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+ + + + diff --git a/_book/April-2021.md b/_book/April-2021.md new file mode 100644 index 0000000..1431ac9 --- /dev/null +++ b/_book/April-2021.md @@ -0,0 +1,121 @@ +# April 2021 {-} + +## April 1 {-} + +

Before accepting regression model estimates, if multicollinearity is problematic (it doesn't necessarily have to be) needs to be checked.

The {check_collinearity} function from {performance} ๐Ÿ“ฆ offers pretty way to check this ๐Ÿ“Šhttps://t.co/sFMj9vbVIe#rstats #DataScience pic.twitter.com/xdrsVbz40f

— R Function A Day (@rfunctionaday) April 1, 2021
+ +## April 2 {-} + +

Sometimes you wish to visually highlight only a certain portion of the data while retaining the full dataset for reference.

The {gghighlight} function from the eponymous ๐Ÿ“ฆ helps you do this effortlessly:https://t.co/eibrcjxQ1I#rstats #DataScience pic.twitter.com/TNUFlLnnMs

— R Function A Day (@rfunctionaday) April 2, 2021
+ +## April 3 {-} + +

k-means is a popular clustering algorithm but has a disadvantage (in unsupervised context) that k should be picked in advance.

The {kmeansruns} function from {fpc} ๐Ÿ“ฆ aids by running k-means over a range of k and returns the best k:https://t.co/bBYsUmhrRz#rstats #DataScience pic.twitter.com/U0ecl17XT0

— R Function A Day (@rfunctionaday) April 3, 2021
+ +## April 4 {-} + +

On a (Easter) Sunday, if you don't want to work but have to, you deserve some light entertainment for yourself.

For such occasions, the {kittyR} function from the eponymous ๐Ÿ“ฆ can do the trick ๐Ÿˆhttps://t.co/74vJiwQSAx#rstats #DataScience pic.twitter.com/Cfq5azg6KQ

— R Function A Day (@rfunctionaday) April 4, 2021
+ +## April 5 {-} + +

Often a survey platform might collapse multiple entries of data into a single column which then need to be separated into individual rows.

The {separate_rows} function from {tidy} is designed to make this easy:https://t.co/rESDf7zU3i#rstats #DataScience pic.twitter.com/S1j8cjBaqX

— R Function A Day (@rfunctionaday) April 5, 2021
+ +## April 6 {-} + +

Wordclouds help visualize word frequencies in qualitative work, and a dedicated geom in grammar of graphics framework can give more flexibility to create them.

The {ggwordcloud} function from the eponymous ๐Ÿ“ฆ provides such a geom ๐Ÿ‘Œhttps://t.co/mk8DnYJY8p#rstats #DataScience pic.twitter.com/yiwFn7QHiR

— R Function A Day (@rfunctionaday) April 6, 2021
+ +## April 7 {-} + +

As a developer or a user, if you are curious about how your favorite R package has performed (in terms of usage) over years, you can create an informative visualization using the {cranDownloads} function from {packageRank} ๐Ÿ“ฆ
https://t.co/64DqUbmAFF#rstats #DataScience pic.twitter.com/GPGIgZiDrf

— R Function A Day (@rfunctionaday) April 7, 2021
+ +## April 8 {-} + +

Often when we are reporting quantities (time, information, etc. units), we wish to report them in human-readable form.

The {pretty_} function family from {prettyunits} ๐Ÿ“ฆ is designed to do exactly this! โœ…https://t.co/9QMWvZIODB#rstats #DataScience pic.twitter.com/ZUX6vbRNpF

— R Function A Day (@rfunctionaday) April 8, 2021
+ +## April 9 {-} + +

Factor analysis (FA) can help reduce many features to a few latent features. But one first needs to check if data is suitable for FA.

The {check_factorstructure} function from {parameters} provides an informative and verbose way ๐Ÿ”https://t.co/MNQZMkum30#rstats #DataScience pic.twitter.com/hdeS0w3NJF

— R Function A Day (@rfunctionaday) April 9, 2021
+ +## April 10 {-} + +

For pedagogical, research, etc. purposes, one may sometimes wish to create fake data.

The {ch_} function family from {charlatan} ๐Ÿ“ฆ supports creation of different types of data across multiple languages ๐Ÿช„https://t.co/mERCQ6aCzf#rstats #DataScience pic.twitter.com/JClu9pnzqO

— R Function A Day (@rfunctionaday) April 10, 2021
+ +## April 11 {-} + +

If you are fluent in {dplyr} and wish to learn more about {data.table}, it can be nifty to have a function that can provide a syntax translation between the two.

The {show_query} function from {dtplyr} ๐Ÿ“ฆ does just that! ๐Ÿ”„https://t.co/RKhCwjgNSt#rstats #DataScience pic.twitter.com/uj9NrXyNhY

— R Function A Day (@rfunctionaday) April 11, 2021
+ +## April 12 {-} + +

Highest Density Interval (HDI) is a credible interval that contains the most probable effect values.

The {hdi} function from {bayestestR} helps to compute and visualize HDI easily for posterior distributions from Bayesian models ๐Ÿ“Šhttps://t.co/ui1FRXgqzq#rstats #DataScience pic.twitter.com/w4TcHpaVOP

— R Function A Day (@rfunctionaday) April 12, 2021
+ +## April 13 {-} + +

Google trends analytics is helpful in the study of global web search patterns.

The {gtrends} function from {gtrendsR} ๐Ÿ“ฆ helps extract and visualize this data for specified periods and geolocations ๐Ÿ”Žhttps://t.co/yS01ELq5q4#rstats #DataScience pic.twitter.com/mhGTSXB2rN

— R Function A Day (@rfunctionaday) April 13, 2021
+ +## April 14 {-} + +

Plots in the grammar of graphics framework are a combination of layers of geometric elements.

The {layer_} function family in {ggplot2} ๐Ÿ“ฆ extracts layer details, which can be helpful for testing and exploring aesthetic defaults ๐Ÿ—‚https://t.co/mIq2G3qAo8#rstats #DataScience pic.twitter.com/jNUcLQ6nsC

— R Function A Day (@rfunctionaday) April 14, 2021
+ +## April 15 {-} + +

In an age where virtual assistant programs have become ubiquitous, you may also wish to have one that helps you find solutions to common ggplot formatting problems.

The {gghelp} function {ggx} mimics behavior of such an assistant ๐Ÿ“https://t.co/6VQzRqbrgp#rstats #DataScience pic.twitter.com/esK1W6xMEj

— R Function A Day (@rfunctionaday) April 15, 2021
+ +## April 16 {-} + +

Although a no. of ๐Ÿ“ฆs help assess validity of regression model assumptions visually, only a handful cover time series analysis.

The {ggtsdiag} function from {ggfortify} ๐Ÿ“ฆ provides a comprehensive diagnostic check for such models ๐Ÿ“ˆhttps://t.co/iJaZ6bD6e1#rstats #DataScience pic.twitter.com/aeGlPV3IXn

— R Function A Day (@rfunctionaday) April 16, 2021
+ +## April 17 {-} + +

Often one needs to report statistical analysis in a publication/report, and formatting them manually can be tedious and error-prone ๐Ÿ“„

The {report} function from the eponymous ๐Ÿ“ฆ automates this process to follow best practices โœ๏ธhttps://t.co/gAaRN4Qqr1#rstats #DataScience pic.twitter.com/59QS89HiLE

— R Function A Day (@rfunctionaday) April 17, 2021
+ +## April 18 {-} + +

If your choice of color palette is not color-blind friendly, color differences in a plot may not be obvious to color-blind people.

The {replacePlotColor} function from {colorBlindness} ๐Ÿ“ฆ helps you replace colors with safe colorshttps://t.co/AN9lyD9fpc#rstats #DataScience pic.twitter.com/coudegYOfm

— R Function A Day (@rfunctionaday) April 18, 2021
+ +## April 19 {-} + +

Exploratory data analysis often involves specifying and comparing multiple regression models.

The {modelplot} function from {modelsummary} ๐Ÿ“ฆ provides pretty dot-and-whisker plots to display/compare regression estimates from models:https://t.co/0vyzYY9bUy#rstats #DataScience pic.twitter.com/ZtkV0EUjVd

— R Function A Day (@rfunctionaday) April 19, 2021
+ +## April 20 {-} + +

Next to statistical significance, we are often interested in the practical relevance of an effect.

The {interpret_} function family from {effectsize} ๐Ÿ“ฆ provides such interpretation guidelines, which can differ across disciplines ๐Ÿ“https://t.co/CaqThRABhy#rstats #DataScience pic.twitter.com/3ywbl2imPj

— R Function A Day (@rfunctionaday) April 20, 2021
+ +## April 21 {-} + +

The {purrr::map_} functions apply a function to list elements.
But what if one wants to apply a function not to each element of the list but to all combinations of elements?

The {xmap_} functions from {crossmap} ๐Ÿ“ฆ do exactly this!https://t.co/9ITKhUqV9P#rstats #DataScience pic.twitter.com/woQenHnhfN

— R Function A Day (@rfunctionaday) April 21, 2021
+ +## April 22 {-} + +

PCA is a popular method to reduce the dimensionality of multivariate data and a biplot is a useful visualization method for the same.

The {fviz_pca_biplot} function from {factoextra} ๐Ÿ“ฆ makes it effortless to make elegant biplots:https://t.co/DRGesAG4vq#rstats #DataScience pic.twitter.com/RB4BoCDp1S

— R Function A Day (@rfunctionaday) April 22, 2021
+ +## April 23 {-} + +

Sometimes you just want to convert the source code from R script (.R) into a new Markdown (.md) document/report.

The {spin} function from {knitr} ๐Ÿ“ฆ makes this conversion effortless! ๐Ÿช„https://t.co/cBaYSC5nc8#rstats #DataScience pic.twitter.com/u1kIa8Of9T

— R Function A Day (@rfunctionaday) April 23, 2021
+ +## April 24 {-} + +

If one correlation is significant, while the other isn't, it's a fallacy to conclude that difference in correlations itself is statistically significant.

The {cocor} function from eponymous ๐Ÿ“ฆ helps to formally test this difference:https://t.co/9CjLNcqffG#rstats #DataScience pic.twitter.com/XiJ5OqIExv

— R Function A Day (@rfunctionaday) April 24, 2021
+ +## April 25 {-} + +

Confusion matrix visualization helps assess the performance of a (binary or multi-class) classification algorithm.

The {plot_confusion_matrix} function from {cvms} ๐Ÿ“ฆ produces elegant and informative confusion matrix plots ๐Ÿ—„https://t.co/NHxGNS8XnD#rstats #DataScience pic.twitter.com/SZWvibYAQp

— R Function A Day (@rfunctionaday) April 25, 2021
+ +## April 26 {-} + +

As trivial as combining multiple characters to form a single phrase sounds, the common solutions return outputs that are imperfect for human readers.

The {combine_words} helper function from {knitr} ๐Ÿ“ฆ fills in this gap! ๐Ÿ™Œhttps://t.co/M0CgOUU0ed#rstats #DataScience pic.twitter.com/XuCNHf3BFi

— R Function A Day (@rfunctionaday) April 26, 2021
+ +## April 27 {-} + +

Although a number of ๐Ÿ“ฆs provide functions to visualize a one-way ANOVA design, few support visualizing more complex, multi-way ANOVA designs.

The {afex_plot} function from {afex} ๐Ÿ“ฆ is one such function ! ๐Ÿ™Œ๐Ÿ“Šhttps://t.co/3px5ySCzrd#rstats #DataScience pic.twitter.com/3g9hkZ3ySy

— R Function A Day (@rfunctionaday) April 27, 2021
+ +## April 28 {-} + +

Markdown has a syntax that is enviable for its ease and simplicity. So one might naturally wish to use it for annotations in {ggplot2} plots.

The {element_markdown} function from {ggtext} ๐Ÿ“ฆ magically helps to do exactly this! ๐ŸŽ‰https://t.co/54WpoyrVje#rstats #DataScience pic.twitter.com/irr8gRVaxf

— R Function A Day (@rfunctionaday) April 28, 2021
+ +## April 29 {-} + +

If we have sensitive categorical data (e.g., gender, race, etc.), we might sometimes be required to anonymize them before carrying out any analysis.

The {fct_anon} function from {forcats} ๐Ÿ“ฆ helps exactly with this step ๐Ÿ—ƒ๏ธhttps://t.co/xsR3HipYqu#rstats #DataScience pic.twitter.com/C6apoc6fEP

— R Function A Day (@rfunctionaday) April 29, 2021
+ +## April 30 {-} + +

Google's Tesseract (https://t.co/tJkaT2vH2j), a powerful optical character recognition engine, can extract text embedded in images from over 100 languages!

The {ocr} function from {tesseract} ๐Ÿ“ฆ provides access to this engine ๐Ÿ‘ฝhttps://t.co/H0vHvn59sQ#rstats #DataScience pic.twitter.com/g0GMKB9JEy

— R Function A Day (@rfunctionaday) April 30, 2021
diff --git a/_book/August-2021.md b/_book/August-2021.md new file mode 100644 index 0000000..8bbc242 --- /dev/null +++ b/_book/August-2021.md @@ -0,0 +1,125 @@ +# August 2021 {-} + +## August 1 {-} + +

Sometimes you may wish to insert a textual table in a {ggplot2} plot the same way you insert text labels at a specified position.

The {geom_table} function from {ggpp} ๐Ÿ“ฆ offers just the geometric layer! ๐Ÿ™Œhttps://t.co/bQdaxiMffR#rstats #DataScience pic.twitter.com/6raba4JIDV

— R Function A Day (@rfunctionaday) August 1, 2021
+ +## August 2 {-} + +

If you are used to {dplyr}'s pipeable syntax (using `%>%`) and wish to continue using it when you switch to {data.table}, the {dt} helper function from {tidytable} ๐Ÿ“ฆ can help! ๐Ÿ‘ฉโ€๐Ÿ”งhttps://t.co/YzFfw2meLF#rstats #DataScience pic.twitter.com/3SQqnjj3Bz

— R Function A Day (@rfunctionaday) August 2, 2021
+ +## August 3 {-} + +

If you are used to reading code with syntax highlighting, you may wish the same when code is printed in the terminal/console.

The {prettycode} function from the eponymous ๐Ÿ“ฆ does exactly this! ๐ŸŽจhttps://t.co/dF1WYPJSSc#rstats #DataScience pic.twitter.com/4fK9FjnTOu

— R Function A Day (@rfunctionaday) August 3, 2021
+ +## August 4 {-} + +

Compared to CSS, Sass is a more flexible HTML styling framework. As it becomes ubiquitous, you may wish to learn how Sass code you are reading translates to familiar CSS.

The {sass} function from the eponymous ๐Ÿ“ฆ does this ๐Ÿ”„https://t.co/FR8UlmQyTY#rstats #DataScience pic.twitter.com/3ZgYl0E6j3

— R Function A Day (@rfunctionaday) August 4, 2021
+ +## August 5 {-} + +

A dot plot is a good visualization to represent the distribution of labeled numeric data.

The {ggdotplotstats} function from {ggstatsplot} ๐Ÿ“ฆ easily produces these charts (with additional descriptive and inferential statistics) ๐Ÿ’https://t.co/OLcqX643Vx#rstats #DataScience pic.twitter.com/GZ3huWi9qU

— R Function A Day (@rfunctionaday) August 5, 2021
+ +## August 6 {-} + +

If you know C and wish to understand the implementation of R data structures at C-level as S-expressions or SEXPs (https://t.co/pCGK6tlgNB), the {sxp} function from {lobstr} ๐Ÿ“ฆ can be helpful! โ›๏ธhttps://t.co/pb7um2e1RR#rstats #DataScience pic.twitter.com/NYrdqEJqJW

— R Function A Day (@rfunctionaday) August 6, 2021
+ +## August 7 {-} + +

For teaching or for quality assessment, we may sometimes wish to visualize residuals of a model to check the goodness of fit.

The {stat_fit_deviations} function from {ggpmisc} ๐Ÿ“ฆ makes this easy in {ggplot2} ๐Ÿ™Œhttps://t.co/YezhmuyXgP#rstats #DataScience pic.twitter.com/M6o4MAcnIH

— R Function A Day (@rfunctionaday) August 7, 2021
+ +## August 8 {-} + +

If we need to write an "impure" function that modifies R landscape (e.g. changes working directory), we'd do it so that behavior of other R functions doesn't change.

The {local_} function family in {withr} ๐Ÿ“ฆ can help to do so! ๐Ÿฅกhttps://t.co/CgklVPJp7n#rstats #DataScience pic.twitter.com/rrpbojxoev

— R Function A Day (@rfunctionaday) August 8, 2021
+ +## August 9 {-} + +

Gelman and Hill (2007) propose a binned residual plot to check the under-or over-fitting of binomial logistic regression models. 0โƒฃ1โƒฃ

The {binned_residuals} function from {performance} ๐Ÿ“ฆeasily produces such visualization ๐Ÿ”https://t.co/gAAjFNIf92#rstats #DataScience pic.twitter.com/cKw8CxpJfC

— R Function A Day (@rfunctionaday) August 9, 2021
+ +## August 10 {-} + +

Even after doing everything you can in R, sometimes you may want to further improve the performance of your code by rewriting it in C++.

The {cpp_function} from {cpp11} ๐Ÿ“ฆ provides a convenient way to do so ๐Ÿ‘https://t.co/5fdrx9KPc6#rstats #DataScience pic.twitter.com/IbKeeuZdjb

— R Function A Day (@rfunctionaday) August 10, 2021
+ +## August 11 {-} + +

For common statistical tests, we may wish to extract not only the details of inferential statistics but also effect size estimates.

Functions, like {two_sample_test}, from {statsExpressions} ๐Ÿ“ฆ provide a tidy way to do so ๐Ÿ™Œhttps://t.co/MJ1avaR6WJ#rstats #DataScience pic.twitter.com/zKFUetTeXR

— R Function A Day (@rfunctionaday) August 11, 2021
+ +## August 12 {-} + +

Sometimes data analysis may require finding the first non-missing value (to replace it with another value, to create a new variable, etc.) across vectors or columns.

The {coalesce} function from {dplyr} ๐Ÿ“ฆ comes in handy here ๐Ÿ”https://t.co/Q7EPEAmFZC#rstats #DataScience pic.twitter.com/Upqhjd5eJY

— R Function A Day (@rfunctionaday) August 12, 2021
+ +## August 13 {-} + +

Unified Modeling Language provides a way to visualize the design of a system, and nomnoml (https://t.co/X9yAhs166L) is online tool to draw UML diagrams.

The {nomnoml} function from eponymous ๐Ÿ“ฆ renders UML diagrams from R itself! ๐Ÿ“https://t.co/CBZgudgltr#rstats #DataScience pic.twitter.com/KmksKDgQPz

— R Function A Day (@rfunctionaday) August 13, 2021
+ +## August 14 {-} + +

Sometimes you might need to analyze the scanned text data present in a PDF.

The {pdf_ocr_text} function from {pdftools} ๐Ÿ“ฆ can extract such text โ›https://t.co/cnoqBhZ0Ys#rstats #DataScience pic.twitter.com/1LMVHfGgsL

— R Function A Day (@rfunctionaday) August 14, 2021
+ +## August 15 {-} + +

Although {rmarkdown} ๐Ÿ“ฆ makes writing reports easy, it lacks automatic numbering of and cross-referencing (for figures, tables, sections, etc) features.

The 2nd variant of function family in {bookdown} ๐Ÿ“ฆ provides these features โœ๏ธhttps://t.co/EKFeVqQJb1#rstats #DataScience pic.twitter.com/RcIAtscT0K

— R Function A Day (@rfunctionaday) August 15, 2021
+ +## August 16 {-} + +

The {tmap} ๐Ÿ“ฆ provides API to create (static and interactive) thematic maps with a syntax resembling that of {ggplot2}.

The {qtm} function provides a convenient short-hand to draw such maps quickly! ๐Ÿ—บ๏ธhttps://t.co/WDYN5ofasw#rstats #DataScience pic.twitter.com/PZk4thnd4q

— R Function A Day (@rfunctionaday) August 16, 2021
+ +## August 17 {-} + +

Sometimes we need to convert a deeply nested list into a rectangular dataframe. But in doing so, we may not wish to bring to the top all list elements.

The {hoist} function from {tidyr} ๐Ÿ“ฆ can help with such transformations ๐Ÿ› ๏ธhttps://t.co/yrhHtF2YVl#rstats #DataScience pic.twitter.com/m5TvEse0bm

— R Function A Day (@rfunctionaday) August 17, 2021
+ +## August 18 {-} + +

Sometimes we want to draw text in a {ggplot2} plot so that it fits inside a defined area without manually fiddling around with font size.

The {geom_fit_text} function from {ggfittext} ๐Ÿ“ฆ resizes text to fit inside a defined box ๐Ÿ—œ๏ธhttps://t.co/GiaXx9QEgb#rstats #DataScience pic.twitter.com/krnvdRqIWz

— R Function A Day (@rfunctionaday) August 18, 2021
+ +## August 19 {-} + +

Posterior predictive check for a regression model uses simulated data under the fitted model to check for any discrepancies from observed data.

The {pp_check} function from {performance} ๐Ÿ“ฆ carries out and visualizes this check ๐Ÿ”https://t.co/qk7dnlHyP5#rstats #DataScience pic.twitter.com/R95Evuhhdp

— R Function A Day (@rfunctionaday) August 19, 2021
+ +## August 20 {-} + +

If you use {dplyr} ๐Ÿ“ฆ, you may wish to avoid the {group_by} + {ungroup} workflow for grouped analysis if you just need to carry out a single operation.

The {with_groups} function provides an alternative for such contexts โœ‚๏ธhttps://t.co/FiEeP2loxm#rstats #DataScience pic.twitter.com/XANjLS22ki

— R Function A Day (@rfunctionaday) August 20, 2021
+ +## August 21 {-} + +

Sometimes you may wish to display results from a statistical test in a graphics you have prepared.

The {statsExpressions} ๐Ÿ“ฆ functions, like {corr_test}, provide the necessary expressions ๐Ÿ‘https://t.co/ERUsOJKliZ#rstats #DataScience pic.twitter.com/4T3awwOSFQ

— R Function A Day (@rfunctionaday) August 21, 2021
+ +## August 22 {-} + +

For some reason, you may need to prepare a calendar in R and you might prefer to do so in the {ggplot2} framework.

The {calendR} function from the eponymous ๐Ÿ“ฆ provides customizable monthly or yearly calendars ๐Ÿ“…https://t.co/KPx4ozhjrV#rstats #DataScience pic.twitter.com/C1dM0FSllv

— R Function A Day (@rfunctionaday) August 22, 2021
+ +## August 23 {-} + +

The {mlr3} project is a modern and powerful object-oriented framework for doing machine learning (ML) in R.

The {autoplot} function from {mlr3viz} ๐Ÿ“ฆ provides a model-agnostic general-purpose tool for visualizing ML algorithms! ๐Ÿ“Š๐Ÿ“ˆhttps://t.co/ky8yhLtYyG#rstats #DataScience pic.twitter.com/YBu2rLz44K

— R Function A Day (@rfunctionaday) August 23, 2021
+ +## August 24 {-} + +

Instead of using third-party softwares, you may sometimes wish to edit (join, convert, etc.) images from within R itself.

The {image_} function family from {magick} provide such helpers ๐Ÿ“ท๐ŸŽžhttps://t.co/ald6jelSWG#rstats #DataScience pic.twitter.com/y4H67cTPnY

— R Function A Day (@rfunctionaday) August 24, 2021
+ +## August 25 {-} + +

For correlation hypotheses, in addition to the joint distribution in a scatterplot, one may also wish to visualize the marginal distributions for each variable.

This is easy to do using the {_side} function family from {ggside} ๐Ÿ“ฆ !https://t.co/vQYV4ELEaG#rstats #DataScience pic.twitter.com/VSBpVqtO6K

— R Function A Day (@rfunctionaday) August 25, 2021
+ +## August 26 {-} + +

The UpSet plot is an efficient alternative to the Venn Diagram for visualizing a complex intersections of multiple sets.

The {scale_x_upset} function from {ggupset} ๐Ÿ“ฆ provides a {ggplot2} scale to create UpSet plots easily ๐Ÿงฑhttps://t.co/5rwIgAhzxa#rstats #DataScience pic.twitter.com/wk352FtXb0

— R Function A Day (@rfunctionaday) August 26, 2021
+ +## August 27 {-} + +

Sometimes (e.g. for meta-analysis) we may need to either extract effect size from a test statistic or convert the available effect size to a more suitable one.

The {_to_} function family from {effectsize} can do such conversions ๐Ÿ”„https://t.co/EO6hb9NkER#rstats #DataScience pic.twitter.com/2ItAfBPwps

— R Function A Day (@rfunctionaday) August 27, 2021
+ +## August 28 {-} + +

A correlation matrix is a nifty visualization for displaying relationships between multiple variables.

The {ggcorrmat} function from {ggstatsplot} ๐Ÿ“ฆ creates such matrices with significance testing and other descriptive details โ†”๏ธhttps://t.co/rbwEs6qVON#rstats #DataScience pic.twitter.com/EDPanyNE0x

— R Function A Day (@rfunctionaday) August 28, 2021
+ +## August 29 {-} + +

For a given generic or class, sometimes we may be interested to systematically list and investigate the available S3/S4 methods.

The {_methods_} function family in {sloop} ๐Ÿ“ฆ provides the needed helpers ๐Ÿ“https://t.co/EV7AVaSHPF#rstats #DataScience pic.twitter.com/XxFb3Es3pB

— R Function A Day (@rfunctionaday) August 29, 2021
+ +## August 30 {-} + +

When we are interested in studying group differences, sometimes we may wish to annotate our visualizations with results from significance testing.

The {geom_signif} from {ggsignif} ๐Ÿ“ฆ provides just the geometric layer! โš–๏ธhttps://t.co/O9XM5nHdgf#rstats #DataScience pic.twitter.com/iZhilHeGO3

— R Function A Day (@rfunctionaday) August 30, 2021
+ +## August 31 {-} + +

YAML language is ubiquitous, esp. in configuration files, and sometime we may need to convert R object to a YAML object.

The {as_yml} function from {ymlthis} ๐Ÿ“ฆ makes this conversion easy ๐Ÿ”„https://t.co/GSTD3slB0k#rstats #DataScience pic.twitter.com/YeVunUZoR8

— R Function A Day (@rfunctionaday) August 31, 2021
diff --git a/_book/December-2021.md b/_book/December-2021.md new file mode 100644 index 0000000..4ef07c0 --- /dev/null +++ b/_book/December-2021.md @@ -0,0 +1,125 @@ +# December 2021 {-} + +## December 1 {-} + +

ROC curves provide a convenient way to compare responses and predictions of a binomial model, and sometimes you may also need to plot multiple curves.

The {geom_roc} function {plotROC} ๐Ÿ“ฆ can do it all! ๐Ÿ’ชhttps://t.co/dbmEIkLfNM#rstats #DataScience pic.twitter.com/H2gejkYnvI

— R Function A Day (@rfunctionaday) December 1, 2021
+ +## December 2 {-} + +

Either while learning or exploring the distribution of our data, we may wish to visualize the shape of various statistical distributions.

The {vdist_*} function family from {vistributions} ๐Ÿ“ฆ makes this easy! ๐Ÿ””https://t.co/iqSh2nnKWF#rstats #DataScience pic.twitter.com/XDccj6H5Dy

— R Function A Day (@rfunctionaday) December 2, 2021
+ +## December 3 {-} + +

A level plot displays spatial data in two rather than three dimensions, and sometimes we may wish to create such visualisation for `RasterLayer` objects.

The {levelplot} function from {rasterVis} ๐Ÿ“ฆ does so elegantly and flexibly!https://t.co/TTYvQLpIbZ#rstats #DataScience pic.twitter.com/S5g6fQUAw7

— R Function A Day (@rfunctionaday) December 3, 2021
+ +## December 4 {-} + +

Sometimes you wish to remove only certain rows or columns that are completely empty instead of partly empty.

The {remove_empty_*} helper function family from {sjmisc} ๐Ÿ“ฆ do exactly this! ๐Ÿงนhttps://t.co/P6z56VFxPD#rstats #DataScience pic.twitter.com/zKDdFpxscn

— R Function A Day (@rfunctionaday) December 4, 2021
+ +## December 5 {-} + +

Modern OS, devices, etc. offer dark mode, and you may wish to use it also for your visualizations to match this color scheme.

The {dark_theme_*} function family from {ggdark} ๐Ÿ“ฆ offers dark mode for {ggplot2} visualizations! ๐ŸŒ‘https://t.co/MAAA7DadAQ#rstats #DataScience pic.twitter.com/foWfo0Hquh

— R Function A Day (@rfunctionaday) December 5, 2021
+ +## December 6 {-} + +

When we are interested in studying group differences, sometimes we may wish to annotate our visualizations with results from significance testing.

The {add_pval} from {ggpval} ๐Ÿ“ฆprovides a way to do this! โš–๏ธhttps://t.co/X1pKEeFHnG#rstats #DataScience pic.twitter.com/xixLLhcHlE

— R Function A Day (@rfunctionaday) December 6, 2021
+ +## December 7 {-} + +

If you use Qualtrics surveys to collect data on a crowdsourcing website (like MTurk), you may wish to exclude responses based on specified exclusion criteria.

The {exclude_*} function family from {excluder} ๐Ÿ“ฆ makes this easy! โœ‚๏ธhttps://t.co/Nhl8MqQEXH#rstats #DataScience pic.twitter.com/CDqpV10RN1

— R Function A Day (@rfunctionaday) December 7, 2021
+ +## December 8 {-} + +

To have a consistent coding style, we need to adopt a naming convention and stick to it in the codebase.

The {to_any_case} function from {snakecase} ๐Ÿ“ฆ can help convert strings to any desired case! ๐Ÿ๐Ÿชhttps://t.co/sHkKj1dLgV#rstats #DataScience pic.twitter.com/MNVuzLIrjF

— R Function A Day (@rfunctionaday) December 8, 2021
+ +## December 9 {-} + +

Although descriptive statistics are helpful, computing them can be tricky, especially for complicated designs.

The {suberbPlot} function from {superb} ๐Ÿ“ฆ makes this easy for various designs, sampling schemes, population sizes, etc.!https://t.co/5bXZF7pt5x#rstats #DataScience pic.twitter.com/VOEofPuSxn

— R Function A Day (@rfunctionaday) December 9, 2021
+ +## December 10 {-} + +

Sometimes you may wish to encode values to a shape in a visualisation but may find the default shapes to be not distinctive enough.

The {geom_star} function from {ggstar} ๐Ÿ“ฆ provides just the shapes! ๐ŸงŠโญ•๏ธโญ๏ธhttps://t.co/ecTTbN44cv#rstats #DataScience pic.twitter.com/icpTqfUK2W

— R Function A Day (@rfunctionaday) December 10, 2021
+ +## December 11 {-} + +

Line charts can represent variation in quantitative values across ordered measurements (e.g. across time).

The {gg_line_facet} function from {simplevis} ๐Ÿ“ฆ creates such charts with elegant defaults ๐Ÿ“ˆhttps://t.co/A4k8DMTDUf#rstats #DataScience pic.twitter.com/1p1TqWH4Bz

— R Function A Day (@rfunctionaday) December 11, 2021
+ +## December 12 {-} + +

Sometimes we want to adjust data by regressing out effects of selected variables.

The {adjust} function from {datawizard} ๐Ÿ“ฆ does so easily and flexibly! ๐ŸŽ›https://t.co/lI6JSpnlfb#rstats #DataScience pic.twitter.com/A3No7I2ymW

— R Function A Day (@rfunctionaday) December 12, 2021
+ +## December 13 {-} + +

Bar charts provide a great way to visualize the magnitude of the quantitative values across different categories.

The {bar_chart} function from {ggcharts} ๐Ÿ“ฆ produces them easily! ๐Ÿ“Šhttps://t.co/j4c3C1gVZI#rstats #DataScience pic.twitter.com/ma0Wl1J6cK

— R Function A Day (@rfunctionaday) December 13, 2021
+ +## December 14 {-} + +

You may wish to prepare shareable project analysis docs, with emphasis on reproducibility and efficient project management.

The {wflow_*} function family from {workflowr} ๐Ÿ“ฆ makes this easy with a version-controlled website! ๐Ÿ“https://t.co/GTMWYeYuEm#rstats #DataScience pic.twitter.com/Rqi5yE6dl8

— R Function A Day (@rfunctionaday) December 14, 2021
+ +## December 15 {-} + +

While annotating plotted data with labels, we may wish to do so in a way that overlap between labels is minimized.

The {geom_label_repel} function from {ggrepel} ๐Ÿ“ฆ provides a clever and performant way to do so! ๐Ÿ“https://t.co/GU3SQS1YWt#rstats #DataScience pic.twitter.com/1UX56FSeNX

— R Function A Day (@rfunctionaday) December 15, 2021
+ +## December 16 {-} + +

Sometimes we need to operate over a list or vector, either by position or by name, to get both the index and the value of interest.

The {imap} function family from {purrr} ๐Ÿ“ฆ can be of help here ๐Ÿ”https://t.co/U5IHEt8hpt#rstats #DataScience pic.twitter.com/Fsyahp4tb8

— R Function A Day (@rfunctionaday) December 16, 2021
+ +## December 17 {-} + +

Sometimes the rmarkdown source file alone isn't enough to reproduce the report, and additional files (e.g. data) need to be embedded.

The {embed_*} function family from {xfun} ๐Ÿ“ฆ does so by encoding the files to base64 format! ๐ŸŽhttps://t.co/oA04uKnV3F#rstats #DataScience pic.twitter.com/NA6jzbr2YN

— R Function A Day (@rfunctionaday) December 17, 2021
+ +## December 18 {-} + +

Often we need to work, not with a specific date, but rather with intervals containing start and end dates.

The {int_*} function family from {lubridate} ๐Ÿ“ฆ makes it easy to operate on intervals ๐Ÿ—“https://t.co/beoBq6Rlr3#rstats #DataScience pic.twitter.com/8mMnL0nJwC

— R Function A Day (@rfunctionaday) December 18, 2021
+ +## December 19 {-} + +

If you inherit a folder with many files, it can be cumbersome to go through them and find missing packages to install.

The {show_*} function family from {reinstallr} ๐Ÿ“ฆ can list used packages and highlight the ones missing! ๐Ÿ”https://t.co/KrhtH0DT2X#rstats #DataScience pic.twitter.com/nZLLvDN9jP

— R Function A Day (@rfunctionaday) December 19, 2021
+ +## December 20 {-} + +

Sometimes it can be useful to mark the centroid of a group of observations with a point or with a label.

The {stat_centroid} function from {ggpp} ๐Ÿ“ฆprovides a way to do so! ๐Ÿ“Œhttps://t.co/FaAYMnOFW7#rstats #DataScience pic.twitter.com/gwfY7uyrPK

— R Function A Day (@rfunctionaday) December 20, 2021
+ +## December 21 {-} + +

During data wrangling stage, sometimes we may need to rotate the dataframe (such that rows become columns and vice versa).

The {rotate_df} function from {sjmisc} ๐Ÿ“ฆ provides just the tool! ๐Ÿ”งhttps://t.co/fB1vqf4uqu#rstats #DataScience pic.twitter.com/yw2aO2oLxZ

— R Function A Day (@rfunctionaday) December 21, 2021
+ +## December 22 {-} + +

Sometimes you may wish to quickly build an elegant and sharable website.

The {create_postcard} function from {postcards} ๐Ÿ“ฆ creates pretty, single-page websites with customizable Rmarkdown template! โœจ๐Ÿ“‡https://t.co/0eZUTkkd16#rstats #DataScience pic.twitter.com/izucxUyfQ6

— R Function A Day (@rfunctionaday) December 22, 2021
+ +## December 23 {-} + +

If you wish to write functions that save data to a user's computer, you need to find an appropriate directory depending on the OS.

The {user_*_dir} function family from {rappdirs} ๐Ÿ“ฆ makes this easy! ๐Ÿ—‚https://t.co/XUPMngVMz9#rstats #DataScience pic.twitter.com/ps2ISHwql6

— R Function A Day (@rfunctionaday) December 23, 2021
+ +## December 24 {-} + +

JSON data representation format is ubiquitous, and sometimes we might need to convert a dataframe in R to a JSON object.

The {to_json} function from {jsonify} ๐Ÿ“ฆ provides a convenient and fast way to do so! ๐Ÿ”€https://t.co/CDUlMuB6fE#rstats #DataScience pic.twitter.com/GHQgTRWYB5

— R Function A Day (@rfunctionaday) December 24, 2021
+ +## December 25 {-} + +

Sometimes you may wish to open files, directories, URLs, etc. programmatically, instead of doing it manually.

The {xopen} function from the eponymous ๐Ÿ“ฆ provides a way to so from R! ๐Ÿ”งhttps://t.co/5HxeOdvIIK#rstats #DataScience pic.twitter.com/zVpcSHOdXe

— R Function A Day (@rfunctionaday) December 25, 2021
+ +## December 26 {-} + +

If you use {R6}โ€™s implementation of encapsulated OOP in R, and sometimes you may wish to use more idiomatic functional OOP with such objects.

The eponymous function from {R62S3} ๐Ÿ“ฆ generate S3 generics/methods for R6 classes ๐Ÿ”€https://t.co/s82kX21cKc#rstats #DataScience pic.twitter.com/DCXfB6wpgu

— R Function A Day (@rfunctionaday) December 26, 2021
+ +## December 27 {-} + +

A Tufte handout is a thoughtfully styled document with footnotes, integration of graphics with text, and elegant typography.

The {tufte_*} function family from {tufte} ๐Ÿ“ฆ helps create them via Rmarkdown ๐Ÿ“ƒhttps://t.co/Ca2FPorPnW#rstats #DataScience pic.twitter.com/dhyEsyj1l7

— R Function A Day (@rfunctionaday) December 27, 2021
+ +## December 28 {-} + +

Archive files (zip, tar, etc.) collect multiple files in a single file, and you may sometimes need to use them for portability or compression.

The {archive_*} function family from {archive} ๐Ÿ“ฆ provides relevant operations in R! ๐Ÿ“https://t.co/E97cRlKZrO#rstats #DataScience pic.twitter.com/a1GlbccJfV

— R Function A Day (@rfunctionaday) December 28, 2021
+ +## December 29 {-} + +

YAML language is ubiquitous, especially in configuration files, and sometimes we may wish to convert R object to a YAML string.

The {as.yaml} function from {yaml} ๐Ÿ“ฆ makes this easy ๐Ÿ”€https://t.co/31Iwor6B6N#rstats #DataScience pic.twitter.com/S3YfVZovnD

— R Function A Day (@rfunctionaday) December 29, 2021
+ +## December 30 {-} + +

Cox regression model is widely used in medical research to assess the effect of several risk factors on the survival time of patients.

The {ggforest} function from {survminer} ๐Ÿ“ฆeasily creates a forest plot of its model estimates ๐Ÿ“Šhttps://t.co/YXA8LjVQY5#rstats #DataScience pic.twitter.com/S6F2KdFAUn

— R Function A Day (@rfunctionaday) December 30, 2021
+ +## December 31 {-} + +

While crawling webpages, sometimes you may wish to switch between linking strategies; using either absolute or relative URLs.

The {url_*} function family from {xml2} ๐Ÿ“ฆ makes these conversions easy in R! ๐Ÿ”€https://t.co/x1KW07bqQZ#rstats #DataScience pic.twitter.com/8cZaZ6afiy

— R Function A Day (@rfunctionaday) December 31, 2021
diff --git a/_book/February-2021.md b/_book/February-2021.md new file mode 100644 index 0000000..2deb861 --- /dev/null +++ b/_book/February-2021.md @@ -0,0 +1,113 @@ +# February 2021 {-} + +## February 1 {-} + +

The {dplyr} syntax is heavily inspired by #SQL, which means it is easy to *translate* the {dplyr} code to its equivalent query and this is supported in {dplyr} itself! ๐Ÿ™Œ https://t.co/rjE4S2MK8o#rstats #DataScience pic.twitter.com/SffAhyRmWh

— R Function A Day (@rfunctionaday) February 1, 2021
+ +## February 2 {-} + +

In case you want to peruse how a ggplot (and its various layers) are built, you can use the {ggplot_build} function from {ggplot2}! ๐Ÿ— ๐Ÿšง ๐Ÿฆบ https://t.co/mIq2G3qAo8

Useful also to investigate what are the aesthetic defaults for utilized {geom_}s.#rstats #DataScience pic.twitter.com/UnQdpZYCJ8

— R Function A Day (@rfunctionaday) February 2, 2021
+ +## February 3 {-} + +

If you want to get a quick overview of descriptive statistics for a numeric variable, {describe_distribution} from {parameters} ๐Ÿ“ฆ is your friend ๐Ÿ™Œ https://t.co/KlhJNsdL90

Also works with a {grouped_} dataframe from {dplyr} ๐Ÿ‘#rstats #DataScience pic.twitter.com/FoBio2p8yk

— R Function A Day (@rfunctionaday) February 3, 2021
+ +## February 4 {-} + +

If you want to see a beautiful, colorful tree of your directory file structure, you can use {dir_tree} function from {fs} package! ๐ŸŒณ๐ŸŒˆ https://t.co/UpjcB7z7Qf#rstats #DataScience pic.twitter.com/SlXAIcePoA

— R Function A Day (@rfunctionaday) February 4, 2021
+ +## February 5 {-} + +

If you work a lot with web scraping and need to extract a particular node from HTML page, {html_nodes} from {rvest} ๐Ÿ“ฆ is your friend ๐Ÿ‘ฏโ€โ™€๏ธhttps://t.co/ob6beJEkZN#rstats #DataScience pic.twitter.com/kcouQ5rTfC

— R Function A Day (@rfunctionaday) February 5, 2021
+ +## February 6 {-} + +

In a long script, it can become tiresome to keep duplicating the same object name when it is being self-assigned.

