resources.Rmd

---
title: "Resources"
output: 
  html_document
---

```{r, include = FALSE}
library(dplyr)
library(kableExtra)
```

Here are additional resources to help you on your R journey - either before, during, or after the class! Click each section to expand.

<br>

<details open><summary> <span style = "color: #5383bb;"> **Help Getting Started**</span></summary><br>

- [Guide to using Slack]( https://slack.com/help/articles/218080037-Getting-started-for-new-Slack-users)
- [R reference card](http://cran.r-project.org/doc/contrib/Short-refcard.pdf) 
- [R introductory guide](https://cran.r-project.org/doc/manuals/r-release/R-intro.html) 
- [R jargon](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf) 
- [R terminology](https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf)
- [What is the Tidyverse/what packages are in it?](https://www.tidyverse.org/packages/)
- [Where do package names come from?](https://www.statworx.com/en/content-hub/blog/why-is-it-called-that-way-origin-and-meaning-of-r-package-names/)
- [Lawlor et al. 2022](https://doi.org/10.1371/journal.pcbi.1010372): Ten simple rules for teaching yourself R 

</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **Class Cheatsheets**</span></summary><br>

- [Day 1 - RStudio & Reproducibility Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-1.pdf)
- [Day 2 - Basic R & Data Input/Output Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-2.pdf)
- [Day 3 - Data Subsetting  Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-3.pdf)
- [Day 4 - Data Summarization & Data Classes Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-4.pdf)
- [Day 5 - Data Cleaning Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-5.pdf)
- [Day 6 - Data Manipulation & Data Visualization `esquisse` Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-6.pdf)
- [Day 7 - Data Visualization `ggplot2` & Factors Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-7.pdf)
- [Day 8 - Statistics Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-8.pdf)
- [Day 9 - Functions Cheatsheet](https://jhudatascience.org/intro_to_r/modules/cheatsheets/Day-9.pdf)
- [Additional RStudio "Cheatsheets"](https://www.rstudio.com/resources/cheatsheets/)
- [Really great `stringr` Cheatsheet](https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_strings.pdf)
</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **Troubleshooting Guides**</span></summary><br>

- [Guide on when to use quotes or backticks](https://jhudatascience.org/intro_to_r/resources/quotes_vs_backticks.html)
- [Guide on what functions require pulling values out first](https://jhudatascience.org/intro_to_r/resources/functions_for_vectors.html)

</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **Keyboard Shortcuts**</span></summary><br>

- RStudio shortcuts can be found [here](<http://www.rstudio.com/ide/docs/using/keyboard_shortcuts>) 

- Shortcut for the `%in%` operator can be found [here](https://stackoverflow.com/questions/58714081/is-there-a-shortcut-for-in)
</details>

<br>

<details open><summary> <span style = "color: #5383bb;"> **Extra help**</span></summary><br>

**Need help with data import?** 

- [Video on data import](https://youtu.be/LEkNfJgpunQ)
- [Video](https://www.youtube.com/watch?v=we6vwB7DsNU) for PC users who want to see how to move files around (especially from downloads) 
- [Video](https://www.youtube.com/watch?v=Ao9e0cDzMrE) for Mac users who want to see how to move files around (especially from downloads)
- [Extra information about file paths](https://docs.google.com/presentation/d/18u1Vhd3Uq-QprC0btpxS_-Ka-LKVUvncyoqdbGdb-g4/edit?usp=sharing)

**Need extra guidance on wrangling?**

- [Guide on `janitor`](https://hutchdatascience.org/data_snacks/r_snacks/janitor.html)
- [Guide on cleaning complicated names](https://jhudatascience.org/intro_to_r/resources/cleaning_names.html)

**Need help with joins?**

- [`full-join()` animation](https://github.com/gadenbuie/tidyexplain/blob/master/images/full-join.gif)
- [`inner_join()` animation](https://github.com/gadenbuie/tidyexplain/blob/master/images/inner-join.gif)
- [`left-join()` animation](https://github.com/gadenbuie/tidyexplain/blob/master/images/left-join.gif)
- [`right-join()` animation](https://github.com/gadenbuie/tidyexplain/blob/master/images/right-join.gif)


**Need help with plots?**

- [`ggplot2` gallery](https://r-graph-gallery.com/ggplot2-package.html) - See what is possible with `ggplot2` to create graphs in R
- [`ggplot2` theme cheatsheet](https://github.com/claragranell/ggplot2/blob/main/ggplot_theme_system_cheatsheet.pdf)
- [Visualization best practices](https://jhudatascience.org/tidyversecourse/dataviz.html#making-good-plots)
- [Guide on when to use which plot](https://infogram.com/page/choose-the-right-chart-data-visualization)
- [Guide to building up a `ggplot2` figure](https://hopstat.wordpress.com/2016/02/18/how-i-build-up-a-ggplot2-figure/)  

