From fddb4cd3ac71a37f8ddaa7ca0903f4c325d0b582 Mon Sep 17 00:00:00 2001 From: luisesanmartin Date: Tue, 14 Mar 2023 19:59:11 -0400 Subject: [PATCH 1/7] session 1 edits, solves #92 - used term "dataframe" consistently, instead of "dataset" - moved vector subset from dfs to dataframes section - added note that manually loading data also opens the viewer - updated script captures to not show rm() command - updated instructor names and dates --- Presentations/01-intro-to-R.Rmd | 179 ++++++++++---------- Presentations/01-intro-to-R.html | 200 +++++++++++------------ Presentations/img/script1.png | Bin 0 -> 69668 bytes Presentations/img/script2.png | Bin 0 -> 78983 bytes Presentations/img/script3.png | Bin 0 -> 78602 bytes Presentations/img/script4.png | Bin 0 -> 78553 bytes Presentations/img/scritpt1.png | Bin 75312 -> 0 bytes Presentations/img/scritpt2.png | Bin 85946 -> 0 bytes Presentations/img/scritpt3.png | Bin 85758 -> 0 bytes Presentations/img/scritpt4.png | Bin 85604 -> 0 bytes Presentations/libs/panelset/panelset.css | 115 +++++++++++++ Presentations/libs/panelset/panelset.js | 1 + 12 files changed, 293 insertions(+), 202 deletions(-) create mode 100644 Presentations/img/script1.png create mode 100644 Presentations/img/script2.png create mode 100644 Presentations/img/script3.png create mode 100644 Presentations/img/script4.png delete mode 100644 Presentations/img/scritpt1.png delete mode 100644 Presentations/img/scritpt2.png delete mode 100644 Presentations/img/scritpt3.png delete mode 100644 Presentations/img/scritpt4.png diff --git a/Presentations/01-intro-to-R.Rmd b/Presentations/01-intro-to-R.Rmd index 5824351..1818d5f 100644 --- a/Presentations/01-intro-to-R.Rmd +++ b/Presentations/01-intro-to-R.Rmd @@ -1,8 +1,8 @@ --- title: "Session 1 - Introduction to R" subtitle: "R for Stata Users" -author: "Luiza Andrade, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti" -date: "The World Bank | [WB Github](https://github.com/worldbank)
May 2022" +author: "Luiza Andrade, Marc-Andrea Fiorina, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti" +date: "The World Bank | [WB Github](https://github.com/worldbank)
March 2023" output: xaringan::moon_reader: css: ["libs/remark-css/default.css", "libs/remark-css/metropolis.css", "libs/remark-css/metropolis-fonts.css"] @@ -99,9 +99,9 @@ We're glad you're joining us today! ## Format -- Every session has two TAs. For this session, our TAs are __Luiza Cardoso De Andrade__ and __Rony Rodriguez-Ramirez__ +- Every session has two TAs. For this session, our TAs are __Luiza Cardoso De Andrade__ and __Marc-Andrea Fiorina__ -- The TAs will help you troubleshooting __particular issues__ which make you unable to follow along the presentation. Send them a message over the chat whenever you need help +- The TAs will help you troubleshooting __particular issues__ which make you unable to follow along the presentation. Send a message over the chat whenever you need help --- @@ -118,7 +118,7 @@ We're glad you're joining us today! - The materials of each session will be shared in the OSF page of the course by the end of each session: https://osf.io/86g3b/ -- The recordings will be shared in a WB internal link +- The recordings will be shared each day after the session --- @@ -165,7 +165,7 @@ knitr::include_graphics("img/Interface.png") # Getting started - RStudio interface ```{r echo = FALSE, out.width = "70%"} -knitr::include_graphics("img/scritpt1.png") +knitr::include_graphics("img/script1.png") ``` --- @@ -173,14 +173,14 @@ knitr::include_graphics("img/scritpt1.png") # Getting started - RStudio interface ```{r echo = FALSE, out.width = "70%"} -knitr::include_graphics("img/scritpt2.png") +knitr::include_graphics("img/script2.png") ``` --- # Getting started - RStudio interface ```{r echo = FALSE, out.width = "70%"} -knitr::include_graphics("img/scritpt3.png") +knitr::include_graphics("img/script3.png") ``` --- @@ -188,24 +188,25 @@ knitr::include_graphics("img/scritpt3.png") # Getting started - RStudio interface ```{r echo = FALSE, out.width = "70%"} -knitr::include_graphics("img/scritpt4.png") +knitr::include_graphics("img/script4.png") ``` --- # Getting started - Importing