In such instances, the assignment pipe from {magrittr} ๐Ÿ“ฆ proves handy! ๐Ÿ‘Œhttps://t.co/NVHXfOZOI9#rstats #DataScience pic.twitter.com/3iGuHsUZjG

— R Function A Day (@rfunctionaday) February 6, 2021
+ +## February 7 {-} + +

Often you might want to split an existing column into a combination of a few other columns and {separate} from {tidyr} ๐Ÿ“ฆ is exactly what you are looking for! ๐Ÿช„ https://t.co/imqGN7ridk#rstats #DataScience pic.twitter.com/wrSFpNtNeM

— R Function A Day (@rfunctionaday) February 7, 2021
+ +## February 8 {-} + +

The {row_number} function from {dplyr} is a timesaver if you have to create a unique identifier for observations, especially if they belong to different groups 1๏ธโƒฃ2๏ธโƒฃ3๏ธโƒฃhttps://t.co/XSagfpLHVg#rstats #DataScience pic.twitter.com/JWb0W2XMjz

— R Function A Day (@rfunctionaday) February 8, 2021
+ +## February 9 {-} + +

Sometimes you just want to extract the source code present in the source document (e.g., Rmd) to a separate script, and {purl} from {knitr} ๐Ÿ“ฆ makes that effortless! ๐Ÿฆชhttps://t.co/2lwSJ2HhKG#rstats #DataScience pic.twitter.com/RpqD7zsQst

— R Function A Day (@rfunctionaday) February 9, 2021
+ +## February 10 {-} + +

Often you need to convert country names/codes from different conventions to standardized names, and {countrycode} function from the eponymous ๐Ÿ“ฆ can handle most of such conversions! โš™๏ธhttps://t.co/QVF8hBe7BE#rstats #DataScience pic.twitter.com/RByINBRutU

— R Function A Day (@rfunctionaday) February 10, 2021
+ +## February 11 {-} + +

Sometimes a factor level can be missing implicitly, you can make it explicit using {fct_explicit_na} function from {forcats} ๐Ÿ“ฆ :https://t.co/GexbNwDl4L#rstats #DataScience pic.twitter.com/7XI2z0cXR8

— R Function A Day (@rfunctionaday) February 11, 2021
+ +## February 12 {-} + +

Cleaning column names so that they have a consistent pattern is probably the first and the most important step in data analysis and {clean_names} function from {janitor} ๐Ÿ“ฆ is peerless in this regard ๐Ÿงผ https://t.co/7w14DlhEvA#rstats #DataScience pic.twitter.com/GmmVwHVrOA

— R Function A Day (@rfunctionaday) February 12, 2021
+ +## February 13 {-} + +

The {markdown_} function family from {commonmark} ๐Ÿ“ฆ can help you to convert markdown text into various formats (e.g., latex, html, etc.) โœ๏ธ https://t.co/kADYv6J4lB

Can be a useful tool for teaching, say html, if one is already comfortable with rmarkdown.#rstats #DataScience pic.twitter.com/AZyxYsPxvX

— R Function A Day (@rfunctionaday) February 13, 2021
+ +## February 14 {-} + +

Some graphics ๐Ÿ“ฆs (e.g. {hrbrthemes}) require special fonts. But it can be a pain to list and interrogate installed fonts.

The {system_fonts} function from {systemfonts} ๐Ÿ“ฆ outputs a beautiful richly informative table with a one-line command ๐Ÿ‘#rstats #DataScience pic.twitter.com/aTiJdg7gZs

— R Function A Day (@rfunctionaday) February 14, 2021
+ +## February 15 {-} + +

When you just need to create a list of all possible combinations of values in a vector, {combn} function from {utils} ๐Ÿ“ฆ comes in handy ๐Ÿงฐhttps://t.co/5tPzuFp1P9#rstats #DataScience pic.twitter.com/RoyLDraNty

— R Function A Day (@rfunctionaday) February 15, 2021
+ +## February 16 {-} + +

When you have to present results from a regression model in a well-formatted table, the {tbl_regression} function from {gtsummary} ๐Ÿ“ฆ will be a serious time-saver ๐Ÿ—‚https://t.co/EVoZ6ZRbgY#rstats #DataScience pic.twitter.com/q3MvqXIIog

— R Function A Day (@rfunctionaday) February 16, 2021
+ +## February 17 {-} + +

In a deeply nested data structure (an object from JSON, e.g.), indexing can be a bit tedious in base-R.

The {pluck} function from {purrr} ๐Ÿ“ฆ provides a less tiresome way to index ๐Ÿ˜Œhttps://t.co/ePJ6HHVKm4#rstats #DataScience pic.twitter.com/nhHEJKjkAu

— R Function A Day (@rfunctionaday) February 17, 2021
+ +## February 18 {-} + +

For comparing performance of different functions, or plain out of curiosity, sometimes we wish to benchmark an expression, and the {mark} function from {bench} ๐Ÿ“ฆ makes this very easy! ๐Ÿ‹๏ธโ€โ™€๏ธhttps://t.co/0qP7sY7J9c

Note how {cor} is faster than {cor.test}.#rstats #DataScience pic.twitter.com/8AGUg3rE4O

— R Function A Day (@rfunctionaday) February 18, 2021
+ +## February 19 {-} + +

Nothing improves the readability of the code like a style guide, and this is exactly what {style_*} function family from {styler} ๐Ÿ“ฆ does! ๐Ÿ’…

The easiest thing to do is to run this function in the directory with your R scripts ๐Ÿงน https://t.co/Xjh4j4HuhQ#rstats #DataScience pic.twitter.com/NBhgffztFN

— R Function A Day (@rfunctionaday) February 19, 2021
+ +## February 20 {-} + +

The #rstats has a native {IN} operator but sometimes you might miss the {NOT IN} operator from SQL. ๐Ÿ˜ช

The {% raw %}{%nin%}{% endraw %} operator from {sjmisc} has you covered! ๐Ÿ‘https://t.co/7IzXkPxlYD#rstats #DataScience pic.twitter.com/5dpgqHa8Vj

— R Function A Day (@rfunctionaday) February 20, 2021
+ +## February 21 {-} + +

If you are well-versed in SQL and looking to learn {dplyr}, the {show_dplyr} function from {tidyquery} ๐Ÿ“ฆ can be a helpful teaching assistant while translating from a SQL query to equivalent {dplyr} code! ๐Ÿ‘ฉโ€๐Ÿซhttps://t.co/gT4ESz4h4y#rstats #DataScience pic.twitter.com/duKYJRuJN0

— R Function A Day (@rfunctionaday) February 21, 2021
+ +## February 22 {-} + +

Although a number of functions tend to have a {data} argument, some donโ€™t. For such functions, the pipe operator (%>%) from {magrittr} ๐Ÿ“ฆ wonโ€™t work.

In such contexts, one can use the exposition pipe operator (%$%) ๐Ÿ‘https://t.co/1RODRqilaL#rstats #DataScience pic.twitter.com/qxFmzJROKU

— R Function A Day (@rfunctionaday) February 22, 2021
+ +## February 23 {-} + +

The first important step of any data analysis workflow is to make sure that everything about your data โ€œmakes senseโ€ and a few other tools out there do as good of a job describing your data as {skim} function from {skimr} ๐Ÿ“ฆ!https://t.co/vg3t4v8Ixq#rstats #DataScience pic.twitter.com/FtpFi8ReZY

— R Function A Day (@rfunctionaday) February 23, 2021
+ +## February 24 {-} + +

The infix operator (%||%) from {rlang} ๐Ÿ“ฆ can be helpful for having a default value in case it is {NULL} ๐Ÿช„https://t.co/qdMs5VAgjo

Helpful for collaborative scripts or ๐Ÿ“ฆ functions where users might enter different spellings to specify the same argument.#rstats #DataScience pic.twitter.com/RDeKBpcFD1

— R Function A Day (@rfunctionaday) February 24, 2021
+ +## February 25 {-} + +

Checking association between variables often involves carrying out correlation analysis and a few functions make this as easy as {correlation} from eponymous ๐Ÿ“ฆhttps://t.co/VGZhhq9L0P

Supports a huge variety of correlation methods.#rstats #DataScience pic.twitter.com/piTCGqE5Ek

— R Function A Day (@rfunctionaday) February 25, 2021
+ +## February 26 {-} + +

If you want to conditionally select values in a dataframe, you can use {dplyr::filter}, but what if you want to do the same for a vector or a list?

The {keep} function from {purrr} does exactly that! ๐Ÿฑ https://t.co/pAXndQ9Pl2#rstats #DataScience pic.twitter.com/6yixL2ZKae

— R Function A Day (@rfunctionaday) February 26, 2021
+ +## February 27 {-} + +

JSON data representation format is ubiquitous and some time we might need to convert our dataframe in R to a JSON object.

The {toJSON} function from {jsonlite} makes this a childโ€™s play ๐Ÿ‘ถhttps://t.co/0lOvOE19EW#rstats #DataScience pic.twitter.com/PAbVQy4Kiz

— R Function A Day (@rfunctionaday) February 27, 2021
+ +## February 28 {-} + +

When you are working with free-form survey inputs, you need to consider the possibility of mistakes in data entry.

In such contexts {stringdist_join} function from {fuzzyjoin} ๐Ÿ“ฆ can save you headache while joining dataframes! โœŒ๏ธhttps://t.co/HNANB6IjH2#rstats #DataScience pic.twitter.com/b3HT9Soqoa

— R Function A Day (@rfunctionaday) February 28, 2021
diff --git a/_book/January-2021.md b/_book/January-2021.md new file mode 100644 index 0000000..4fa521d --- /dev/null +++ b/_book/January-2021.md @@ -0,0 +1,34 @@ +# January 2021 {-} + +## January 24 {-} + +

Use {tabyl} function from {janitor} to quickly prepare a frequency table ๐Ÿ‘Œ

Additionally, you can also use {adorn_} functions to make the printing even prettier!#RStats #DataScience pic.twitter.com/3BbKO83CSE

— R Function A Day (@rfunctionaday) January 24, 2021
+ +## January 25 {-} + +

Often you might want to make a new column as a combination of a few other columns in a dataframe and {tidyr::unite} is exactly what you are looking for! ๐Ÿช„ #rstats #DataScience pic.twitter.com/BeNs5nXz0T

— R Function A Day (@rfunctionaday) January 25, 2021
+ +## January 26 {-} + +

Use the powerful parser functions from {readr} to parse a vector into the expected type ๐Ÿ—‚

Helpful with messy, user-entered data where these functions can make educated guesses about what the input was supposed to be.#rstats #DataScience pic.twitter.com/5MGHmEM9Pu

— R Function A Day (@rfunctionaday) January 26, 2021
+ +## January 27 {-} + +

The {model_performance} function from {performance} ๐Ÿ“ฆ is a quick way to get detailed regression model summary indices for a huge no. of models! ๐Ÿ“https://t.co/vMpMVbz0x5#rstats #DataScience pic.twitter.com/VzPRICbIAg

— R Function A Day (@rfunctionaday) January 27, 2021
+ +## January 28 {-} + +

Tibbles are modern data frames and are ubiquitous in the {tidyverse}. But what if you want to build a tibble from scratch?

The {tribble} function provides an intuitive row-by-row syntax to do so! ๐Ÿงฑ๐Ÿ‘ทhttps://t.co/UBRRJLyM47#rstats #DataScience pic.twitter.com/X8KlYkRCTF

— R Function A Day (@rfunctionaday) January 28, 2021
+ +## January 29 {-} + +

Some functions may remove {NA}s in the data by default when one wishes to retain them.

{replace_na} from {tidyr} can help here by replacing {NA}s with a specified value ๐Ÿ•ต๏ธโ€โ™€๏ธhttps://t.co/y4Of3tcexf#rstats #DataScience pic.twitter.com/XIodtKU7Xn

— R Function A Day (@rfunctionaday) January 29, 2021
+ +## January 30 {-} + +

If you are feeling a bit ambitious and want to understand what is the abstract syntactic structure of your R code, you can use the {ast} function from {lobstr} package to draw a tree representation! ๐Ÿฆž https://t.co/WbyECG3a4H#rstats #datascience pic.twitter.com/cgMZDz1vz1

— R Function A Day (@rfunctionaday) January 30, 2021
+ +## January 31 {-} + +

"All work and no play makes Jack a dull boy"
So it's important to also have a little fun as part of your #rstats workflows.

To that end, you can use {say} function from {cowsay}! ๐Ÿฎ ๐Ÿ’ฌ https://t.co/SmDKtEHbMC#rstats #DataScience pic.twitter.com/8AROLW8r3d

— R Function A Day (@rfunctionaday) January 31, 2021
+ diff --git a/_book/January-2022.md b/_book/January-2022.md new file mode 100644 index 0000000..f28c255 --- /dev/null +++ b/_book/January-2022.md @@ -0,0 +1,99 @@ +# January 2022 {-} + +## January 1 {-} + +

If you use {dplyr} and {tidyr} ๐Ÿ“ฆs to wrangle data, you may wish to see some feedback about what different operations did, especially in long piped chains.

The "print and pipe" operator (%P>%) from {ViewPipeSteps} ๐Ÿ“ฆ does this ๐Ÿ“https://t.co/izehRpk23g#rstats #DataScience pic.twitter.com/jEev5v27te

— R Function A Day (@rfunctionaday) January 1, 2022
+ +## January 2 {-} + +

Inspired by other popular languages (C++, JavaScript, Python, etc.), you may also wish to use arithmetic operators in R that modify objects in place.

The operator family from {infix} ๐Ÿ“ฆ provide these! ๐Ÿ‘https://t.co/LXJmDRYAfQ#rstats #DataScience pic.twitter.com/e4AKp7a6BF

— R Function A Day (@rfunctionaday) January 2, 2022
+ +## January 3 {-} + +

Bar charts are a good way to visualize categorical data, but making a well-labelled bar chart can be time-consuming.

The {PlotXTabs2} function from {CGPfunctions} ๐Ÿ“ฆ easily produces this chart (with additional statistical details)!

https://t.co/Ig9tgpd9Gu#rstats #DataScience pic.twitter.com/kZBxlLPBZ2

— R Function A Day (@rfunctionaday) January 3, 2022
+ +## January 4 {-} + +

Sometimes you need to check if a remote computer or web server is up and running.

You can do so in R using the {ping} function from {pingr} ๐Ÿ“ฆ! ๐Ÿ›Žhttps://t.co/ahm9IdRc3y#rstats #DataScience pic.twitter.com/FPZiRwyz0s

— R Function A Day (@rfunctionaday) January 4, 2022
+ +## January 5 {-} + +

Sometimes you wish to remove only certain rows or columns that are completely empty instead of partly empty.

The {remove_empty_*} function family from {janitor} ๐Ÿ“ฆ do exactly this! ๐Ÿงนhttps://t.co/j1RAFZv4pY#rstats #DataScience pic.twitter.com/QHb1bDzHzB

— R Function A Day (@rfunctionaday) January 5, 2022
+ +## January 6 {-} + +

Kaplan-Meier estimates of survival curves can help infer differences in survival times between compared groups.

The {ggsurvplot} function from {survminer} ๐Ÿ“ฆ plots such curves with rich details! ๐Ÿ‘https://t.co/n25xxlgZiV#rstats #DataScience pic.twitter.com/H1FbLNSlhv

— R Function A Day (@rfunctionaday) January 6, 2022
+ +## January 7 {-} + +

A Bland-Altman plot helps visually assess agreement between scores of any kind (psychological questionnaires, chemical assays, etc.).

The {BAC_plot} function from {ufs} ๐Ÿ“ฆ easily creates them with elegant defaults! ๐ŸŽ๐Ÿhttps://t.co/zl7vSzwe26#rstats #DataScience pic.twitter.com/g73a0A6k11

— R Function A Day (@rfunctionaday) January 7, 2022
+ +## January 8 {-} + +

Sometimes we may need to edit images in R, e.g. changing transparency, spatial anti-aliasing, converting to black and white, etc.

The {image_convert} function from {magick} ๐Ÿ“ฆ allows many options for doing so! ๐Ÿ“ธhttps://t.co/ald6jeDtOe#rstats #DataScience pic.twitter.com/ZV74ynzCtN

— R Function A Day (@rfunctionaday) January 8, 2022
+ +## January 9 {-} + +

To ensure reproducibility of R script, you may wish it to download needed package versions on a certain date.

The {create_checkpoint} function from {checkpoint} ๐Ÿ“ฆ creates a local library with the needed package versions! ๐Ÿ“‘https://t.co/L8uArRuRiN

#rstats #DataScience pic.twitter.com/T6R9b5W5yY

— R Function A Day (@rfunctionaday) January 9, 2022
+ +## January 10 {-} + +

PCA is a popular dimensionality reduction technique and sometimes you may wish to reports its results in a report.

The {tab_pca} function from {sjPlot} ๐Ÿ“ฆ produces publication-ready HTML table for PCA with elegant defaults! ๐Ÿ“https://t.co/mvakHooBSf#rstats #DataScience pic.twitter.com/tULXxnRShU

— R Function A Day (@rfunctionaday) January 10, 2022
+ +## January 11 {-} + +

A list is a non-atomic vector, and sometimes you may wish to convert (or flatten) it to an atomic one.

The {flatten} function family from {purrr} ๐Ÿ“ฆ provides helpers to do this with type stability! ๐Ÿ—œhttps://t.co/tMAS8S8RcM#rstats #DataScience pic.twitter.com/NA7zvA2Rtb

— R Function A Day (@rfunctionaday) January 11, 2022
+ +## January 12 {-} + +

Across disciplines (physics, engineering, etc.), a quiver plot helps visualize vector fields as arrows, and we may need to create such a plot in R.

The {geom_quiver} function from {ggquiver} ๐Ÿ“ฆ offers just the geometric layer! ๐ŸŒชhttps://t.co/DNwPEmLFDe#rstats #DataScience pic.twitter.com/a2RZuXtKX6

— R Function A Day (@rfunctionaday) January 12, 2022
+ +## January 13 {-} + +

Filename extensions (.py, .csv, .pdf, etc.) decide the characteristics and intended usage of files, and we may wish to work with them further in R.

The {*_ext} function family in {xfun} ๐Ÿ“ฆ provides helpers to do so! ๐Ÿ—‚https://t.co/Qq8IQsMb6Q#rstats #DataScience pic.twitter.com/l47PKMvzrL

— R Function A Day (@rfunctionaday) January 13, 2022
+ +## January 14 {-} + +

Sometimes you want to quickly compute and visualize frequencies for all categorical variables in the data.

The {inspect_cat} function from {inspectdf} ๐Ÿ“ฆ does so, while labeling most frequent levels and highlighting missing data! ๐Ÿ“Šhttps://t.co/loDxpvrDGP#rstats #DataScience pic.twitter.com/aamrTwLhQh

— R Function A Day (@rfunctionaday) January 14, 2022
+ +## January 15 {-} + +

Although often you can easily read data from a single CSV file, sometimes you have to read a dataset stored in one of the Excel spreadsheets.

The {read_xlsx} function from {readxl} ๐Ÿ“ฆ provides an easy syntax to do so! ๐Ÿ“https://t.co/TE7GJQVSS8#rstats #DataScience pic.twitter.com/XTqST8rbou

— R Function A Day (@rfunctionaday) January 15, 2022
+ +## January 16 {-} + +

To save space, IP addresses are often stored as integers, and if we receive such data, we may wish to convert them to the familiar human-readable form.

The {integer_to_ip} function from {ipaddress} ๐Ÿ“ฆ makes this conversion easy! ๐Ÿ•ธhttps://t.co/IqK3ihOIjc#rstats #DataScience pic.twitter.com/CvDYbKPuej

— R Function A Day (@rfunctionaday) January 16, 2022
+ +## January 17 {-} + +

A horizon plot is a compact time-series data visualization to plot and compare different moving values.

The {geom_horizon} function from {ggHoriPlot} ๐Ÿ“ฆ provides just the geometric layer! โฌ†๏ธโฌ‡๏ธhttps://t.co/NSA3RYIoiE#rstats #DataScience pic.twitter.com/7uqC45DQOf

— R Function A Day (@rfunctionaday) January 17, 2022
+ +## January 18 {-} + +

Sometimes, especially in the context of statistical modeling, there might be infinite or NaN's present in outputs that we may wish to replace with NAs.

The {zap_inf} helper function from {sjmisc} ๐Ÿ“ฆ does exactly this! โ™พhttps://t.co/mrWehQheOi#rstats #DataScience pic.twitter.com/CL7qiucGd7

— R Function A Day (@rfunctionaday) January 18, 2022
+ +## January 19 {-} + +

Python dictionary is an unordered data type with key-value pairs that allows accessing values, not by indexing, but via unique keys.

The {py_dict} function from {reticulate} ๐Ÿ“ฆ gives you access to this data type in R! ๐Ÿ“–https://t.co/stxGhcedjI#rstats #DataScience pic.twitter.com/C7jiezmfad

— R Function A Day (@rfunctionaday) January 19, 2022
+ +## January 20 {-} + +

Sometimes you may wish to include some patterns or images in plots for enhanced storytelling.

The {image*} or {pattern*} function family from {patternplot} ๐Ÿ“ฆ provides a way to do so! โšœ๏ธhttps://t.co/lRhdnmbQoQ#rstats #DataScience pic.twitter.com/S1ze86OJTk

— R Function A Day (@rfunctionaday) January 20, 2022
+ +## January 21 {-} + +

Serialization changes objects to a byte stream that can be saved to a binary file, while deserializing does the reverse.

The {q*} function family from {qs} ๐Ÿ“ฆ provides a performant way to serialize or deserialize any R object! โšก๏ธhttps://t.co/i8BNgtAQWw#rstats #DataScience pic.twitter.com/5yTJWk3z5Z

— R Function A Day (@rfunctionaday) January 21, 2022
+ +## January 22 {-} + +

Visualizing data along an Archimedean spiral can efficiently reveal periodic patterns in time series data.

The {spiral_*} function family from {spiralize} ๐Ÿ“ฆ draws such spiral plots flexibly! ๐ŸŒ€https://t.co/5vybsvWv2e#rstats #DataScience pic.twitter.com/c168IWhcXL

— R Function A Day (@rfunctionaday) January 22, 2022
+ +## January 23 {-} + +

While working in a piped workflow, if you need to extract a single column, the syntax to do so can be a bit cumbersome.

The {pull} function from {dplyr} ๐Ÿ“ฆ provides a more readable syntax to this end! ๐Ÿ’„https://t.co/rCT1vcMZzX#rstats #DataScience pic.twitter.com/vEs2cc7Lw8

— R Function A Day (@rfunctionaday) January 23, 2022
+ +## January 24 {-} + +

If we need to bind multiple dataframes by rows, we may first wish to check if this can be done successfully.

The {compare_df_cols} function from {janitor} ๐Ÿ“ฆ provides a summary of data types to check this! ๐ŸŽ๐Ÿhttps://t.co/88zYQFdIWa#rstats #DataScience pic.twitter.com/kSCLESOBP9

— R Function A Day (@rfunctionaday) January 24, 2022
+ + diff --git a/_book/July-2021.md b/_book/July-2021.md new file mode 100644 index 0000000..f3b9c7f --- /dev/null +++ b/_book/July-2021.md @@ -0,0 +1,125 @@ +# July 2021 {-} + +## July 1 {-} + +

If you share code plus data, it's a good idea to adopt a few defensive techniques to ensure that the data are what the code expects.

The {assert_that} function from {assertthat} ๐Ÿ“ฆ provides just the tool! ๐Ÿฅ‹https://t.co/kQT74S0AQX#rstats #DataScience pic.twitter.com/ScTmUxQxnr

— R Function A Day (@rfunctionaday) July 1, 2021
+ +## July 2 {-} + +

Either for aesthetic or for highlighting purposes, you may sometimes wish to draw borders around legend keys in {ggplot2} plots. ๐Ÿ–ผ๏ธ

The {keybox} from {ggfun} ๐Ÿ“ฆ does exactly this, easily and flexibly ๐Ÿ™Œhttps://t.co/dBAjEqKzY2#rstats #DataScience pic.twitter.com/mvFxIeInyj

— R Function A Day (@rfunctionaday) July 2, 2021
+ +## July 3 {-} + +

If you don't use RMarkdown and copy-paste software output to report statistics, you'd want to check that no errors were made in the process.

The {statcheck} function from eponymous ๐Ÿ“ฆ does this (for single or multiple files)! ๐Ÿ”https://t.co/HdW6CKKPO8#rstats #DataScience pic.twitter.com/tgC5ysvz6T

— R Function A Day (@rfunctionaday) July 3, 2021
+ +## July 4 {-} + +

Correctly specifying a distribution family for regression model can improve estimate accuracy. But what if weโ€™re unsure?

The {check_distribution} function from {performance} ๐Ÿ“ฆ uses Random Forest to help you reconsider the choice โš ๏ธhttps://t.co/qD7cQvNLLz#rstats #DataScience pic.twitter.com/MLhkCT2cwh

— R Function A Day (@rfunctionaday) July 4, 2021
+ +## July 5 {-} + +

While scraping web data in R, sometimes we may wish that the extracted text layout mimics its browser/HTML behavior (e.g. ignore whitespace).

The {html_text2} function from {rvest} ๐Ÿ“ฆ helps with exactly this! ๐Ÿ™‡โ€โ™€๏ธhttps://t.co/QJwe3nXx43#rstats #DataScience pic.twitter.com/T7NU9MYKqL

— R Function A Day (@rfunctionaday) July 5, 2021
+ +## July 6 {-} + +

Simple slopes analyses can help understand interaction effects in linear regression.

The {sim_slopes} function from {interactions} ๐Ÿ“ฆ provides an easy way to both run and visualize this analysis for 2-way or 3-way interactions! ๐ŸŽš๏ธhttps://t.co/4Xvc4FMrEr#rstats #DataScience pic.twitter.com/70AFS2QQcF

— R Function A Day (@rfunctionaday) July 6, 2021
+ +## July 7 {-} + +

A histogram is a good visualization to represent the distribution of numeric data. ๐Ÿ“Š

The {gghistostats} function from {ggstatsplot} ๐Ÿ“ฆ provides ready-made histograms (with additional descriptive and inferential statistics) ๐Ÿ’https://t.co/zSmT4MBGjX#rstats #DataScience pic.twitter.com/xMFwXedQSO

— R Function A Day (@rfunctionaday) July 7, 2021
+ +## July 8 {-} + +

If you want to use non-standard fonts or characters, getting graphics devices to work with them can be a pain.

The {showtext_auto} function from {showtext} ๐Ÿ“ฆ supports a large collection of font formats and graphics devices! ๐Ÿ™Œhttps://t.co/bt3SzZfyhI#rstats #DataScience pic.twitter.com/bYmyfZEfhL

— R Function A Day (@rfunctionaday) July 8, 2021
+ +## July 9 {-} + +

Sometimes we may wish to provide descriptive labels for colors we are using, but may not know how to label them.

The {name} function from {ColorNameR} ๐Ÿ“ฆ produces color labels in multiple languages and colorspaces! ๐ŸŽจhttps://t.co/RVkqjq7Ksz#rstats #DataScience pic.twitter.com/MmD1MWPHF3

— R Function A Day (@rfunctionaday) July 9, 2021
+ +## July 10 {-} + +

Sometimes we may wish to check all relevant assumptions for a regression model in one go. ๐Ÿ”

The {check_model} function from {performance} ๐Ÿ“ฆ does exactly this and also provides elegant visualizations with helpful pointers โœ…๐Ÿ“Šhttps://t.co/4SIIL0u9Jn#rstats #DataScience pic.twitter.com/EMmye4qAZk

— R Function A Day (@rfunctionaday) July 10, 2021
+ +## July 11 {-} + +

If you are writing manuscripts in RMarkdown, you may wish to auto-generate citations for all R packages used in the document. โœ๏ธ

The {write_bib} function from {knitr} ๐Ÿ“ฆ does exactly this! ๐Ÿ“„https://t.co/JYux01tB7h#rstats #DataScience pic.twitter.com/YvOiaPeYfY

— R Function A Day (@rfunctionaday) July 11, 2021
+ +## July 12 {-} + +

While plotting time series data, we may wish to plot several subseries corresponding to the periods of interest (seasons, months, etc.).

The {ggfreqplot} function from {ggfortify} ๐Ÿ“ฆ makes this task effortless! ๐Ÿ“ˆ๐Ÿ“‰ https://t.co/rRmcQbKv4U#rstats #DataScience pic.twitter.com/Vic17LXkM9

— R Function A Day (@rfunctionaday) July 12, 2021
+ +## July 13 {-} + +

If ๐Ÿ“ฆs being used happen to have a function with an identical name, using that function *may* fail.

Aside from `::` qualifier, {conflicted_prefer} function from {conflicted} ๐Ÿ“ฆ can solve this conflict by prioritizing one function ๐Ÿฅ‡https://t.co/n96Mt8kHx3#rstats #DataScience pic.twitter.com/V9jZwxADoM

— R Function A Day (@rfunctionaday) July 13, 2021
+ +## July 14 {-} + +

Exploratory data analysis often involves specifying and comparing multiple regression models.

The {compare_parameters} function from {parameters} ๐Ÿ“ฆ provides dot-and-whisker plots to display and compare regression estimates! ๐Ÿฅ‡๐Ÿฅˆ๐Ÿฅ‰https://t.co/vmuGta5W9i#rstats #DataScience pic.twitter.com/Xyve8SMk1Q

— R Function A Day (@rfunctionaday) July 14, 2021
+ +## July 15 {-} + +

Either computing environment or good practice recommendations may compel you to check for file paths, character encoding, etc. in your script. ๐Ÿ”

The {is_} function family from {xfun} ๐Ÿ“ฆ provides tools to run such checks easily โœ…https://t.co/LdRTIKXOCA#rstats #DataSciencee pic.twitter.com/YCtgZEufae

— R Function A Day (@rfunctionaday) July 15, 2021
+ +## July 16 {-} + +

Sometimes we may wish to assess the polarity (positive, negative, neutral) of text data.

The {sentiment} function from {sentimentr} ๐Ÿ“ฆ provides a convenient and flexible way to approximate the sentiment of the text by sentence โž•โž–https://t.co/mMpxUxXyVP#rstats #DataScience pic.twitter.com/9oXGUwVTnf

— R Function A Day (@rfunctionaday) July 16, 2021
+ +## July 17 {-} + +

During exploratory phase, we may wish to visualize and model data quickly and thoroughly. โžฟ

The {ggwithinstats} function from {ggstatsplot} ๐Ÿ“ฆ does this for one-way repeated measures designs via plots with statistical details ๐Ÿ“Šhttps://t.co/lOT7qa37z8#rstats #DataScience pic.twitter.com/UxoUQr1cvB

— R Function A Day (@rfunctionaday) July 17, 2021
+ +## July 18 {-} + +

To keep related data together, you might sometimes create dataframes with columns that themselves contain dataframes. ๐Ÿงณ

Since working with them can be a pain, the {unpack} function from {tidyr} ๐Ÿ“ฆ helps you "unpack" them! ๐Ÿ”จhttps://t.co/rt0ekSiMUg#rstats #DataScience pic.twitter.com/m2O8OQRDgF

— R Function A Day (@rfunctionaday) July 18, 2021
+ +## July 19 {-} + +

If you use {dplyr} ๐Ÿ“ฆ for data analysis, you may sometimes wish to carry out statistical analysis on a grouped data frame.

If the statistical function requires the whole dataframe, the {cur_data} function provides just the tool! ๐Ÿ—„๏ธhttps://t.co/IemOGQqqrH#rstats #DataScience pic.twitter.com/MzEnBq0tp0

— R Function A Day (@rfunctionaday) July 19, 2021
+ +## July 20 {-} + +

Visualizing variable distribution via violin plot is easy in {ggplot2}, but we may wish to avoid redundant mirroring of the density plot.

The {stat_density_ridges} function from {ggridges} ๐Ÿ“ฆ provides just the geometric layer! ๐Ÿ™Œhttps://t.co/x6yAh0hHW9#rstats #DataScience pic.twitter.com/V3JhOamnFb

— R Function A Day (@rfunctionaday) July 20, 2021
+ +## July 21 {-} + +

During analysis, model selection may involve the specification of multiple models and formally testing if they are different.

The {test_performance} function from {performance} ๐Ÿ“ฆ performs and summarizes indices from these tests ๐Ÿฅ‡https://t.co/Qd25p55XPq#rstats #DataScience pic.twitter.com/EvmEOVXNRn

— R Function A Day (@rfunctionaday) July 21, 2021
+ +## July 22 {-} + +

While visualizing data across a combination of variables, {facet_wrap} in {ggplot2} creates small multiples. But what if the variables are nested?

The {facet_nested_wrap} function from {ggh4x} ๐Ÿ“ฆ handles exactly such designs! ๐Ÿ“Š๐Ÿ™Œhttps://t.co/WVp154GvPA#rstats #DataScience pic.twitter.com/P6ZjOUF9eV

— R Function A Day (@rfunctionaday) July 22, 2021
+ +## July 23 {-} + +

Sometimes you may wish to do something in R and can't think of any package that might be helpful.

In such cases, the {findPackage} function from {packagefinder} ๐Ÿ“ฆ can search and return relevant CRAN packages given the keywords ๐Ÿ”https://t.co/0IiwJtafTq#rstats #DataScience pic.twitter.com/n946xE4cAS

— R Function A Day (@rfunctionaday) July 23, 2021
+ +## July 24 {-} + +

Sometimes you may wish to write SQL queries (for practice?) without access to a database. ๐Ÿ˜ฉ

In such cases, you can use the {dbwritetable} function from {DBI} ๐Ÿ“ฆ to copy a dataframe to a database table, and then write queries! โœ๏ธ๐Ÿ™Œhttps://t.co/Gu3HwjrR0S#rstats #DataScience pic.twitter.com/z6IZnW0o1o

— R Function A Day (@rfunctionaday) July 24, 2021
+ +## July 25 {-} + +

Simulating parameter draws can sometimes be a (computationally faster) alternative to bootstrapping. โฒ๏ธ

The {simulate_parameters} function from {parameters} ๐Ÿ“ฆ can run and visualize such simulations for various regression models ๐ŸŽฒhttps://t.co/SHdA3JANeq#rstats #DataScience pic.twitter.com/sRe2BhPKjW

— R Function A Day (@rfunctionaday) July 25, 2021
+ +## July 26 {-} + +

In case you are looking for an alternate, "operator" way to access object attributes in R, you can use the infix attribute accessor (%@%) from {rlang} ๐Ÿ“ฆ!๏ธhttps://t.co/C0T97lOtWV#rstats #DataScience pic.twitter.com/zAYw4JCo5i

— R Function A Day (@rfunctionaday) July 26, 2021
+ +## July 27 {-} + +

Sometimes you need to plot data from different geographical entities into a grid and may wish to preserve the original geographical orientation of the entities.

The {facet_geo} function from {geofacet} ๐Ÿ“ฆ produces such a grid! ๐Ÿ—บ๏ธhttps://t.co/IUIQ1B7rUw#rstats #DataScience pic.twitter.com/MamwLytO4F

— R Function A Day (@rfunctionaday) July 27, 2021
+ +## July 28 {-} + +

Operating on multiple columns in a row-wise manner is fairly straightforward in {dplyr} ๐Ÿ“ฆ.

In this workflow, the {c_across} function allows you to use the tidy selection syntax to select columns to operate on ๐Ÿ”https://t.co/gmFEid0WDK#rstats #DataScience pic.twitter.com/8uMiwmcxtq

— R Function A Day (@rfunctionaday) July 28, 2021
+ +## July 29 {-} + +

Either out of curiosity or to improve its performance, sometimes you may want to time your R code.

The {tic}/{toc} function from {tictoc} ๐Ÿ“ฆ provides just the tool โฒ๏ธhttps://t.co/yCu9ArIdBd#rstats #DataScience pic.twitter.com/PyHJCSm2NG

— R Function A Day (@rfunctionaday) July 29, 2021
+ +## July 30 {-} + +

To ensure reproducibility of R script, you may wish it to download needed package versions on a certain date.

The {groundhog.library} function from {groundhog} ๐Ÿ“ฆ creates a local library with the needed package versions ๐Ÿ“‘https://t.co/bOhOXRzfMZ#rstats #DataScience pic.twitter.com/1pLfn8uU4q

— R Function A Day (@rfunctionaday) July 30, 2021
+ +## July 31 {-} + +

ROC curves provide a convenient way to compare responses and predictions of a binomial model.

The {performance_roc} function from {performance} ๐Ÿ“ฆ computes AUC metric and visualizes ROC curves for a collection of models ๐Ÿฅ‡๐Ÿฅˆ๐Ÿฅ‰https://t.co/ZKty2kA5Br#rstats #DataScience pic.twitter.com/LYJgfvZHIF

— R Function A Day (@rfunctionaday) July 31, 2021
diff --git a/_book/June-2021.md b/_book/June-2021.md new file mode 100644 index 0000000..9f959ec --- /dev/null +++ b/_book/June-2021.md @@ -0,0 +1,121 @@ +# June 2021 {-} + +## June 1 {-} + +

Outliers are influential observations that can cause problems for (interpreting) regression models, and they need to be detected for further scrutiny.

The {check_outliers} function from {performance} ๐Ÿ“ฆ can do this effortlessly ๐Ÿ‹๏ธโ€โ™€๏ธhttps://t.co/kb9ipoi0l7#rstats #DataScience pic.twitter.com/Qatg8YgQAx

— R Function A Day (@rfunctionaday) June 1, 2021
+ +## June 2 {-} + +

Project Gutenberg is a digital library with > 65K full-texts freely available. Sometimes you may wish to query if a book you want to read is available ๐Ÿ“š

The {gutenberg_works} function from {gutenbergr} ๐Ÿ“ฆ provides this info! ๐Ÿ”https://t.co/xxXL0wx3wQ#rstats #DataScience pic.twitter.com/jgGngCTmqk

— R Function A Day (@rfunctionaday) June 2, 2021
+ +## June 3 {-} + +

Although visualizing variable distribution via violin plot is easy in {ggplot2}, we may wish to avoid redundant mirroring of the density plot.

The {stat_halfeye} function from {ggdist} ๐Ÿ“ฆ provides exactly such a geometric layer! ๐Ÿ™Œhttps://t.co/I3ohHjJG1s#rstats #DataScience pic.twitter.com/eVpC6OxEx9

— R Function A Day (@rfunctionaday) June 3, 2021
+ +## June 4 {-} + +

For correlation hypotheses, one may wish to simultaneously visualize both the joint and the marginal distributions.

The {ggscatterstats} function from {ggstatsplot} ๐Ÿ“ฆ does this by default (with statistical summary as a plus) ๐Ÿ’ https://t.co/09RGm3D8f5#rstats #DataScience pic.twitter.com/939n98ag9O

— R Function A Day (@rfunctionaday) June 4, 2021
+ +## June 5 {-} + +

At times, we may wish to create "safe" versions of functions that notify us if the function fails but also doesn't error and stop the workflow that relies on it.

The {safely} function from {purrr} ๐Ÿ“ฆ creates such "safe" functions ๐Ÿช‚https://t.co/TgO3VCFznT#rstats #DataScience pic.twitter.com/Au0O7G3Lgb

— R Function A Day (@rfunctionaday) June 5, 2021
+ +## June 6 {-} + +

Sometimes we need to convert an HTML to a PDF, PNG, etc. ๐Ÿ”

The {chrome_print} function from {pagedown} can do this either for a local file (including an Rmd file that outputs HTML) or for a URL ๐Ÿ“ https://t.co/IqHMH3kBVz#rstats #DataScience pic.twitter.com/J5sGQVTDbt

— R Function A Day (@rfunctionaday) June 6, 2021
+ +## June 7 {-} + +

Sometimes the data is missing because the output format doesn't repeat values until they change. Such missing values can be filled in using the next or the previous entry. ๐Ÿงฑ

The {fill} function from {tidyr} does exactly this! ๐Ÿ™Œhttps://t.co/QTlw2j4luQ#rstats #DataScience pic.twitter.com/9YLldOUtrH

— R Function A Day (@rfunctionaday) June 7, 2021
+ +## June 8 {-} + +

A test of practical equivalence checks if the effect sizes are equivalent to null effect (a range of values considered to be practically unimportant).