**Need help with stats?**

- [Guide on when to use what stats test](https://www.scribbr.com/statistics/statistical-tests/)
- [Modeling 101](https://jhudatascience.org/tidyversecourse/model.html#linear-modeling)
- [Common statistical tests are linear models](https://lindeloev.github.io/tests-as-linear/) (why understanding linear models will get you far!)
- [Interpreting GLM output (e.g., deviance)](https://www.statology.org/null-residual-deviance/)
- [Guide on why `set.seed` can be useful](https://rsample.tidymodels.org/reference/bootstraps.html)

**Want help creating tables?**

- [Guide on making nice tables from stats tests in R](https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html)
- [Guide on making custom styled tables in R with the `kableExtra` package](https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html)
- [Guide on using `DT table` to make interactive tables](https://rstudio.github.io/DT/)

</details>
<br>

<details><summary> <span style = "color: #5383bb;"> **Courses & Conferences**</span></summary><br>

#### **Online Courses and Resources**

- [Tidyverse Skills for Data Science Book](https://jhudatascience.org/tidyversecourse/)  (**a great next step** to learn more about the tidyverse, some modeling, and machine learning)
- [Tidyverse Skills for Data Science Course](https://www.coursera.org/specializations/tidyverse-data-science-r)  (same content with quizzes, can get certificate with $)
- [Open Case Studies](https://www.opencasestudies.org/)  
(resource for specific public health cases with statistical implementation and interpretation - **a great next step** for learning more about stats and wrangling!)
- [R for Data Science](http://r4ds.had.co.nz/)  (great general information)
- [R for Applied Epidemiology](https://epirhandbook.com/en/)  (Similar general introductory course)  
- [R basics chapter of Introduction to Data Science by Rafael A. Irizarry](https://rafalab.github.io/dsbook/r-basics.html)(great general information)
- [Dataquest](https://www.dataquest.io/)  (general interactive resource)
- [Quick R Guide]( http://statmethods.net/)  (nice free general resource)
- [Introduction to Reproducibility](https://jhudatascience.org/Reproducibility_in_Cancer_Informatics/)
- [Advanced Reproducibility](https://jhudatascience.org/Adv_Reproducibility_in_Cancer_Informatics/)

#### **Classes at JHU**

- [PH.140.621, PH.140.622, PH.140.623, PH.140.62 - Statistical Methods in Public Health I, II, III, and IV](https://e-catalogue.jhu.edu/course-descriptions/biostatistics/) - The class is mostly taught in STATA, but you can also join a group of students working in R. The class covers many topics in statistical analysis for public health data.
- [PH.140.778 - Statistical Computing, Algorithm, and Software Development](https://www.jhsph.edu/courses/course/36737/2022/140.778.01/statistical-computing-algorithm-and-software-devel) - A more advanced course for working with data in R.
    - Content for similar topics as this course can also be found on [Leanpub](https://leanpub.com/advstatcomp).

#### **R Conferences**

- The [RStudio/Posit conference](https://posit.co/conference/) has lots of useful workshops! 
- useR! — International R User Conference information can be found [here](https://www.r-project.org/conferences/).

</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **R for Stata, SPSS, and SAS files**</span></summary><br>

- The [Haven](https://haven.tidyverse.org/) package  
 (This package is super useful for reading and writing files so that they are compatible across Stata, SPSS, SAS, and R) 
- [R vs Stata](https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf)  
(See page 505)
- [R <-> SAS Cheatsheet](https://raw.githubusercontent.com/rstudio/cheatsheets/main/sas-r.pdf)
- [SAS to R Converter](https://www.codeconvert.ai/sas-to-r-converter)  
- [Guide to learning R as a SAS user](https://hutchdatascience.org/data_snacks/r_snacks/sas2r.html)
- You might also find large language models like ChatGPT useful for code conversion. Be sure to check the output because AI makes mistakes!  

</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **Comparison of Python and R**</span></summary><br>

- A helpful [article about the difference between these two languages](https://www.ibm.com/think/topics/python-vs-r).

</details>

<br>

<details><summary> <span style = "color: #5383bb;"> **Videos of Previous Online Lectures**</span></summary><br>