data -Let's start by loading the data set we'll be using: +Let's start by loading the data we'll be using: ## Exercise 1: Import data manually (`r fa("clock")` 3 min) -1. Go to the OSF page of the course (https://osf.io/86g3b/) and download the file located in `R for Stata Users - 2022 May` > `Data` > `whr_panel.csv` +1. Go to the OSF page of the course (https://osf.io/86g3b/) and download the file located in `R for Stata Users - 2023 March` > `Data` > `whr_panel.csv` 2. In RStudio, go to `File` > `Import Dataset` > `From Text (base)` and open the `whr_panel.csv` file. + Depending on your Rstudio version, it might be `File` > `Import Dataset` > `From CSV` -3. Assign the name `whr` to the dataset on the import window. +3. Assign the name `whr` to the dataframe on the import window. + + If you solved the exercise correctly, you'll see that RStudio opens a tab with a viewer of the dataframe --- @@ -258,11 +259,11 @@ name: data-in-r ## In R: -__R__ works in a completely different way: +Datasets are called __dataframes__. R works with them in a different way: -* You can load __as many datasets as you wish__ or your computer's memory allows +* You can load __as many dataframes as you wish__ or your computer's memory allows -* Operations will have lasting effects __only if you store them__ +* Operations will have lasting effects __only if you store their results__ --- @@ -270,7 +271,7 @@ __R__ works in a completely different way: ## In R: -* Everything that exists in R's memory -- variables, datasets, functions -- __is an object__ +* Everything that exists in R's memory -variables, dataframes, functions- __is an object__ * You could think of an object like a chunk of data with some properties that has a name by which you call it @@ -294,7 +295,7 @@ View(whr) # <--- Note that the first letter is uppercase ``` -```{r echo = FALSE, out.width = "45%"} +```{r echo = FALSE, out.width = "40%"} knitr::include_graphics("img/View.png") ``` @@ -316,7 +317,7 @@ Now, let's try some simple manipulations. First, assume we're only interested in ## Exercise 2: Subset the data (`r fa("clock")` 1 min) -- Subset the data set, keeping only observations where variable `year` equals `2016`. +- Subset the dataframe, keeping only observations where variable `year` equals `2016`. ```{r, eval=FALSE} # To do that we'll use the subset() function @@ -388,7 +389,7 @@ knitr::include_graphics("img/x_42.png") ## Exercise 3: Create an object (`r fa("clock")` 1 min) -Create a new dataset, called `whr2016`, that is a subset of the `whr` data set containing only data from the year 2016. +Create a new dataframe, called `whr2016`, that is a subset of the `whr` dataframe containing only data from the year 2016. ```{r, include = T, results = "hide"} # Using the same function but now assigning it to an object @@ -397,23 +398,9 @@ whr2016 <- subset(whr, year == 2016) # Display the 6 first obs. of the new data head(whr2016) -# Notice that we still have the original data set intact -head(whr) - -``` - ---- - -# Data in R - -```{r, include = T, results = "hide"} -whr2016 <- subset(whr, year == 2016) -head(whr2016) +# Notice that we still have the original dataframe intact head(whr) -``` -```{r echo=FALSE} -knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") ``` --- @@ -511,7 +498,7 @@ knitr::include_graphics("img/subset_arguments.png") * Usually the first argument is the object you want to use the function on, e.g. `subset(whr, ...)` -* Functions usually return values that you can store in an object, print or use directly as an argument of another function. +* Functions usually return values that you can store in an object, print or use directly as an argument of another function. __They rarely modify an object in-place__ We will explore these ideas in depth in a later session. @@ -542,7 +529,7 @@ This will give you the foundation to explore your data and construct analytical * An object is like a global or local in Stata, it's __something you can refer to later__ in your code to get a value -* But while you can only put a number or a string in a global, __you can put anything into an object__: scalars, strings, datasets, vectors, plots, functions, etc. +* But