The {equivalence_test} from {effectsize} ๐Ÿ“ฆ performs and visualizes this test ๐Ÿ”https://t.co/r3sWIusHcH#rstats #DataScience pic.twitter.com/rurbfkD04l

— R Function A Day (@rfunctionaday) June 8, 2021
+ +## June 9 {-} + +

Tables are typically presented outside of graphics. But sometimes we may wish to display them alongside graphics, and this requires making "graphical" tables.

The {tableGrob} function from {gridExtra} ๐Ÿ“ฆ can do exactly this! ๐Ÿคฉhttps://t.co/yatEDnIiHJ#rstats #DataScience pic.twitter.com/obnLMJSSEh

— R Function A Day (@rfunctionaday) June 9, 2021
+ +## June 10 {-} + +

A scatterplot matrix elegantly visualizes multi-dimensional data with variable pair graphs arranged in a matrix with shared scales.

The {ggpairs} function from {GGally} ๐Ÿ“ฆ provides an easy and flexible way to generate them! ๐Ÿ‘ https://t.co/liWHH9PAUe#rstats #DataScience pic.twitter.com/z94y7K7IMk

— R Function A Day (@rfunctionaday) June 10, 2021
+ +## June 11 {-} + +

Sometimes you just want to quickly convert the source code from R script (.R) into a report (can be a markdown, PDF, HTML).

The {stitch} function family from {knitr} ๐Ÿ“ฆ makes this conversion effortless! ๐Ÿงถ https://t.co/F7q6zYqn84#rstats #DataScience pic.twitter.com/bPwdrVMEOw

— R Function A Day (@rfunctionaday) June 11, 2021
+ +## June 12 {-} + +

If data has grouped/clustered structure, we may wish to highlight this in a visualization. ๐Ÿชถ

The {geom_mark_ellipse} function from {ggforce} ๐Ÿ“ฆ provides a perfect geometric layer to achieve this by drawing an annotated ellipse! ๐Ÿ’ชhttps://t.co/CBfw7Uj52e#rstats #DataScience pic.twitter.com/iIkVhL6ZXc

— R Function A Day (@rfunctionaday) June 12, 2021
+ +## June 13 {-} + +

Sometimes youโ€™d wish to download fulltext for a paper to read from the comfort of R console. ๐Ÿก

The {ft_get} function from {fulltext} ๐Ÿ“ฆ can do exactly this; you only need to provide a doi! ๐Ÿ“‘https://t.co/AL9zFqN1wj#rstats #DataScience pic.twitter.com/jnAShY92I4

— R Function A Day (@rfunctionaday) June 13, 2021
+ +## June 14 {-} + +

If you have a script that takes a long time to run, you may wish to get notified when it finishes. โœ…

The {beep} function from {beepr} ๐Ÿ“ฆ can do this by producing a sound of your choosing! ๐Ÿ”Šhttps://t.co/tXSziFWu88#rstats #DataScience pic.twitter.com/okF71yE6ZV

— R Function A Day (@rfunctionaday) June 14, 2021
+ +## June 15 {-} + +

Significance-testing for regression models assumes that errors have constant variance.

The validity of this key assumption can be statistically and visually checked using the {check_heteroscedasticity} function from {performance} ๐Ÿ“ฆhttps://t.co/TOemt5pXes#rstats #DataScience pic.twitter.com/4y4txOtnSs

— R Function A Day (@rfunctionaday) June 15, 2021
+ +## June 16 {-} + +

Sometimes we might have a dataframe containing information that we ideally prefer in a list.

The {deframe} function from {tibble} ๐Ÿ“ฆ can do the needed transformation to extract a list ๐Ÿ”งhttps://t.co/dsjo0w5aYD#rstats #DataScience pic.twitter.com/uRFlJMoJ0Q

— R Function A Day (@rfunctionaday) June 16, 2021
+ +## June 17 {-} + +

While working with text data, we may wish to create page-styled visualizations (to highlight certain words, to provide a quick visual summary, etc.).

The {ggpage_plot} function from {ggpage} ๐Ÿ“ฆ creates them in {ggplot}-framework! ๐Ÿ“ƒhttps://t.co/TW6OPH5otb#rstats #DataScience pic.twitter.com/CURqBhD9cU

— R Function A Day (@rfunctionaday) June 17, 2021
+ +## June 18 {-} + +

Sometimes we may wish to print colorful output to the R terminal.

The color functions (red, green, etc.) from {crayon} ๐Ÿ“ฆ make this really easy (via ANSI escape codes)! ๐ŸŽจhttps://t.co/RGvmzBQR7B#rstats #DataScience pic.twitter.com/X7jGXxHb9h

— R Function A Day (@rfunctionaday) June 18, 2021
+ +## June 19 {-} + +

Population pyramids help visualize the overall age distribution of a population and are useful across many fields (e.g., ecology).

The {pyramid_chart} function from {ggcharts} ๐Ÿ“ฆ provides an easy syntax to create them ๐Ÿ‘https://t.co/vvhumXF3s5#rstats #DataScience pic.twitter.com/O7RB54NLnX

— R Function A Day (@rfunctionaday) June 19, 2021
+ +## June 20 {-} + +

Sometimes youโ€™d wish to download and install the needed softwares from the comfort of your R console itself. ๐Ÿก

The {install.} function family from {installr} ๐Ÿ“ฆ can do exactly this *only on Windows* OS โฌhttps://t.co/MdlaWRGskO#rstats #DataScience pic.twitter.com/RQOgFGyJYS

— R Function A Day (@rfunctionaday) June 20, 2021
+ +## June 21 {-} + +

Pie charts are a popular way to visualize categorical data, but making a well-labeled pie chart can be time-consuming.

The {ggpiestats} function from {ggstatsplot} ๐Ÿ“ฆ produces them easily (with additional statistical details) ๐Ÿ’https://t.co/8RAAjLUP7H#rstats #DataScience pic.twitter.com/Eqbsd7ACLN

— R Function A Day (@rfunctionaday) June 21, 2021
+ +## June 22 {-} + +

If you have experience with object-oriented programming (OOP) languages, you may wish to learn its implementation in R. ๐Ÿ‘ฉโ€๐Ÿ’ป

The first step would be to know object types in R, and the {otype} function from {sloop} ๐Ÿ“ฆ makes this easy!https://t.co/cfl3iD4n9m#rstats #DataScience pic.twitter.com/avIaOGvlHC

— R Function A Day (@rfunctionaday) June 22, 2021
+ +## June 23 {-} + +

Even if you know HTML, sometimes you may wish to generate the HTML code/tags using R itself.

The tag builder function family from {htmltools} ๐Ÿ“ฆ makes this straightforward!https://t.co/X0cN4XB0WC

Potentially also helpful for self-teaching HTML via R. ๐Ÿ‘ฉโ€๐Ÿซ#rstats #DataScience pic.twitter.com/Tv8jES7TYt

— R Function A Day (@rfunctionaday) June 23, 2021
+ +## June 24 {-} + +

Significance-testing for ANOVAs assumes that all comparison groups have the same variance.

The validity of this assumption can be statistically and visually checked using the {check_homogeneity} function from {performance} ๐Ÿ“ฆ! โœ…https://t.co/YVlloAfSdo#rstats #DataScience pic.twitter.com/advuxCgV6t

— R Function A Day (@rfunctionaday) June 24, 2021
+ +## June 25 {-} + +

While writing in RMarkdown, we may be required to embed local or remote assets to enrich the document.

The {include_} function family from {knitr} ๐Ÿ“ฆ makes this easy and also provides many advantages over Markdown syntax ๐Ÿงฐhttps://t.co/3ffCSIRvCw#rstats #DataScience pic.twitter.com/gbOUCChpXX

— R Function A Day (@rfunctionaday) June 25, 2021
+ +## June 26 {-} + +

While deciding to use a color palette, not only do we wish to know the hex color codes but also how the color *looks*.

The {paletteer_d} function from {paletteer} ๐Ÿ“ฆ does exactly this! ๐ŸŽจhttps://t.co/zNlTlQtytB

P.S. For continuous palettes: {paletteer_c}#rstats #DataScience pic.twitter.com/gASyRT94mg

— R Function A Day (@rfunctionaday) June 26, 2021
+ +## June 27 {-} + +

For multivariate data, a radar chart helps visualize multiple quantitative variables on a two-dimensional chart. ๐Ÿ•ธ๏ธ

The {ggRadar} function from {ggiraphExtra} ๐Ÿ“ฆ makes it really easy to create static or interactive radar charts! ๐Ÿ‘https://t.co/JXXAQbNOyE#rstats #DataScience pic.twitter.com/cHbCKDd8Ig

— R Function A Day (@rfunctionaday) June 27, 2021
+ +## June 28 {-} + +

Sometimes R objects need to be compared (e.g. while implementing a caching strategy), and this can be done by creating unique hash digests for them. ๐Ÿ•ต๏ธ

The {digest} function from the eponymous ๐Ÿ“ฆ does exactly this! 3โƒฃ2โƒฃhttps://t.co/dFy9o3RpbC#rstats #DataScience pic.twitter.com/GJNB48TXGc

— R Function A Day (@rfunctionaday) June 28, 2021
+ +## June 29 {-} + +

If data has grouped/clustered structure, we may wish to highlight this in a visualization. ๐Ÿชถ

The {geom_mark_hull} function from {ggforce} ๐Ÿ“ฆ provides a geometric layer to achieve this by annotating sets of points via hulls โœ๏ธhttps://t.co/fnjCPTf2Ki#rstats #DataScience pic.twitter.com/wbvFiHaEyt

— R Function A Day (@rfunctionaday) June 29, 2021
+ +## June 30 {-} + +

Clustering algorithms can often disagree on the number of clusters present in the data.

To address this, the {n_clusters} function from {parameters} ๐Ÿ“ฆ runs 28 different algorithms and suggests a number based on maximum consensus ๐Ÿค–https://t.co/DIxPXIhhLg#rstats #DataScience pic.twitter.com/gMIT5jd1QO

— R Function A Day (@rfunctionaday) June 30, 2021
diff --git a/_book/March-2021.md b/_book/March-2021.md new file mode 100644 index 0000000..1f045bc --- /dev/null +++ b/_book/March-2021.md @@ -0,0 +1,125 @@ +# March 2021 {-} + +## March 1 {-} + +

This one is an oldie but a goodie:
The {switch} function from {base} ๐Ÿ“ฆ helps you select from a list of alternative actions depending on a condition of interest ๐Ÿ—‚https://t.co/IYYI1dIvpm

Note how, if unspecified, values can trickle down.#rstats #DataScience pic.twitter.com/6iqBh85XMY

— R Function A Day (@rfunctionaday) March 1, 2021
+ +## March 2 {-} + +

In case you want to create a new variable conditional on checking whether values for a different variable lie inside an interval, the {between} function in {dplyr} can be a big help ๐Ÿ‘Œhttps://t.co/JrEHPo5oDv

Works with date-time objects as well! ๐Ÿ™Œ#rstats #DataScience pic.twitter.com/J5TtCwLBE3

— R Function A Day (@rfunctionaday) March 2, 2021
+ +## March 3 {-} + +

Sometimes you might wish to know which ๐Ÿ“ฆ some of the unfamiliar functions are from, or where some of the objects in your session are located.

The {find} function in {base} ๐Ÿ“ฆ can be helpful here ๐Ÿ”https://t.co/vTvhBnFogF#rstats #DataScience pic.twitter.com/BG9E8cnS9w

— R Function A Day (@rfunctionaday) March 3, 2021
+ +## March 4 {-} + +

No matter the programming language I code in, I rarely wish to leave the comfort zone of #rmarkdown ๐Ÿก

But how does one check details about the languages currently supported? Using the {knit_engine} function from {knitr} ๐Ÿงถ https://t.co/bVEzn990dE#rstats #DataScience pic.twitter.com/LCHtsb1b1T

— R Function A Day (@rfunctionaday) March 4, 2021
+ +## March 5 {-} + +

If you are used to creating tibble columns sequentially, you might think a similar procedure works while creating lists, but it doesnโ€™t, at least not with {base::list}.

You can instead use {lst} function from {tibble} ๐Ÿ“ฆ! ๐Ÿ› https://t.co/K97EBCEsYz#rstats #DataScience pic.twitter.com/p152AVXFDb

— R Function A Day (@rfunctionaday) March 5, 2021
+ +## March 6 {-} + +

Most R packages are standing on the shoulders of other packages.

In case you are curious to find out which ones, you can use the {plot_dependencies} function from {deepdep} ๐Ÿ“ฆ for visualizing the said dependence:https://t.co/3tl1A7aMW3#rstats #DataScience pic.twitter.com/dUeHWz3MBA

— R Function A Day (@rfunctionaday) March 6, 2021
+ +## March 7 {-} + +

Missing values can be troublesome for some statistical analysis and need to be attended to during the data exploration phase.

The {vis_dat} function from {visdat} ๐Ÿ“ฆ makes it effortless to glean the structure of missing values ๐Ÿ”https://t.co/ciff6eE06w#rstats #DataScience pic.twitter.com/eM4uJgWApO

— R Function A Day (@rfunctionaday) March 7, 2021
+ +## March 8 {-} + +

In case you want to access statistics about your system memory without leaving R, you can use the {ps_system_memory} function from the {ps} ๐Ÿ“ฆhttps://t.co/hSVUebKkIG

The measure to watch out for is the available memory.#rstats #DataScience pic.twitter.com/D4YoWLNsku

— R Function A Day (@rfunctionaday) March 8, 2021
+ +## March 9 {-} + +

Sometimes your data is tabled, i.e. a single row corresponds to multiple observations, not one.

To convert it to tidy data, you can untable it using the {uncount} function from {tidyr} ๐Ÿ“ฆhttps://t.co/LVXog4CWVM#rstats #DataScience pic.twitter.com/r7tcMVkIFL

— R Function A Day (@rfunctionaday) March 9, 2021
+ +## March 10 {-} + +

Sometimes you have multiple functions that you always use in conjunction and wish you can compose them into a single function.

Thanks to {compose} function from {purrr} ๐Ÿ“ฆ, you can do exactly that! ๐ŸŽผhttps://t.co/zdydXfEM35#rstats #DataScience pic.twitter.com/teZ5w65uHS

— R Function A Day (@rfunctionaday) March 10, 2021
+ +## March 11 {-} + +

If you like a certain color from a palette and wish to use it in your plots, you need to find out whatโ€™s the hexadecimal code for that color.

You can use the {show_col} function from {scales} ๐Ÿ“ฆ to achieve this ๐ŸŽจhttps://t.co/vxlr8ff035#rstats #DataScience pic.twitter.com/UDN1IFMvO9

— R Function A Day (@rfunctionaday) March 11, 2021
+ +## March 12 {-} + +

If you want to substitute a certain string pattern in data, you can use {gsub}. But what if you wish to do this across *files*?

You can use the {gsub_file} function from {xfun} ๐Ÿ“ฆ to do such substitutions! ๐Ÿ”https://t.co/1PFI7vdRn0#rstats #DataScience pic.twitter.com/09C9GNbKPi

— R Function A Day (@rfunctionaday) March 12, 2021
+ +## March 13 {-} + +

If you have tidy data and wish to carry out operations that require the data to be in wide format, the โ€œspread-operate-retidyโ€ pattern can be a bit cumbersome.

The {pairwise_} function family from {widyr} ๐Ÿ“ฆ makes this less painful:https://t.co/sIcFOKSJSM#rstats #DataScience pic.twitter.com/iaQTtAmzPS

— R Function A Day (@rfunctionaday) March 13, 2021
+ +## March 14 {-} + +

While working with text data, sometimes you can have a long running paragraph that needs to be wrapped so that it is more readable (and pretty). ๐Ÿ“–

The {str_wrap} function from {stringr} does the trick! ๐Ÿ’กhttps://t.co/OF9drlXHl0#rstats #DataScience pic.twitter.com/8QVlPZ0vTW

— R Function A Day (@rfunctionaday) March 14, 2021
+ +## March 15 {-} + +

If you have regression model with interaction effect, it can be tricky to visualize the interaction in a statistically informative way.

Thankfully, {interplot} ๐Ÿ“ฆ has already done that thinking for you in its eponymous function ๐Ÿ™https://t.co/DhhrVrUxyG#rstats #DataScience pic.twitter.com/rWYxVqnpVV

— R Function A Day (@rfunctionaday) March 15, 2021
+ +## March 16 {-} + +

In case you are used to the python-style tuple assignment, you can also do something similar in R using the infix operator from {tidytidbits} ๐Ÿ“ฆ!https://t.co/0g6LDoZfmq#rstats #DataScience pic.twitter.com/M14Qt9p1rg

— R Function A Day (@rfunctionaday) March 16, 2021
+ +## March 17 {-} + +

To report posterior distribution of a Bayesian model, you might report a point estimate, uncertainty around it, and an index for importance of the effect.

You can do all this using {describe_posterior} function from {bayestestR} ๐Ÿ“ฆhttps://t.co/OsEGXjo0Nr#rstats #DataScience pic.twitter.com/68fHWiuOM1

— R Function A Day (@rfunctionaday) March 17, 2021
+ +## March 18 {-} + +

If you see a beautiful image and think to yourself: "I would really like to use that color palette in my plot!", you can do so! ๐ŸŽจ

You can extract the color palette from image using the {create_palette} function from {paletter} ๐Ÿ“ฆhttps://t.co/uZsB1ovCsf#rstats #DataScience pic.twitter.com/vMIv9eZARJ

— R Function A Day (@rfunctionaday) March 18, 2021
+ +## March 19 {-} + +

If you wish to include mathematical expressions in a plot/text, but you are more comfortable writing them in LaTeX than in Rโ€™s plotmath, you can use the {TeX} function from {latex2exp} ๐Ÿ“ฆ for a translation! ๐Ÿ‘https://t.co/cpyeNcFL1j#rstats #DataScience pic.twitter.com/gRPLIFljTa

— R Function A Day (@rfunctionaday) March 19, 2021
+ +## March 20 {-} + +

When you have to format or interpolate strings using piped data, you can do so easily using the {glue_data} function from {glue} ๐Ÿ“ฆhttps://t.co/INusSE9k2u#rstats #DataScience pic.twitter.com/YGgl1ZAGNC

— R Function A Day (@rfunctionaday) March 20, 2021
+ +## March 21 {-} + +

If you wish to embed a well-formatted HTML table containing a summary of your regression model, the {tab_model} function from {sjPlot} ๐Ÿ“ฆ can do so effortlessly:https://t.co/dtVUrfWVkB

Supports simple, mixed-effects, Bayesian, etc. regression models.#rstats #DataScience pic.twitter.com/8wZha5ohNi

— R Function A Day (@rfunctionaday) March 21, 2021
+ +## March 22 {-} + +

It can be a daunting task to construct regular expressions, especially complex ones.

The {rx_} function family from {RVerbalExpressions} ๐Ÿ“ฆ makes this more accessible by allowing one to construct regex using verbal expressions ๐Ÿ”กhttps://t.co/5G77171gYA#rstats #DataScience pic.twitter.com/MbLmKSW0KX

— R Function A Day (@rfunctionaday) March 22, 2021
+ +## March 23 {-} + +

If data has hierarchical categories, you may wish to see their breakdown into subcategories (e.g., to check if enough observations per cell for mixed-effects model).

You can easily do so using {sizetree} function from {plotrix} ๐Ÿ“ฆ!https://t.co/tH5hUetZ2A#rstats #DataScience pic.twitter.com/5d52tK8Mx7

— R Function A Day (@rfunctionaday) March 23, 2021
+ +## March 24 {-} + +

If you have a list-of-lists, sometimes you may wish to turn it inside out, i.e. convert a pair of lists into a list of pairs. ๐Ÿ”€

The {transpose} function from {purrr} ๐Ÿ“ฆ makes this easy:https://t.co/TBQHC2jw3r#rstats #DataScience pic.twitter.com/7wS8by9ysf

— R Function A Day (@rfunctionaday) March 24, 2021
+ +## March 25 {-} + +

While writing functions, either for a ๐Ÿ“ฆ or for colleagues, you can construct an elegant user-interface (UI) using {ui_} function family from {usethis} ๐Ÿ“ฆhttps://t.co/PxL3FRV8Qk

Such UI provides helpful info, esp. for functions that do multiple things.#rstats #DataScience pic.twitter.com/kCgSLY9gMz

— R Function A Day (@rfunctionaday) March 25, 2021
+ +## March 26 {-} + +

When you are visualizing data, in addition to plotting the full dataset, you may also wish to emphasize/zoom in on its subset for a special consideration.

You can do this easily with the {facet_zoom} function from {ggforce} ๐Ÿ“ฆ! ๐Ÿ™Œhttps://t.co/Dm7Lbzv0Pt#rstats #DataScience pic.twitter.com/3EKrTafXRi

— R Function A Day (@rfunctionaday) March 26, 2021
+ +## March 27 {-} + +

If in the *middle* of your piped workflow, you wish to access a side effect (e.g., plot or print something) and then continue on with chained operations, you can do so using the tee pipe operator {%T>%} from {magrittr} ๐Ÿ“ฆ ! https://t.co/ibga7yyf5X#rstats #DataScience pic.twitter.com/5H0MoPOERY

— R Function A Day (@rfunctionaday) March 27, 2021
+ +## March 28 {-} + +

For correlation hypotheses, in addition to the joint distribution in a scatterplot, one may also wish to visualize the marginal distributions for each variable.

This is easy to do using the {ggMarginal} function from {ggExtra} ๐Ÿ“ฆ!https://t.co/OgS1XsmY1U#rstats #DataScience pic.twitter.com/wE8mmJwnIx

— R Function A Day (@rfunctionaday) March 28, 2021
+ +## March 29 {-} + +

If you need to convert a vector or a list into a dataframe, you can use the {enframe} function from {tibble} ๐Ÿ“ฆ to do this conveniently:https://t.co/dsjo0w5aYD

Compare its behavior with that of {data.frame} function to see when you want to prefer it.#rstats #DataScience pic.twitter.com/BxCBR64XF8

— R Function A Day (@rfunctionaday) March 29, 2021
+ +## March 30 {-} + +

Sometimes you need to display two plots, not in a grid, but one inside the other (aka "inset" plot).

Assuming you are using the grammar of graphics approach, this is easy to do using {annotation_custom} function from {ggplot2} ๐Ÿ“ฆ:https://t.co/GTV3UX2XV6#rstats #DataScience pic.twitter.com/cVH9J0dHTJ

— R Function A Day (@rfunctionaday) March 30, 2021
+ +## March 31 {-} + +

If you need to pad strings, the appropriately named {str_pad} function from {stringr} ๐Ÿ“ฆ makes this effortless ๐Ÿงตhttps://t.co/Ar7LehWFQl

See examples below to see all the different ways in which you can do such padding.#rstats #DataScience pic.twitter.com/TZpXJSWvwv

— R Function A Day (@rfunctionaday) March 31, 2021
diff --git a/_book/May-2021.md b/_book/May-2021.md new file mode 100644 index 0000000..015cf11 --- /dev/null +++ b/_book/May-2021.md @@ -0,0 +1,125 @@ +# May 2021 {-} + +## May 1 {-} + +

In {ggplot2}, the missing values are removed by default, but sometimes we may wish to visualize them to see if there is a pattern in missingness.

The {geom_miss_point} function from {naniar} ๐Ÿ“ฆ offers exactly this functionality ๐Ÿ”https://t.co/zJrYSMhnX4#rstats #DataScience pic.twitter.com/IrXFHoLVG1

— R Function A Day (@rfunctionaday) May 1, 2021
+ +## May 2 {-} + +

While exploring data, one often needs a quick and easy way to create dot-and-whisker plots of coefficient estimates (or other quantities) for regression models.

The {ggcoefstats} function from {ggstatsplot} ๐Ÿ“ฆ offers one such optionhttps://t.co/e4iZpPoNIC#rstats #DataScience pic.twitter.com/suxiOKjuLo

— R Function A Day (@rfunctionaday) May 2, 2021
+ +## May 3 {-} + +

Not all functions can properly deal with labelled data, which can cause problems for data analysis. So often one may wish to just remove all labels.

The {remove_all_labels} function from {sjlabelled} ๐Ÿ“ฆ does exactly this โœ‚๏ธhttps://t.co/9mJHPTRlVD#rstats #DataScience pic.twitter.com/dt6Pbfkn58

— R Function A Day (@rfunctionaday) May 3, 2021
+ +## May 4 {-} + +

The {ggplot2} syntax to carry out few common routines (e.g, changing legend position, axes, etc.) can be surprisingly hard to remember.

The {easy_} function family from {ggeasy} ๐Ÿ“ฆ provides memorable shortcuts for such routines ๐Ÿ™Œhttps://t.co/vqFYzcvxKZ#rstats #DataScience pic.twitter.com/eZvdrDwmNf

— R Function A Day (@rfunctionaday) May 4, 2021
+ +## May 5 {-} + +

Google Scholar is an essential platform to organize one's scholarly research output.

The {get_} function family from {scholar} ๐Ÿ“ฆ provides various helpers to extract information about the scholar, publications, citations, etc. ๐Ÿงชhttps://t.co/ndoPs9Yy8S#rstats #DataScience pic.twitter.com/lAMo79rZu3

— R Function A Day (@rfunctionaday) May 5, 2021
+ +## May 6 {-} + +

Linear and linear mixed-effects models assume normally distributed residuals and random effects.

The validity of the assumption can be statistically and visually checked using the {check_normality} function from {performance} ๐Ÿ“ฆ! ๐Ÿ”https://t.co/7XbOrSrYxq#rstats #DataScience pic.twitter.com/AK7AX7iF6o

— R Function A Day (@rfunctionaday) May 6, 2021
+ +## May 7 {-} + +

Often we wish to append visualizations of regression model estimates with a tabular summary of inferential statistics.

The {forest_model} function from {forestmodel} ๐Ÿ“ฆ satisfies these requirements via an informative forest plot ๐ŸŒณhttps://t.co/YYD6dEJXSM#rstats #DataScience pic.twitter.com/rSEodymQx4

— R Function A Day (@rfunctionaday) May 7, 2021
+ +## May 8 {-} + +

Post reproducibility crisis news, replications are on the rise across disciplines. This has led to a need to compare multiple studies.

To this end, the function {sci_figure} from {scifigure} ๐Ÿ“ฆ proves to be a helpful visual tool โš–๏ธhttps://t.co/GnwtwoUcar#rstats #DataScience pic.twitter.com/TXbndnA0Bu

— R Function A Day (@rfunctionaday) May 8, 2021
+ +## May 9 {-} + +

Sometimes the data contain implicitly missing values and one may wish to make them explicit by completing missing information.

The {complete} function from {tidyr} ๐Ÿ“ฆ does exactly this! ๐Ÿ‘https://t.co/tHbK4YAJlZ#rstats #DataScience pic.twitter.com/xbDCkW0kLG

— R Function A Day (@rfunctionaday) May 9, 2021
+ +## May 10 {-} + +

Although {patchwork} ๐Ÿ“ฆ's arithmetic syntax (`+`, `*`, etc.) to combine plots together is extremely easy and intuitive, it can be difficult to use programmatically.

The {wrap_plots} function is the perfect alternative solution!๐Ÿ’กhttps://t.co/AGLkfbPUja#rstats #DataScience pic.twitter.com/fJS5V9weJ3

— R Function A Day (@rfunctionaday) May 10, 2021
+ +## May 11 {-} + +

Sometimes one may wish to include plots or visualizations in a table, e.g. to show the distribution of a variable.

The {spec_} function family from {kableExtra} ๐Ÿ“ฆ make doing so effortless ๐Ÿ™Œhttps://t.co/X4YY8Drm7C#rstats #DataScience pic.twitter.com/9HRDTxEpB8

— R Function A Day (@rfunctionaday) May 11, 2021
+ +## May 12 {-} + +

ROPE defines part of the posterior distribution practically equivalent to a null effect and is useful for hypothesis testing.

The {rope} function from {bayestestR} ๐Ÿ“ฆ helps calculate and visualize it for Bayesian regression models:https://t.co/mfXACyKOfC#rstats #DataScience pic.twitter.com/W2iL072RP5

— R Function A Day (@rfunctionaday) May 12, 2021
+ +## May 13 {-} + +

Sometimes we might need to convert numbers into English words (e.g., for assistant systems to read them out loud). ๐Ÿ“ข

The {numbers_to_words} function from {xfun} ๐Ÿ“ฆ makes this routine effortless and customizable ๐Ÿ”ขhttps://t.co/l2mYGmQGnx#rstats #DataScience pic.twitter.com/B9atDLK3nQ

— R Function A Day (@rfunctionaday) May 13, 2021
+ +## May 14 {-} + +

The Gardnerโ€“Altman mean difference plot simultaneously displays the raw data and highlights the group difference estimate ๐Ÿ“Š

The {mean_diff} function from {dabestr} ๐Ÿ“ฆ creates such plots for two or multiple groups easily โš–๏ธhttps://t.co/dhtg2Nh62r#rstats #DataScience pic.twitter.com/fjv2Vkpi19

— R Function A Day (@rfunctionaday) May 14, 2021
+ +## May 15 {-} + +

For regression analysis with big datasets, we may at times wish to reduce memory assigned to model objects.

The {axe_} function family from {butcher} ๐Ÿ“ฆ offer ways to remove different parts of the model object to make it leaner ๐Ÿ‹๏ธhttps://t.co/ayY86U0zOy#rstats #DataScience pic.twitter.com/9CIU1NSqKU

— R Function A Day (@rfunctionaday) May 15, 2021
+ +## May 16 {-} + +

During exploratory phase, we may wish to visualize and model data quickly, thoroughly, and iteratively โžฟ

The {ggbetweenstats} function from {ggstatsplot} ๐Ÿ“ฆ facilitates this for one-way design via plots with statistical details ๐Ÿ“Šhttps://t.co/fQkmmLSOh3#rstats #DataScience pic.twitter.com/uxsJ8Odv5b

— R Function A Day (@rfunctionaday) May 16, 2021
+ +## May 17 {-} + +

One often needs to compare two values on the same axis (e.g. pre-and post-treatment scores) and a dumbbell chart is an efficient way to visualize such comparison.

The {dumbbell_chart} function from {ggcharts} ๐Ÿ“ฆ creates them easily:https://t.co/Y6YGS0h0DS#rstats #DataScience pic.twitter.com/Wpwey00gGV

— R Function A Day (@rfunctionaday) May 17, 2021
+ +## May 18 {-} + +

Programming is a challenging task and we - as a teacher, a collaborator, a developer, etc. - might wish to add a touch of encouragement to this activity sometimes.

The {praise} function from the eponymous ๐Ÿ“ฆ can be of help here ๐Ÿ’Œhttps://t.co/wImExbbaT7#rstats #DataScience pic.twitter.com/1SnLZ7ctS7

— R Function A Day (@rfunctionaday) May 18, 2021
+ +## May 19 {-} + +

After model-fitting, we often need to convert model objects into a dataframe, either for reporting or for visualizations. ๐Ÿ“œ

The {model_parameters} function from the {parameters} ๐Ÿ“ฆ does this for many regression model objects ๐ŸŒŸhttps://t.co/4jOHsZDCaz#rstats #DataScience pic.twitter.com/uI7sUSF0bz

— R Function A Day (@rfunctionaday) May 19, 2021
+ +## May 20 {-} + +

In modern scientific work, the traditional credit assignment systems seem outmoded, and a contributions table is better suited.

The {generate} function from {contribution} ๐Ÿ“ฆ can easily generate such tables in ggplot2-framework ๐Ÿ”ขhttps://t.co/FCTli0Ht1f#rstats #DataScience pic.twitter.com/IqcLPMFxOa

— R Function A Day (@rfunctionaday) May 20, 2021
+ +## May 21 {-} + +

If you are fluent in {dplyr} and wish to learn how to write SQL queries, it can be nifty to have a function that can provide a syntax translation between the two.

The {show_query} function from {dbplyr} ๐Ÿ“ฆ does just that! ๐Ÿ”„https://t.co/GAjWk5qfVC#rstats #DataScience pic.twitter.com/qxOAPD4PAX

— R Function A Day (@rfunctionaday) May 21, 2021
+ +## May 22 {-} + +

While working with a directory, we sometimes need to get more information about its contents.

Instead of using the terminal, one can simply use the {dir_ls} function from {fs} ๐Ÿ“ฆ to return a richly informative summary ๐Ÿ—‚https://t.co/s0UxLxPykG#rstats #DataScience pic.twitter.com/kAzYf2jPfB

— R Function A Day (@rfunctionaday) May 22, 2021
+ +## May 23 {-} + +

During analysis, model selection may involve specification and comparison of multiple models.

The {compare_performance} function from {performance} ๐Ÿ“ฆ creates tabular + graphical summaries comparing performance indices for models ๐Ÿฅ‡https://t.co/6cAI5ZbNkl#rstats #DataScience pic.twitter.com/CxyfyRCCHY

— R Function A Day (@rfunctionaday) May 23, 2021
+ +## May 24 {-} + +

While writing functions that print text to the user's terminal or console, we may wish to construct a pleasant command-line interface.

The {cli_} function family from {cli} ๐Ÿ“ฆ provides a collection of tools to make it pretty ๐Ÿ’…https://t.co/HJeQ6MBgue#rstats #DataScience pic.twitter.com/ZJZNBP5SAa

— R Function A Day (@rfunctionaday) May 24, 2021
+ +## May 25 {-} + +

While teaching or reporting methods, we may sometimes wish to report LaTeX equations for regression models, which can be frustrating and time-consuming.

The {extract_eq} function from {equatiomatic} ๐Ÿ“ฆ makes this really easy! ๐Ÿ™Œhttps://t.co/LyQ9bMxal0#rstats #DataScience pic.twitter.com/mlghSLFeGJ

— R Function A Day (@rfunctionaday) May 25, 2021
+ +## May 26 {-} + +

Either due to reading or formatting errors, sometimes data entries that belong together in a cell might break off across rows.

The {unbreak_vals} function from {unheadr} ๐Ÿ“ฆ makes it easy to join them using regular expressions ๐Ÿฉนhttps://t.co/gouxjvPows#rstats #DataScience pic.twitter.com/3a4ZA73xxP

— R Function A Day (@rfunctionaday) May 26, 2021
+ +## May 27 {-} + +

Bar charts are a good way to visualize categorical data but making a well-labeled bar chart can be time-consuming.

The {ggbarstats} function from {ggstatsplot} ๐Ÿ“ฆ easily produces such a chart (with additional statistical details) ๐Ÿ“Šhttps://t.co/dNl2IJAJX3#rstats #DataScience pic.twitter.com/0UySWaMxei

— R Function A Day (@rfunctionaday) May 27, 2021
+ +## May 28 {-} + +

A common data wrangling routine involves checking for presence or absence of certain combinations when the data have combinatorial structure.

The {expand} function from {tidyr} ๐Ÿ“ฆ does exactly this! ๐Ÿ‘https://t.co/oj3THZs4RW#rstats #DataScience pic.twitter.com/SjHZo7bpxv

— R Function A Day (@rfunctionaday) May 28, 2021
+ +## May 29 {-} + +

Although adding horizontal and vertical lines to {ggplot2} is easy, sometimes we need to add them both and with multiple intercepts.

The {geom_vhlines} function from {ggpp} ๐Ÿ“ฆ provides a perfect geometric layer to do so quickly! โšก๏ธhttps://t.co/gNfzDhRdhu#rstats #DataScience pic.twitter.com/1oA1ZMWDKI

— R Function A Day (@rfunctionaday) May 29, 2021
+ +## May 30 {-} + +

Across projects, we may have a similar directory structure and, therefore, we may prefer to create directories algorithmically over manually.

The {dir_create} function from {fs} ๐Ÿ“ฆ is perfect for safely doing this! ๐Ÿ—ƒ๏ธhttps://t.co/SvS78Z0hIN#rstats #DataScience pic.twitter.com/uHEXy3n1N6

— R Function A Day (@rfunctionaday) May 30, 2021
+ +## May 31 {-} + +

The {latex2exp} ๐Ÿ“ฆ is useful to convert LaTeX equations to Rโ€™s {plotmath}. But what if we instead want to do the opposite: convert {plotmath} expressions to LaTeX?

The {expr2latex} function from {simsalapar} ๐Ÿ“ฆ does exactly this! ๐Ÿ”€https://t.co/MvVrIEOdwe#rstats #DataScience pic.twitter.com/kSMSdp4gIC

— R Function A Day (@rfunctionaday) May 31, 2021
diff --git a/_book/November-2021.md b/_book/November-2021.md new file mode 100644 index 0000000..ef1c6d1 --- /dev/null +++ b/_book/November-2021.md @@ -0,0 +1,121 @@ +# November 2021 {-} + +## November 1 {-} + +

If you write documents using R, you may wish to spell check to find possible spelling mistakes.

The {spell_check_*} function family from {spelling} ๐Ÿ“ฆ makes this easy for selected text, a collection of documents, or a package ๐Ÿ”https://t.co/IxvzK0f9r1#rstats #DataScience pic.twitter.com/9bkSWUjakn

— R Function A Day (@rfunctionaday) November 1, 2021
+ +## November 2 {-} + +

Sometimes we may wish to check all relevant assumptions for a linear regression model in one go.

The {gg_diagnose} function from {lindia} ๐Ÿ“ฆ does this for lm objects and provides helpful {ggplot2} visualizations โœ…๐Ÿ“Šhttps://t.co/12Q5NT3c6P#rstats #DataScience pic.twitter.com/1bMYhDxosT

— R Function A Day (@rfunctionaday) November 2, 2021
+ +## November 3 {-} + +

Sometimes you need to display two plots, not in a grid, but one inside the other (aka "inset" plot).

Assuming you are using the grammar of graphics approach, this is easy to do using {inset_element} function from {patchwork} ๐Ÿ“ฆ! ๐Ÿ‘ฉโ€๐Ÿญhttps://t.co/H2A9Yh0W6f#rstats #DataScience pic.twitter.com/2diq1akqay

— R Function A Day (@rfunctionaday) November 3, 2021
+ +## November 4 {-} + +

Even after doing everything you can in R, sometimes you may want to further improve the performance of your code by rewriting it in C++.

The {cppFunction} from {Rcpp} ๐Ÿ“ฆprovides a convenient way to do so! โšก๏ธhttps://t.co/xL92m6Jlub#rstats #DataScience pic.twitter.com/MIlRxRciMN

— R Function A Day (@rfunctionaday) November 4, 2021
+ +## November 5 {-} + +

A dot plot is a simple yet informative way to visualize the distribution of continuous or quantitative variables.

The {geom_dotsinterval} function from {ggdist} ๐Ÿ“ฆ provides the needed geometric layer! ๐Ÿ”ต๐Ÿ”ดhttps://t.co/n67JhkqJfr#rstats #DataScience pic.twitter.com/XuqjlSWBbj

— R Function A Day (@rfunctionaday) November 5, 2021
+ +## November 6 {-} + +

When numerous key-pair values are specified in an environment, R's performance can degrade because of a significant amount of memory leakage.

The {fastmap} function from the eponymous ๐Ÿ“ฆ provides a more performant alternative! ๐Ÿ”https://t.co/H7dZxlZixI#rstats #DataScience pic.twitter.com/Hxai0xP0KZ

— R Function A Day (@rfunctionaday) November 6, 2021
+ +## November 7 {-} + +

While exploring data, one often needs a quick and easy way to create dot-and-whisker plots of coefficient estimates for regression models.

The {plot_model} function from {sjPlot} ๐Ÿ“ฆ produces such plots for a wide range models ๐Ÿ“Šhttps://t.co/S4iMdhxUbB#rstats #DataScience pic.twitter.com/azkYut7LkL

— R Function A Day (@rfunctionaday) November 7, 2021
+ +## November 8 {-} + +

While analyzing text data, if you filter out stop words, you may wish to have them readily available in R.

The {generate_stoplist} function from {tidystopwords} ๐Ÿ“ฆ provides them in more than 100 languages! ๐Ÿ›‘https://t.co/fZK57AClCG#rstats #DataScience pic.twitter.com/R4aYJzr7CF

— R Function A Day (@rfunctionaday) November 8, 2021
+ +## November 9 {-} + +

If you have a complex study design, before simulating it, you may first wish to validate it with a mock dataset.

The {check_design} function from {faux} ๐Ÿ“ฆ provides a quick and easy visual way to do so! ๐Ÿ“Šhttps://t.co/N20EV0caL7#rstats #DataScience pic.twitter.com/dcG5cpLJ3j

— R Function A Day (@rfunctionaday) November 9, 2021
+ +## November 10 {-} + +

Sometimes you may need to operate on video files in R (e.g., change file format, convert to images, etc.).

The {av_*} function family from {av} ๐Ÿ“ฆ provides numerous helpers to this end! ๐ŸŽฅhttps://t.co/v5mgjqqCQr#rstats #DataScience pic.twitter.com/21DWBeT0gq

— R Function A Day (@rfunctionaday) November 10, 2021
+ +## November 11 {-} + +

If RStudio is your preferred IDE for R, you may wish to list and change your current preferences programmatically, rather than pointing and clicking.