  **From Summer Institute 2023**
  
  ```{r, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "Introduction", "https://youtu.be/aIJrFKQYnP8",
    "RStudio", "https://youtu.be/cxHVf5rTK1c",
    "Reproducibility", "https://youtu.be/af9B9-_df1o",
    "Basic R", "https://youtu.be/5io-iZDutH8",
    "Data Input", "https://youtu.be/sllVVRD5YE4",
    "Subsetting Data", "https://youtu.be/dWdK3bnAGm8",
    "Data Classes", "https://youtu.be/Q4ubnU35TUs",
    "Data Summarization", "https://youtu.be/xsI30yCGgTQ",
    "Data Cleaning", "https://youtu.be/qL6_yfiR9Jk",
    "Data Manipulation", "https://youtu.be/fQ7lDp8Svw0",
    "Intro to Data Visualization", "https://youtu.be/D5RdNwadtR0",
    "Data Visualization", "https://youtu.be/fKj1iBxLwyk",
    "Factors", "https://youtu.be/yqU5zHVh-qA",
    "Statistics", "https://youtu.be/T9Oh0miYhZ0",
    "Data Output", "https://youtu.be/x2OuR4JhPLY",
    "Functions", "https://youtu.be/jWv5RSXq5mo"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```

  **From Summer Institute 2022**
  
  ```{r, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "RStudio and Reproducibility", "https://www.youtube.com/watch?v=eCsD0f0q6rY&t=9s",
    "Basic R", "https://www.youtube.com/watch?v=2YRgDG3qsho",
    "Data IO", "https://www.youtube.com/watch?v=ejnBAdA2N1c",
    "Subsetting Data", "https://www.youtube.com/watch?v=GFa6dXAezJg",
    "Data Summarization", "https://www.youtube.com/watch?v=kj_69maSANk",
    "Data Classes", "https://www.youtube.com/watch?v=zoVciFJieLY&t=21s",
    "Data Cleaning", "https://www.youtube.com/watch?v=GhK8xMUBNwg",
    "Data Manipulation", "https://www.youtube.com/watch?v=qP73AWUjfAU",
    "Intro to Data Visualization", "https://www.youtube.com/watch?v=OCVo6vWrKL4",
    "Data Visualization", "https://www.youtube.com/watch?v=9UPlZqOfT_s",
    "Factors", "https://www.youtube.com/watch?v=0fE756trEEE",
    "Statistics", "https://www.youtube.com/watch?v=jZ5sskAdAJU&t=4s",
    "Functions", "https://www.youtube.com/watch?v=dx-85RzN1G0"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```
  
  **From Winter Institute 2022**
  
  ```{r, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "RStudio", "https://youtu.be/zpAQrglIJb0",
    "Basic R", "https://youtu.be/_8ZG0G1nNlA",
    "Reproducibility", "https://youtu.be/ChFTiZ7Clo4",
    "Data IO", "https://youtu.be/bnYN7AfYGNM",
    "Subsetting Data", "https://youtu.be/kPU3dl25ox8",
    "Data Summarization", "https://youtu.be/C_a8bGGdrIA",
    "Data Classes", "https://youtu.be/ZCe30kIJ0Xc",
    "Data Cleaning", "https://youtu.be/siFL49CCsJg",
    "Data Manipulation", "https://youtu.be/7M81XUIY5SE",
    "Intro to Data Visualization", "https://youtu.be/Md46muvSrYE",
    "Data Visualization", "https://youtu.be/YjDT3ZUSiR4",
    "Factors", "https://youtu.be/Co4l0YhuYSk",
    "Statistics", "https://youtu.be/3nSIN1mu8uw",
    "Functions", "https://youtu.be/XUKJtUYU1Ic"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```
  
  **From Summer Institute 2021**
  
  ```{r, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "RStudio", "https://youtu.be/zpAQrglIJb0",
    "Basic R", "https://youtu.be/md30mwFJ2_Q",
    "Data IO", "https://youtu.be/6xNyETqIqZU",
    "Subsetting Data", "https://youtu.be/YeBSc2YXr4U",
    "Data Summarization Part 1 + 2", "https://youtu.be/yL3BGDWtVC4",
    "Data Classes", "https://youtu.be/zBCvbikMTAc",
    "Data Cleaning", "https://youtu.be/TaREvr5evwk",
    "Data Manipulation", "https://youtu.be/-039V99I-PE",
    "Reproducibility", "https://youtu.be/ui9dJLqtdCs",
    "Statistics", "https://youtu.be/EJwlBoBHoz4",
    "Data Visualization", "https://youtu.be/ngQX9khx7UQ",
    "Functions", "https://youtu.be/epIujSKgNi4"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```
  
  **From Winter Institute 2020**
  
  ```{r, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "RStudio and Data Classes", "https://youtu.be/vyIsDnsq5jY",
    "Subsetting Data", "https://youtu.be/mT8lSagYbjM",
    "Data Summarization Part 1", "https://youtu.be/SZYpzt9zy0g",
    "Data Classes", "https://youtu.be/82zSA1N0mnA",
    "Data Cleaning", "https://youtu.be/G3V2YPaQN34",
    "Data Manipulation", "https://youtu.be/43MPdw5bf4o",
    "Statistics 1", "https://youtu.be/Jr4ljyzrr4U",
    "Statistics 2", "https://youtu.be/ub2BSbK9lMM"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```
  
  **From Summer Institute 2017**
  
  ```{r videos, echo = FALSE, message = FALSE, results='asis'}
  mat <- matrix(c(
    "Day 1", "https://youtu.be/Xi-wsACc7p0",
    "Day 2", "https://youtu.be/u1FQt9Hr8iw",
    "Day 3", "https://youtu.be/woc7AGJTzZw",
    "Day 4", "https://youtu.be/RZ7eVIMPIes",
    "Day 5", "https://youtu.be/e8cFV8wluC0"
  ), ncol = 2, byrow = TRUE)
  mat <- data.frame(mat, stringsAsFactors = FALSE)
  colnames(mat) <- c("Day", "Link to Video")
  knitr::kable(mat, format = "html") %>%
    kable_styling()
  ```

</details>

<br>