while you can only put a number or a string in a global, __you can put anything into an object__: scalars, strings, dataframes, vectors, plots, functions * Objects also have attributes that can be used to manipulate them @@ -556,7 +543,7 @@ Here are the object classes we will cover in this first session: * __Vectors:__ an uni-dimensional object that __stores a sequence of values of the same class__ -* __Data frames:__ a combination of different vectors of the same length (the same as your dataset in Stata) +* __Dataframes:__ a combination of different vectors of the same length (the same as a dataset in Stata) * __Lists:__ a multidimensional object that can store several objects __of different classes and dimensions__ @@ -607,49 +594,13 @@ knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") --- -# R objects - Vectors - -## Vectors - -To R, each of the columns of the object `whr` is a vector. - -### Calling a vector from a `data.frame` column: - -We use the `$` character (operator) to extract vectors (variables) by their names in a `data.frame` - -For example: - -```{r} -# Create a vector with the values of the "year" variable -year_vector <- whr$year - -# See the 3 first elements of the year column -whr$year[1:3] - -``` - ---- - -# R objects - Vectors - -```{r, eval=F} -year_vector <- whr$year # creates a vector with the values of the "year" variable -whr$year[1:3] # see the 3 first elements of the year column -``` - -```{r echo=FALSE} -knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") -``` - ---- - -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes -The `whr` and `whr2016` objects are both data frames. You can also construct a new data frame from scratch by __combining vectors with the same number of elements__. +The `whr` and `whr2016` objects are both dataframes. You can also construct a new dataframe from scratch by __combining vectors with the same number of elements__ with the command `data.frame()`. -#### Now, type the following code to create a new data frame +#### Now, type the following code to create a new dataframe ```{r} # Dataframe created by biding vectors df1 <- data.frame(v1,v2) @@ -658,7 +609,7 @@ df1 --- -# R objects - Data frames +# R objects - Dataframes ```{r, eval=F} df1 <- data.frame(v1,v2) #creates a df by binding to existing vectors @@ -671,11 +622,11 @@ knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") --- -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes -Since a data frame has two dimensions, you can use indices for both. The first index indicates the row selection and the second indicates the column. +Since a dataframe has two dimensions, you can use indices for both. The first index indicates the row selection and the second indicates the column. ### Numeric indexing ```{r, eval = F} @@ -692,7 +643,7 @@ whr[45,1] --- -# R objects - Data frames +# R objects - Dataframes ```{r, eval = F} whr[,1] # The first column of whr @@ -706,9 +657,9 @@ knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") --- -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes Alternatively, you can use the column names for indexing, which is the same as using the `$` sign. @@ -721,7 +672,7 @@ whr[22,"country"] # The same as whr$country[22] --- -# R objects - Data frames +# R objects - Dataframes ```{r, eval=F} # The 22th element of the country column @@ -734,15 +685,51 @@ knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") --- +# R objects - Dataframes + +## Vectors in dataframes + +To R, each of the columns of the object `whr` is a vector. + +### Calling a vector from a dataframe: + +We use the `$` character to extract vectors (variables) by their names in a dataframe + +For example: + +```{r} +# Create a vector with the values of the "year" variable +year_vector <- whr$year + +# See the 3 first elements of the year column +whr$year[1:3] + +``` + +--- + +# R objects - Dataframes + +```{r, eval=F} +year_vector <- whr$year # creates a vector with the values of the "year" variable +whr$year[1:3] # see the 3 first elements of the year column +``` + +```{r echo=FALSE} +knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") +``` + +--- + # R