The {* _rstudio_prefs} function family in {rstudio.prefs} ๐Ÿ“ฆ provides this! โœ…https://t.co/IXw9ciTFZ7#rstats #DataScience pic.twitter.com/IwTJHUFjle

— R Function A Day (@rfunctionaday) November 11, 2021
+ +## November 12 {-} + +

Lorenz curves provide a good way to visualize inequality in the distribution of a variable (like wealth).

The {stat_lorenz} function from {gglorenz} ๐Ÿ“ฆprovides the needed geometric layer to create such a curve easily ๐Ÿ™Œhttps://t.co/AJpWz8rHCr#rstats #DataScience pic.twitter.com/4bCGp9mrQq

— R Function A Day (@rfunctionaday) November 12, 2021
+ +## November 13 {-} + +

Sometimes you may wish to quickly prepare a frequency table for categorical variables.

The {datasummary_crosstab} function from {modelsummary} ๐Ÿ“ฆ provides output with rich details! ๐Ÿงฎ
https://t.co/ropUkhXb1v#rstats #DataScience pic.twitter.com/plsTArmYA1

— R Function A Day (@rfunctionaday) November 13, 2021
+ +## November 14 {-} + +

If you have a number of rows in your visualization, you may wish to individuate them with the help of different background colours.

The {geom_stripped_rows} function from {GGally} ๐Ÿ“ฆ provides the perfect geometric layer! ๐Ÿฆ“https://t.co/S12QxH4r8Y#rstats #DataScience pic.twitter.com/DnEgVri8yc

— R Function A Day (@rfunctionaday) November 14, 2021
+ +## November 15 {-} + +

If you are used to doing string interpolation in R using the {glue} ๐Ÿ“ฆ, you may wish to have a similar helper while writing SQL queries.

The {glue_sql} function cleverly handles SQL quoting while writing queries โœ๏ธhttps://t.co/3zGFUR1m2e#rstats #DataScience pic.twitter.com/jeAVIRZaYS

— R Function A Day (@rfunctionaday) November 15, 2021
+ +## November 16 {-} + +

Given the diversity of time series classes in the R ecosystem, you may wish to have a universal converter for interchanging classes.

The {ts_*} function family in {tsbox} ๐Ÿ“ฆ provides just the tools! ๐Ÿ› https://t.co/yqNT5DaLns#rstats #DataScience pic.twitter.com/Yf8vMAMIjt

— R Function A Day (@rfunctionaday) November 16, 2021
+ +## November 17 {-} + +

For correlation hypotheses, one may wish to simultaneously visualize both the joint and the marginal distributions.

The {ScatterHist} function from {WVPlots} ๐Ÿ“ฆ does this by default (with statistical summary as a plus) ๐Ÿ’https://t.co/DjZxh7OGG5#rstats #DataScience pic.twitter.com/Se1tOBNLR0

— R Function A Day (@rfunctionaday) November 17, 2021
+ +## November 18 {-} + +

If you use project-oriented workflows, instead of changing a working directory, you may wish to construct file paths relative to the top-level directory.

The eponymous function from {here} ๐Ÿ“ฆ provides this functionality ๐Ÿ  https://t.co/qutCsLDu8e#rstats #DataScience pic.twitter.com/0HYc2zVDu6

— R Function A Day (@rfunctionaday) November 18, 2021
+ +## November 19 {-} + +

Sometimes you need to rank-transform your data, present either in a vector or in a dataframe.

The {ranktransform} function from {datawizard} ๐Ÿ“ฆ allows you to do this quite flexibly! 1๏ธโƒฃ2๏ธโƒฃ3๏ธโƒฃhttps://t.co/S5i3g69jn6#rstats #DataScience pic.twitter.com/ySPWE7VpFe

— R Function A Day (@rfunctionaday) November 19, 2021
+ +## November 20 {-} + +

Pie charts provide an informative but imperfect way to visualize categorical data, but squared pie (or waffle) charts overcome some of these imperfections.

The {waffle} function from the eponymous ๐Ÿ“ฆ easily produces them ๐Ÿง‡ https://t.co/OxtCuCMhHF#rstats #DataScience pic.twitter.com/1ui2OqHJ8k

— R Function A Day (@rfunctionaday) November 20, 2021
+ +## November 21 {-} + +

Apache Spark is a popular platform for large-scale data processing, and you may wish to use the familiar dataframe-based workflows with it from R.

The {copy_to} function from {sparklyr} ๐Ÿ“ฆ can convert dataframes to Spark objects! โšก๏ธhttps://t.co/YpsY9738WF#rstats #DataScience pic.twitter.com/TPWcsNraPK

— R Function A Day (@rfunctionaday) November 21, 2021
+ +## November 22 {-} + +

Sometimes we wish to combine various figures into a single, compound figure to convey an overarching point.

The {plot_grid} function from {cowplot} ๐Ÿ“ฆ provides a way to do so flexibly! ๐Ÿง‘โ€๐Ÿญhttps://t.co/5cQUrPQpJz#rstats #DataScience pic.twitter.com/VONCYetz1x

— R Function A Day (@rfunctionaday) November 22, 2021
+ +## November 23 {-} + +

If you use {dm} ๐Ÿ“ฆ to create data model objects representing tables in a relational database, you may wish to visualise the relations between the tables.

The {dm_draw} function does exactly this! ๐Ÿ•ธhttps://t.co/OmDHyi8Ig9#rstats #DataScience pic.twitter.com/OHIHpLbdgG

— R Function A Day (@rfunctionaday) November 23, 2021
+ +## November 24 {-} + +

Either while debugging or learning a new function, you may wish to understand its implementation logic.

The {flow_view} function from {flow} ๐Ÿ“ฆ helps visualizes this logic via a flow diagram! ๐Ÿ”ท๐Ÿ”ถhttps://t.co/f5ubm3fq7T#rstats #DataScience pic.twitter.com/yR7AS9stSk

— R Function A Day (@rfunctionaday) November 24, 2021
+ +## November 25 {-} + +

During the data-wrangling stage, sometimes you may wish to compare how the dataframe at the current stage differs from the original one.

The {rCompare} function from {dataCompareR} ๐Ÿ“ฆ provides just the tool! ๐ŸŽ๐Ÿhttps://t.co/btUd1vK0dj#rstats #DataScience pic.twitter.com/x8xGL4MUqn

— R Function A Day (@rfunctionaday) November 25, 2021
+ +## November 26 {-} + +

Sometimes we may wish to assess the polarity (positive, negative, neutral) of text data.

The {get_sentiment} function from {syuzhet} ๐Ÿ“ฆ provides a convenient way to approximate the sentiment of the text by sentence! โž•โž–https://t.co/Pv9tCBxzYM#rstats #DataScience pic.twitter.com/us9PiBzRcb

— R Function A Day (@rfunctionaday) November 26, 2021
+ +## November 27 {-} + +

The first important step of a data analysis workflow is to make sure that everything about your data โ€œmakes senseโ€.

The {diagnose} function from {dlookr} ๐Ÿ“ฆ provides a detailed data diagnosis report that makes this step easy! ๐Ÿ”https://t.co/UoUEdrfUTU#rstats #DataScience pic.twitter.com/eVxrpd0rjc

— R Function A Day (@rfunctionaday) November 27, 2021
+ +## November 28 {-} + +

Chloropleth maps provide a convenient way to show variation in a quantitative variable across locations.

The {mf_map} function from {mapsf} ๐Ÿ“ฆ easily draws such maps with elegant defaults! ๐Ÿ—บhttps://t.co/QitknL32Jh#rstats #DataScience pic.twitter.com/JMCtpdEYnY

— R Function A Day (@rfunctionaday) November 28, 2021
+ +## November 29 {-} + +

If we want to print an HTML document produced by Rmarkdown, we may also wish that it were paginated.

The {html_paged} function from {pagedown} ๐Ÿ“ฆ provides an HTML document split into multiple pages! ๐Ÿ“‘๐Ÿ–จhttps://t.co/wMofeBVYuc#rstats #DataScience pic.twitter.com/L1sXZrh4Xz

— R Function A Day (@rfunctionaday) November 29, 2021
+ +## November 30 {-} + +

A correlation matrix compactly shows relationships between multiple variables. But, for pipe-based workflows, it is often helpful to have them in a dataframe.

The {correlate} function from {corrr} ๐Ÿ“ฆ makes it easy to extract them!https://t.co/TAMnjQQTAT#rstats #DataScience pic.twitter.com/aLN7k9uvGy

— R Function A Day (@rfunctionaday) November 30, 2021
diff --git a/_book/October-2021.md b/_book/October-2021.md new file mode 100644 index 0000000..a7f405a --- /dev/null +++ b/_book/October-2021.md @@ -0,0 +1,125 @@ +# October 2021 {-} + +## October 1 {-} + +

Correspondence analysis extends PCA to summarizes and visualizes multivariate categorical data in two-dimension plots.

The {fviz_ca} function from {factoextra} ๐Ÿ“ฆ provides elegant ggplot2-based visualizations for this analysis! ๐Ÿ’„https://t.co/mtFtXrE0VJ#rstats #DataScience pic.twitter.com/FT2vNH70Vx

— R Function A Day (@rfunctionaday) October 1, 2021
+ +## October 2 {-} + +

Text comes in a variety of encodings, and many functions for reading in text assume UTF-8 encoding. But what if this assumption doesn't hold up?

In such cases, the {utf8_valid} function from {utf8} ๐Ÿ“ฆ can find the offending text ๐Ÿ“https://t.co/vwL09X0zrq#rstats #DataScience pic.twitter.com/cU8tkACV75

— R Function A Day (@rfunctionaday) October 2, 2021
+ +## October 3 {-} + +

While exploring data, one often needs a quick and easy way to display distributions of coefficient estimates for regression models.

The {dwplot} function from {dotwhisker} ๐Ÿ“ฆ produces such plots easily and flexibly! ๐Ÿ””https://t.co/aHrGaAG8Kv#rstats #DataScience pic.twitter.com/IwGGnBQN82

— R Function A Day (@rfunctionaday) October 3, 2021
+ +## October 4 {-} + +

In the hypothesis testing framework, sometimes we may wish to generate a distribution corresponding to our null hypothesis.

The {generate} function from {infer} ๐Ÿ“ฆ easily resamples observed data to create such a distribution! ๐Ÿ“Šhttps://t.co/39GlrUrqsp#rstats #DataScience pic.twitter.com/rt60yA7YFQ

— R Function A Day (@rfunctionaday) October 4, 2021
+ +## October 5 {-} + +

Having stand-out comment lines decorated with bands of characters can make different sections of R scripts easy to identify and navigate.

The {xxx_*} function family from {ARTofR} ๐Ÿ“ฆ offers a collection of such comments ๐Ÿšจhttps://t.co/in3KGiwDXs#rstats #DataScience pic.twitter.com/c75VrfhniD

— R Function A Day (@rfunctionaday) October 5, 2021
+ +## October 6 {-} + +

Association rule mining is an unsupervised method to find attributes that frequently occur together in a dataset.

The {plot} method from {arulesViz} ๐Ÿ“ฆ can visualize found association rules in the ggplot2-framework โ›https://t.co/s4pUQAvqXA#rstats #DataScience pic.twitter.com/dDy9Fstgq1

— R Function A Day (@rfunctionaday) October 6, 2021
+ +## October 7 {-} + +

A calendar-based graphic visualizes daily activities, but getting the data in the needed format can be a challenge.

The {frame_calendar} function from {sugrrants} ๐Ÿ“ฆ provides convenient syntax to format data in the right format ๐Ÿ—“๏ธhttps://t.co/JZHB0oUb7q#rstats #DataScience pic.twitter.com/q5Vv5wcShM

— R Function A Day (@rfunctionaday) October 7, 2021
+ +## October 8 {-} + +

Sometimes you have to present results from a regression model in a well-formatted table.

The {as_flextable} function from {flextable} ๐Ÿ“ฆ can easily produce such tables with rich details ๐Ÿ—‚https://t.co/trfvqzqhvp#rstats #DataScience pic.twitter.com/cXx7iyUXh6

— R Function A Day (@rfunctionaday) October 8, 2021
+ +## October 9 {-} + +

Sometimes you may wish to sample only a portion of the data.

The {slice} function family from {dplyr} ๐Ÿ“ฆ provides helpers to do so โœ‚๏ธhttps://t.co/4tt17FnvnM#rstats #DataScience pic.twitter.com/9e8pS7KjFn

— R Function A Day (@rfunctionaday) October 9, 2021
+ +## October 10 {-} + +

In order to ensure reproducibility, we may want to include detailed session information in our report.

The {session_info} function from {sessioninfo} ๐Ÿ“ฆ creates such a log ๐Ÿ“https://t.co/C9RyxMnXNk#rstats #DataScience pic.twitter.com/QH0SkjztL6

— R Function A Day (@rfunctionaday) October 10, 2021
+ +## October 11 {-} + +

If you use a function that outputs content that you often need to copy-paste, you may wish to do this programmatically than manually.

The {write_clip} function from {clipr} ๐Ÿ“ฆ allows you to do this! โœ๏ธhttps://t.co/LI9QxOqguP#rstats #DataScience pic.twitter.com/CZM86KxLlL

— R Function A Day (@rfunctionaday) October 11, 2021
+ +## October 12 {-} + +

Sometimes you may wish to customize how {ggplot2} axes are drawn (e.g. adding brackets).

The {coord_flex_*} function family from {lemon} ๐Ÿ“ฆ supports such customizations ๐Ÿ‹https://t.co/DNHBbSx5Io#rstats #DataScience pic.twitter.com/SJzVPftyqQ

— R Function A Day (@rfunctionaday) October 12, 2021
+ +## October 13 {-} + +

Often we may wish to compute descriptive and inferential statistics by giving different weights across data points.

The {weighted_*} function family from {sjstats} ๐Ÿ“ฆ provides familiar syntax to carry out weighted analysis ๐Ÿ‹๏ธhttps://t.co/x9Ng6317As#rstats #DataScience pic.twitter.com/mEE7MIlkPE

— R Function A Day (@rfunctionaday) October 13, 2021
+ +## October 14 {-} + +

Treemaps visualize nested proportions for multiple categorical variables by recursively nesting rectangles (area = proportion) inside each other.

The {geom_treemap} function from {treemapify} ๐Ÿ“ฆ supplies needed geometric layer! ๐ŸŒณhttps://t.co/i1Pc58pewr#rstats #DataScience pic.twitter.com/9ccyphWC76

— R Function A Day (@rfunctionaday) October 14, 2021
+ +## October 15 {-} + +

Although often you can write data to a single CSV file, sometimes you may need to write several datasets to multiple Excel spreadsheets.

The {write_xlsx} function from {writexl} ๐Ÿ“ฆ provides an easy syntax to do so! ๐Ÿ“https://t.co/EsBmWyupeB#rstats #DataScience pic.twitter.com/9631CZzFM5

— R Function A Day (@rfunctionaday) October 15, 2021
+ +## October 16 {-} + +

Sometimes we may wish to center variables (subtract from mean, i.e.) in a dataframe (e.g. to interpret interaction terms in regression analysis).

The {center} function from {datawizard} ๐Ÿ“ฆ does this flexibly 0๏ธโƒฃhttps://t.co/2KaDrn47w4#rstats #DataScience pic.twitter.com/KcfRGT1qF5

— R Function A Day (@rfunctionaday) October 16, 2021
+ +## October 17 {-} + +

Sometimes you not only wish to create a visualization with {ggplot2} ๐Ÿ“ฆ, but also to add additional filters to it.

The {with_*} function family from {ggfx} ๐Ÿ“ฆ provides a rich collection of such filters! ๐Ÿ’…https://t.co/ekCrUTvFHV#rstats #DataScience pic.twitter.com/nnlVunnFyR

— R Function A Day (@rfunctionaday) October 17, 2021
+ +## October 18 {-} + +

If the data to be wrangled is highly non-rectangular, you may wish to melt it down first and then wrangle it further.

The {melt_*} function family from {meltr} ๐Ÿ“ฆ allows you to do this โ™จ๏ธhttps://t.co/gYmWI1xIQA#rstats #DataScience pic.twitter.com/3nJcnrB9rP

— R Function A Day (@rfunctionaday) October 18, 2021
+ +## October 19 {-} + +

If you work with text data, sometimes you might need to detect the text language.

The eponymous function from {franc} ๐Ÿ“ฆ can detect text from 310 languages! ๐Ÿ•ต๏ธโ€โ™‚๏ธhttps://t.co/KOSWDsHQWH#rstats #DataScience pic.twitter.com/0KZbgor1iW

— R Function A Day (@rfunctionaday) October 19, 2021
+ +## October 20 {-} + +

Sometimes you may wish to quickly prepare a frequency table but present it as a visualization.

The {ggtable} function from {GGally} ๐Ÿ“ฆ provides an easy syntax to do so in {ggplot2}-framework! ๐Ÿงฎ
https://t.co/NqgdhgImbo#rstats #DataScience pic.twitter.com/TqA4mQI06R

— R Function A Day (@rfunctionaday) October 20, 2021
+ +## October 21 {-} + +

In order to speed up, you may want to return cached results if same inputs are provided to a computationally expensive function.

The {memoise} function from the eponymous ๐Ÿ“ฆ does exactly this! โฉ https://t.co/vT2edZhCZo#rstats #DataScience pic.twitter.com/BidZlofr8v

— R Function A Day (@rfunctionaday) October 21, 2021
+ +## October 22 {-} + +

If you have a lot of overlapping data points, you could wish to simultaneously estimate their overlap and individuate them.

The {geom_pointdensity} function from {ggpointdensity} ๐Ÿ“ฆ provides the perfect geometric layer! ๐Ÿงนhttps://t.co/OeFMabDKZg#rstats #DataScience pic.twitter.com/tUtP6Jw6pI

— R Function A Day (@rfunctionaday) October 22, 2021
+ +## October 23 {-} + +

Often you need to list all active processes on your system, but instead of using a task manager, you may wish to do it from R itself.

The {ps} function from the eponymous ๐Ÿ“ฆ creates an informative dataframe of active processes ๐Ÿ—’https://t.co/KVrBiD4mUE#rstats #DataScience pic.twitter.com/ORYvnaTGV5

— R Function A Day (@rfunctionaday) October 23, 2021
+ +## October 24 {-} + +

If you are fluent in R and wish to learn SQL, it can be nifty to have a function that can provide a syntax translation between the two.

The {translate_sql} function from {dbplyr} ๐Ÿ“ฆ provides such translations ๐Ÿ”€https://t.co/cS6zPgdFNn#rstats #DataScience pic.twitter.com/Jafa31i673

— R Function A Day (@rfunctionaday) October 24, 2021
+ +## October 25 {-} + +

If the data is stored in Excel spreadsheets and needs to be imported into R, we may sometimes wish to have it in the tidy data format.

The {xlsx_cells} function from {tidyxl} ๐Ÿ“ฆ makes such import straightforward ๐Ÿ”งhttps://t.co/hcAgiuyCON#rstats #DataScience pic.twitter.com/8VFCVEDaok

— R Function A Day (@rfunctionaday) October 25, 2021
+ +## October 26 {-} + +

Donut charts are a popular way to visualize categorical data, but making a well-labelled donut chart can be time-consuming.

The {ggdonutchart} function from {ggpubr} ๐Ÿ“ฆ produces them easily! ๐Ÿฉhttps://t.co/VdLvfSyFZH#rstats #DataScience pic.twitter.com/l1k5na75wL

— R Function A Day (@rfunctionaday) October 26, 2021
+ +## October 27 {-} + +

If you do iterative analysis in list columns using {purrr} ๐Ÿ“ฆ, you understand the frustration of figuring out which iteration produces warnings or errors.

The {map_peacefully} wrapper function from {collateral} ๐Ÿ“ฆ comes to help ๐Ÿ‘https://t.co/gqFb0FM8ov#rstats #DataScience pic.twitter.com/CGzrIMSwI6

— R Function A Day (@rfunctionaday) October 27, 2021
+ +## October 28 {-} + +

Sometimes we may need to convert a {ggplot2} image in a vector graphics format to a raster image.

The {rasterise} function from {ggrastr} ๐Ÿ“ฆ can convert one or many ggplot2 layer(s) into a rasterized output ๐Ÿ”€https://t.co/nrLWwoYwEi#rstats #DataScience pic.twitter.com/e3wI9Y5bvk

— R Function A Day (@rfunctionaday) October 28, 2021
+ +## October 29 {-} + +

Google spreadsheets make it easy to create and edit files in real-time while collaborating online, and sometimes we may wish to access them from R.

The {read_sheet} function from {googlesheets4} ๐Ÿ“ฆ provides a way to do so ๐Ÿ‘https://t.co/dj4tkk9My2#rstats #DataScience pic.twitter.com/zHFgcWqNxf

— R Function A Day (@rfunctionaday) October 29, 2021
+ +## October 30 {-} + +

If you work a lot with time-of-day data, the {hms} function from the eponymous ๐Ÿ“ฆ provides a convenient way to construct and store them in a dataframe, and has consistent coercion rules ๐Ÿ•ฐhttps://t.co/0ViYWZrGLX#rstats #DataScience pic.twitter.com/jbwyxuzLiC

— R Function A Day (@rfunctionaday) October 30, 2021
+ +## October 31 {-} + +

If you have a lot of overlapping data points, you could wish to visualize their density by plotting points next to each other.

The {geom_beeswarm} function from {ggbeeswarm} ๐Ÿ“ฆ provides the perfect geometric layer! ๐Ÿhttps://t.co/YCc94sGUTN#rstats #DataScience pic.twitter.com/rbdaQtqqqB

— R Function A Day (@rfunctionaday) October 31, 2021
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    + R Function A Day +

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    About +

    +

    A book that collects (and provides an easy way to access and search) tweets from R Function A Day account that I created and maintained as a hobby project for 1 year (from 24.01.2021 to 24.01.2022).

    +

    There are 365 posts1 in this book, one for each day.

    +
    +

    +โš ๏ธโš ๏ธ Note Before โš ๏ธโš ๏ธ +

    +

    Since the tweets are embedded, the web browser will be slow to load them. So please be patient. +Apologies for these performance issues.

    +
    +

    If you find that the embedded tweet is assigned to wrong date in the book, or if you wish to add something or change something about the book layout, please file an issue on GitHub.

    + +
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    "R Function A Day" was written by Indrajeet Patil. It was last built on 2024-12-13.

    +
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    This book was built by the bookdown R package.

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    + + diff --git a/_book/index.md b/_book/index.md new file mode 100644 index 0000000..551ca3e --- /dev/null +++ b/_book/index.md @@ -0,0 +1,33 @@ +--- +title: "R Function A Day" +author: 'Indrajeet Patil' +date: "2024-12-13" +site: bookdown::bookdown_site +documentclass: book +url: https://bookdown.org/IndrajeetPatil/R-Function-A-Day-book/ +cover-image: "images/cover.jpg" +description: | + "A book that collects (and provides an easy way to access and search) tweets from *R Function A Day* [account](https://twitter.com/rfunctionaday) that I created and maintained as a hobby project for 1 year (from 24.01.2021 to 24.01.2022)." +github-repo: "IndrajeetPatil/R-Function-A-Day-book" +keywords: "CRAN, R, Book, Data Science" +# bibliography: [book.bib, packages.bib] # no references +# biblio-style: apalike # no references +# csl: chicago-fullnote-bibliography.csl # no references +--- + +# About {-} + +A book that collects (and provides an easy way to access and search) tweets from *R Function A Day* [account](https://twitter.com/rfunctionaday) that I created and maintained as a hobby project for 1 year (from **24.01.2021** to **24.01.2022**). + +There are 365 posts^[Actually, there are 366 posts to be exact. But I accidentally wrote about one function on two separate occasion, so I count them as one post. It's a fun game to find which one ๐Ÿ˜‰] in this book, one for each day. + +--- + +

    โš ๏ธโš ๏ธ Note Before โš ๏ธโš ๏ธ

    + +Since the tweets are embedded, the web browser will be slow to load them. So please be patient. +Apologies for these performance issues. + +--- + +If you find that the embedded tweet is assigned to wrong date in the book, or if you wish to add something or change something about the book layout, please file an [issue on GitHub](https://github.com/IndrajeetPatil/R-Function-A-Day-book/issues). diff --git a/_book/january-2021.html b/_book/january-2021.html new file mode 100644 index 0000000..ab463c7 --- /dev/null +++ b/_book/january-2021.html @@ -0,0 +1,205 @@ + + + + + + +January 2021 | R Function A Day + + + + + + + + + + + + + + + + + + + + +
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    "R Function A Day" was written by Indrajeet Patil. It was last built on 2024-12-13.

    +
    + +
    +

    This book was built by the bookdown R package.

    +
    + +
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    + + diff --git a/_book/january-2022.html b/_book/january-2022.html new file mode 100644 index 0000000..4ae55df --- /dev/null +++ b/_book/january-2022.html @@ -0,0 +1,397 @@ + + + + + + +January 2022 | R Function A Day + + + + + + + + + + + + + + + + + + + + +
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    "R Function A Day" was written by Indrajeet Patil. It was last built on 2024-12-13.

    +
    + +
    +

    This book was built by the bookdown R package.

    +
    + +
    +
    + + diff --git a/_book/july-2021.html b/_book/july-2021.html new file mode 100644 index 0000000..4034b4e --- /dev/null +++ b/_book/july-2021.html @@ -0,0 +1,481 @@ + + + + + + +July 2021 | R Function A Day + + + + + + + + + + + + + + + + + + + + +
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document.documentElement&&0===i.default(r).closest(".navbar-nav").length&&i.default(document.body).children().on("mouseover",null,i.default.noop),this._element.focus(),this._element.setAttribute("aria-expanded",!0),i.default(this._menu).toggleClass("show"),i.default(r).toggleClass("show").trigger(i.default.Event("shown.bs.dropdown",n))}}},e.hide=function(){if(!this._element.disabled&&!i.default(this._element).hasClass("disabled")&&i.default(this._menu).hasClass("show")){var e={relatedTarget:this._element},n=i.default.Event("hide.bs.dropdown",e),o=t._getParentFromElement(this._element);i.default(o).trigger(n),n.isDefaultPrevented()||(this._popper&&this._popper.destroy(),i.default(this._menu).toggleClass("show"),i.default(o).toggleClass("show").trigger(i.default.Event("hidden.bs.dropdown",e)))}},e.dispose=function(){i.default.removeData(this._element,"bs.dropdown"),i.default(this._element).off(".bs.dropdown"),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},e.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},e._addEventListeners=function(){var t=this;i.default(this._element).on("click.bs.dropdown",(function(e){e.preventDefault(),e.stopPropagation(),t.toggle()}))},e._getConfig=function(t){return t=a({},this.constructor.Default,i.default(this._element).data(),t),l.typeCheckConfig(Ot,t,this.constructor.DefaultType),t},e._getMenuElement=function(){if(!this._menu){var e=t._getParentFromElement(this._element);e&&(this._menu=e.querySelector(".dropdown-menu"))}return this._menu},e._getPlacement=function(){var t=i.default(this._element.parentNode),e="bottom-start";return t.hasClass("dropup")?e=i.default(this._menu).hasClass("dropdown-menu-right")?"top-end":"top-start":t.hasClass("dropright")?e="right-start":t.hasClass("dropleft")?e="left-start":i.default(this._menu).hasClass("dropdown-menu-right")&&(e="bottom-end"),e},e._detectNavbar=function(){return i.default(this._element).closest(".navbar").length>0},e._getOffset=function(){var t=this,e={};return"function"==typeof this._config.offset?e.fn=function(e){return e.offsets=a({},e.offsets,t._config.offset(e.offsets,t._element)||{}),e}:e.offset=this._config.offset,e},e._getPopperConfig=function(){var t={placement:this._getPlacement(),modifiers:{offset:this._getOffset(),flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}};return"static"===this._config.display&&(t.modifiers.applyStyle={enabled:!1}),a({},t,this._config.popperConfig)},t._jQueryInterface=function(e){return this.each((function(){var n=i.default(this).data("bs.dropdown");if(n||(n=new t(this,"object"==typeof e?e:null),i.default(this).data("bs.dropdown",n)),"string"==typeof e){if("undefined"==typeof n[e])throw new TypeError('No method named "'+e+'"');n[e]()}}))},t._clearMenus=function(e){if(!e||3!==e.which&&("keyup"!==e.type||9===e.which))for(var n=[].slice.call(document.querySelectorAll('[data-toggle="dropdown"]')),o=0,r=n.length;o0&&a--,40===e.which&&adocument.documentElement.clientHeight;n||(this._element.style.overflowY="hidden"),this._element.classList.add("modal-static");var 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zI6~Iv5J?-Q4*&oF000000FV;^004t70Z6Qk1Xl{X9oJ{sRC2(cs?- literal 0 HcmV?d00001 diff --git a/_book/libs/bs3compat-0.8.0/bs3compat.js b/_book/libs/bs3compat-0.8.0/bs3compat.js new file mode 100644 index 0000000..4f029dc --- /dev/null +++ b/_book/libs/bs3compat-0.8.0/bs3compat.js @@ -0,0 +1,48 @@ +// Inform the world that we have the ability to use BS3 nav/navbar markup in BS4 +window.BS3_COMPAT = true; + +// This logic needs to execute after both the BS4+ (new) as well as BS3 (legacy) +// jQuery plugins have been registered. For BS5, plugin registration happens +// after DOM content is loaded, which is why we do the same here. +// https://github.com/twbs/bootstrap/blob/08139c22/js/dist/tab.js#L87 +$(function() { + + // The legacy plugin needs to be registered after the new one + if (!$.fn.tab.Constructor.VERSION.match(/^3\./)) { + (console.warn || console.error || console.log)("bs3compat.js couldn't find bs3 tab impl; bs3 tabs will not be properly supported"); + return; + } + var legacyTabPlugin = $.fn.tab.noConflict(); + + if (!$.fn.tab || !$.fn.tab.Constructor || !$.fn.tab.noConflict) { + (console.warn || console.error || console.log)("bs3compat.js couldn't find a jQuery tab impl; bs3 tabs will not be properly supported"); + } + var newTabPlugin = $.fn.tab.noConflict(); + + // Re-define the tab click event + // https://github.com/twbs/bootstrap/blob/08139c2/js/src/tab.js#L33 + var EVENT_KEY = "click.bs.tab.data-api"; + $(document).off(EVENT_KEY); + + var SELECTOR = '[data-toggle="tab"], [data-toggle="pill"], [data-bs-toggle="tab"], [data-bs-toggle="pill"]'; + $(document).on(EVENT_KEY, SELECTOR, function(event) { + event.preventDefault(); + $(this).tab("show"); + }); + + function TabPlugin(config) { + // Legacy (bs3) tabs: li.active > a + // New (bs4+) tabs: li.nav-item > a.active.nav-link + var legacy = $(this).closest(".nav").find("li:not(.dropdown).active > a").length > 0; + var plugin = legacy ? legacyTabPlugin : newTabPlugin; + plugin.call($(this), config); + } + + var noconflict = $.fn.tab; + $.fn.tab = TabPlugin; + $.fn.tab.Constructor = newTabPlugin.Constructor; + $.fn.tab.noConflict = function() { + $.fn.tab = noconflict; + return TabPlugin; + }; +}); diff --git a/_book/libs/bs3compat-0.8.0/tabs.js b/_book/libs/bs3compat-0.8.0/tabs.js new file mode 100644 index 0000000..79825cc --- /dev/null +++ b/_book/libs/bs3compat-0.8.0/tabs.js @@ -0,0 +1,157 @@ +/* ======================================================================== + * Bootstrap: tab.js v3.4.1 + * https://getbootstrap.com/docs/3.4/javascript/#tabs + * ======================================================================== + * Copyright 2011-2019 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + * ======================================================================== */ + +// Register tab plugin after DOM content loaded in order to +// override BS5's plugin +// https://github.com/twbs/bootstrap/blob/08139c22/js/dist/tab.js#L87 +$(function() { + 'use strict'; + + // TAB CLASS DEFINITION + // ==================== + + var Tab = function (element) { + // jscs:disable requireDollarBeforejQueryAssignment + this.element = $(element) + // jscs:enable requireDollarBeforejQueryAssignment + } + + Tab.VERSION = '3.4.1' + + Tab.TRANSITION_DURATION = 150 + + Tab.prototype.show = function () { + var $this = this.element + var $ul = $this.closest('ul:not(.dropdown-menu)') + var selector = $this.data('target') + + if (!selector) { + selector = $this.attr('href') + selector = selector && selector.replace(/.*(?=#[^\s]*$)/, '') // strip for ie7 + } + + if ($this.parent('li').hasClass('active')) return + + var $previous = $ul.find('.active:last a') + var hideEvent = $.Event('hide.bs.tab', { + relatedTarget: $this[0] + }) + var showEvent = $.Event('show.bs.tab', { + relatedTarget: $previous[0] + }) + + $previous.trigger(hideEvent) + $this.trigger(showEvent) + + if (showEvent.isDefaultPrevented() || hideEvent.isDefaultPrevented()) return + + var $target = $(document).find(selector) + + this.activate($this.closest('li'), $ul) + this.activate($target, $target.parent(), function () { + $previous.trigger({ + type: 'hidden.bs.tab', + relatedTarget: $this[0] + }) + $this.trigger({ + type: 'shown.bs.tab', + relatedTarget: $previous[0] + }) + }) + } + + Tab.prototype.activate = function (element, container, callback) { + var $active = container.find('> .active') + var transition = callback + && $.support.transition + && ($active.length && $active.hasClass('fade') || !!container.find('> .fade').length) + + function next() { + $active + .removeClass('active') + .find('> .dropdown-menu > .active') + .removeClass('active') + .end() + .find('[data-toggle="tab"]') + .attr('aria-expanded', false) + + element + .addClass('active') + .find('[data-toggle="tab"]') + .attr('aria-expanded', true) + + if (transition) { + element[0].offsetWidth // reflow for transition + element.addClass('in') + } else { + element.removeClass('fade') + } + + if (element.parent('.dropdown-menu').length) { + element + .closest('li.dropdown') + .addClass('active') + .end() + .find('[data-toggle="tab"]') + .attr('aria-expanded', true) + } + + callback && callback() + } + + $active.length && transition ? + $active + .one('bsTransitionEnd', next) + .emulateTransitionEnd(Tab.TRANSITION_DURATION) : + next() + + $active.removeClass('in') + } + + + // TAB PLUGIN DEFINITION + // ===================== + + function Plugin(option) { + return this.each(function () { + var $this = $(this) + var data = $this.data('bs.tab') + + if (!data) $this.data('bs.tab', (data = new Tab(this))) + if (typeof option == 'string') data[option]() + }) + } + + var old = $.fn.tab + + $.fn.tab = Plugin + $.fn.tab.Constructor = Tab + + + // TAB NO CONFLICT + // =============== + + $.fn.tab.noConflict = function () { + $.fn.tab = old + return this + } + + + // TAB DATA-API + // ============ + + var clickHandler = function (e) { + e.preventDefault() + Plugin.call($(this), 'show') + } + + $(document) + .on('click.bs.tab.data-api', '[data-toggle="tab"]', clickHandler) + .on('click.bs.tab.data-api', '[data-toggle="pill"]', clickHandler) + +}); diff --git a/_book/libs/bs3compat-0.8.0/transition.js b/_book/libs/bs3compat-0.8.0/transition.js new file mode 100644 index 0000000..81e7122 --- /dev/null +++ b/_book/libs/bs3compat-0.8.0/transition.js @@ -0,0 +1,59 @@ +/* ======================================================================== + * Bootstrap: transition.js v3.4.1 + * https://getbootstrap.com/docs/3.4/javascript/#transitions + * ======================================================================== + * Copyright 2011-2019 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/v3-dev/LICENSE) + * ======================================================================== */ + + ++function ($) { + 'use strict'; + + // CSS TRANSITION SUPPORT (Shoutout: https://modernizr.com/) + // ============================================================ + + function transitionEnd() { + var el = document.createElement('bootstrap') + + var transEndEventNames = { + WebkitTransition : 'webkitTransitionEnd', + MozTransition : 'transitionend', + OTransition : 'oTransitionEnd otransitionend', + transition : 'transitionend' + } + + for (var name in transEndEventNames) { + if (el.style[name] !== undefined) { + return { end: transEndEventNames[name] } + } + } + + return false // explicit for ie8 ( ._.) + } + + // https://blog.alexmaccaw.com/css-transitions + $.fn.emulateTransitionEnd = function (duration) { + var called = false + var $el = this + $(this).one('bsTransitionEnd', function () { called = true }) + var callback = function () { if (!called) $($el).trigger($.support.transition.end) } + setTimeout(callback, duration) + return this + } + + $(function () { + $.support.transition = transitionEnd() + + if (!$.support.transition) return + + $.event.special.bsTransitionEnd = { + bindType: $.support.transition.end, + delegateType: $.support.transition.end, + handle: function (e) { + if ($(e.target).is(this)) return e.handleObj.handler.apply(this, arguments) + } + } + }) + +}(jQuery); diff --git a/_book/libs/bs4_book-1.0.0/bs4_book.css b/_book/libs/bs4_book-1.0.0/bs4_book.css new file mode 100644 index 0000000..0bc45a0 --- /dev/null +++ b/_book/libs/bs4_book-1.0.0/bs4_book.css @@ -0,0 +1,555 @@ +/* Page structure ---------------------------------------------------------- + ++-----+-----------------+------+--------------+ +| | sidebar-chapter | main | sidebar-book | ++=====+=================+======+==============+ +| sml | 12 (collapsed) | 12 | - | +| md | 12 (collapsed) | 9 | 3 | +| lg | 3 | 7 | 2 | ++-----+-----------------+------+--------------+ + +Side uses container-fluid so we set up some additional breakpoints, to ensure +that the columns never get too wide, either individually or collectively. + +*/ + + +@media (min-width: 1200px) { + .container-fluid { + max-width: 95rem; + } + .container-fluid .row { + justify-content: space-evenly; + } + .container-fluid main { + max-width: 45rem; + } + .sidebar { + max-width: 15rem; + } + + /* Increase font-size for very wide devices */ + body { + font-size: 18px + } +} + +main {margin-top: 1rem;} + +@media (max-width: 991.98px) { + .sidebar { + max-width: 100%; + } + + .collapse-lg { + display: none; + padding: 1rem; + border-radius: 0.2rem; + background: #fafafa; + margin-top: 0.5rem; + margin-bottom: 1rem; + box-shadow: 5px 5px 10px rgba(0.1, 0.1, 0.1, 0.5); + border: 1px solid var(--primary); + } + .book-toc { + column-count: 2; + } + .sidebar-book, main { + padding-left: 1rem; + } + .sidebar-book { + margin-top: 1rem; + } +} +@media (min-width: 992px) { + .collapse-lg { + display: block !important; + } +} +.collapse-lg.show { + display: block; +} + +@media (min-width: 768px) { + .sidebar-chapter { + position: sticky; + max-height: 100vh; + top: 0; + overflow-y: auto; + } +} + +@media (min-width: 992px) { + .sidebar-book { + position: sticky; + max-height: 100vh; + top: 0; + overflow-y: auto; + } +} + +/* Chapter nav ----------------------------------------- */ + +.chapter-nav { + display: flex; + justify-content: space-between; + margin-top: 2rem; +} +.chapter-nav .prev, .chapter-nav .next { + padding: 0.5rem; + border: 1px solid #eee; + border-radius: 0.2rem; + box-shadow: 0 .5rem 1rem rgba(0,0,0,.15); +} +.chapter-nav .empty { + border: none; +} +.chapter-nav .prev a:before { + content: "ยซ "; +} +.chapter-nav .next a:after { + content: " ยป"; +} + +/* Sidebar ------------------------------------------------------ */ + +.sidebar h1, .sidebar h2 { + margin-top: 1.5rem; + margin-bottom: 0.5rem; +} +.sidebar h1 { + font-size: 1.1rem; +} +@media (max-width: 991.98px) { + .sidebar h1 { + font-size: 1.5rem; + margin-top: 0rem; + } +} +.sidebar h2 { + font-size: 0.9rem; +} + +.sidebar hr { + margin: 0 0 0.5rem 0; +} + +.sidebar li { + margin-bottom: 0.5rem; + font-size: 0.9rem; + line-height: 1.5; +} + +.sidebar li.book-part { + margin-top: 1rem; +} + +.book-toc .active { + font-weight: bolder; +} + +.book-extra { + border-top: 1px solid #ccc; + margin-top: 0.5rem; + padding-top: 0.5rem; + font-size: 0.9rem; +} + +.book-extra i { + font-size: 1.2em; +} + +/* Sticky footer ----------------------------------------- */ +html, body {height: 100%} + +body { + display: flex; + flex-direction: column; +} +.container-fluid { + flex: 1 0 auto; +} +footer { + flex-shrink: 0; + font-size: 0.9rem; + +} +footer a { + text-decoration: underline; +} + +/* Scrollspy --------------------------------------------- */ + +nav[data-toggle="toc"] .nav > li { + margin-bottom: calc(0.5rem - 3px); +} + +nav[data-toggle="toc"] .nav > li > a { + padding: 3px; + display: block; +} + +nav[data-toggle="toc"] .nav > li > a:hover { + text-decoration: underline; +} + +nav[data-toggle="toc"] .nav a.nav-link.active, +nav[data-toggle="toc"] .nav .nav-link.active > li > a { + background-color: #eee; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle="toc"] .nav-link + ul { + display: none; +} +nav[data-toggle="toc"] .nav-link.active + ul { + margin-top: 3px; + display: block; +} + +nav[data-toggle="toc"] .nav .nav > li { + margin-bottom: 0; +} +nav[data-toggle="toc"] .nav .nav > li > a { + margin-left: 10px; +} +/* Figures -------------------------------------------- */ + +.figure, .inline-figure { + width: 100%; + overflow-x: auto; +} + +.inline-figure { + border: solid 2px #f1f1f1; + margin-bottom: 1rem; /* to match

    */ +} + +.figure { + border-top: 2px solid #eee; + border-bottom: 2px solid #eee; + margin: 1.5rem -0.5rem 1rem -0.5rem; + padding: 1.5rem 0 1rem 1rem; +} + +@media (max-width: 767.98px) { + .figure { + margin: 1.5rem -1rem 1.5rem -1rem; + padding: 1.5rem; + width: 100vw; + } +} + +caption, p.caption { + text-align: left; + margin-top: 1rem; + margin-bottom: 0; + font-size: 0.9rem; + color: #777; +} + +/* Headings -------------------------------------------- */ + +h2 { + margin-top: 2rem; + margin-bottom: 1rem; + font-size: 1.5rem; +} +h3 { margin-top: 1.5em; font-size: 1.2rem; } +h4 { margin-top: 1.5em; font-size: 1.1rem; } +h5 { margin-top: 1.5em; font-size: 1rem; } + +h1, h2, h3, h4, h5 { + line-height: 1.3; +} + +.header-section-number { + color: #6C6C6C; + font-weight: normal; +} + +.dropdown-item .header-section-number { + position: absolute; + width: 2rem; + left: -1rem; + display: block; + text-align: right; +} + +.anchor { + font-size: max(0.5em, 1rem); + margin-left: 0.5rem; + display: none; +} +h1:hover .anchor, +h2:hover .anchor, +h3:hover .anchor, +h4:hover .anchor, +h5:hover .anchor, +h6:hover .anchor { + display: inline; +} + +/* Tables ---------------------------------------------- */ + +.inline-table { + overflow-x: auto; +} + +table.kable_wrapper td { + vertical-align: top; +} + + +/* Footnotes --------------------------------------------- */ + +.popover { + max-width: min(100vw, 32rem); + font-size: 0.9rem; + box-shadow: 4px 4px 8px rgba(0, 0, 0, 0.3); +} +.popover-body { + padding: 0.75rem; +} +.popover-body p:last-child { + margin-bottom: 0; +} + +a.footnote-ref { + cursor: pointer; +} + +/* Search ---------------------------------------------- */ + +mark { + background: linear-gradient(-100deg, + hsla(48,92%,75%,.3), + hsla(48,92%,75%,.7) 95%, + hsla(48,92%,75%,.1) + ) +} + +.algolia-autocomplete .aa-hint { + color: #999; +} +.algolia-autocomplete .aa-dropdown-menu { + width: min(100%, 20rem); + background-color: #fff; + border: 1px solid var(--gray); + border-radius: 0.2rem; + margin-top: 2px; + + max-height: 50vh; + overflow-y: auto; +} +.algolia-autocomplete .aa-dropdown-menu .aa-suggestion { + cursor: pointer; + padding: 5px 4px; + border-bottom: 1px #ddd solid; + font-size: 0.9rem; +} +.algolia-autocomplete .aa-dropdown-menu .aa-suggestion.aa-cursor { + background-color: #B2D7FF; +} + +/* Code ------------------------------------------------ */ + +pre { + position: relative; + overflow: auto; + border: 1px solid #eee; + padding: 0.5rem; + margin: 0 -0.5rem 1rem -0.5rem; + background-image: linear-gradient(160deg,#f8f8f8 0,#f1f1f1 100%); +} + +@media (max-width: 767.98px) { + /* Make background span full width on mobile */ + .section > .sourceCode > pre { + margin: 0 -1rem 1rem -1rem; + padding: 0.5rem 1rem; + width: 100vw; + } +} + +code { + background-color: #f8f8f8; +} + +pre code { + background-color: transparent; + word-break: normal; /* force wide blocks to scroll, not wrap */ + word-wrap: normal; +} + +pre, code { + border-radius: 0.2rem; + color: #212529; /* match text colour */ +} +code a:any-link { + color: inherit; /* use colour from syntax highlighting */ + text-decoration: underline; + text-decoration-color: #ccc; +} + +/* copy button */ + +div.sourceCode { + position: relative; +} + +.btn-copy { + position: absolute; + top: 0rem; + right: -0.5rem; /* coherent with pre margin rule */ +} + +div.sourceCode > button { + filter: opacity(50%); +} + +div.sourceCode > button:hover { + filter: opacity(100%); +} + +div.sourceCode > button > i.bi::before { + display: inline-block; + height: 1rem; + width: 1rem; + content: ""; + vertical-align: -0.125em; + background-image: url('data:image/svg+xml,'); + background-repeat: no-repeat; + background-size: 1rem 1rem; +} + +div.sourceCode > button.btn-copy-checked > .bi::before { + background-image: url('data:image/svg+xml,'); +} + +/* https://github.com/rstudio/distill/blob/master/inst/rmarkdown/templates/distill_article/resources/a11y.theme + https://gist.github.com/hadley/f53b6e92df20994fdabe6562d284728a */ +code span.ot {color:#007faa} +code span.at {color:#7d9029} +code span.ss {color:#bb6688} +code span.an {color:#545454;} +code span.fu {color:#4254A7} +code span.st {color:#008000} +code span.cf {color:#007faa;} +code span.op {color:#696969} +code span.er {color:#ff0000;} +code span.bn {color:#a1024a} +code span.al {color:#ff0000;} +code span.va {color:#19177c} +code span.bu {color: #007faa;} +code span.ex {} +code span.pp {color:#bc7a00} +code span.in {color:#545454;} +code span.vs {color:#008000} +code span.wa {color:#545454; font-style: italic} +code span.do {color:#ba2121; font-style: italic} +code span.im {color:#007faa; font-weight: bold;} +code span.ch {color:#008000} +code span.dt {color:#aa5d00} +code span.fl {color:#a1024a} +code span.co {color:#545454} +code span.cv {color:#545454; font-style: italic} +code span.cn {color:#d91e18} +code span.sc {color:#008000} +code span.dv {color:#a1024a} +code span.kw {color:#007faa} + +/* Misc typography ---------------------------------------------- */ + +a { + overflow-wrap: break-word; + word-wrap: break-word; +} + +blockquote { + border-left: 0.5rem solid #eee; + padding-left: 0.5rem; + margin-left: -0.5rem; +} + +body { + line-height: 1.6; +} + +.smallcaps { + font-variant: small-caps; +} + +/* special callout blocks */ + +.rmdnote, .rmdcaution, .rmdimportant, .rmdtip, .rmdwarning { + margin: 1rem calc(-2px - 0.5em); + padding: 1rem; + border: 2px solid #eee; +} + +.rmdnote > *:last-child, .rmdcaution > *:last-child, .rmdimportant > *:last-child, .rmdtip > *:last-child, .rmdwarning > *:last-child { + margin-bottom: 0; +} + +@media (max-width: 767.98px) { + .rmdnote, .rmdcaution, .rmdimportant, .rmdtip, .rmdwarning { + margin: 1rem -1rem; + border-width: 4px; + } +} + +.rmdnote { + border-color: var(--primary); +} +.rmdimportant { + border-color: var(--success); +} +.rmdcaution { + border-color: var(--danger); +} +.rmdwarning { + border-color: var(--warning); +} +.rmdtip { + border-color: var(--info); +} + +.rmdcaution pre, .rmdimportant pre, .rmdnote pre, .rmdtip pre, .rmdwarning pre { + /* Make code blocks full width in rmdnote */ + margin: 0 -1rem 1rem -1rem; + padding: 1rem; +} + +.rmdcaution .btn-copy, .rmdimportant .btn-copy, .rmdnote .btn-copy, .rmdtip .btn-copy, .rmdwarning .btn-copy { + /* Needs to be set according to margin in callout pre block */ + right: -1rem; +} + +main ul { + list-style-type: square; +} +main ol, main ul { + padding-left: 25px; + margin-bottom: 0; +} +main li { + margin-bottom: 0.5rem; +} +main ol > li:first-child, main ul > li:first-child { + margin-top: 0.5rem; +} + +/* Cover image */ + +img.cover { + float: right; + margin: 0 1rem 0 1rem; + box-shadow: 0 .5rem 1rem rgba(0,0,0,.15); +} +@media (max-width: 767.98px) { + img.cover { + float: none; + display: block; + margin: 0 auto 1rem auto; + } +} diff --git a/_book/libs/bs4_book-1.0.0/bs4_book.js b/_book/libs/bs4_book-1.0.0/bs4_book.js new file mode 100644 index 0000000..667c7d9 --- /dev/null +++ b/_book/libs/bs4_book-1.0.0/bs4_book.js @@ -0,0 +1,137 @@ +$(function () { + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + // Activate popovers + $('[data-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); + $('[data-toggle="tooltip"]').tooltip(); +}) + +// Search ---------------------------------------------------------------------- + +var fuse; + +$(function () { + // Initialise search index on focus + $("#search").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + + var response = await fetch('search.json'); + var data = await response.json(); + + var options = { + keys: ["heading", "text", "code"], + ignoreLocation: true, + ignoreFieldNorm: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + + $("#search").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.chapter == s.heading) { + return `${s.chapter}`; + } else { + return `${s.chapter} /
    ${s.heading}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); +}); + +var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + } + + callback(items); +} + +// Copy to clipboard ----------------------------------------------------------- + +function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-original-title'); + element.setAttribute('data-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-original-title', tooltipOriginalTitle); +} + +$(document).ready(function() { + if(ClipboardJS.isSupported()) { + // Insert copy buttons + var copyButton = ""; + $(copyButton).appendTo("div.sourceCode"); + // Initialize tooltips: + $('.btn-copy').tooltip({container: 'body', boundary: 'window'}); + + // Initialize clipboard: + var clipboard = new ClipboardJS('.btn-copy', { + text: function(trigger) { + return trigger.parentNode.textContent; + } + }); + + clipboard.on('success', function(e) { + const btn = e.trigger; + changeTooltipMessage(btn, 'Copied!'); + btn.classList.add('btn-copy-checked'); + setTimeout(function() { + btn.classList.remove('btn-copy-checked'); + }, 2000); + e.clearSelection(); + }); + + clipboard.on('error', function() { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + }; +}); diff --git a/_book/libs/jquery-3.6.0/jquery-3.6.0.min.js b/_book/libs/jquery-3.6.0/jquery-3.6.0.min.js new file mode 100644 index 0000000..c4c6022 --- /dev/null +++ b/_book/libs/jquery-3.6.0/jquery-3.6.0.min.js @@ -0,0 +1,2 @@ +/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */ +!function(e,t){"use strict";"object"==typeof module&&"object"==typeof 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๐Ÿช„ #rstats #DataScience pic.twitter.com/BeNs5nXz0T\n","code":""},{"path":"january-2021.html","id":"january-26","chapter":"January 2021","heading":"January 26","text":"\nUse powerful parser functions {readr} parse vector expected type ๐Ÿ—‚Helpful messy, user-entered data functions can make educated guesses input supposed .#rstats #DataScience pic.twitter.com/5MGHmEM9Pu\n","code":""},{"path":"january-2021.html","id":"january-27","chapter":"January 2021","heading":"January 27","text":"\n{model_performance} function {performance} ๐Ÿ“ฆ quick way get detailed regression model summary indices huge . models! ๐Ÿ“https://t.co/vMpMVbz0x5#rstats #DataScience pic.twitter.com/VzPRICbIAg\n","code":""},{"path":"january-2021.html","id":"january-28","chapter":"January 2021","heading":"January 28","text":"\nTibbles modern data frames ubiquitous {tidyverse}. want build tibble scratch?{tribble} function provides intuitive row--row syntax ! ๐Ÿงฑ๐Ÿ‘ทhttps://t.co/UBRRJLyM47#rstats #DataScience pic.twitter.com/X8KlYkRCTF\n","code":""},{"path":"january-2021.html","id":"january-29","chapter":"January 2021","heading":"January 29","text":"\nfunctions may remove {NA}s data default one wishes retain .{replace_na} {tidyr} can help replacing {NA}s specified value ๐Ÿ•ต๏ธโ€โ™€๏ธhttps://t.co/y4Of3tcexf#rstats #DataScience pic.twitter.com/XIodtKU7Xn\n","code":""},{"path":"january-2021.html","id":"january-30","chapter":"January 2021","heading":"January 30","text":"\nfeeling bit ambitious want understand abstract syntactic structure R code, can use {ast} function {lobstr} package draw tree representation! ๐Ÿฆž https://t.co/WbyECG3a4H#rstats #datascience pic.twitter.com/cgMZDz1vz1\n","code":""},{"path":"january-2021.html","id":"january-31","chapter":"January 2021","heading":"January 31","text":"\n\"work play makes Jack dull boy\"important also little fun part #rstats workflows. end, can use {say} function {cowsay}! ๐Ÿฎ ๐Ÿ’ฌ https://t.co/SmDKtEHbMC#rstats #DataScience pic.twitter.com/8AROLW8r3d\n","code":""},{"path":"february-2021.html","id":"february-2021","chapter":"February 2021","heading":"February 2021","text":"","code":""},{"path":"february-2021.html","id":"february-1","chapter":"February 2021","heading":"February 1","text":"\n{dplyr} syntax heavily inspired #SQL, means easy translate {dplyr} code equivalent query supported {dplyr} ! ๐Ÿ™Œ https://t.co/rjE4S2MK8o#rstats #DataScience pic.twitter.com/SffAhyRmWh\n","code":""},{"path":"february-2021.html","id":"february-2","chapter":"February 2021","heading":"February 2","text":"\ncase want peruse ggplot (various layers) built, can use {ggplot_build} function {ggplot2}! ๐Ÿ— ๐Ÿšง ๐Ÿฆบ https://t.co/mIq2G3qAo8Useful also investigate aesthetic defaults utilized {geom_}s.#rstats #DataScience pic.twitter.com/UnQdpZYCJ8\n","code":""},{"path":"february-2021.html","id":"february-3","chapter":"February 2021","heading":"February 3","text":"\nwant get quick overview descriptive statistics numeric variable, {describe_distribution} {parameters} ๐Ÿ“ฆ friend ๐Ÿ™Œ https://t.co/KlhJNsdL90Also works {grouped_} dataframe {dplyr} ๐Ÿ‘#rstats #DataScience pic.twitter.com/FoBio2p8yk\n","code":""},{"path":"february-2021.html","id":"february-4","chapter":"February 2021","heading":"February 4","text":"\nwant see beautiful, colorful tree directory file structure, can use {dir_tree} function {fs} package! ๐ŸŒณ๐ŸŒˆ https://t.co/UpjcB7z7Qf#rstats #DataScience pic.twitter.com/SlXAIcePoA\n","code":""},{"path":"february-2021.html","id":"february-5","chapter":"February 2021","heading":"February 5","text":"\nwork lot web scraping need extract particular node HTML page, {html_nodes} {rvest} ๐Ÿ“ฆ friend ๐Ÿ‘ฏโ€โ™€๏ธhttps://t.co/ob6beJEkZN#rstats #DataScience pic.twitter.com/kcouQ5rTfC\n","code":""},{"path":"february-2021.html","id":"february-6","chapter":"February 2021","heading":"February 6","text":"\nlong script, can become tiresome keep duplicating object name self-assigned. instances, assignment pipe {magrittr} ๐Ÿ“ฆ proves handy! ๐Ÿ‘Œhttps://t.co/NVHXfOZOI9#rstats #DataScience pic.twitter.com/3iGuHsUZjG\n","code":""},{"path":"february-2021.html","id":"february-7","chapter":"February 2021","heading":"February 7","text":"\nOften might want split existing column combination columns {separate} {tidyr} ๐Ÿ“ฆ exactly looking ! ๐Ÿช„ https://t.co/imqGN7ridk#rstats #DataScience pic.twitter.com/wrSFpNtNeM\n","code":""},{"path":"february-2021.html","id":"february-8","chapter":"February 2021","heading":"February 8","text":"\n{row_number} function {dplyr} timesaver create unique identifier observations, especially belong different groups 1๏ธโƒฃ2๏ธโƒฃ3๏ธโƒฃhttps://t.co/XSagfpLHVg#rstats #DataScience pic.twitter.com/JWb0W2XMjz\n","code":""},{"path":"february-2021.html","id":"february-9","chapter":"February 2021","heading":"February 9","text":"\nSometimes just want extract source code present source document (e.g., Rmd) separate script, {purl} {knitr} ๐Ÿ“ฆ makes effortless! ๐Ÿฆชhttps://t.co/2lwSJ2HhKG#rstats #DataScience pic.twitter.com/RpqD7zsQst\n","code":""},{"path":"february-2021.html","id":"february-10","chapter":"February 2021","heading":"February 10","text":"\nOften need convert country names/codes different conventions standardized names, {countrycode} function eponymous ๐Ÿ“ฆ can handle conversions! โš™๏ธhttps://t.co/QVF8hBe7BE#rstats #DataScience pic.twitter.com/RByINBRutU\n","code":""},{"path":"february-2021.html","id":"february-11","chapter":"February 2021","heading":"February 11","text":"\nSometimes factor level can missing implicitly, can make explicit using {fct_explicit_na} function {forcats} ๐Ÿ“ฆ :https://t.co/GexbNwDl4L#rstats #DataScience pic.twitter.com/7XI2z0cXR8\n","code":""},{"path":"february-2021.html","id":"february-12","chapter":"February 2021","heading":"February 12","text":"\nCleaning column names consistent pattern probably first important step data analysis {clean_names} function {janitor} ๐Ÿ“ฆ peerless regard ๐Ÿงผ https://t.co/7w14DlhEvA#rstats #DataScience pic.twitter.com/GmmVwHVrOA\n","code":""},{"path":"february-2021.html","id":"february-13","chapter":"February 2021","heading":"February 13","text":"\n{markdown_} function family {commonmark} ๐Ÿ“ฆ can help convert markdown text various formats (e.g., latex, html, etc.) โœ๏ธ https://t.co/kADYv6J4lBCan useful tool teaching, say html, one already comfortable rmarkdown.#rstats #DataScience pic.twitter.com/AZyxYsPxvX\n","code":""},{"path":"february-2021.html","id":"february-14","chapter":"February 2021","heading":"February 14","text":"\ngraphics ๐Ÿ“ฆs (e.g.ย {hrbrthemes}) require special fonts. can pain list interrogate installed fonts.{system_fonts} function {systemfonts} ๐Ÿ“ฆ outputs beautiful richly informative table one-line command ๐Ÿ‘#rstats #DataScience pic.twitter.com/aTiJdg7gZs\n","code":""},{"path":"february-2021.html","id":"february-15","chapter":"February 2021","heading":"February 15","text":"\njust need create list possible combinations values vector, {combn} function {utils} ๐Ÿ“ฆ comes handy ๐Ÿงฐhttps://t.co/5tPzuFp1P9#rstats #DataScience pic.twitter.com/RoyLDraNty\n","code":""},{"path":"february-2021.html","id":"february-16","chapter":"February 2021","heading":"February 16","text":"\npresent results regression model well-formatted table, {tbl_regression} function {gtsummary} ๐Ÿ“ฆ serious time-saver ๐Ÿ—‚https://t.co/EVoZ6ZRbgY#rstats #DataScience pic.twitter.com/q3MvqXIIog\n","code":""},{"path":"february-2021.html","id":"february-17","chapter":"February 2021","heading":"February 17","text":"\ndeeply nested data structure (object JSON, e.g.), indexing can bit tedious base-R.{pluck} function {purrr} ๐Ÿ“ฆ provides less tiresome way index ๐Ÿ˜Œhttps://t.co/ePJ6HHVKm4#rstats #DataScience pic.twitter.com/nhHEJKjkAu\n","code":""},{"path":"february-2021.html","id":"february-18","chapter":"February 2021","heading":"February 18","text":"\ncomparing performance different functions, plain curiosity, sometimes wish benchmark expression, {mark} function {bench} ๐Ÿ“ฆ makes easy! ๐Ÿ‹๏ธโ€โ™€๏ธhttps://t.co/0qP7sY7J9cNote {cor} faster {cor.test}.#rstats #DataScience pic.twitter.com/8AGUg3rE4O\n","code":""},{"path":"february-2021.html","id":"february-19","chapter":"February 2021","heading":"February 19","text":"\nNothing improves readability code like style guide, exactly {style_*} function family {styler} ๐Ÿ“ฆ ! ๐Ÿ’…easiest thing run function directory R scripts ๐Ÿงน https://t.co/Xjh4j4HuhQ#rstats #DataScience pic.twitter.com/NBhgffztFN\n","code":""},{"path":"february-2021.html","id":"february-20","chapter":"February 2021","heading":"February 20","text":"\n#rstats native {} operator sometimes might miss {} operator SQL. ๐Ÿ˜ช{% raw %}{%nin%}{% endraw %} operator {sjmisc} covered! ๐Ÿ‘https://t.co/7IzXkPxlYD#rstats #DataScience pic.twitter.com/5dpgqHa8Vj\n","code":""},{"path":"february-2021.html","id":"february-21","chapter":"February 2021","heading":"February 21","text":"\nwell-versed SQL looking learn {dplyr}, {show_dplyr} function {tidyquery} ๐Ÿ“ฆ can helpful teaching assistant translating SQL query equivalent {dplyr} code! ๐Ÿ‘ฉโ€๐Ÿซhttps://t.co/gT4ESz4h4y#rstats #DataScience pic.twitter.com/duKYJRuJN0\n","code":""},{"path":"february-2021.html","id":"february-22","chapter":"February 2021","heading":"February 22","text":"\nAlthough number functions tend {data} argument, donโ€™t. functions, pipe operator (%>%) {magrittr} ๐Ÿ“ฆ wonโ€™t work. contexts, one can use exposition pipe operator (%$%) ๐Ÿ‘https://t.co/1RODRqilaL#rstats #DataScience pic.twitter.com/qxFmzJROKU\n","code":""},{"path":"february-2021.html","id":"february-23","chapter":"February 2021","heading":"February 23","text":"\nfirst important step data analysis workflow make sure everything data โ€œmakes senseโ€ tools good job describing data {skim} function {skimr} ๐Ÿ“ฆ!https://t.co/vg3t4v8Ixq#rstats #DataScience pic.twitter.com/FtpFi8ReZY\n","code":""},{"path":"february-2021.html","id":"february-24","chapter":"February 2021","heading":"February 24","text":"\ninfix operator (%||%) {rlang} ๐Ÿ“ฆ can helpful default value case {NULL} ๐Ÿช„https://t.co/qdMs5VAgjoHelpful collaborative scripts ๐Ÿ“ฆ functions users might enter different spellings specify argument.#rstats #DataScience pic.twitter.com/RDeKBpcFD1\n","code":""},{"path":"february-2021.html","id":"february-25","chapter":"February 2021","heading":"February 25","text":"\nChecking association variables often involves carrying correlation analysis functions make easy {correlation} eponymous ๐Ÿ“ฆhttps://t.co/VGZhhq9L0PSupports huge variety correlation methods.#rstats #DataScience pic.twitter.com/piTCGqE5Ek\n","code":""},{"path":"february-2021.html","id":"february-26","chapter":"February 2021","heading":"February 26","text":"\nwant conditionally select values dataframe, can use {dplyr::filter}, want vector list?{keep} function {purrr} exactly ! ๐Ÿฑ https://t.co/pAXndQ9Pl2#rstats #DataScience pic.twitter.com/6yixL2ZKae\n","code":""},{"path":"february-2021.html","id":"february-27","chapter":"February 2021","heading":"February 27","text":"\nJSON data representation format ubiquitous time might need convert dataframe R JSON object.{toJSON} function {jsonlite} makes childโ€™s play ๐Ÿ‘ถhttps://t.co/0lOvOE19EW#rstats #DataScience pic.twitter.com/PAbVQy4Kiz\n","code":""},{"path":"february-2021.html","id":"february-28","chapter":"February 2021","heading":"February 28","text":"\nworking free-form survey inputs, need consider possibility mistakes data entry.contexts {stringdist_join} function {fuzzyjoin} ๐Ÿ“ฆ can save headache joining dataframes! โœŒ๏ธhttps://t.co/HNANB6IjH2#rstats #DataScience pic.twitter.com/b3HT9Soqoa\n","code":""},{"path":"march-2021.html","id":"march-2021","chapter":"March 2021","heading":"March 2021","text":"","code":""},{"path":"march-2021.html","id":"march-1","chapter":"March 2021","heading":"March 1","text":"\none oldie goodie:{switch} function {base} ๐Ÿ“ฆ helps select list alternative actions depending condition interest ๐Ÿ—‚https://t.co/IYYI1dIvpmNote , unspecified, values can trickle .#rstats #DataScience pic.twitter.com/6iqBh85XMY\n","code":""},{"path":"march-2021.html","id":"march-2","chapter":"March 2021","heading":"March 2","text":"\ncase want create new variable conditional checking whether values different variable lie inside interval, {} function {dplyr} can big help ๐Ÿ‘Œhttps://t.co/JrEHPo5oDvWorks date-time objects well! ๐Ÿ™Œ#rstats #DataScience pic.twitter.com/J5TtCwLBE3\n","code":""},{"path":"march-2021.html","id":"march-3","chapter":"March 2021","heading":"March 3","text":"\nSometimes might wish know ๐Ÿ“ฆ unfamiliar functions , objects session located. {find} function {base} ๐Ÿ“ฆ can helpful ๐Ÿ”https://t.co/vTvhBnFogF#rstats #DataScience pic.twitter.com/BG9E8cnS9w\n","code":""},{"path":"march-2021.html","id":"march-4","chapter":"March 2021","heading":"March 4","text":"\nmatter programming language code , rarely wish leave comfort zone #rmarkdown ๐Ÿก one check details languages currently supported? Using {knit_engine} function {knitr} ๐Ÿงถ https://t.co/bVEzn990dE#rstats #DataScience pic.twitter.com/LCHtsb1b1T\n","code":""},{"path":"march-2021.html","id":"march-5","chapter":"March 2021","heading":"March 5","text":"\nused creating tibble columns sequentially, might think similar procedure works creating lists, doesnโ€™t, least {base::list}.can instead use {lst} function {tibble} ๐Ÿ“ฆ! ๐Ÿ› https://t.co/K97EBCEsYz#rstats #DataScience pic.twitter.com/p152AVXFDb\n","code":""},{"path":"march-2021.html","id":"march-6","chapter":"March 2021","heading":"March 6","text":"\nR packages standing shoulders packages. case curious find ones, can use {plot_dependencies} function {deepdep} ๐Ÿ“ฆ visualizing said dependence:https://t.co/3tl1A7aMW3#rstats #DataScience pic.twitter.com/dUeHWz3MBA\n","code":""},{"path":"march-2021.html","id":"march-7","chapter":"March 2021","heading":"March 7","text":"\nMissing values can troublesome statistical analysis need attended data exploration phase.{vis_dat} function {visdat} ๐Ÿ“ฆ makes effortless glean structure missing values ๐Ÿ”https://t.co/ciff6eE06w#rstats #DataScience pic.twitter.com/eM4uJgWApO\n","code":""},{"path":"march-2021.html","id":"march-8","chapter":"March 2021","heading":"March 8","text":"\ncase want access statistics system memory without leaving R, can use {ps_system_memory} function {ps} ๐Ÿ“ฆhttps://t.co/hSVUebKkIGThe measure watch available memory.#rstats #DataScience pic.twitter.com/D4YoWLNsku\n","code":""},{"path":"march-2021.html","id":"march-9","chapter":"March 2021","heading":"March 9","text":"\nSometimes data tabled, .e.ย single row corresponds multiple observations, one.convert tidy data, can untable using {uncount} function {tidyr} ๐Ÿ“ฆhttps://t.co/LVXog4CWVM#rstats #DataScience pic.twitter.com/r7tcMVkIFL\n","code":""},{"path":"march-2021.html","id":"march-10","chapter":"March 2021","heading":"March 10","text":"\nSometimes multiple functions always use conjunction wish can compose single function.Thanks {compose} function {purrr} ๐Ÿ“ฆ, can exactly ! ๐ŸŽผhttps://t.co/zdydXfEM35#rstats #DataScience pic.twitter.com/teZ5w65uHS\n","code":""},{"path":"march-2021.html","id":"march-11","chapter":"March 2021","heading":"March 11","text":"\nlike certain color palette wish use plots, need find โ€™s hexadecimal code color.can use {show_col} function {scales} ๐Ÿ“ฆ achieve ๐ŸŽจhttps://t.co/vxlr8ff035#rstats #DataScience pic.twitter.com/UDN1IFMvO9\n","code":""},{"path":"march-2021.html","id":"march-12","chapter":"March 2021","heading":"March 12","text":"\nwant substitute certain string pattern data, can use {gsub}. wish across files?can use {gsub_file} function {xfun} ๐Ÿ“ฆ substitutions! ๐Ÿ”https://t.co/1PFI7vdRn0#rstats #DataScience pic.twitter.com/09C9GNbKPi\n","code":""},{"path":"march-2021.html","id":"march-13","chapter":"March 2021","heading":"March 13","text":"\ntidy data wish carry operations require data wide format, โ€œspread-operate-retidyโ€ pattern can bit cumbersome.{pairwise_} function family {widyr} ๐Ÿ“ฆ makes less painful:https://t.co/sIcFOKSJSM#rstats #DataScience pic.twitter.com/iaQTtAmzPS\n","code":""},{"path":"march-2021.html","id":"march-14","chapter":"March 2021","heading":"March 14","text":"\nworking text data, sometimes can long running paragraph needs wrapped readable (pretty). ๐Ÿ“–{str_wrap} function {stringr} trick! ๐Ÿ’กhttps://t.co/OF9drlXHl0#rstats #DataScience pic.twitter.com/8QVlPZ0vTW\n","code":""},{"path":"march-2021.html","id":"march-15","chapter":"March 2021","heading":"March 15","text":"\nregression model interaction effect, can tricky visualize interaction statistically informative way.Thankfully, {interplot} ๐Ÿ“ฆ already done thinking eponymous function ๐Ÿ™https://t.co/DhhrVrUxyG#rstats #DataScience pic.twitter.com/rWYxVqnpVV\n","code":""},{"path":"march-2021.html","id":"march-16","chapter":"March 2021","heading":"March 16","text":"\ncase used python-style tuple assignment, can also something similar R using infix operator {tidytidbits} ๐Ÿ“ฆ!https://t.co/0g6LDoZfmq#rstats #DataScience pic.twitter.com/M14Qt9p1rg\n","code":""},{"path":"march-2021.html","id":"march-17","chapter":"March 2021","heading":"March 17","text":"\nreport posterior distribution Bayesian model, might report point estimate, uncertainty around , index importance effect.can using {describe_posterior} function {bayestestR} ๐Ÿ“ฆhttps://t.co/OsEGXjo0Nr#rstats #DataScience pic.twitter.com/68fHWiuOM1\n","code":""},{"path":"march-2021.html","id":"march-18","chapter":"March 2021","heading":"March 18","text":"\nsee beautiful image think : \"really like use color palette plot!\", can ! ๐ŸŽจcan extract color palette image using {create_palette} function {paletter} ๐Ÿ“ฆhttps://t.co/uZsB1ovCsf#rstats #DataScience pic.twitter.com/vMIv9eZARJ\n","code":""},{"path":"march-2021.html","id":"march-19","chapter":"March 2021","heading":"March 19","text":"\nwish include mathematical expressions plot/text, comfortable writing LaTeX Rโ€™s plotmath, can use {TeX} function {latex2exp} ๐Ÿ“ฆ translation! ๐Ÿ‘https://t.co/cpyeNcFL1j#rstats #DataScience pic.twitter.com/gRPLIFljTa\n","code":""},{"path":"march-2021.html","id":"march-20","chapter":"March 2021","heading":"March 20","text":"\nformat interpolate strings using piped data, can easily using {glue_data} function {glue} ๐Ÿ“ฆhttps://t.co/INusSE9k2u#rstats #DataScience pic.twitter.com/YGgl1ZAGNC\n","code":""},{"path":"march-2021.html","id":"march-21","chapter":"March 2021","heading":"March 21","text":"\nwish embed well-formatted HTML table containing summary regression model, {tab_model} function {sjPlot} ๐Ÿ“ฆ can effortlessly:https://t.co/dtVUrfWVkBSupports simple, mixed-effects, Bayesian, etc. regression models.#rstats #DataScience pic.twitter.com/8wZha5ohNi\n","code":""},{"path":"march-2021.html","id":"march-22","chapter":"March 2021","heading":"March 22","text":"\ncan daunting task construct regular expressions, especially complex ones.{rx_} function family {RVerbalExpressions} ๐Ÿ“ฆ makes accessible allowing one construct regex using verbal expressions ๐Ÿ”กhttps://t.co/5G77171gYA#rstats #DataScience pic.twitter.com/MbLmKSW0KX\n","code":""},{"path":"march-2021.html","id":"march-23","chapter":"March 2021","heading":"March 23","text":"\ndata hierarchical categories, may wish see breakdown subcategories (e.g., check enough observations per cell mixed-effects model).can easily using {sizetree} function {plotrix} ๐Ÿ“ฆ!https://t.co/tH5hUetZ2A#rstats #DataScience pic.twitter.com/5d52tK8Mx7\n","code":""},{"path":"march-2021.html","id":"march-24","chapter":"March 2021","heading":"March 24","text":"\nlist--lists, sometimes may wish turn inside , .e.ย convert pair lists list pairs. ๐Ÿ”€{transpose} function {purrr} ๐Ÿ“ฆ makes easy:https://t.co/TBQHC2jw3r#rstats #DataScience pic.twitter.com/7wS8by9ysf\n","code":""},{"path":"march-2021.html","id":"march-25","chapter":"March 2021","heading":"March 25","text":"\nwriting functions, either ๐Ÿ“ฆ colleagues, can construct elegant user-interface (UI) using {ui_} function family {usethis} ๐Ÿ“ฆhttps://t.co/PxL3FRV8QkSuch UI provides helpful info, esp.ย functions multiple things.#rstats #DataScience pic.twitter.com/kCgSLY9gMz\n","code":""},{"path":"march-2021.html","id":"march-26","chapter":"March 2021","heading":"March 26","text":"\nvisualizing data, addition plotting full dataset, may also wish emphasize/zoom subset special consideration.can easily {facet_zoom} function {ggforce} ๐Ÿ“ฆ! ๐Ÿ™Œhttps://t.co/Dm7Lbzv0Pt#rstats #DataScience pic.twitter.com/3EKrTafXRi\n","code":""},{"path":"march-2021.html","id":"march-27","chapter":"March 2021","heading":"March 27","text":"\nmiddle piped workflow, wish access side effect (e.g., plot print something) continue chained operations, can using tee pipe operator {%T>%} {magrittr} ๐Ÿ“ฆ ! https://t.co/ibga7yyf5X#rstats #DataScience pic.twitter.com/5H0MoPOERY\n","code":""},{"path":"march-2021.html","id":"march-28","chapter":"March 2021","heading":"March 28","text":"\ncorrelation hypotheses, addition joint distribution scatterplot, one may also wish visualize marginal distributions variable.easy using {ggMarginal} function {ggExtra} ๐Ÿ“ฆ!https://t.co/OgS1XsmY1U#rstats #DataScience pic.twitter.com/wE8mmJwnIx\n","code":""},{"path":"march-2021.html","id":"march-29","chapter":"March 2021","heading":"March 29","text":"\nneed convert vector list dataframe, can use {enframe} function {tibble} ๐Ÿ“ฆ conveniently:https://t.co/dsjo0w5aYDCompare behavior {data.frame} function see want prefer .#rstats #DataScience pic.twitter.com/BxCBR64XF8\n","code":""},{"path":"march-2021.html","id":"march-30","chapter":"March 2021","heading":"March 30","text":"\nSometimes need display two plots, grid, one inside (aka \"inset\" plot).Assuming using grammar graphics approach, easy using {annotation_custom} function {ggplot2} ๐Ÿ“ฆ:https://t.co/GTV3UX2XV6#rstats #DataScience pic.twitter.com/cVH9J0dHTJ\n","code":""},{"path":"march-2021.html","id":"march-31","chapter":"March 2021","heading":"March 31","text":"\nneed pad strings, appropriately named {str_pad} function {stringr} ๐Ÿ“ฆ makes effortless ๐Ÿงตhttps://t.co/Ar7LehWFQlSee examples see different ways can padding.#rstats #DataScience pic.twitter.com/TZpXJSWvwv\n","code":""},{"path":"april-2021.html","id":"april-2021","chapter":"April 2021","heading":"April 2021","text":"","code":""},{"path":"april-2021.html","id":"april-1","chapter":"April 2021","heading":"April 1","text":"\naccepting regression model estimates, multicollinearity problematic (necessarily ) needs checked.{check_collinearity} function {performance} ๐Ÿ“ฆ offers pretty way check ๐Ÿ“Šhttps://t.co/sFMj9vbVIe#rstats #DataScience pic.twitter.com/xdrsVbz40f\n","code":""},{"path":"april-2021.html","id":"april-2","chapter":"April 2021","heading":"April 2","text":"\nSometimes wish visually highlight certain portion data retaining full dataset reference.{gghighlight} function eponymous ๐Ÿ“ฆ helps effortlessly:https://t.co/eibrcjxQ1I#rstats #DataScience pic.twitter.com/TNUFlLnnMs\n","code":""},{"path":"april-2021.html","id":"april-3","chapter":"April 2021","heading":"April 3","text":"\nk-means popular clustering algorithm disadvantage (unsupervised context) k picked advance.{kmeansruns} function {fpc} ๐Ÿ“ฆ aids running k-means range k returns best k:https://t.co/bBYsUmhrRz#rstats #DataScience pic.twitter.com/U0ecl17XT0\n","code":""},{"path":"april-2021.html","id":"april-4","chapter":"April 2021","heading":"April 4","text":"\n(Easter) Sunday, want work , deserve light entertainment .occasions, {kittyR} function eponymous ๐Ÿ“ฆ can trick ๐Ÿˆhttps://t.co/74vJiwQSAx#rstats #DataScience pic.twitter.com/Cfq5azg6KQ\n","code":""},{"path":"april-2021.html","id":"april-5","chapter":"April 2021","heading":"April 5","text":"\nOften survey platform might collapse multiple entries data single column need separated individual rows.{separate_rows} function {tidy} designed make easy:https://t.co/rESDf7zU3i#rstats #DataScience pic.twitter.com/S1j8cjBaqX\n","code":""},{"path":"april-2021.html","id":"april-6","chapter":"April 2021","heading":"April 6","text":"\nWordclouds help visualize word frequencies qualitative work, dedicated geom grammar graphics framework can give flexibility create .{ggwordcloud} function eponymous ๐Ÿ“ฆ provides geom ๐Ÿ‘Œhttps://t.co/mk8DnYJY8p#rstats #DataScience pic.twitter.com/yiwFn7QHiR\n","code":""},{"path":"april-2021.html","id":"april-7","chapter":"April 2021","heading":"April 7","text":"\ndeveloper user, curious favorite R package performed (terms usage) years, can create informative visualization using {cranDownloads} function {packageRank} ๐Ÿ“ฆhttps://t.co/64DqUbmAFF#rstats #DataScience pic.twitter.com/GPGIgZiDrf\n","code":""},{"path":"april-2021.html","id":"april-8","chapter":"April 2021","heading":"April 8","text":"\nOften reporting quantities (time, information, etc. units), wish report human-readable form.{pretty_} function family {prettyunits} ๐Ÿ“ฆ designed exactly ! โœ…https://t.co/9QMWvZIODB#rstats #DataScience pic.twitter.com/ZUX6vbRNpF\n","code":""},{"path":"april-2021.html","id":"april-9","chapter":"April 2021","heading":"April 9","text":"\nFactor analysis (FA) can help reduce many features latent features. one first needs check data suitable FA.{check_factorstructure} function {parameters} provides informative verbose way ๐Ÿ”https://t.co/MNQZMkum30#rstats #DataScience pic.twitter.com/hdeS0w3NJF\n","code":""},{"path":"april-2021.html","id":"april-10","chapter":"April 2021","heading":"April 10","text":"\npedagogical, research, etc. purposes, one may sometimes wish create fake data.{ch_} function family {charlatan} ๐Ÿ“ฆ supports creation different types data across multiple languages ๐Ÿช„https://t.co/mERCQ6aCzf#rstats #DataScience pic.twitter.com/JClu9pnzqO\n","code":""},{"path":"april-2021.html","id":"april-11","chapter":"April 2021","heading":"April 11","text":"\nfluent {dplyr} wish learn {data.table}, can nifty function can provide syntax translation two.{show_query} function {dtplyr} ๐Ÿ“ฆ just ! ๐Ÿ”„https://t.co/RKhCwjgNSt#rstats #DataScience pic.twitter.com/uj9NrXyNhY\n","code":""},{"path":"april-2021.html","id":"april-12","chapter":"April 2021","heading":"April 12","text":"\nHighest Density Interval (HDI) credible interval contains probable effect values.{hdi} function {bayestestR} helps compute visualize HDI easily posterior distributions Bayesian models ๐Ÿ“Šhttps://t.co/ui1FRXgqzq#rstats #DataScience pic.twitter.com/w4TcHpaVOP\n","code":""},{"path":"april-2021.html","id":"april-13","chapter":"April 2021","heading":"April 13","text":"\nGoogle trends analytics helpful study global web search patterns.{gtrends} function {gtrendsR} ๐Ÿ“ฆ helps extract visualize data specified periods geolocations ๐Ÿ”Žhttps://t.co/yS01ELq5q4#rstats #DataScience pic.twitter.com/mhGTSXB2rN\n","code":""},{"path":"april-2021.html","id":"april-14","chapter":"April 2021","heading":"April 14","text":"\nPlots grammar graphics framework combination layers geometric elements.{layer_} function family {ggplot2} ๐Ÿ“ฆ extracts layer details, can helpful testing exploring aesthetic defaults ๐Ÿ—‚https://t.co/mIq2G3qAo8#rstats #DataScience pic.twitter.com/jNUcLQ6nsC\n","code":""},{"path":"april-2021.html","id":"april-15","chapter":"April 2021","heading":"April 15","text":"\nage virtual assistant programs become ubiquitous, may also wish one helps find solutions common ggplot formatting problems.{gghelp} function {ggx} mimics behavior assistant ๐Ÿ“https://t.co/6VQzRqbrgp#rstats #DataScience pic.twitter.com/esK1W6xMEj\n","code":""},{"path":"april-2021.html","id":"april-16","chapter":"April 2021","heading":"April 16","text":"\nAlthough . ๐Ÿ“ฆs help assess validity regression model assumptions visually, handful cover time series analysis.{ggtsdiag} function {ggfortify} ๐Ÿ“ฆ provides comprehensive diagnostic check models ๐Ÿ“ˆhttps://t.co/iJaZ6bD6e1#rstats #DataScience pic.twitter.com/aeGlPV3IXn\n","code":""},{"path":"april-2021.html","id":"april-17","chapter":"April 2021","heading":"April 17","text":"\nOften one needs report statistical analysis publication/report, formatting manually can tedious error-prone ๐Ÿ“„ {report} function eponymous ๐Ÿ“ฆ automates process follow best practices โœ๏ธhttps://t.co/gAaRN4Qqr1#rstats #DataScience pic.twitter.com/59QS89HiLE\n","code":""},{"path":"april-2021.html","id":"april-18","chapter":"April 2021","heading":"April 18","text":"\nchoice color palette color-blind friendly, color differences plot may obvious color-blind people.{replacePlotColor} function {colorBlindness} ๐Ÿ“ฆ helps replace colors safe colorshttps://t.co/AN9lyD9fpc#rstats #DataScience pic.twitter.com/coudegYOfm\n","code":""},{"path":"april-2021.html","id":"april-19","chapter":"April 2021","heading":"April 19","text":"\nExploratory data analysis often involves specifying comparing multiple regression models.{modelplot} function {modelsummary} ๐Ÿ“ฆ provides pretty dot--whisker plots display/compare regression estimates models:https://t.co/0vyzYY9bUy#rstats #DataScience pic.twitter.com/ZtkV0EUjVd\n","code":""},{"path":"april-2021.html","id":"april-20","chapter":"April 2021","heading":"April 20","text":"\nNext statistical significance, often interested practical relevance effect.{interpret_} function family {effectsize} ๐Ÿ“ฆ provides interpretation guidelines, can differ across disciplines ๐Ÿ“https://t.co/CaqThRABhy#rstats #DataScience pic.twitter.com/3ywbl2imPj\n","code":""},{"path":"april-2021.html","id":"april-21","chapter":"April 2021","heading":"April 21","text":"\n{purrr::map_} functions apply function list elements.one wants apply function element list combinations elements?{xmap_} functions {crossmap} ๐Ÿ“ฆ exactly !https://t.co/9ITKhUqV9P#rstats #DataScience pic.twitter.com/woQenHnhfN\n","code":""},{"path":"april-2021.html","id":"april-22","chapter":"April 2021","heading":"April 22","text":"\nPCA popular method reduce dimensionality multivariate data biplot useful visualization method . {fviz_pca_biplot} function {factoextra} ๐Ÿ“ฆ makes effortless make elegant biplots:https://t.co/DRGesAG4vq#rstats #DataScience pic.twitter.com/RB4BoCDp1S\n","code":""},{"path":"april-2021.html","id":"april-23","chapter":"April 2021","heading":"April 23","text":"\nSometimes just want convert source code R script (.R) new Markdown (.md) document/report.{spin} function {knitr} ๐Ÿ“ฆ makes conversion effortless! ๐Ÿช„https://t.co/cBaYSC5nc8#rstats #DataScience pic.twitter.com/u1kIa8Of9T\n","code":""},{"path":"april-2021.html","id":"april-24","chapter":"April 2021","heading":"April 24","text":"\none correlation significant, , fallacy conclude difference correlations statistically significant.{cocor} function eponymous ๐Ÿ“ฆ helps formally test difference:https://t.co/9CjLNcqffG#rstats #DataScience pic.twitter.com/XiJ5OqIExv\n","code":""},{"path":"april-2021.html","id":"april-25","chapter":"April 2021","heading":"April 25","text":"\nConfusion matrix visualization helps assess performance (binary multi-class) classification algorithm.{plot_confusion_matrix} function {cvms} ๐Ÿ“ฆ produces elegant informative confusion matrix plots ๐Ÿ—„https://t.co/NHxGNS8XnD#rstats #DataScience pic.twitter.com/SZWvibYAQp\n","code":""},{"path":"april-2021.html","id":"april-26","chapter":"April 2021","heading":"April 26","text":"\ntrivial combining multiple characters form single phrase sounds, common solutions return outputs imperfect human readers.{combine_words} helper function {knitr} ๐Ÿ“ฆ fills gap! ๐Ÿ™Œhttps://t.co/M0CgOUU0ed#rstats #DataScience pic.twitter.com/XuCNHf3BFi\n","code":""},{"path":"april-2021.html","id":"april-27","chapter":"April 2021","heading":"April 27","text":"\nAlthough number ๐Ÿ“ฆs provide functions visualize one-way ANOVA design, support visualizing complex, multi-way ANOVA designs.{afex_plot} function {afex} ๐Ÿ“ฆ one function ! ๐Ÿ™Œ๐Ÿ“Šhttps://t.co/3px5ySCzrd#rstats #DataScience pic.twitter.com/3g9hkZ3ySy\n","code":""},{"path":"april-2021.html","id":"april-28","chapter":"April 2021","heading":"April 28","text":"\nMarkdown syntax enviable ease simplicity. one might naturally wish use annotations {ggplot2} plots.{element_markdown} function {ggtext} ๐Ÿ“ฆ magically helps exactly ! ๐ŸŽ‰https://t.co/54WpoyrVje#rstats #DataScience pic.twitter.com/irr8gRVaxf\n","code":""},{"path":"april-2021.html","id":"april-29","chapter":"April 2021","heading":"April 29","text":"\nsensitive categorical data (e.g., gender, race, etc.), might sometimes required anonymize carrying analysis.{fct_anon} function {forcats} ๐Ÿ“ฆ helps exactly step ๐Ÿ—ƒ๏ธhttps://t.co/xsR3HipYqu#rstats #DataScience pic.twitter.com/C6apoc6fEP\n","code":""},{"path":"april-2021.html","id":"april-30","chapter":"April 2021","heading":"April 30","text":"\nGoogle's Tesseract (https://t.co/tJkaT2vH2j), powerful optical character recognition engine, can extract text embedded images 100 languages!{ocr} function {tesseract} ๐Ÿ“ฆ provides access engine ๐Ÿ‘ฝhttps://t.co/H0vHvn59sQ#rstats #DataScience pic.twitter.com/g0GMKB9JEy\n","code":""},{"path":"may-2021.html","id":"may-2021","chapter":"May 2021","heading":"May 2021","text":"","code":""},{"path":"may-2021.html","id":"may-1","chapter":"May 2021","heading":"May 1","text":"\n{ggplot2}, missing values removed default, sometimes may wish visualize see pattern missingness.{geom_miss_point} function {naniar} ๐Ÿ“ฆ offers exactly functionality ๐Ÿ”https://t.co/zJrYSMhnX4#rstats #DataScience pic.twitter.com/IrXFHoLVG1\n","code":""},{"path":"may-2021.html","id":"may-2","chapter":"May 2021","heading":"May 2","text":"\nexploring data, one often needs quick easy way create dot--whisker plots coefficient estimates (quantities) regression models.{ggcoefstats} function {ggstatsplot} ๐Ÿ“ฆ offers one optionhttps://t.co/e4iZpPoNIC#rstats #DataScience pic.twitter.com/suxiOKjuLo\n","code":""},{"path":"may-2021.html","id":"may-3","chapter":"May 2021","heading":"May 3","text":"\nfunctions can properly deal labelled data, can cause problems data analysis. often one may wish just remove labels.{remove_all_labels} function {sjlabelled} ๐Ÿ“ฆ exactly โœ‚๏ธhttps://t.co/9mJHPTRlVD#rstats #DataScience pic.twitter.com/dt6Pbfkn58\n","code":""},{"path":"may-2021.html","id":"may-4","chapter":"May 2021","heading":"May 4","text":"\n{ggplot2} syntax carry common routines (e.g, changing legend position, axes, etc.) can surprisingly hard remember.{easy_} function family {ggeasy} ๐Ÿ“ฆ provides memorable shortcuts routines ๐Ÿ™Œhttps://t.co/vqFYzcvxKZ#rstats #DataScience pic.twitter.com/eZvdrDwmNf\n","code":""},{"path":"may-2021.html","id":"may-5","chapter":"May 2021","heading":"May 5","text":"\nGoogle Scholar essential platform organize one's scholarly research output.{get_} function family {scholar} ๐Ÿ“ฆ provides various helpers extract information scholar, publications, citations, etc. ๐Ÿงชhttps://t.co/ndoPs9Yy8S#rstats #DataScience pic.twitter.com/lAMo79rZu3\n","code":""},{"path":"may-2021.html","id":"may-6","chapter":"May 2021","heading":"May 6","text":"\nLinear linear mixed-effects models assume normally distributed residuals random effects.validity assumption can statistically visually checked using {check_normality} function {performance} ๐Ÿ“ฆ! ๐Ÿ”https://t.co/7XbOrSrYxq#rstats #DataScience pic.twitter.com/AK7AX7iF6o\n","code":""},{"path":"may-2021.html","id":"may-7","chapter":"May 2021","heading":"May 7","text":"\nOften wish append visualizations regression model estimates tabular summary inferential statistics.{forest_model} function {forestmodel} ๐Ÿ“ฆ satisfies requirements via informative forest plot ๐ŸŒณhttps://t.co/YYD6dEJXSM#rstats #DataScience pic.twitter.com/rSEodymQx4\n","code":""},{"path":"may-2021.html","id":"may-8","chapter":"May 2021","heading":"May 8","text":"\nPost reproducibility crisis news, replications rise across disciplines. led need compare multiple studies.end, function {sci_figure} {scifigure} ๐Ÿ“ฆ proves helpful visual tool โš–๏ธhttps://t.co/GnwtwoUcar#rstats #DataScience pic.twitter.com/TXbndnA0Bu\n","code":""},{"path":"may-2021.html","id":"may-9","chapter":"May 2021","heading":"May 9","text":"\nSometimes data contain implicitly missing values one may wish make explicit completing missing information.{complete} function {tidyr} ๐Ÿ“ฆ exactly ! ๐Ÿ‘https://t.co/tHbK4YAJlZ#rstats #DataScience pic.twitter.com/xbDCkW0kLG\n","code":""},{"path":"may-2021.html","id":"may-10","chapter":"May 2021","heading":"May 10","text":"\nAlthough {patchwork} ๐Ÿ“ฆ's arithmetic syntax (+, *, etc.) combine plots together extremely easy intuitive, can difficult use programmatically.{wrap_plots} function perfect alternative solution!๐Ÿ’กhttps://t.co/AGLkfbPUja#rstats #DataScience pic.twitter.com/fJS5V9weJ3\n","code":""},{"path":"may-2021.html","id":"may-11","chapter":"May 2021","heading":"May 11","text":"\nSometimes one may wish include plots visualizations table, e.g.ย show distribution variable.{spec_} function family {kableExtra} ๐Ÿ“ฆ make effortless ๐Ÿ™Œhttps://t.co/X4YY8Drm7C#rstats #DataScience pic.twitter.com/9HRDTxEpB8\n","code":""},{"path":"may-2021.html","id":"may-12","chapter":"May 2021","heading":"May 12","text":"\nROPE defines part posterior distribution practically equivalent null effect useful hypothesis testing.{rope} function {bayestestR} ๐Ÿ“ฆ helps calculate visualize Bayesian regression models:https://t.co/mfXACyKOfC#rstats #DataScience pic.twitter.com/W2iL072RP5\n","code":""},{"path":"may-2021.html","id":"may-13","chapter":"May 2021","heading":"May 13","text":"\nSometimes might need convert numbers English words (e.g., assistant systems read loud). ๐Ÿ“ข{numbers_to_words} function {xfun} ๐Ÿ“ฆ makes routine effortless customizable ๐Ÿ”ขhttps://t.co/l2mYGmQGnx#rstats #DataScience pic.twitter.com/B9atDLK3nQ\n","code":""},{"path":"may-2021.html","id":"may-14","chapter":"May 2021","heading":"May 14","text":"\nGardnerโ€“Altman mean difference plot simultaneously displays raw data highlights group difference estimate ๐Ÿ“Š{mean_diff} function {dabestr} ๐Ÿ“ฆ creates plots two multiple groups easily โš–๏ธhttps://t.co/dhtg2Nh62r#rstats #DataScience pic.twitter.com/fjv2Vkpi19\n","code":""},{"path":"may-2021.html","id":"may-15","chapter":"May 2021","heading":"May 15","text":"\nregression analysis big datasets, may times wish reduce memory assigned model objects.{axe_} function family {butcher} ๐Ÿ“ฆ offer ways remove different parts model object make leaner ๐Ÿ‹๏ธhttps://t.co/ayY86U0zOy#rstats #DataScience pic.twitter.com/9CIU1NSqKU\n","code":""},{"path":"may-2021.html","id":"may-16","chapter":"May 2021","heading":"May 16","text":"\nexploratory phase, may wish visualize model data quickly, thoroughly, iteratively โžฟ{ggbetweenstats} function {ggstatsplot} ๐Ÿ“ฆ facilitates one-way design via plots statistical details ๐Ÿ“Šhttps://t.co/fQkmmLSOh3#rstats #DataScience pic.twitter.com/uxsJ8Odv5b\n","code":""},{"path":"may-2021.html","id":"may-17","chapter":"May 2021","heading":"May 17","text":"\nOne often needs compare two values axis (e.g.ย pre-post-treatment scores) dumbbell chart efficient way visualize comparison.{dumbbell_chart} function {ggcharts} ๐Ÿ“ฆ creates easily:https://t.co/Y6YGS0h0DS#rstats #DataScience pic.twitter.com/Wpwey00gGV\n","code":""},{"path":"may-2021.html","id":"may-18","chapter":"May 2021","heading":"May 18","text":"\nProgramming challenging task - teacher, collaborator, developer, etc. - might wish add touch encouragement activity sometimes.{praise} function eponymous ๐Ÿ“ฆ can help ๐Ÿ’Œhttps://t.co/wImExbbaT7#rstats #DataScience pic.twitter.com/1SnLZ7ctS7\n","code":""},{"path":"may-2021.html","id":"may-19","chapter":"May 2021","heading":"May 19","text":"\nmodel-fitting, often need convert model objects dataframe, either reporting visualizations. ๐Ÿ“œ{model_parameters} function {parameters} ๐Ÿ“ฆ many regression model objects ๐ŸŒŸhttps://t.co/4jOHsZDCaz#rstats #DataScience pic.twitter.com/uI7sUSF0bz\n","code":""},{"path":"may-2021.html","id":"may-20","chapter":"May 2021","heading":"May 20","text":"\nmodern scientific work, traditional credit assignment systems seem outmoded, contributions table better suited.{generate} function {contribution} ๐Ÿ“ฆ can easily generate tables ggplot2-framework ๐Ÿ”ขhttps://t.co/FCTli0Ht1f#rstats #DataScience pic.twitter.com/IqcLPMFxOa\n","code":""},{"path":"may-2021.html","id":"may-21","chapter":"May 2021","heading":"May 21","text":"\nfluent {dplyr} wish learn write SQL queries, can nifty function can provide syntax translation two.{show_query} function {dbplyr} ๐Ÿ“ฆ just ! ๐Ÿ”„https://t.co/GAjWk5qfVC#rstats #DataScience pic.twitter.com/qxOAPD4PAX\n","code":""},{"path":"may-2021.html","id":"may-22","chapter":"May 2021","heading":"May 22","text":"\nworking directory, sometimes need get information contents.Instead using terminal, one can simply use {dir_ls} function {fs} ๐Ÿ“ฆ return richly informative summary ๐Ÿ—‚https://t.co/s0UxLxPykG#rstats #DataScience pic.twitter.com/kAzYf2jPfB\n","code":""},{"path":"may-2021.html","id":"may-23","chapter":"May 2021","heading":"May 23","text":"\nanalysis, model selection may involve specification comparison multiple models.{compare_performance} function {performance} ๐Ÿ“ฆ creates tabular + graphical summaries comparing performance indices models ๐Ÿฅ‡https://t.co/6cAI5ZbNkl#rstats #DataScience pic.twitter.com/CxyfyRCCHY\n","code":""},{"path":"may-2021.html","id":"may-24","chapter":"May 2021","heading":"May 24","text":"\nwriting functions print text user's terminal console, may wish construct pleasant command-line interface.{cli_} function family {cli} ๐Ÿ“ฆ provides collection tools make pretty ๐Ÿ’…https://t.co/HJeQ6MBgue#rstats #DataScience pic.twitter.com/ZJZNBP5SAa\n","code":""},{"path":"may-2021.html","id":"may-25","chapter":"May 2021","heading":"May 25","text":"\nteaching reporting methods, may sometimes wish report LaTeX equations regression models, can frustrating time-consuming.{extract_eq} function {equatiomatic} ๐Ÿ“ฆ makes really easy! ๐Ÿ™Œhttps://t.co/LyQ9bMxal0#rstats #DataScience pic.twitter.com/mlghSLFeGJ\n","code":""},{"path":"may-2021.html","id":"may-26","chapter":"May 2021","heading":"May 26","text":"\nEither due reading formatting errors, sometimes data entries belong together cell might break across rows.{unbreak_vals} function {unheadr} ๐Ÿ“ฆ makes easy join using regular expressions ๐Ÿฉนhttps://t.co/gouxjvPows#rstats #DataScience pic.twitter.com/3a4ZA73xxP\n","code":""},{"path":"may-2021.html","id":"may-27","chapter":"May 2021","heading":"May 27","text":"\nBar charts good way visualize categorical data making well-labeled bar chart can time-consuming.{ggbarstats} function {ggstatsplot} ๐Ÿ“ฆ easily produces chart (additional statistical details) ๐Ÿ“Šhttps://t.co/dNl2IJAJX3#rstats #DataScience pic.twitter.com/0UySWaMxei\n","code":""},{"path":"may-2021.html","id":"may-28","chapter":"May 2021","heading":"May 28","text":"\ncommon data wrangling routine involves checking presence absence certain combinations data combinatorial structure.{expand} function {tidyr} ๐Ÿ“ฆ exactly ! ๐Ÿ‘https://t.co/oj3THZs4RW#rstats #DataScience pic.twitter.com/SjHZo7bpxv\n","code":""},{"path":"may-2021.html","id":"may-29","chapter":"May 2021","heading":"May 29","text":"\nAlthough adding horizontal vertical lines {ggplot2} easy, sometimes need add multiple intercepts.{geom_vhlines} function {ggpp} ๐Ÿ“ฆ provides perfect geometric layer quickly! โšก๏ธhttps://t.co/gNfzDhRdhu#rstats #DataScience pic.twitter.com/1oA1ZMWDKI\n","code":""},{"path":"may-2021.html","id":"may-30","chapter":"May 2021","heading":"May 30","text":"\nAcross projects, may similar directory structure , therefore, may prefer create directories algorithmically manually.{dir_create} function {fs} ๐Ÿ“ฆ perfect safely ! ๐Ÿ—ƒ๏ธhttps://t.co/SvS78Z0hIN#rstats #DataScience pic.twitter.com/uHEXy3n1N6\n","code":""},{"path":"may-2021.html","id":"may-31","chapter":"May 2021","heading":"May 31","text":"\n{latex2exp} ๐Ÿ“ฆ useful convert LaTeX equations Rโ€™s {plotmath}. instead want opposite: convert {plotmath} expressions LaTeX?{expr2latex} function {simsalapar} ๐Ÿ“ฆ exactly ! ๐Ÿ”€https://t.co/MvVrIEOdwe#rstats #DataScience pic.twitter.com/kSMSdp4gIC\n","code":""},{"path":"june-2021.html","id":"june-2021","chapter":"June 2021","heading":"June 2021","text":"","code":""},{"path":"june-2021.html","id":"june-1","chapter":"June 2021","heading":"June 1","text":"\nOutliers influential observations can cause problems (interpreting) regression models, need detected scrutiny.{check_outliers} function {performance} ๐Ÿ“ฆ can effortlessly ๐Ÿ‹๏ธโ€โ™€๏ธhttps://t.co/kb9ipoi0l7#rstats #DataScience pic.twitter.com/Qatg8YgQAx\n","code":""},{"path":"june-2021.html","id":"june-2","chapter":"June 2021","heading":"June 2","text":"\nProject Gutenberg digital library > 65K full-texts freely available. Sometimes may wish query book want read available ๐Ÿ“š{gutenberg_works} function {gutenbergr} ๐Ÿ“ฆ provides info! ๐Ÿ”https://t.co/xxXL0wx3wQ#rstats #DataScience pic.twitter.com/jgGngCTmqk\n","code":""},{"path":"june-2021.html","id":"june-3","chapter":"June 2021","heading":"June 3","text":"\nAlthough visualizing variable distribution via violin plot easy {ggplot2}, may wish avoid redundant mirroring density plot.{stat_halfeye} function {ggdist} ๐Ÿ“ฆ provides exactly geometric layer! ๐Ÿ™Œhttps://t.co/I3ohHjJG1s#rstats #DataScience pic.twitter.com/eVpC6OxEx9\n","code":""},{"path":"june-2021.html","id":"june-4","chapter":"June 2021","heading":"June 4","text":"\ncorrelation hypotheses, one may wish simultaneously visualize joint marginal distributions.{ggscatterstats} function {ggstatsplot} ๐Ÿ“ฆ default (statistical summary plus) ๐Ÿ’ https://t.co/09RGm3D8f5#rstats #DataScience pic.twitter.com/939n98ag9O\n","code":""},{"path":"june-2021.html","id":"june-5","chapter":"June 2021","heading":"June 5","text":"\ntimes, may wish create \"safe\" versions functions notify us function fails also error stop workflow relies .{safely} function {purrr} ๐Ÿ“ฆ creates \"safe\" functions ๐Ÿช‚https://t.co/TgO3VCFznT#rstats #DataScience pic.twitter.com/Au0O7G3Lgb\n","code":""},{"path":"june-2021.html","id":"june-6","chapter":"June 2021","heading":"June 6","text":"\nSometimes need convert HTML PDF, PNG, etc. ๐Ÿ”{chrome_print} function {pagedown} can either local file (including Rmd file outputs HTML) URL ๐Ÿ“ https://t.co/IqHMH3kBVz#rstats #DataScience pic.twitter.com/J5sGQVTDbt\n","code":""},{"path":"june-2021.html","id":"june-7","chapter":"June 2021","heading":"June 7","text":"\nSometimes data missing output format repeat values change. missing values can filled using next previous entry. ๐Ÿงฑ {fill} function {tidyr} exactly ! ๐Ÿ™Œhttps://t.co/QTlw2j4luQ#rstats #DataScience pic.twitter.com/9YLldOUtrH\n","code":""},{"path":"june-2021.html","id":"june-8","chapter":"June 2021","heading":"June 8","text":"\ntest practical equivalence checks effect sizes equivalent null effect (range values considered practically unimportant).{equivalence_test} {effectsize} ๐Ÿ“ฆ performs visualizes test ๐Ÿ”https://t.co/r3sWIusHcH#rstats #DataScience pic.twitter.com/rurbfkD04l\n","code":""},{"path":"june-2021.html","id":"june-9","chapter":"June 2021","heading":"June 9","text":"\nTables typically presented outside graphics. sometimes may wish display alongside graphics, requires making \"graphical\" tables.{tableGrob} function {gridExtra} ๐Ÿ“ฆ can exactly ! ๐Ÿคฉhttps://t.co/yatEDnIiHJ#rstats #DataScience pic.twitter.com/obnLMJSSEh\n","code":""},{"path":"june-2021.html","id":"june-10","chapter":"June 2021","heading":"June 10","text":"\nscatterplot matrix elegantly visualizes multi-dimensional data variable pair graphs arranged matrix shared scales.{ggpairs} function {GGally} ๐Ÿ“ฆ provides easy flexible way generate ! ๐Ÿ‘ https://t.co/liWHH9PAUe#rstats #DataScience pic.twitter.com/z94y7K7IMk\n","code":""},{"path":"june-2021.html","id":"june-11","chapter":"June 2021","heading":"June 11","text":"\nSometimes just want quickly convert source code R script (.R) report (can markdown, PDF, HTML).{stitch} function family {knitr} ๐Ÿ“ฆ makes conversion effortless! ๐Ÿงถ https://t.co/F7q6zYqn84#rstats #DataScience pic.twitter.com/bPwdrVMEOw\n","code":""},{"path":"june-2021.html","id":"june-12","chapter":"June 2021","heading":"June 12","text":"\ndata grouped/clustered structure, may wish highlight visualization. ๐Ÿชถ{geom_mark_ellipse} function {ggforce} ๐Ÿ“ฆ provides perfect geometric layer achieve drawing annotated ellipse! ๐Ÿ’ชhttps://t.co/CBfw7Uj52e#rstats #DataScience pic.twitter.com/iIkVhL6ZXc\n","code":""},{"path":"june-2021.html","id":"june-13","chapter":"June 2021","heading":"June 13","text":"\nSometimes โ€™d wish download fulltext paper read comfort R console. ๐Ÿก {ft_get} function {fulltext} ๐Ÿ“ฆ can exactly ; need provide doi! ๐Ÿ“‘https://t.co/AL9zFqN1wj#rstats #DataScience pic.twitter.com/jnAShY92I4\n","code":""},{"path":"june-2021.html","id":"june-14","chapter":"June 2021","heading":"June 14","text":"\nscript takes long time run, may wish get notified finishes. โœ…{beep} function {beepr} ๐Ÿ“ฆ can producing sound choosing! ๐Ÿ”Šhttps://t.co/tXSziFWu88#rstats #DataScience pic.twitter.com/okF71yE6ZV\n","code":""},{"path":"june-2021.html","id":"june-15","chapter":"June 2021","heading":"June 15","text":"\nSignificance-testing regression models assumes errors constant variance.validity key assumption can statistically visually checked using {check_heteroscedasticity} function {performance} ๐Ÿ“ฆhttps://t.co/TOemt5pXes#rstats #DataScience pic.twitter.com/4y4txOtnSs\n","code":""},{"path":"june-2021.html","id":"june-16","chapter":"June 2021","heading":"June 16","text":"\nSometimes might dataframe containing information ideally prefer list.{deframe} function {tibble} ๐Ÿ“ฆ can needed transformation extract list ๐Ÿ”งhttps://t.co/dsjo0w5aYD#rstats #DataScience pic.twitter.com/uRFlJMoJ0Q\n","code":""},{"path":"june-2021.html","id":"june-17","chapter":"June 2021","heading":"June 17","text":"\nworking text data, may wish create page-styled visualizations (highlight certain words, provide quick visual summary, etc.).{ggpage_plot} function {ggpage} ๐Ÿ“ฆ creates {ggplot}-framework! ๐Ÿ“ƒhttps://t.co/TW6OPH5otb#rstats #DataScience pic.twitter.com/CURqBhD9cU\n","code":""},{"path":"june-2021.html","id":"june-18","chapter":"June 2021","heading":"June 18","text":"\nSometimes may wish print colorful output R terminal.color functions (red, green, etc.) {crayon} ๐Ÿ“ฆ make really easy (via ANSI escape codes)! ๐ŸŽจhttps://t.co/RGvmzBQR7B#rstats #DataScience pic.twitter.com/X7jGXxHb9h\n","code":""},{"path":"june-2021.html","id":"june-19","chapter":"June 2021","heading":"June 19","text":"\nPopulation pyramids help visualize overall age distribution population useful across many fields (e.g., ecology).{pyramid_chart} function {ggcharts} ๐Ÿ“ฆ provides easy syntax create ๐Ÿ‘https://t.co/vvhumXF3s5#rstats #DataScience pic.twitter.com/O7RB54NLnX\n","code":""},{"path":"june-2021.html","id":"june-20","chapter":"June 2021","heading":"June 20","text":"\nSometimes โ€™d wish download install needed softwares comfort R console . ๐Ÿก {install.} function family {installr} ๐Ÿ“ฆ can exactly Windows OS โฌhttps://t.co/MdlaWRGskO#rstats #DataScience pic.twitter.com/RQOgFGyJYS\n","code":""},{"path":"june-2021.html","id":"june-21","chapter":"June 2021","heading":"June 21","text":"\nPie charts popular way visualize categorical data, making well-labeled pie chart can time-consuming.{ggpiestats} function {ggstatsplot} ๐Ÿ“ฆ produces easily (additional statistical details) ๐Ÿ’https://t.co/8RAAjLUP7H#rstats #DataScience pic.twitter.com/Eqbsd7ACLN\n","code":""},{"path":"june-2021.html","id":"june-22","chapter":"June 2021","heading":"June 22","text":"\nexperience object-oriented programming (OOP) languages, may wish learn implementation R. ๐Ÿ‘ฉโ€๐Ÿ’ปfirst step know object types R, {otype} function {sloop} ๐Ÿ“ฆ makes easy!https://t.co/cfl3iD4n9m#rstats #DataScience pic.twitter.com/avIaOGvlHC\n","code":""},{"path":"june-2021.html","id":"june-23","chapter":"June 2021","heading":"June 23","text":"\nEven know HTML, sometimes may wish generate HTML code/tags using R .tag builder function family {htmltools} ๐Ÿ“ฆ makes straightforward!https://t.co/X0cN4XB0WCPotentially also helpful self-teaching HTML via R. ๐Ÿ‘ฉโ€๐Ÿซ#rstats #DataScience pic.twitter.com/Tv8jES7TYt\n","code":""},{"path":"june-2021.html","id":"june-24","chapter":"June 2021","heading":"June 24","text":"\nSignificance-testing ANOVAs assumes comparison groups variance.validity assumption can statistically visually checked using {check_homogeneity} function {performance} ๐Ÿ“ฆ! โœ…https://t.co/YVlloAfSdo#rstats #DataScience pic.twitter.com/advuxCgV6t\n","code":""},{"path":"june-2021.html","id":"june-25","chapter":"June 2021","heading":"June 25","text":"\nwriting RMarkdown, may required embed local remote assets enrich document.{include_} function family {knitr} ๐Ÿ“ฆ makes easy also provides many advantages Markdown syntax ๐Ÿงฐhttps://t.co/3ffCSIRvCw#rstats #DataScience pic.twitter.com/gbOUCChpXX\n","code":""},{"path":"june-2021.html","id":"june-26","chapter":"June 2021","heading":"June 26","text":"\ndeciding use color palette, wish know hex color codes also color looks.{paletteer_d} function {paletteer} ๐Ÿ“ฆ exactly ! ๐ŸŽจhttps://t.co/zNlTlQtytBP.S. continuous palettes: {paletteer_c}#rstats #DataScience pic.twitter.com/gASyRT94mg\n","code":""},{"path":"june-2021.html","id":"june-27","chapter":"June 2021","heading":"June 27","text":"\nmultivariate data, radar chart helps visualize multiple quantitative variables two-dimensional chart. ๐Ÿ•ธ๏ธ{ggRadar} function {ggiraphExtra} ๐Ÿ“ฆ makes really easy create static interactive radar charts! ๐Ÿ‘https://t.co/JXXAQbNOyE#rstats #DataScience pic.twitter.com/cHbCKDd8Ig\n","code":""},{"path":"june-2021.html","id":"june-28","chapter":"June 2021","heading":"June 28","text":"\nSometimes R objects need compared (e.g.ย implementing caching strategy), can done creating unique hash digests . ๐Ÿ•ต๏ธ{digest} function eponymous ๐Ÿ“ฆ exactly ! 3โƒฃ2โƒฃhttps://t.co/dFy9o3RpbC#rstats #DataScience pic.twitter.com/GJNB48TXGc\n","code":""},{"path":"june-2021.html","id":"june-29","chapter":"June 2021","heading":"June 29","text":"\ndata grouped/clustered structure, may wish highlight visualization. ๐Ÿชถ{geom_mark_hull} function {ggforce} ๐Ÿ“ฆ provides geometric layer achieve annotating sets points via hulls โœ๏ธhttps://t.co/fnjCPTf2Ki#rstats #DataScience pic.twitter.com/wbvFiHaEyt\n","code":""},{"path":"june-2021.html","id":"june-30","chapter":"June 2021","heading":"June 30","text":"\nClustering algorithms can often disagree number clusters present data.address , {n_clusters} function {parameters} ๐Ÿ“ฆ runs 28 different algorithms suggests number based maximum consensus ๐Ÿค–https://t.co/DIxPXIhhLg#rstats #DataScience pic.twitter.com/gMIT5jd1QO\n","code":""},{"path":"july-2021.html","id":"july-2021","chapter":"July 2021","heading":"July 2021","text":"","code":""},{"path":"july-2021.html","id":"july-1","chapter":"July 2021","heading":"July 1","text":"\nshare code plus data, good idea adopt defensive techniques ensure data code expects.{assert_that} function {assertthat} ๐Ÿ“ฆ provides just tool! ๐Ÿฅ‹https://t.co/kQT74S0AQX#rstats #DataScience pic.twitter.com/ScTmUxQxnr\n","code":""},{"path":"july-2021.html","id":"july-2","chapter":"July 2021","heading":"July 2","text":"\nEither aesthetic highlighting purposes, may sometimes wish draw borders around legend keys {ggplot2} plots. ๐Ÿ–ผ๏ธ{keybox} {ggfun} ๐Ÿ“ฆ exactly , easily flexibly ๐Ÿ™Œhttps://t.co/dBAjEqKzY2#rstats #DataScience pic.twitter.com/mvFxIeInyj\n","code":""},{"path":"july-2021.html","id":"july-3","chapter":"July 2021","heading":"July 3","text":"\nuse RMarkdown copy-paste software output report statistics, want check errors made process.{statcheck} function eponymous ๐Ÿ“ฆ (single multiple files)! ๐Ÿ”https://t.co/HdW6CKKPO8#rstats #DataScience pic.twitter.com/tgC5ysvz6T\n","code":""},{"path":"july-2021.html","id":"july-4","chapter":"July 2021","heading":"July 4","text":"\nCorrectly specifying distribution family regression model can improve estimate accuracy. โ€™re unsure?{check_distribution} function {performance} ๐Ÿ“ฆ uses Random Forest help reconsider choice โš ๏ธhttps://t.co/qD7cQvNLLz#rstats #DataScience pic.twitter.com/MLhkCT2cwh\n","code":""},{"path":"july-2021.html","id":"july-5","chapter":"July 2021","heading":"July 5","text":"\nscraping web data R, sometimes may wish extracted text layout mimics browser/HTML behavior (e.g.ย ignore whitespace).{html_text2} function {rvest} ๐Ÿ“ฆ helps exactly ! ๐Ÿ™‡โ€โ™€๏ธhttps://t.co/QJwe3nXx43#rstats #DataScience pic.twitter.com/T7NU9MYKqL\n","code":""},{"path":"july-2021.html","id":"july-6","chapter":"July 2021","heading":"July 6","text":"\nSimple slopes analyses can help understand interaction effects linear regression.{sim_slopes} function {interactions} ๐Ÿ“ฆ provides easy way run visualize analysis 2-way 3-way interactions! ๐ŸŽš๏ธhttps://t.co/4Xvc4FMrEr#rstats #DataScience pic.twitter.com/70AFS2QQcF\n","code":""},{"path":"july-2021.html","id":"july-7","chapter":"July 2021","heading":"July 7","text":"\nhistogram good visualization represent distribution numeric data. ๐Ÿ“Š{gghistostats} function {ggstatsplot} ๐Ÿ“ฆ provides ready-made histograms (additional descriptive inferential statistics) ๐Ÿ’https://t.co/zSmT4MBGjX#rstats #DataScience pic.twitter.com/xMFwXedQSO\n","code":""},{"path":"july-2021.html","id":"july-8","chapter":"July 2021","heading":"July 8","text":"\nwant use non-standard fonts characters, getting graphics devices work can pain.{showtext_auto} function {showtext} ๐Ÿ“ฆ supports large collection font formats graphics devices! ๐Ÿ™Œhttps://t.co/bt3SzZfyhI#rstats #DataScience pic.twitter.com/bYmyfZEfhL\n","code":""},{"path":"july-2021.html","id":"july-9","chapter":"July 2021","heading":"July 9","text":"\nSometimes may wish provide descriptive labels colors using, may know label .{name} function {ColorNameR} ๐Ÿ“ฆ produces color labels multiple languages colorspaces! ๐ŸŽจhttps://t.co/RVkqjq7Ksz#rstats #DataScience pic.twitter.com/MmD1MWPHF3\n","code":""},{"path":"july-2021.html","id":"july-10","chapter":"July 2021","heading":"July 10","text":"\nSometimes may wish check relevant assumptions regression model one go. ๐Ÿ”{check_model} function {performance} ๐Ÿ“ฆ exactly also provides elegant visualizations helpful pointers โœ…๐Ÿ“Šhttps://t.co/4SIIL0u9Jn#rstats #DataScience pic.twitter.com/EMmye4qAZk\n","code":""},{"path":"july-2021.html","id":"july-11","chapter":"July 2021","heading":"July 11","text":"\nwriting manuscripts RMarkdown, may wish auto-generate citations R packages used document. โœ๏ธ{write_bib} function {knitr} ๐Ÿ“ฆ exactly ! ๐Ÿ“„https://t.co/JYux01tB7h#rstats #DataScience pic.twitter.com/YvOiaPeYfY\n","code":""},{"path":"july-2021.html","id":"july-12","chapter":"July 2021","heading":"July 12","text":"\nplotting time series data, may wish plot several subseries corresponding periods interest (seasons, months, etc.).{ggfreqplot} function {ggfortify} ๐Ÿ“ฆ makes task effortless! ๐Ÿ“ˆ๐Ÿ“‰ https://t.co/rRmcQbKv4U#rstats #DataScience pic.twitter.com/Vic17LXkM9\n","code":""},{"path":"july-2021.html","id":"july-13","chapter":"July 2021","heading":"July 13","text":"\n๐Ÿ“ฆs used happen function identical name, using function may fail.Aside :: qualifier, {conflicted_prefer} function {conflicted} ๐Ÿ“ฆ can solve conflict prioritizing one function ๐Ÿฅ‡https://t.co/n96Mt8kHx3#rstats #DataScience pic.twitter.com/V9jZwxADoM\n","code":""},{"path":"july-2021.html","id":"july-14","chapter":"July 2021","heading":"July 14","text":"\nExploratory data analysis often involves specifying comparing multiple regression models.{compare_parameters} function {parameters} ๐Ÿ“ฆ provides dot--whisker plots display compare regression estimates! ๐Ÿฅ‡๐Ÿฅˆ๐Ÿฅ‰https://t.co/vmuGta5W9i#rstats #DataScience pic.twitter.com/Xyve8SMk1Q\n","code":""},{"path":"july-2021.html","id":"july-15","chapter":"July 2021","heading":"July 15","text":"\nEither computing environment good practice recommendations may compel check file paths, character encoding, etc. script. ๐Ÿ”{is_} function family {xfun} ๐Ÿ“ฆ provides tools run checks easily โœ…https://t.co/LdRTIKXOCA#rstats #DataSciencee pic.twitter.com/YCtgZEufae\n","code":""},{"path":"july-2021.html","id":"july-16","chapter":"July 2021","heading":"July 16","text":"\nSometimes may wish assess polarity (positive, negative, neutral) text data.{sentiment} function {sentimentr} ๐Ÿ“ฆ provides convenient flexible way approximate sentiment text sentence โž•โž–https://t.co/mMpxUxXyVP#rstats #DataScience pic.twitter.com/9oXGUwVTnf\n","code":""},{"path":"july-2021.html","id":"july-17","chapter":"July 2021","heading":"July 17","text":"\nexploratory phase, may wish visualize model data quickly thoroughly. โžฟ{ggwithinstats} function {ggstatsplot} ๐Ÿ“ฆ one-way repeated measures designs via plots statistical details ๐Ÿ“Šhttps://t.co/lOT7qa37z8#rstats #DataScience pic.twitter.com/UxoUQr1cvB\n","code":""},{"path":"july-2021.html","id":"july-18","chapter":"July 2021","heading":"July 18","text":"\nkeep related data together, might sometimes create dataframes columns contain dataframes. ๐ŸงณSince working can pain, {unpack} function {tidyr} ๐Ÿ“ฆ helps \"unpack\" ! ๐Ÿ”จhttps://t.co/rt0ekSiMUg#rstats #DataScience pic.twitter.com/m2O8OQRDgF\n","code":""},{"path":"july-2021.html","id":"july-19","chapter":"July 2021","heading":"July 19","text":"\nuse {dplyr} ๐Ÿ“ฆ data analysis, may sometimes wish carry statistical analysis grouped data frame.statistical function requires whole dataframe, {cur_data} function provides just tool! ๐Ÿ—„๏ธhttps://t.co/IemOGQqqrH#rstats #DataScience pic.twitter.com/MzEnBq0tp0\n","code":""},{"path":"july-2021.html","id":"july-20","chapter":"July 2021","heading":"July 20","text":"\nVisualizing variable distribution via violin plot easy {ggplot2}, may wish avoid redundant mirroring density plot.{stat_density_ridges} function {ggridges} ๐Ÿ“ฆ provides just geometric layer! ๐Ÿ™Œhttps://t.co/x6yAh0hHW9#rstats #DataScience pic.twitter.com/V3JhOamnFb\n","code":""},{"path":"july-2021.html","id":"july-21","chapter":"July 2021","heading":"July 21","text":"\nanalysis, model selection may involve specification multiple models formally testing different.{test_performance} function {performance} ๐Ÿ“ฆ performs summarizes indices tests ๐Ÿฅ‡https://t.co/Qd25p55XPq#rstats #DataScience pic.twitter.com/EvmEOVXNRn\n","code":""},{"path":"july-2021.html","id":"july-22","chapter":"July 2021","heading":"July 22","text":"\nvisualizing data across combination variables, {facet_wrap} {ggplot2} creates small multiples. variables nested?{facet_nested_wrap} function {ggh4x} ๐Ÿ“ฆ handles exactly designs! ๐Ÿ“Š๐Ÿ™Œhttps://t.co/WVp154GvPA#rstats #DataScience pic.twitter.com/P6ZjOUF9eV\n","code":""},{"path":"july-2021.html","id":"july-23","chapter":"July 2021","heading":"July 23","text":"\nSometimes may wish something R think package might helpful.cases, {findPackage} function {packagefinder} ๐Ÿ“ฆ can search return relevant CRAN packages given keywords ๐Ÿ”https://t.co/0IiwJtafTq#rstats #DataScience pic.twitter.com/n946xE4cAS\n","code":""},{"path":"july-2021.html","id":"july-24","chapter":"July 2021","heading":"July 24","text":"\nSometimes may wish write SQL queries (practice?) without access database. ๐Ÿ˜ฉcases, can use {dbwritetable} function {DBI} ๐Ÿ“ฆ copy dataframe database table, write queries! โœ๏ธ๐Ÿ™Œhttps://t.co/Gu3HwjrR0S#rstats #DataScience pic.twitter.com/z6IZnW0o1o\n","code":""},{"path":"july-2021.html","id":"july-25","chapter":"July 2021","heading":"July 25","text":"\nSimulating parameter draws can sometimes (computationally faster) alternative bootstrapping. โฒ๏ธ{simulate_parameters} function {parameters} ๐Ÿ“ฆ can run visualize simulations various regression models ๐ŸŽฒhttps://t.co/SHdA3JANeq#rstats #DataScience pic.twitter.com/sRe2BhPKjW\n","code":""},{"path":"july-2021.html","id":"july-26","chapter":"July 2021","heading":"July 26","text":"\ncase looking alternate, \"operator\" way access object attributes R, can use infix attribute accessor (%@%) {rlang} ๐Ÿ“ฆ!๏ธhttps://t.co/C0T97lOtWV#rstats #DataScience pic.twitter.com/zAYw4JCo5i\n","code":""},{"path":"july-2021.html","id":"july-27","chapter":"July 2021","heading":"July 27","text":"\nSometimes need plot data different geographical entities grid may wish preserve original geographical orientation entities.{facet_geo} function {geofacet} ๐Ÿ“ฆ produces grid! ๐Ÿ—บ๏ธhttps://t.co/IUIQ1B7rUw#rstats #DataScience pic.twitter.com/MamwLytO4F\n","code":""},{"path":"july-2021.html","id":"july-28","chapter":"July 2021","heading":"July 28","text":"\nOperating multiple columns row-wise manner fairly straightforward {dplyr} ๐Ÿ“ฆ.workflow, {c_across} function allows use tidy selection syntax select columns operate ๐Ÿ”https://t.co/gmFEid0WDK#rstats #DataScience pic.twitter.com/8uMiwmcxtq\n","code":""},{"path":"july-2021.html","id":"july-29","chapter":"July 2021","heading":"July 29","text":"\nEither curiosity improve performance, sometimes may want time R code.{tic}/{toc} function {tictoc} ๐Ÿ“ฆ provides just tool โฒ๏ธhttps://t.co/yCu9ArIdBd#rstats #DataScience pic.twitter.com/PyHJCSm2NG\n","code":""},{"path":"july-2021.html","id":"july-30","chapter":"July 2021","heading":"July 30","text":"\nensure reproducibility R script, may wish download needed package versions certain date.{groundhog.library} function {groundhog} ๐Ÿ“ฆ creates local library needed package versions ๐Ÿ“‘https://t.co/bOhOXRzfMZ#rstats #DataScience pic.twitter.com/1pLfn8uU4q\n","code":""},{"path":"july-2021.html","id":"july-31","chapter":"July 2021","heading":"July 31","text":"\nROC curves provide convenient way compare responses predictions binomial model.{performance_roc} function {performance} ๐Ÿ“ฆ computes AUC metric visualizes ROC curves collection models ๐Ÿฅ‡๐Ÿฅˆ๐Ÿฅ‰https://t.co/ZKty2kA5Br#rstats #DataScience pic.twitter.com/LYJgfvZHIF\n","code":""},{"path":"august-2021.html","id":"august-2021","chapter":"August 2021","heading":"August 2021","text":"","code":""},{"path":"august-2021.html","id":"august-1","chapter":"August 2021","heading":"August 1","text":"\nSometimes may wish insert textual table {ggplot2} plot way insert text labels specified position.{geom_table} function {ggpp} ๐Ÿ“ฆ offers just geometric layer! ๐Ÿ™Œhttps://t.co/bQdaxiMffR#rstats #DataScience pic.twitter.com/6raba4JIDV\n","code":""},{"path":"august-2021.html","id":"august-2","chapter":"August 2021","heading":"August 2","text":"\nused {dplyr}'s pipeable syntax (using %>%) wish continue using switch {data.table}, {dt} helper function {tidytable} ๐Ÿ“ฆ can help! ๐Ÿ‘ฉโ€๐Ÿ”งhttps://t.co/YzFfw2meLF#rstats #DataScience pic.twitter.com/3SQqnjj3Bz\n","code":""},{"path":"august-2021.html","id":"august-3","chapter":"August 2021","heading":"August 3","text":"\nused reading code syntax highlighting, may wish code printed terminal/console.{prettycode} function eponymous ๐Ÿ“ฆ exactly ! ๐ŸŽจhttps://t.co/dF1WYPJSSc#rstats #DataScience pic.twitter.com/4fK9FjnTOu\n","code":""},{"path":"august-2021.html","id":"august-4","chapter":"August 2021","heading":"August 4","text":"\nCompared CSS, Sass flexible HTML styling framework. becomes ubiquitous, may wish learn Sass code reading translates familiar CSS.{sass} function eponymous ๐Ÿ“ฆ ๐Ÿ”„https://t.co/FR8UlmQyTY#rstats #DataScience pic.twitter.com/3ZgYl0E6j3\n","code":""},{"path":"august-2021.html","id":"august-5","chapter":"August 2021","heading":"August 5","text":"\ndot plot good visualization represent distribution labeled numeric data.{ggdotplotstats} function {ggstatsplot} ๐Ÿ“ฆ easily produces charts (additional descriptive inferential statistics) ๐Ÿ’https://t.co/OLcqX643Vx#rstats #DataScience pic.twitter.com/GZ3huWi9qU\n","code":""},{"path":"august-2021.html","id":"august-6","chapter":"August 2021","heading":"August 6","text":"\nknow C wish understand implementation R data structures C-level S-expressions SEXPs (https://t.co/pCGK6tlgNB), {sxp} function {lobstr} ๐Ÿ“ฆ can helpful! โ›๏ธhttps://t.co/pb7um2e1RR#rstats #DataScience pic.twitter.com/NYrdqEJqJW\n","code":""},{"path":"august-2021.html","id":"august-7","chapter":"August 2021","heading":"August 7","text":"\nteaching quality assessment, may sometimes wish visualize residuals model check goodness fit.{stat_fit_deviations} function {ggpmisc} ๐Ÿ“ฆ makes easy {ggplot2} ๐Ÿ™Œhttps://t.co/YezhmuyXgP#rstats #DataScience pic.twitter.com/M6o4MAcnIH\n","code":""},{"path":"august-2021.html","id":"august-8","chapter":"August 2021","heading":"August 8","text":"\nneed write \"impure\" function modifies R landscape (e.g.ย changes working directory), behavior R functions change.{local_} function family {withr} ๐Ÿ“ฆ can help ! ๐Ÿฅกhttps://t.co/CgklVPJp7n#rstats #DataScience pic.twitter.com/rrpbojxoev\n","code":""},{"path":"august-2021.html","id":"august-9","chapter":"August 2021","heading":"August 9","text":"\nGelman Hill (2007) propose binned residual plot check --fitting binomial logistic regression models. 0โƒฃ1โƒฃ{binned_residuals} function {performance} ๐Ÿ“ฆeasily produces visualization ๐Ÿ”https://t.co/gAAjFNIf92#rstats #DataScience pic.twitter.com/cKw8CxpJfC\n","code":""},{"path":"august-2021.html","id":"august-10","chapter":"August 2021","heading":"August 10","text":"\nEven everything can R, sometimes may want improve performance code rewriting C++.{cpp_function} {cpp11} ๐Ÿ“ฆ provides convenient way ๐Ÿ‘https://t.co/5fdrx9KPc6#rstats #DataScience pic.twitter.com/IbKeeuZdjb\n","code":""},{"path":"august-2021.html","id":"august-11","chapter":"August 2021","heading":"August 11","text":"\ncommon statistical tests, may wish extract details inferential statistics also effect size estimates.Functions, like {two_sample_test}, {statsExpressions} ๐Ÿ“ฆ provide tidy way ๐Ÿ™Œhttps://t.co/MJ1avaR6WJ#rstats #DataScience pic.twitter.com/zKFUetTeXR\n","code":""},{"path":"august-2021.html","id":"august-12","chapter":"August 2021","heading":"August 12","text":"\nSometimes data analysis may require finding first non-missing value (replace another value, create new variable, etc.) across vectors columns.{coalesce} function {dplyr} ๐Ÿ“ฆ comes handy ๐Ÿ”https://t.co/Q7EPEAmFZC#rstats #DataScience pic.twitter.com/Upqhjd5eJY\n","code":""},{"path":"august-2021.html","id":"august-13","chapter":"August 2021","heading":"August 13","text":"\nUnified Modeling Language provides way visualize design system, nomnoml (https://t.co/X9yAhs166L) online tool draw UML diagrams.{nomnoml} function eponymous ๐Ÿ“ฆ renders UML diagrams R ! ๐Ÿ“https://t.co/CBZgudgltr#rstats #DataScience pic.twitter.com/KmksKDgQPz\n","code":""},{"path":"august-2021.html","id":"august-14","chapter":"August 2021","heading":"August 14","text":"\nSometimes might need analyze scanned text data present PDF.{pdf_ocr_text} function {pdftools} ๐Ÿ“ฆ can extract text โ›https://t.co/cnoqBhZ0Ys#rstats #DataScience pic.twitter.com/1LMVHfGgsL\n","code":""},{"path":"august-2021.html","id":"august-15","chapter":"August 2021","heading":"August 15","text":"\nAlthough {rmarkdown} ๐Ÿ“ฆ makes writing reports easy, lacks automatic numbering cross-referencing (figures, tables, sections, etc) features.2nd variant function family {bookdown} ๐Ÿ“ฆ provides features โœ๏ธhttps://t.co/EKFeVqQJb1#rstats #DataScience pic.twitter.com/RcIAtscT0K\n","code":""},{"path":"august-2021.html","id":"august-16","chapter":"August 2021","heading":"August 16","text":"\n{tmap} ๐Ÿ“ฆ provides API create (static interactive) thematic maps syntax resembling {ggplot2}.{qtm} function provides convenient short-hand draw maps quickly! ๐Ÿ—บ๏ธhttps://t.co/WDYN5ofasw#rstats #DataScience pic.twitter.com/PZk4thnd4q\n","code":""},{"path":"august-2021.html","id":"august-17","chapter":"August 2021","heading":"August 17","text":"\nSometimes need convert deeply nested list rectangular dataframe. , may wish bring top list elements.{hoist} function {tidyr} ๐Ÿ“ฆ can help transformations ๐Ÿ› ๏ธhttps://t.co/yrhHtF2YVl#rstats #DataScience pic.twitter.com/m5TvEse0bm\n","code":""},{"path":"august-2021.html","id":"august-18","chapter":"August 2021","heading":"August 18","text":"\nSometimes want draw text {ggplot2} plot fits inside defined area without manually fiddling around font size.{geom_fit_text} function {ggfittext} ๐Ÿ“ฆ resizes text fit inside defined box ๐Ÿ—œ๏ธhttps://t.co/GiaXx9QEgb#rstats #DataScience pic.twitter.com/krnvdRqIWz\n","code":""},{"path":"august-2021.html","id":"august-19","chapter":"August 2021","heading":"August 19","text":"\nPosterior predictive check regression model uses simulated data fitted model check discrepancies observed data.{pp_check} function {performance} ๐Ÿ“ฆ carries visualizes check ๐Ÿ”https://t.co/qk7dnlHyP5#rstats #DataScience pic.twitter.com/R95Evuhhdp\n","code":""},{"path":"august-2021.html","id":"august-20","chapter":"August 2021","heading":"August 20","text":"\nuse {dplyr} ๐Ÿ“ฆ, may wish avoid {group_by} + {ungroup} workflow grouped analysis just need carry single operation.{with_groups} function provides alternative contexts โœ‚๏ธhttps://t.co/FiEeP2loxm#rstats #DataScience pic.twitter.com/XANjLS22ki\n","code":""},{"path":"august-2021.html","id":"august-21","chapter":"August 2021","heading":"August 21","text":"\nSometimes may wish display results statistical test graphics prepared.{statsExpressions} ๐Ÿ“ฆ functions, like {corr_test}, provide necessary expressions ๐Ÿ‘https://t.co/ERUsOJKliZ#rstats #DataScience pic.twitter.com/4T3awwOSFQ\n","code":""},{"path":"august-2021.html","id":"august-22","chapter":"August 2021","heading":"August 22","text":"\nreason, may need prepare calendar R might prefer {ggplot2} framework.{calendR} function eponymous ๐Ÿ“ฆ provides customizable monthly yearly calendars ๐Ÿ“…https://t.co/KPx4ozhjrV#rstats #DataScience pic.twitter.com/C1dM0FSllv\n","code":""},{"path":"august-2021.html","id":"august-23","chapter":"August 2021","heading":"August 23","text":"\n{mlr3} project modern powerful object-oriented framework machine learning (ML) R.{autoplot} function {mlr3viz} ๐Ÿ“ฆ provides model-agnostic general-purpose tool visualizing ML algorithms! ๐Ÿ“Š๐Ÿ“ˆhttps://t.co/ky8yhLtYyG#rstats #DataScience pic.twitter.com/YBu2rLz44K\n","code":""},{"path":"august-2021.html","id":"august-24","chapter":"August 2021","heading":"August 24","text":"\nInstead using third-party softwares, may sometimes wish edit (join, convert, etc.) images within R .{image_} function family {magick} provide helpers ๐Ÿ“ท๐ŸŽžhttps://t.co/ald6jelSWG#rstats #DataScience pic.twitter.com/y4H67cTPnY\n","code":""},{"path":"august-2021.html","id":"august-25","chapter":"August 2021","heading":"August 25","text":"\ncorrelation hypotheses, addition joint distribution scatterplot, one may also wish visualize marginal distributions variable.easy using {_side} function family {ggside} ๐Ÿ“ฆ !https://t.co/vQYV4ELEaG#rstats #DataScience pic.twitter.com/VSBpVqtO6K\n","code":""},{"path":"august-2021.html","id":"august-26","chapter":"August 2021","heading":"August 26","text":"\nUpSet plot efficient alternative Venn Diagram visualizing complex intersections multiple sets.{scale_x_upset} function {ggupset} ๐Ÿ“ฆ provides {ggplot2} scale create UpSet plots easily ๐Ÿงฑhttps://t.co/5rwIgAhzxa#rstats #DataScience pic.twitter.com/wk352FtXb0\n","code":""},{"path":"august-2021.html","id":"august-27","chapter":"August 2021","heading":"August 27","text":"\nSometimes (e.g.ย meta-analysis) may need either extract effect size test statistic convert available effect size suitable one.{} function family {effectsize} can conversions ๐Ÿ”„https://t.co/EO6hb9NkER#rstats #DataScience pic.twitter.com/2ItAfBPwps\n","code":""},{"path":"august-2021.html","id":"august-28","chapter":"August 2021","heading":"August 28","text":"\ncorrelation matrix nifty visualization displaying relationships multiple variables.{ggcorrmat} function {ggstatsplot} ๐Ÿ“ฆ creates matrices significance testing descriptive details โ†”๏ธŽ๏ธhttps://t.co/rbwEs6qVON#rstats #DataScience pic.twitter.com/EDPanyNE0x\n","code":""},{"path":"august-2021.html","id":"august-29","chapter":"August 2021","heading":"August 29","text":"\ngiven generic class, sometimes may interested systematically list investigate available S3/S4 methods.{methods} function family {sloop} ๐Ÿ“ฆ provides needed helpers ๐Ÿ“https://t.co/EV7AVaSHPF#rstats #DataScience pic.twitter.com/XxFb3Es3pB\n","code":""},{"path":"august-2021.html","id":"august-30","chapter":"August 2021","heading":"August 30","text":"\ninterested studying group differences, sometimes may wish annotate visualizations results significance testing.{geom_signif} {ggsignif} ๐Ÿ“ฆ provides just geometric layer! โš–๏ธhttps://t.co/O9XM5nHdgf#rstats #DataScience pic.twitter.com/iZhilHeGO3\n","code":""},{"path":"august-2021.html","id":"august-31","chapter":"August 2021","heading":"August 31","text":"\nYAML language ubiquitous, esp.ย configuration files, sometime may need convert R object YAML object.{as_yml} function {ymlthis} ๐Ÿ“ฆ makes conversion easy ๐Ÿ”„https://t.co/GSTD3slB0k#rstats #DataScience pic.twitter.com/YeVunUZoR8\n","code":""},{"path":"september-2021.html","id":"september-2021","chapter":"September 2021","heading":"September 2021","text":"","code":""},{"path":"september-2021.html","id":"september-1","chapter":"September 2021","heading":"September 1","text":"\nSometimes may wish estimate density priors posteriors regression model.{estimate_density} function {bayestestR} ๐Ÿ“ฆ computes visualizes estimates ๐Ÿงฎhttps://t.co/gm8Wf9qr3f#rstats #DataScience pic.twitter.com/SjuoAiuXPo\n","code":""},{"path":"september-2021.html","id":"september-2","chapter":"September 2021","heading":"September 2","text":"\nuse {dplyr} {tidyr} ๐Ÿ“ฆs wrangle data, may wish see feedback different operations , especially long piped chains.{tidylog} ๐Ÿ“ฆ functions exactly ๐Ÿ“https://t.co/MfpoUZoBRQ#rstats #DataScience pic.twitter.com/hOnonyFHzx\n","code":""},{"path":"september-2021.html","id":"september-3","chapter":"September 2021","heading":"September 3","text":"\ncheatsheet helpful resource hand learning new R package.{get_all_cheatsheets} function {cheatsheet} ๐Ÿ“ฆ makes convenient download available cheatsheets computer one go! ๐Ÿ™Œhttps://t.co/CxW1EOoaLK#rstats #DataScience pic.twitter.com/CXkPpPWZ5d\n","code":""},{"path":"september-2021.html","id":"september-4","chapter":"September 2021","heading":"September 4","text":"\nSometimes may wish display collection images {ggplot2} graph.{geom_point_img} function {ggimg} ๐Ÿ“ฆ provides just needed geometric layer! ๐Ÿ“ทhttps://t.co/YinETxrQdk#rstats #DataScience pic.twitter.com/rhB2jwgsZe\n","code":""},{"path":"september-2021.html","id":"september-5","chapter":"September 2021","heading":"September 5","text":"\nSometimes may wish execute computation clean new R process without affecting R processes.{r} function {callr} ๐Ÿ“ฆ exactly ! โœ๏ธhttps://t.co/CXYdyo3d1P#rstats #DataScience pic.twitter.com/2cBDDRC2nj\n","code":""},{"path":"september-2021.html","id":"september-6","chapter":"September 2021","heading":"September 6","text":"\nCox regression model widely used medical research assess effect several risk factors survival time patients.{ggcoxdiagnostics} function {survminer} ๐Ÿ“ฆ can help visually assess goodness model fit ๐Ÿงhttps://t.co/BzVW6ReYFm#rstats #DataScience pic.twitter.com/3z6D7FQgL9\n","code":""},{"path":"september-2021.html","id":"september-7","chapter":"September 2021","heading":"September 7","text":"\nSometimes may need high-quality conversion pdf page(s) image (png, jpeg, etc.) format.{pdf_convert} function {pdftools} ๐Ÿ“ฆ provides needed functionality ๐Ÿ”€https://t.co/DgielR9zoQ#rstats #DataScience pic.twitter.com/bHKPNcnPLQ\n","code":""},{"path":"september-2021.html","id":"september-8","chapter":"September 2021","heading":"September 8","text":"\nSometimes may want plot different probability distributions parameters interest.{ggdistribution} function {ggfortify} ๐Ÿ“ฆ provides convenient helper ! ๐Ÿ””https://t.co/8ue1b7Drh2#rstats #DataScience pic.twitter.com/aOHHeMT5KT\n","code":""},{"path":"september-2021.html","id":"september-9","chapter":"September 2021","heading":"September 9","text":"\nSometimes need perform addition subtraction quantity (e.g.ย computing confidence intervals estimate).plus-minus operator (%+-%) {dipsaus} ๐Ÿ“ฆ can conveniently various data types ๐Ÿงฎhttps://t.co/1Ia5zzsM9L#rstats #DataScience pic.twitter.com/JimL38L0Sl\n","code":""},{"path":"september-2021.html","id":"september-10","chapter":"September 2021","heading":"September 10","text":"\nvisualizing group differences, sometimes may also wish include additional descriptive statistics plot .{ggsummarystats} function {ggpubr} ๐Ÿ“ฆ provides flexible way ! ๐Ÿ“Šhttps://t.co/DMzyXViicx#rstats #DataScience pic.twitter.com/lIrBWo1Gmz\n","code":""},{"path":"september-2021.html","id":"september-11","chapter":"September 2021","heading":"September 11","text":"\n{purrr} ๐Ÿ“ฆโ€™s {map} {map2} function families can apply single- two-argument functions list. want apply function multiple arguments?{pmap_} function family addresses exactly problem ๐Ÿ™Œhttps://t.co/FrCzOUFLtJ#rstats #DataScience pic.twitter.com/oSQRgS4ywS\n","code":""},{"path":"september-2021.html","id":"september-12","chapter":"September 2021","heading":"September 12","text":"\nEuler diagram, way visualize set relationships, generalization Venn diagram since relaxes requirement set interactions present.{euler} function {eulerr} ๐Ÿ“ฆ easily produces diagrams ๐Ÿ–‡https://t.co/7eM2ZqEeVG#rstats #DataScience pic.twitter.com/aEBXBdIdt2\n","code":""},{"path":"september-2021.html","id":"september-13","chapter":"September 2021","heading":"September 13","text":"\nexploring data, one often needs quick easy way create dot--whisker plots coefficient estimates regression models.{ggcoef_model} function {GGally} ๐Ÿ“ฆ produces plots elegant defaults ๐Ÿคฉhttps://t.co/atW3RTPWEH#rstats #DataScience pic.twitter.com/j0Zj2fp50Q\n","code":""},{"path":"september-2021.html","id":"september-14","chapter":"September 2021","heading":"September 14","text":"\nSometimes may wish take snapshot webpage, rmarkdown document, Shiny application R .{*shot} function family {webshot} ๐Ÿ“ฆ provides convenient way ! ๐Ÿ“ธhttps://t.co/SR0NPM36Xl#rstats #DataScience pic.twitter.com/2IvYDAB3yo\n","code":""},{"path":"september-2021.html","id":"september-15","chapter":"September 2021","heading":"September 15","text":"\nuse R python, sometimes may wish convert Jupyter notebooks can modify {rmarkdown}.{convert_ipynb} function {rmarkdown} ๐Ÿ“ฆ can conversion ๐Ÿ”„https://t.co/6pnduEMAZg#rstats #DataScience pic.twitter.com/IxtlF0faKc\n","code":""},{"path":"september-2021.html","id":"september-16","chapter":"September 2021","heading":"September 16","text":"\nDropbox popular file hosting service may sometimes wish interact application comfort R .{drop_*} function family {rdrop2} ๐Ÿ“ฆ provides just toolbox! ๐Ÿงฐ https://t.co/THtnR1fqB2#rstats #DataScience pic.twitter.com/AfrKlSlKCB\n","code":""},{"path":"september-2021.html","id":"september-17","chapter":"September 2021","heading":"September 17","text":"\nSometimes may need standardize (z-score) data, .e.ย express terms standard deviation.{standardize} function {datawizard} ๐Ÿ“ฆ easily computes , provides customizations (e.g.ย robust variant)! ๐Ÿ™Œhttps://t.co/yavQlVXuey#rstats #DataScience pic.twitter.com/mNeyfbohSg\n","code":""},{"path":"september-2021.html","id":"september-18","chapter":"September 2021","heading":"September 18","text":"\nSlope graphs handy tool visualizing trends paired data multiple observations.{newggslopegraph} function {CGPfunctions} ๐Ÿ“ฆ provides helper quickly prepare graph! ๐Ÿ“ˆhttps://t.co/Rw9BmRpPPc#rstats #DataScience pic.twitter.com/XCO6SOocTa\n","code":""},{"path":"september-2021.html","id":"september-19","chapter":"September 2021","heading":"September 19","text":"\nCompiling LaTeX PDF can troublesome; often due missing LaTeX packages. may wish packages automatically installed, making compilation painless.{pdflatex} function {tinytex} ๐Ÿ“ฆ exactly ! ๐Ÿ‘https://t.co/4Ga94fTm47#rstats #DataScience pic.twitter.com/JX6o9YAi1d\n","code":""},{"path":"september-2021.html","id":"september-20","chapter":"September 2021","heading":"September 20","text":"\nLike SQL, may sometimes wish change rows one dataset based rows another dataset.{rows_} function family {dplyr} ๐Ÿ“ฆ provides functionality! ๐Ÿ–‡https://t.co/uSpmlee90H#rstats #DataScience pic.twitter.com/mLTwGqKMvD\n","code":""},{"path":"september-2021.html","id":"september-21","chapter":"September 2021","heading":"September 21","text":"\nvisualizing distributions, boxplots can hide bimodality & violins can show non-existent data.Sina plots, points jittered proportional density, provide good solution & {ggforce::geom_sina} supplies layer! ๐Ÿ‘https://t.co/BR5iiCcf6s#rstats #DataScience pic.twitter.com/IrYGYqXUtt\n","code":""},{"path":"september-2021.html","id":"september-22","chapter":"September 2021","heading":"September 22","text":"\ncoefficient determination (R^2) provides absolute goodness--fit index regression model used assess performance.{r2} function {performance} ๐Ÿ“ฆ computes variety regression models! ๐Ÿงฎhttps://t.co/y3qQQOXuRv#rstats #DataScience pic.twitter.com/pJCrx4MOP4\n","code":""},{"path":"september-2021.html","id":"september-23","chapter":"September 2021","heading":"September 23","text":"\ncommon issue watching movies subtitle files can sync .{srt_shift} function {srt} ๐Ÿ“ฆ can help shift dialogues sync video write new subtitle file! ๐ŸŽฅ๐Ÿฟhttps://t.co/j5V6SnCfZ7#rstats #DataScience pic.twitter.com/JE234uj5e7\n","code":""},{"path":"september-2021.html","id":"september-24","chapter":"September 2021","heading":"September 24","text":"\nHash tables can efficiently look values irrespective size table.{hash} function eponymous ๐Ÿ“ฆ provides hash-like data structure necessary methods hash operations native R style! ๐Ÿ”Žhttps://t.co/QrxzzANqFV#rstats #DataScience pic.twitter.com/b75HRHxS1m\n","code":""},{"path":"september-2021.html","id":"september-25","chapter":"September 2021","heading":"September 25","text":"\nsunset funnel plot includes study-level power info acts diagnostic tool meta-analysis examine power studies detect effect interest.{metaviz::viz_sunset} function can easily create ! ๐ŸŒ… https://t.co/cqlXpmUh0f#rstats #DataScience pic.twitter.com/lf1Pefk5XH\n","code":""},{"path":"september-2021.html","id":"september-26","chapter":"September 2021","heading":"September 26","text":"\nAlthough visualizing variable distribution via boxplot easy, may wish avoid redundant vertical mirroring distribution.{geom_half_boxplot} function {gghalves} ๐Ÿ“ฆ provides exactly geometric layer! ๐Ÿ™Œhttps://t.co/0kfnVPtUWU#rstats #DataScience pic.twitter.com/2KqVyhOPIA\n","code":""},{"path":"september-2021.html","id":"september-27","chapter":"September 2021","heading":"September 27","text":"\nItem analysis, psychometric technique, selects items (large pool) index psychological construct.{tab_itemscale} function {sjPlot} ๐Ÿ“ฆ carries produces elegant HTML tables item analysis ๐Ÿงฝhttps://t.co/8Z5DzerVaU#rstats #DataScience pic.twitter.com/uIXZrVsr0Z\n","code":""},{"path":"september-2021.html","id":"september-28","chapter":"September 2021","heading":"September 28","text":"\nXML language ubiquitous storing transporting wide array data structures sometimes may wish work R.{as_list} function {xml2} ๐Ÿ“ฆ converts XML document nodes familiar R list! ๐Ÿ”€https://t.co/OqvPGvv1Ce#rstats #DataScience pic.twitter.com/fddfsNc0z5\n","code":""},{"path":"september-2021.html","id":"september-29","chapter":"September 2021","heading":"September 29","text":"\nEven know markdown syntax well, sometimes may wish generate algorithmically.{md_} function family {gluedown} ๐Ÿ“ฆ can help ๐Ÿค–https://t.co/VXXO0Ce3Mu#rstats #DataScience pic.twitter.com/EklzKUpHkB\n","code":""},{"path":"september-2021.html","id":"september-30","chapter":"September 2021","heading":"September 30","text":"\ncuriosity working memory-constrained setting, sometimes may wish know roughly much memory used R.{mem_used} function {lobstr} ๐Ÿ“ฆ provides estimate ๐Ÿ“€https://t.co/SwebVXHUrx#rstats #DataScience pic.twitter.com/SbEWyUZdPl\n","code":""},{"path":"october-2021.html","id":"october-2021","chapter":"October 2021","heading":"October 2021","text":"","code":""},{"path":"october-2021.html","id":"october-1","chapter":"October 2021","heading":"October 1","text":"\nCorrespondence analysis extends PCA summarizes visualizes multivariate categorical data two-dimension plots.{fviz_ca} function {factoextra} ๐Ÿ“ฆ provides elegant ggplot2-based visualizations analysis! ๐Ÿ’„https://t.co/mtFtXrE0VJ#rstats #DataScience pic.twitter.com/FT2vNH70Vx\n","code":""},{"path":"october-2021.html","id":"october-2","chapter":"October 2021","heading":"October 2","text":"\nText comes variety encodings, many functions reading text assume UTF-8 encoding. assumption hold ?cases, {utf8_valid} function {utf8} ๐Ÿ“ฆ can find offending text ๐Ÿ“https://t.co/vwL09X0zrq#rstats #DataScience pic.twitter.com/cU8tkACV75\n","code":""},{"path":"october-2021.html","id":"october-3","chapter":"October 2021","heading":"October 3","text":"\nexploring data, one often needs quick easy way display distributions coefficient estimates regression models.{dwplot} function {dotwhisker} ๐Ÿ“ฆ produces plots easily flexibly! ๐Ÿ””https://t.co/aHrGaAG8Kv#rstats #DataScience pic.twitter.com/IwGGnBQN82\n","code":""},{"path":"october-2021.html","id":"october-4","chapter":"October 2021","heading":"October 4","text":"\nhypothesis testing framework, sometimes may wish generate distribution corresponding null hypothesis.{generate} function {infer} ๐Ÿ“ฆ easily resamples observed data create distribution! ๐Ÿ“Šhttps://t.co/39GlrUrqsp#rstats #DataScience pic.twitter.com/rt60yA7YFQ\n","code":""},{"path":"october-2021.html","id":"october-5","chapter":"October 2021","heading":"October 5","text":"\nstand-comment lines decorated bands characters can make different sections R scripts easy identify navigate.{xxx_*} function family {ARTofR} ๐Ÿ“ฆ offers collection comments ๐Ÿšจhttps://t.co/in3KGiwDXs#rstats #DataScience pic.twitter.com/c75VrfhniD\n","code":""},{"path":"october-2021.html","id":"october-6","chapter":"October 2021","heading":"October 6","text":"\nAssociation rule mining unsupervised method find attributes frequently occur together dataset.{plot} method {arulesViz} ๐Ÿ“ฆ can visualize found association rules ggplot2-framework โ›https://t.co/s4pUQAvqXA#rstats #DataScience pic.twitter.com/dDy9Fstgq1\n","code":""},{"path":"october-2021.html","id":"october-7","chapter":"October 2021","heading":"October 7","text":"\ncalendar-based graphic visualizes daily activities, getting data needed format can challenge.{frame_calendar} function {sugrrants} ๐Ÿ“ฆ provides convenient syntax format data right format ๐Ÿ—“๏ธhttps://t.co/JZHB0oUb7q#rstats #DataScience pic.twitter.com/q5Vv5wcShM\n","code":""},{"path":"october-2021.html","id":"october-8","chapter":"October 2021","heading":"October 8","text":"\nSometimes present results regression model well-formatted table.{as_flextable} function {flextable} ๐Ÿ“ฆ can easily produce tables rich details ๐Ÿ—‚https://t.co/trfvqzqhvp#rstats #DataScience pic.twitter.com/cXx7iyUXh6\n","code":""},{"path":"october-2021.html","id":"october-9","chapter":"October 2021","heading":"October 9","text":"\nSometimes may wish sample portion data.{slice} function family {dplyr} ๐Ÿ“ฆ provides helpers โœ‚๏ธhttps://t.co/4tt17FnvnM#rstats #DataScience pic.twitter.com/9e8pS7KjFn\n","code":""},{"path":"october-2021.html","id":"october-10","chapter":"October 2021","heading":"October 10","text":"\norder ensure reproducibility, may want include detailed session information report.{session_info} function {sessioninfo} ๐Ÿ“ฆ creates log ๐Ÿ“https://t.co/C9RyxMnXNk#rstats #DataScience pic.twitter.com/QH0SkjztL6\n","code":""},{"path":"october-2021.html","id":"october-11","chapter":"October 2021","heading":"October 11","text":"\nuse function outputs content often need copy-paste, may wish programmatically manually.{write_clip} function {clipr} ๐Ÿ“ฆ allows ! โœ๏ธhttps://t.co/LI9QxOqguP#rstats #DataScience pic.twitter.com/CZM86KxLlL\n","code":""},{"path":"october-2021.html","id":"october-12","chapter":"October 2021","heading":"October 12","text":"\nSometimes may wish customize {ggplot2} axes drawn (e.g.ย adding brackets).{coord_flex_*} function family {lemon} ๐Ÿ“ฆ supports customizations ๐Ÿ‹https://t.co/DNHBbSx5Io#rstats #DataScience pic.twitter.com/SJzVPftyqQ\n","code":""},{"path":"october-2021.html","id":"october-13","chapter":"October 2021","heading":"October 13","text":"\nOften may wish compute descriptive inferential statistics giving different weights across data points.{weighted_*} function family {sjstats} ๐Ÿ“ฆ provides familiar syntax carry weighted analysis ๐Ÿ‹๏ธhttps://t.co/x9Ng6317As#rstats #DataScience pic.twitter.com/mEE7MIlkPE\n","code":""},{"path":"october-2021.html","id":"october-14","chapter":"October 2021","heading":"October 14","text":"\nTreemaps visualize nested proportions multiple categorical variables recursively nesting rectangles (area = proportion) inside .{geom_treemap} function {treemapify} ๐Ÿ“ฆ supplies needed geometric layer! ๐ŸŒณhttps://t.co/i1Pc58pewr#rstats #DataScience pic.twitter.com/9ccyphWC76\n","code":""},{"path":"october-2021.html","id":"october-15","chapter":"October 2021","heading":"October 15","text":"\nAlthough often can write data single CSV file, sometimes may need write several datasets multiple Excel spreadsheets.{write_xlsx} function {writexl} ๐Ÿ“ฆ provides easy syntax ! ๐Ÿ“https://t.co/EsBmWyupeB#rstats #DataScience pic.twitter.com/9631CZzFM5\n","code":""},{"path":"october-2021.html","id":"october-16","chapter":"October 2021","heading":"October 16","text":"\nSometimes may wish center variables (subtract mean, .e.) dataframe (e.g.ย interpret interaction terms regression analysis).{center} function {datawizard} ๐Ÿ“ฆ flexibly 0๏ธโƒฃhttps://t.co/2KaDrn47w4#rstats #DataScience pic.twitter.com/KcfRGT1qF5\n","code":""},{"path":"october-2021.html","id":"october-17","chapter":"October 2021","heading":"October 17","text":"\nSometimes wish create visualization {ggplot2} ๐Ÿ“ฆ, also add additional filters .{with_*} function family {ggfx} ๐Ÿ“ฆ provides rich collection filters! ๐Ÿ’…https://t.co/ekCrUTvFHV#rstats #DataScience pic.twitter.com/nnlVunnFyR\n","code":""},{"path":"october-2021.html","id":"october-18","chapter":"October 2021","heading":"October 18","text":"\ndata wrangled highly non-rectangular, may wish melt first wrangle .{melt_*} function family {meltr} ๐Ÿ“ฆ allows โ™จ๏ธhttps://t.co/gYmWI1xIQA#rstats #DataScience pic.twitter.com/3nJcnrB9rP\n","code":""},{"path":"october-2021.html","id":"october-19","chapter":"October 2021","heading":"October 19","text":"\nwork text data, sometimes might need detect text language.eponymous function {franc} ๐Ÿ“ฆ can detect text 310 languages! ๐Ÿ•ต๏ธโ€โ™‚๏ธhttps://t.co/KOSWDsHQWH#rstats #DataScience pic.twitter.com/0KZbgor1iW\n","code":""},{"path":"october-2021.html","id":"october-20","chapter":"October 2021","heading":"October 20","text":"\nSometimes may wish quickly prepare frequency table present visualization.{ggtable} function {GGally} ๐Ÿ“ฆ provides easy syntax {ggplot2}-framework! ๐Ÿงฎhttps://t.co/NqgdhgImbo#rstats #DataScience pic.twitter.com/TqA4mQI06R\n","code":""},{"path":"october-2021.html","id":"october-21","chapter":"October 2021","heading":"October 21","text":"\norder speed , may want return cached results inputs provided computationally expensive function.{memoise} function eponymous ๐Ÿ“ฆ exactly ! โฉ https://t.co/vT2edZhCZo#rstats #DataScience pic.twitter.com/BidZlofr8v\n","code":""},{"path":"october-2021.html","id":"october-22","chapter":"October 2021","heading":"October 22","text":"\nlot overlapping data points, wish simultaneously estimate overlap individuate .{geom_pointdensity} function {ggpointdensity} ๐Ÿ“ฆ provides perfect geometric layer! ๐Ÿงนhttps://t.co/OeFMabDKZg#rstats #DataScience pic.twitter.com/tUtP6Jw6pI\n","code":""},{"path":"october-2021.html","id":"october-23","chapter":"October 2021","heading":"October 23","text":"\nOften need list active processes system, instead using task manager, may wish R .{ps} function eponymous ๐Ÿ“ฆ creates informative dataframe active processes ๐Ÿ—’https://t.co/KVrBiD4mUE#rstats #DataScience pic.twitter.com/ORYvnaTGV5\n","code":""},{"path":"october-2021.html","id":"october-24","chapter":"October 2021","heading":"October 24","text":"\nfluent R wish learn SQL, can nifty function can provide syntax translation two.{translate_sql} function {dbplyr} ๐Ÿ“ฆ provides translations ๐Ÿ”€https://t.co/cS6zPgdFNn#rstats #DataScience pic.twitter.com/Jafa31i673\n","code":""},{"path":"october-2021.html","id":"october-25","chapter":"October 2021","heading":"October 25","text":"\ndata stored Excel spreadsheets needs imported R, may sometimes wish tidy data format.{xlsx_cells} function {tidyxl} ๐Ÿ“ฆ makes import straightforward ๐Ÿ”งhttps://t.co/hcAgiuyCON#rstats #DataScience pic.twitter.com/8VFCVEDaok\n","code":""},{"path":"october-2021.html","id":"october-26","chapter":"October 2021","heading":"October 26","text":"\nDonut charts popular way visualize categorical data, making well-labelled donut chart can time-consuming.{ggdonutchart} function {ggpubr} ๐Ÿ“ฆ produces easily! ๐Ÿฉhttps://t.co/VdLvfSyFZH#rstats #DataScience pic.twitter.com/l1k5na75wL\n","code":""},{"path":"october-2021.html","id":"october-27","chapter":"October 2021","heading":"October 27","text":"\niterative analysis list columns using {purrr} ๐Ÿ“ฆ, understand frustration figuring iteration produces warnings errors.{map_peacefully} wrapper function {collateral} ๐Ÿ“ฆ comes help ๐Ÿ‘https://t.co/gqFb0FM8ov#rstats #DataScience pic.twitter.com/CGzrIMSwI6\n","code":""},{"path":"october-2021.html","id":"october-28","chapter":"October 2021","heading":"October 28","text":"\nSometimes may need convert {ggplot2} image vector graphics format raster image.{rasterise} function {ggrastr} ๐Ÿ“ฆ can convert one many ggplot2 layer(s) rasterized output ๐Ÿ”€https://t.co/nrLWwoYwEi#rstats #DataScience pic.twitter.com/e3wI9Y5bvk\n","code":""},{"path":"october-2021.html","id":"october-29","chapter":"October 2021","heading":"October 29","text":"\nGoogle spreadsheets make easy create edit files real-time collaborating online, sometimes may wish access R.{read_sheet} function {googlesheets4} ๐Ÿ“ฆ provides way ๐Ÿ‘https://t.co/dj4tkk9My2#rstats #DataScience pic.twitter.com/zHFgcWqNxf\n","code":""},{"path":"october-2021.html","id":"october-30","chapter":"October 2021","heading":"October 30","text":"\nwork lot time--day data, {hms} function eponymous ๐Ÿ“ฆ provides convenient way construct store dataframe, consistent coercion rules ๐Ÿ•ฐhttps://t.co/0ViYWZrGLX#rstats #DataScience pic.twitter.com/jbwyxuzLiC\n","code":""},{"path":"october-2021.html","id":"october-31","chapter":"October 2021","heading":"October 31","text":"\nlot overlapping data points, wish visualize density plotting points next .{geom_beeswarm} function {ggbeeswarm} ๐Ÿ“ฆ provides perfect geometric layer! ๐Ÿhttps://t.co/YCc94sGUTN#rstats #DataScience pic.twitter.com/rbdaQtqqqB\n","code":""},{"path":"november-2021.html","id":"november-2021","chapter":"November 2021","heading":"November 2021","text":"","code":""},{"path":"november-2021.html","id":"november-1","chapter":"November 2021","heading":"November 1","text":"\nwrite documents using R, may wish spell check find possible spelling mistakes.{spell_check_*} function family {spelling} ๐Ÿ“ฆ makes easy selected text, collection documents, package ๐Ÿ”https://t.co/IxvzK0f9r1#rstats #DataScience pic.twitter.com/9bkSWUjakn\n","code":""},{"path":"november-2021.html","id":"november-2","chapter":"November 2021","heading":"November 2","text":"\nSometimes may wish check relevant assumptions linear regression model one go. {gg_diagnose} function {lindia} ๐Ÿ“ฆ lm objects provides helpful {ggplot2} visualizations โœ…๐Ÿ“Šhttps://t.co/12Q5NT3c6P#rstats #DataScience pic.twitter.com/1bMYhDxosT\n","code":""},{"path":"november-2021.html","id":"november-3","chapter":"November 2021","heading":"November 3","text":"\nSometimes need display two plots, grid, one inside (aka \"inset\" plot).Assuming using grammar graphics approach, easy using {inset_element} function {patchwork} ๐Ÿ“ฆ! ๐Ÿ‘ฉโ€๐Ÿญhttps://t.co/H2A9Yh0W6f#rstats #DataScience pic.twitter.com/2diq1akqay\n","code":""},{"path":"november-2021.html","id":"november-4","chapter":"November 2021","heading":"November 4","text":"\nEven everything can R, sometimes may want improve performance code rewriting C++.{cppFunction} {Rcpp} ๐Ÿ“ฆprovides convenient way ! โšก๏ธhttps://t.co/xL92m6Jlub#rstats #DataScience pic.twitter.com/MIlRxRciMN\n","code":""},{"path":"november-2021.html","id":"november-5","chapter":"November 2021","heading":"November 5","text":"\ndot plot simple yet informative way visualize distribution continuous quantitative variables.{geom_dotsinterval} function {ggdist} ๐Ÿ“ฆ provides needed geometric layer! ๐Ÿ”ต๐Ÿ”ดhttps://t.co/n67JhkqJfr#rstats #DataScience pic.twitter.com/XuqjlSWBbj\n","code":""},{"path":"november-2021.html","id":"november-6","chapter":"November 2021","heading":"November 6","text":"\nnumerous key-pair values specified environment, R's performance can degrade significant amount memory leakage.{fastmap} function eponymous ๐Ÿ“ฆ provides performant alternative! ๐Ÿ”https://t.co/H7dZxlZixI#rstats #DataScience pic.twitter.com/Hxai0xP0KZ\n","code":""},{"path":"november-2021.html","id":"november-7","chapter":"November 2021","heading":"November 7","text":"\nexploring data, one often needs quick easy way create dot--whisker plots coefficient estimates regression models.{plot_model} function {sjPlot} ๐Ÿ“ฆ produces plots wide range models ๐Ÿ“Šhttps://t.co/S4iMdhxUbB#rstats #DataScience pic.twitter.com/azkYut7LkL\n","code":""},{"path":"november-2021.html","id":"november-8","chapter":"November 2021","heading":"November 8","text":"\nanalyzing text data, filter stop words, may wish readily available R.{generate_stoplist} function {tidystopwords} ๐Ÿ“ฆ provides 100 languages! ๐Ÿ›‘https://t.co/fZK57AClCG#rstats #DataScience pic.twitter.com/R4aYJzr7CF\n","code":""},{"path":"november-2021.html","id":"november-9","chapter":"November 2021","heading":"November 9","text":"\ncomplex study design, simulating , may first wish validate mock dataset.{check_design} function {faux} ๐Ÿ“ฆ provides quick easy visual way ! ๐Ÿ“Šhttps://t.co/N20EV0caL7#rstats #DataScience pic.twitter.com/dcG5cpLJ3j\n","code":""},{"path":"november-2021.html","id":"november-10","chapter":"November 2021","heading":"November 10","text":"\nSometimes may need operate video files R (e.g., change file format, convert images, etc.).{av_*} function family {av} ๐Ÿ“ฆ provides numerous helpers end! ๐ŸŽฅhttps://t.co/v5mgjqqCQr#rstats #DataScience pic.twitter.com/21DWBeT0gq\n","code":""},{"path":"november-2021.html","id":"november-11","chapter":"November 2021","heading":"November 11","text":"\nRStudio preferred IDE R, may wish list change current preferences programmatically, rather pointing clicking.{* _rstudio_prefs} function family {rstudio.prefs} ๐Ÿ“ฆ provides ! โœ…https://t.co/IXw9ciTFZ7#rstats #DataScience pic.twitter.com/IwTJHUFjle\n","code":""},{"path":"november-2021.html","id":"november-12","chapter":"November 2021","heading":"November 12","text":"\nLorenz curves provide good way visualize inequality distribution variable (like wealth). {stat_lorenz} function {gglorenz} ๐Ÿ“ฆprovides needed geometric layer create curve easily ๐Ÿ™Œhttps://t.co/AJpWz8rHCr#rstats #DataScience pic.twitter.com/4bCGp9mrQq\n","code":""},{"path":"november-2021.html","id":"november-13","chapter":"November 2021","heading":"November 13","text":"\nSometimes may wish quickly prepare frequency table categorical variables.{datasummary_crosstab} function {modelsummary} ๐Ÿ“ฆ provides output rich details! ๐Ÿงฎhttps://t.co/ropUkhXb1v#rstats #DataScience pic.twitter.com/plsTArmYA1\n","code":""},{"path":"november-2021.html","id":"november-14","chapter":"November 2021","heading":"November 14","text":"\nnumber rows visualization, may wish individuate help different background colours.{geom_stripped_rows} function {GGally} ๐Ÿ“ฆ provides perfect geometric layer! ๐Ÿฆ“https://t.co/S12QxH4r8Y#rstats #DataScience pic.twitter.com/DnEgVri8yc\n","code":""},{"path":"november-2021.html","id":"november-15","chapter":"November 2021","heading":"November 15","text":"\nused string interpolation R using {glue} ๐Ÿ“ฆ, may wish similar helper writing SQL queries.{glue_sql} function cleverly handles SQL quoting writing queries โœ๏ธhttps://t.co/3zGFUR1m2e#rstats #DataScience pic.twitter.com/jeAVIRZaYS\n","code":""},{"path":"november-2021.html","id":"november-16","chapter":"November 2021","heading":"November 16","text":"\nGiven diversity time series classes R ecosystem, may wish universal converter interchanging classes.{ts_*} function family {tsbox} ๐Ÿ“ฆ provides just tools! ๐Ÿ› https://t.co/yqNT5DaLns#rstats #DataScience pic.twitter.com/Yf8vMAMIjt\n","code":""},{"path":"november-2021.html","id":"november-17","chapter":"November 2021","heading":"November 17","text":"\ncorrelation hypotheses, one may wish simultaneously visualize joint marginal distributions.{ScatterHist} function {WVPlots} ๐Ÿ“ฆ default (statistical summary plus) ๐Ÿ’https://t.co/DjZxh7OGG5#rstats #DataScience pic.twitter.com/Se1tOBNLR0\n","code":""},{"path":"november-2021.html","id":"november-18","chapter":"November 2021","heading":"November 18","text":"\nuse project-oriented workflows, instead changing working directory, may wish construct file paths relative top-level directory.eponymous function {} ๐Ÿ“ฆ provides functionality ๐Ÿ  https://t.co/qutCsLDu8e#rstats #DataScience pic.twitter.com/0HYc2zVDu6\n","code":""},{"path":"november-2021.html","id":"november-19","chapter":"November 2021","heading":"November 19","text":"\nSometimes need rank-transform data, present either vector dataframe.{ranktransform} function {datawizard} ๐Ÿ“ฆ allows quite flexibly! 1๏ธโƒฃ2๏ธโƒฃ3๏ธโƒฃhttps://t.co/S5i3g69jn6#rstats #DataScience pic.twitter.com/ySPWE7VpFe\n","code":""},{"path":"november-2021.html","id":"november-20","chapter":"November 2021","heading":"November 20","text":"\nPie charts provide informative imperfect way visualize categorical data, squared pie (waffle) charts overcome imperfections.{waffle} function eponymous ๐Ÿ“ฆ easily produces ๐Ÿง‡ https://t.co/OxtCuCMhHF#rstats #DataScience pic.twitter.com/1ui2OqHJ8k\n","code":""},{"path":"november-2021.html","id":"november-21","chapter":"November 2021","heading":"November 21","text":"\nApache Spark popular platform large-scale data processing, may wish use familiar dataframe-based workflows R.{copy_to} function {sparklyr} ๐Ÿ“ฆ can convert dataframes Spark objects! โšก๏ธhttps://t.co/YpsY9738WF#rstats #DataScience pic.twitter.com/TPWcsNraPK\n","code":""},{"path":"november-2021.html","id":"november-22","chapter":"November 2021","heading":"November 22","text":"\nSometimes wish combine various figures single, compound figure convey overarching point.{plot_grid} function {cowplot} ๐Ÿ“ฆ provides way flexibly! ๐Ÿง‘โ€๐Ÿญhttps://t.co/5cQUrPQpJz#rstats #DataScience pic.twitter.com/VONCYetz1x\n","code":""},{"path":"november-2021.html","id":"november-23","chapter":"November 2021","heading":"November 23","text":"\nuse {dm} ๐Ÿ“ฆ create data model objects representing tables relational database, may wish visualise relations tables.{dm_draw} function exactly ! ๐Ÿ•ธhttps://t.co/OmDHyi8Ig9#rstats #DataScience pic.twitter.com/OHIHpLbdgG\n","code":""},{"path":"november-2021.html","id":"november-24","chapter":"November 2021","heading":"November 24","text":"\nEither debugging learning new function, may wish understand implementation logic.{flow_view} function {flow} ๐Ÿ“ฆ helps visualizes logic via flow diagram! ๐Ÿ”ท๐Ÿ”ถhttps://t.co/f5ubm3fq7T#rstats #DataScience pic.twitter.com/yR7AS9stSk\n","code":""},{"path":"november-2021.html","id":"november-25","chapter":"November 2021","heading":"November 25","text":"\ndata-wrangling stage, sometimes may wish compare dataframe current stage differs original one.{rCompare} function {dataCompareR} ๐Ÿ“ฆ provides just tool! ๐ŸŽ๐Ÿhttps://t.co/btUd1vK0dj#rstats #DataScience pic.twitter.com/x8xGL4MUqn\n","code":""},{"path":"november-2021.html","id":"november-26","chapter":"November 2021","heading":"November 26","text":"\nSometimes may wish assess polarity (positive, negative, neutral) text data.{get_sentiment} function {syuzhet} ๐Ÿ“ฆ provides convenient way approximate sentiment text sentence! โž•โž–https://t.co/Pv9tCBxzYM#rstats #DataScience pic.twitter.com/us9PiBzRcb\n","code":""},{"path":"november-2021.html","id":"november-27","chapter":"November 2021","heading":"November 27","text":"\nfirst important step data analysis workflow make sure everything data โ€œmakes senseโ€.{diagnose} function {dlookr} ๐Ÿ“ฆ provides detailed data diagnosis report makes step easy! ๐Ÿ”https://t.co/UoUEdrfUTU#rstats #DataScience pic.twitter.com/eVxrpd0rjc\n","code":""},{"path":"november-2021.html","id":"november-28","chapter":"November 2021","heading":"November 28","text":"\nChloropleth maps provide convenient way show variation quantitative variable across locations.{mf_map} function {mapsf} ๐Ÿ“ฆ easily draws maps elegant defaults! ๐Ÿ—บhttps://t.co/QitknL32Jh#rstats #DataScience pic.twitter.com/JMCtpdEYnY\n","code":""},{"path":"november-2021.html","id":"november-29","chapter":"November 2021","heading":"November 29","text":"\nwant print HTML document produced Rmarkdown, may also wish paginated.{html_paged} function {pagedown} ๐Ÿ“ฆ provides HTML document split multiple pages! ๐Ÿ“‘๐Ÿ–จhttps://t.co/wMofeBVYuc#rstats #DataScience pic.twitter.com/L1sXZrh4Xz\n","code":""},{"path":"november-2021.html","id":"november-30","chapter":"November 2021","heading":"November 30","text":"\ncorrelation matrix compactly shows relationships multiple variables. , pipe-based workflows, often helpful dataframe.{correlate} function {corrr} ๐Ÿ“ฆ makes easy extract !https://t.co/TAMnjQQTAT#rstats #DataScience pic.twitter.com/aLN7k9uvGy\n","code":""},{"path":"december-2021.html","id":"december-2021","chapter":"December 2021","heading":"December 2021","text":"","code":""},{"path":"december-2021.html","id":"december-1","chapter":"December 2021","heading":"December 1","text":"\nROC curves provide convenient way compare responses predictions binomial model, sometimes may also need plot multiple curves.{geom_roc} function {plotROC} ๐Ÿ“ฆ can ! ๐Ÿ’ชhttps://t.co/dbmEIkLfNM#rstats #DataScience pic.twitter.com/H2gejkYnvI\n","code":""},{"path":"december-2021.html","id":"december-2","chapter":"December 2021","heading":"December 2","text":"\nEither learning exploring distribution data, may wish visualize shape various statistical distributions.{vdist_*} function family {vistributions} ๐Ÿ“ฆ makes easy! ๐Ÿ””https://t.co/iqSh2nnKWF#rstats #DataScience pic.twitter.com/XDccj6H5Dy\n","code":""},{"path":"december-2021.html","id":"december-3","chapter":"December 2021","heading":"December 3","text":"\nlevel plot displays spatial data two rather three dimensions, sometimes may wish create visualisation RasterLayer objects.{levelplot} function {rasterVis} ๐Ÿ“ฆ elegantly flexibly!https://t.co/TTYvQLpIbZ#rstats #DataScience pic.twitter.com/S5g6fQUAw7\n","code":""},{"path":"december-2021.html","id":"december-4","chapter":"December 2021","heading":"December 4","text":"\nSometimes wish remove certain rows columns completely empty instead partly empty.{remove_empty_*} helper function family {sjmisc} ๐Ÿ“ฆ exactly ! ๐Ÿงนhttps://t.co/P6z56VFxPD#rstats #DataScience pic.twitter.com/zKDdFpxscn\n","code":""},{"path":"december-2021.html","id":"december-5","chapter":"December 2021","heading":"December 5","text":"\nModern OS, devices, etc. offer dark mode, may wish use also visualizations match color scheme.{dark_theme_*} function family {ggdark} ๐Ÿ“ฆ offers dark mode {ggplot2} visualizations! ๐ŸŒ‘https://t.co/MAAA7DadAQ#rstats #DataScience pic.twitter.com/foWfo0Hquh\n","code":""},{"path":"december-2021.html","id":"december-6","chapter":"December 2021","heading":"December 6","text":"\ninterested studying group differences, sometimes may wish annotate visualizations results significance testing.{add_pval} {ggpval} ๐Ÿ“ฆprovides way ! โš–๏ธhttps://t.co/X1pKEeFHnG#rstats #DataScience pic.twitter.com/xixLLhcHlE\n","code":""},{"path":"december-2021.html","id":"december-7","chapter":"December 2021","heading":"December 7","text":"\nuse Qualtrics surveys collect data crowdsourcing website (like MTurk), may wish exclude responses based specified exclusion criteria.{exclude_*} function family {excluder} ๐Ÿ“ฆ makes easy! โœ‚๏ธhttps://t.co/Nhl8MqQEXH#rstats #DataScience pic.twitter.com/CDqpV10RN1\n","code":""},{"path":"december-2021.html","id":"december-8","chapter":"December 2021","heading":"December 8","text":"\nconsistent coding style, need adopt naming convention stick codebase.{to_any_case} function {snakecase} ๐Ÿ“ฆ can help convert strings desired case! ๐Ÿ๐Ÿชhttps://t.co/sHkKj1dLgV#rstats #DataScience pic.twitter.com/MNVuzLIrjF\n","code":""},{"path":"december-2021.html","id":"december-9","chapter":"December 2021","heading":"December 9","text":"\nAlthough descriptive statistics helpful, computing can tricky, especially complicated designs.{suberbPlot} function {superb} ๐Ÿ“ฆ makes easy various designs, sampling schemes, population sizes, etc.!https://t.co/5bXZF7pt5x#rstats #DataScience pic.twitter.com/VOEofPuSxn\n","code":""},{"path":"december-2021.html","id":"december-10","chapter":"December 2021","heading":"December 10","text":"\nSometimes may wish encode values shape visualisation may find default shapes distinctive enough. {geom_star} function {ggstar} ๐Ÿ“ฆ provides just shapes! ๐ŸงŠโญ•๏ธโญ๏ธhttps://t.co/ecTTbN44cv#rstats #DataScience pic.twitter.com/icpTqfUK2W\n","code":""},{"path":"december-2021.html","id":"december-11","chapter":"December 2021","heading":"December 11","text":"\nLine charts can represent variation quantitative values across ordered measurements (e.g.ย across time).{gg_line_facet} function {simplevis} ๐Ÿ“ฆ creates charts elegant defaults ๐Ÿ“ˆhttps://t.co/A4k8DMTDUf#rstats #DataScience pic.twitter.com/1p1TqWH4Bz\n","code":""},{"path":"december-2021.html","id":"december-12","chapter":"December 2021","heading":"December 12","text":"\nSometimes want adjust data regressing effects selected variables.{adjust} function {datawizard} ๐Ÿ“ฆ easily flexibly! ๐ŸŽ›https://t.co/lI6JSpnlfb#rstats #DataScience pic.twitter.com/A3No7I2ymW\n","code":""},{"path":"december-2021.html","id":"december-13","chapter":"December 2021","heading":"December 13","text":"\nBar charts provide great way visualize magnitude quantitative values across different categories.{bar_chart} function {ggcharts} ๐Ÿ“ฆ produces easily! ๐Ÿ“Šhttps://t.co/j4c3C1gVZI#rstats #DataScience pic.twitter.com/ma0Wl1J6cK\n","code":""},{"path":"december-2021.html","id":"december-14","chapter":"December 2021","heading":"December 14","text":"\nmay wish prepare shareable project analysis docs, emphasis reproducibility efficient project management.{wflow_*} function family {workflowr} ๐Ÿ“ฆ makes easy version-controlled website! ๐Ÿ“https://t.co/GTMWYeYuEm#rstats #DataScience pic.twitter.com/Rqi5yE6dl8\n","code":""},{"path":"december-2021.html","id":"december-15","chapter":"December 2021","heading":"December 15","text":"\nannotating plotted data labels, may wish way overlap labels minimized.{geom_label_repel} function {ggrepel} ๐Ÿ“ฆ provides clever performant way ! ๐Ÿ“https://t.co/GU3SQS1YWt#rstats #DataScience pic.twitter.com/1UX56FSeNX\n","code":""},{"path":"december-2021.html","id":"december-16","chapter":"December 2021","heading":"December 16","text":"\nSometimes need operate list vector, either position name, get index value interest.{imap} function family {purrr} ๐Ÿ“ฆ can help ๐Ÿ”https://t.co/U5IHEt8hpt#rstats #DataScience pic.twitter.com/Fsyahp4tb8\n","code":""},{"path":"december-2021.html","id":"december-17","chapter":"December 2021","heading":"December 17","text":"\nSometimes rmarkdown source file alone enough reproduce report, additional files (e.g.ย data) need embedded.{embed_*} function family {xfun} ๐Ÿ“ฆ encoding files base64 format! ๐ŸŽhttps://t.co/oA04uKnV3F#rstats #DataScience pic.twitter.com/NA6jzbr2YN\n","code":""},{"path":"december-2021.html","id":"december-18","chapter":"December 2021","heading":"December 18","text":"\nOften need work, specific date, rather intervals containing start end dates.{int_*} function family {lubridate} ๐Ÿ“ฆ makes easy operate intervals ๐Ÿ—“https://t.co/beoBq6Rlr3#rstats #DataScience pic.twitter.com/8mMnL0nJwC\n","code":""},{"path":"december-2021.html","id":"december-19","chapter":"December 2021","heading":"December 19","text":"\ninherit folder many files, can cumbersome go find missing packages install.{show_*} function family {reinstallr} ๐Ÿ“ฆ can list used packages highlight ones missing! ๐Ÿ”https://t.co/KrhtH0DT2X#rstats #DataScience pic.twitter.com/nZLLvDN9jP\n","code":""},{"path":"december-2021.html","id":"december-20","chapter":"December 2021","heading":"December 20","text":"\nSometimes can useful mark centroid group observations point label.{stat_centroid} function {ggpp} ๐Ÿ“ฆprovides way ! ๐Ÿ“Œhttps://t.co/FaAYMnOFW7#rstats #DataScience pic.twitter.com/gwfY7uyrPK\n","code":""},{"path":"december-2021.html","id":"december-21","chapter":"December 2021","heading":"December 21","text":"\ndata wrangling stage, sometimes may need rotate dataframe (rows become columns vice versa).{rotate_df} function {sjmisc} ๐Ÿ“ฆ provides just tool! ๐Ÿ”งhttps://t.co/fB1vqf4uqu#rstats #DataScience pic.twitter.com/yw2aO2oLxZ\n","code":""},{"path":"december-2021.html","id":"december-22","chapter":"December 2021","heading":"December 22","text":"\nSometimes may wish quickly build elegant sharable website.{create_postcard} function {postcards} ๐Ÿ“ฆ creates pretty, single-page websites customizable Rmarkdown template! โœจ๐Ÿ“‡https://t.co/0eZUTkkd16#rstats #DataScience pic.twitter.com/izucxUyfQ6\n","code":""},{"path":"december-2021.html","id":"december-23","chapter":"December 2021","heading":"December 23","text":"\nwish write functions save data user's computer, need find appropriate directory depending OS.{user_*_dir} function family {rappdirs} ๐Ÿ“ฆ makes easy! ๐Ÿ—‚https://t.co/XUPMngVMz9#rstats #DataScience pic.twitter.com/ps2ISHwql6\n","code":""},{"path":"december-2021.html","id":"december-24","chapter":"December 2021","heading":"December 24","text":"\nJSON data representation format ubiquitous, sometimes might need convert dataframe R JSON object.{to_json} function {jsonify} ๐Ÿ“ฆ provides convenient fast way ! ๐Ÿ”€https://t.co/CDUlMuB6fE#rstats #DataScience pic.twitter.com/GHQgTRWYB5\n","code":""},{"path":"december-2021.html","id":"december-25","chapter":"December 2021","heading":"December 25","text":"\nSometimes may wish open files, directories, URLs, etc. programmatically, instead manually.{xopen} function eponymous ๐Ÿ“ฆ provides way R! ๐Ÿ”งhttps://t.co/5HxeOdvIIK#rstats #DataScience pic.twitter.com/zVpcSHOdXe\n","code":""},{"path":"december-2021.html","id":"december-26","chapter":"December 2021","heading":"December 26","text":"\nuse {R6}โ€™s implementation encapsulated OOP R, sometimes may wish use idiomatic functional OOP objects.eponymous function {R62S3} ๐Ÿ“ฆ generate S3 generics/methods R6 classes ๐Ÿ”€https://t.co/s82kX21cKc#rstats #DataScience pic.twitter.com/DCXfB6wpgu\n","code":""},{"path":"december-2021.html","id":"december-27","chapter":"December 2021","heading":"December 27","text":"\nTufte handout thoughtfully styled document footnotes, integration graphics text, elegant typography.{tufte_*} function family {tufte} ๐Ÿ“ฆ helps create via Rmarkdown ๐Ÿ“ƒhttps://t.co/Ca2FPorPnW#rstats #DataScience pic.twitter.com/dhyEsyj1l7\n","code":""},{"path":"december-2021.html","id":"december-28","chapter":"December 2021","heading":"December 28","text":"\nArchive files (zip, tar, etc.) collect multiple files single file, may sometimes need use portability compression.{archive_*} function family {archive} ๐Ÿ“ฆ provides relevant operations R! ๐Ÿ“https://t.co/E97cRlKZrO#rstats #DataScience pic.twitter.com/a1GlbccJfV\n","code":""},{"path":"december-2021.html","id":"december-29","chapter":"December 2021","heading":"December 29","text":"\nYAML language ubiquitous, especially configuration files, sometimes may wish convert R object YAML string.{.yaml} function {yaml} ๐Ÿ“ฆ makes easy ๐Ÿ”€https://t.co/31Iwor6B6N#rstats #DataScience pic.twitter.com/S3YfVZovnD\n","code":""},{"path":"december-2021.html","id":"december-30","chapter":"December 2021","heading":"December 30","text":"\nCox regression model widely used medical research assess effect several risk factors survival time patients.{ggforest} function {survminer} ๐Ÿ“ฆeasily creates forest plot model estimates ๐Ÿ“Šhttps://t.co/YXA8LjVQY5#rstats #DataScience pic.twitter.com/S6F2KdFAUn\n","code":""},{"path":"december-2021.html","id":"december-31","chapter":"December 2021","heading":"December 31","text":"\ncrawling webpages, sometimes may wish switch linking strategies; using either absolute relative URLs.{url_*} function family {xml2} ๐Ÿ“ฆ makes conversions easy R! ๐Ÿ”€https://t.co/x1KW07bqQZ#rstats #DataScience pic.twitter.com/8cZaZ6afiy\n","code":""},{"path":"january-2022.html","id":"january-2022","chapter":"January 2022","heading":"January 2022","text":"","code":""},{"path":"january-2022.html","id":"january-1","chapter":"January 2022","heading":"January 1","text":"\nuse {dplyr} {tidyr} ๐Ÿ“ฆs wrangle data, may wish see feedback different operations , especially long piped chains.\"print pipe\" operator (%P>%) {ViewPipeSteps} ๐Ÿ“ฆ ๐Ÿ“https://t.co/izehRpk23g#rstats #DataScience pic.twitter.com/jEev5v27te\n","code":""},{"path":"january-2022.html","id":"january-2","chapter":"January 2022","heading":"January 2","text":"\nInspired popular languages (C++, JavaScript, Python, etc.), may also wish use arithmetic operators R modify objects place.operator family {infix} ๐Ÿ“ฆ provide ! ๐Ÿ‘https://t.co/LXJmDRYAfQ#rstats #DataScience pic.twitter.com/e4AKp7a6BF\n","code":""},{"path":"january-2022.html","id":"january-3","chapter":"January 2022","heading":"January 3","text":"\nBar charts good way visualize categorical data, making well-labelled bar chart can time-consuming.{PlotXTabs2} function {CGPfunctions} ๐Ÿ“ฆ easily produces chart (additional statistical details)!https://t.co/Ig9tgpd9Gu#rstats #DataScience pic.twitter.com/kZBxlLPBZ2\n","code":""},{"path":"january-2022.html","id":"january-4","chapter":"January 2022","heading":"January 4","text":"\nSometimes need check remote computer web server running.can R using {ping} function {pingr} ๐Ÿ“ฆ! ๐Ÿ›Žhttps://t.co/ahm9IdRc3y#rstats #DataScience pic.twitter.com/FPZiRwyz0s\n","code":""},{"path":"january-2022.html","id":"january-5","chapter":"January 2022","heading":"January 5","text":"\nSometimes wish remove certain rows columns completely empty instead partly empty.{remove_empty_*} function family {janitor} ๐Ÿ“ฆ exactly ! ๐Ÿงนhttps://t.co/j1RAFZv4pY#rstats #DataScience pic.twitter.com/QHb1bDzHzB\n","code":""},{"path":"january-2022.html","id":"january-6","chapter":"January 2022","heading":"January 6","text":"\nKaplan-Meier estimates survival curves can help infer differences survival times compared groups.{ggsurvplot} function {survminer} ๐Ÿ“ฆ plots curves rich details! ๐Ÿ‘https://t.co/n25xxlgZiV#rstats #DataScience pic.twitter.com/H1FbLNSlhv\n","code":""},{"path":"january-2022.html","id":"january-7","chapter":"January 2022","heading":"January 7","text":"\nBland-Altman plot helps visually assess agreement scores kind (psychological questionnaires, chemical assays, etc.).{BAC_plot} function {ufs} ๐Ÿ“ฆ easily creates elegant defaults! ๐ŸŽ๐Ÿhttps://t.co/zl7vSzwe26#rstats #DataScience pic.twitter.com/g73a0A6k11\n","code":""},{"path":"january-2022.html","id":"january-8","chapter":"January 2022","heading":"January 8","text":"\nSometimes may need edit images R, e.g.ย changing transparency, spatial anti-aliasing, converting black white, etc.{image_convert} function {magick} ๐Ÿ“ฆ allows many options ! ๐Ÿ“ธhttps://t.co/ald6jeDtOe#rstats #DataScience pic.twitter.com/ZV74ynzCtN\n","code":""},{"path":"january-2022.html","id":"january-9","chapter":"January 2022","heading":"January 9","text":"\nensure reproducibility R script, may wish download needed package versions certain date.{create_checkpoint} function {checkpoint} ๐Ÿ“ฆ creates local library needed package versions! ๐Ÿ“‘https://t.co/L8uArRuRiN#rstats #DataScience pic.twitter.com/T6R9b5W5yY\n","code":""},{"path":"january-2022.html","id":"january-10","chapter":"January 2022","heading":"January 10","text":"\nPCA popular dimensionality reduction technique sometimes may wish reports results report.{tab_pca} function {sjPlot} ๐Ÿ“ฆ produces publication-ready HTML table PCA elegant defaults! ๐Ÿ“https://t.co/mvakHooBSf#rstats #DataScience pic.twitter.com/tULXxnRShU\n","code":""},{"path":"january-2022.html","id":"january-11","chapter":"January 2022","heading":"January 11","text":"\nlist non-atomic vector, sometimes may wish convert (flatten) atomic one.{flatten} function family {purrr} ๐Ÿ“ฆ provides helpers type stability! ๐Ÿ—œhttps://t.co/tMAS8S8RcM#rstats #DataScience pic.twitter.com/NA7zvA2Rtb\n","code":""},{"path":"january-2022.html","id":"january-12","chapter":"January 2022","heading":"January 12","text":"\nAcross disciplines (physics, engineering, etc.), quiver plot helps visualize vector fields arrows, may need create plot R.{geom_quiver} function {ggquiver} ๐Ÿ“ฆ offers just geometric layer! ๐ŸŒชhttps://t.co/DNwPEmLFDe#rstats #DataScience pic.twitter.com/a2RZuXtKX6\n","code":""},{"path":"january-2022.html","id":"january-13","chapter":"January 2022","heading":"January 13","text":"\nFilename extensions (.py, .csv, .pdf, etc.) decide characteristics intended usage files, may wish work R.{*_ext} function family {xfun} ๐Ÿ“ฆ provides helpers ! ๐Ÿ—‚https://t.co/Qq8IQsMb6Q#rstats #DataScience pic.twitter.com/l47PKMvzrL\n","code":""},{"path":"january-2022.html","id":"january-14","chapter":"January 2022","heading":"January 14","text":"\nSometimes want quickly compute visualize frequencies categorical variables data.{inspect_cat} function {inspectdf} ๐Ÿ“ฆ , labeling frequent levels highlighting missing data! ๐Ÿ“Šhttps://t.co/loDxpvrDGP#rstats #DataScience pic.twitter.com/aamrTwLhQh\n","code":""},{"path":"january-2022.html","id":"january-15","chapter":"January 2022","heading":"January 15","text":"\nAlthough often can easily read data single CSV file, sometimes read dataset stored one Excel spreadsheets.{read_xlsx} function {readxl} ๐Ÿ“ฆ provides easy syntax ! ๐Ÿ“https://t.co/TE7GJQVSS8#rstats #DataScience pic.twitter.com/XTqST8rbou\n","code":""},{"path":"january-2022.html","id":"january-16","chapter":"January 2022","heading":"January 16","text":"\nsave space, IP addresses often stored integers, receive data, may wish convert familiar human-readable form.{integer_to_ip} function {ipaddress} ๐Ÿ“ฆ makes conversion easy! ๐Ÿ•ธhttps://t.co/IqK3ihOIjc#rstats #DataScience pic.twitter.com/CvDYbKPuej\n","code":""},{"path":"january-2022.html","id":"january-17","chapter":"January 2022","heading":"January 17","text":"\nhorizon plot compact time-series data visualization plot compare different moving values.{geom_horizon} function {ggHoriPlot} ๐Ÿ“ฆ provides just geometric layer! โฌ†๏ธโฌ‡๏ธhttps://t.co/NSA3RYIoiE#rstats #DataScience pic.twitter.com/7uqC45DQOf\n","code":""},{"path":"january-2022.html","id":"january-18","chapter":"January 2022","heading":"January 18","text":"\nSometimes, especially context statistical modeling, might infinite NaN's present outputs may wish replace NAs.{zap_inf} helper function {sjmisc} ๐Ÿ“ฆ exactly ! โ™พhttps://t.co/mrWehQheOi#rstats #DataScience pic.twitter.com/CL7qiucGd7\n","code":""},{"path":"january-2022.html","id":"january-19","chapter":"January 2022","heading":"January 19","text":"\nPython dictionary unordered data type key-value pairs allows accessing values, indexing, via unique keys.{py_dict} function {reticulate} ๐Ÿ“ฆ gives access data type R! ๐Ÿ“–https://t.co/stxGhcedjI#rstats #DataScience pic.twitter.com/C7jiezmfad\n","code":""},{"path":"january-2022.html","id":"january-20","chapter":"January 2022","heading":"January 20","text":"\nSometimes may wish include patterns images plots enhanced storytelling.{image} {pattern} function family {patternplot} ๐Ÿ“ฆ provides way ! โšœ๏ธhttps://t.co/lRhdnmbQoQ#rstats #DataScience pic.twitter.com/S1ze86OJTk\n","code":""},{"path":"january-2022.html","id":"january-21","chapter":"January 2022","heading":"January 21","text":"\nSerialization changes objects byte stream can saved binary file, deserializing reverse.{q*} function family {qs} ๐Ÿ“ฆ provides performant way serialize deserialize R object! โšก๏ธhttps://t.co/i8BNgtAQWw#rstats #DataScience pic.twitter.com/5yTJWk3z5Z\n","code":""},{"path":"january-2022.html","id":"january-22","chapter":"January 2022","heading":"January 22","text":"\nVisualizing data along Archimedean spiral can efficiently reveal periodic patterns time series data.{spiral_*} function family {spiralize} ๐Ÿ“ฆ draws spiral plots flexibly! ๐ŸŒ€https://t.co/5vybsvWv2e#rstats #DataScience pic.twitter.com/c168IWhcXL\n","code":""},{"path":"january-2022.html","id":"january-23","chapter":"January 2022","heading":"January 23","text":"\nworking piped workflow, need extract single column, syntax can bit cumbersome.{pull} function {dplyr} ๐Ÿ“ฆ provides readable syntax end! ๐Ÿ’„https://t.co/rCT1vcMZzX#rstats #DataScience pic.twitter.com/vEs2cc7Lw8\n","code":""},{"path":"january-2022.html","id":"january-24-1","chapter":"January 2022","heading":"January 24","text":"\nneed bind multiple dataframes rows, may first wish check can done successfully.{compare_df_cols} function {janitor} ๐Ÿ“ฆ provides summary data types check ! ๐ŸŽ๐Ÿhttps://t.co/88zYQFdIWa#rstats #DataScience pic.twitter.com/kSCLESOBP9\n","code":""},{"path":"retirement.html","id":"retirement","chapter":"Retirement","heading":"Retirement","text":"\nToday concludes 1-year hobby project. wonโ€™t new posts, feel free unfollow, keep revisiting! ๐Ÿ™ƒ> 22K followers 365 posts, guess enjoyed reading much โ€™d preparing . ๐Ÿ˜…Best luck jouRneys! ๐Ÿคž@patilindrajeets\n","code":""}] diff --git a/_book/september-2021.html b/_book/september-2021.html new file mode 100644 index 0000000..230c710 --- /dev/null +++ b/_book/september-2021.html @@ -0,0 +1,469 @@ + + + + + + +September 2021 | R Function A Day + + + + + + + + + + + + + + + + + + + + +
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    "R Function A Day" was written by Indrajeet Patil. It was last built on 2024-12-13.

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    • Sometimes we may wish to estimate density for priors and posteriors of a regression model.

    The {estimate_density} function from {bayestestR} ๐Ÿ“ฆ computes and visualizes these estimates ๐Ÿงฎ    https://t.co/gm8Wf9qr3f#rstats #DataScience pic.twitter.com/SjuoAiuXPo