objects - Lists ## Lists Lists are more complex objects that can contain many objects of __different classes and dimensions__. -The outputs of many functions, a regression for example, are similar to lists. +The outputs of many functions, a regression for example, are similar to lists (more on this in a later session). -It would be beyond the scope of this introduction to go deep into them, but here's a quick example: +Here's a quick example: ### Combine several objects of different types in a list ```{r, include = T, results = "hide"} @@ -1017,7 +1004,7 @@ knitr::include_app("https://rrmaximiliano.shinyapps.io/learnr-app/") ## Booleans -Now let's use the boolean vector `inc_below_avg` to add a dummy variable in the `whr` data set for the same condition. +Now let's use the boolean vector `inc_below_avg` to add a dummy variable in the `whr` dataframe for the same condition. ### Exercise 7 (`r fa("clock")` 3 min) @@ -1138,26 +1125,26 @@ Some possible disadvantages of Stata: Here are some advantages of R: -* R is a free and open source software! +* R is a free and open source software, a huge advantage for open science -* It allows you to have several datasets open simultaneously. +* It allows you to have several dataframes open simultaneously + No need to use `keep`, `preserve`, `restore` -* It can run complex Geographic Information System (GIS) analyses. +* It can run complex Geographic Information System (GIS) analyses -* You can use it for web scrapping. +* You can use it for web scrapping and APIs -* You can run machine learning algorithms with it. +* You can easily run machine learning algorithms with it -* You can create complex Markdown documents. This presentation, for example, is entirely done in R. +* You can create complex Markdown documents. This presentation, for example, is entirely done in R -* You can create interactive dashboards and online applications with the Shiny package. +* You can create interactive dashboards and online applications with the Shiny package --- # Appendix - Syntax -R's syntax is a bit heavier than Stata's: +R's syntax is heavier than Stata's: * Parentheses to separate function names from its arguments. * Commas to separate arguments. diff --git a/Presentations/01-intro-to-R.html b/Presentations/01-intro-to-R.html index 5de57a4..68803e4 100644 --- a/Presentations/01-intro-to-R.html +++ b/Presentations/01-intro-to-R.html @@ -3,7 +3,7 @@ Session 1 - Introduction to R - + @@ -31,10 +31,10 @@ ## R for Stata Users ] .author[ -### Luiza Andrade, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti +### Luiza Andrade, Marc-Andrea Fiorina, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti ] .date[ -### The World Bank | WB Github
May 2022 +### The World Bank | WB Github
March 2023 ] --- @@ -84,9 +84,9 @@ ## Format -- Every session has two TAs. For this session, our TAs are __Luiza Cardoso De Andrade__ and __Rony Rodriguez-Ramirez__ +- Every session has two TAs. For this session, our TAs are __Luiza Cardoso De Andrade__ and __Marc-Andrea Fiorina__ -- The TAs will help you troubleshooting __particular issues__ which make you unable to follow along the presentation. Send them a message over the chat whenever you need help +- The TAs will help you troubleshooting __particular issues__ which make you unable to follow along the presentation. Send a message over the chat whenever you need help --- @@ -103,7 +103,7 @@ - The materials of each session will be shared in the OSF page of the course by the end of each session: https://osf.io/86g3b/ -- The recordings will be shared in a WB internal link +- The recordings will be shared each day after the session --- @@ -147,40 +147,41 @@ # Getting started - RStudio interface -<img src="img/scritpt1.png" width="70%" style="display: block; margin: auto;" /> +<img src="img/script1.png" width="70%" style="display: block; margin: auto;" /> --- # Getting started - RStudio interface -<img src="img/scritpt2.png" width="70%" style="display: block; margin: auto;" /> +<img src="img/script2.png" width="70%" style="display: block; margin: auto;" /> --- # Getting started - RStudio interface -<img src="img/scritpt3.png" width="70%" style="display: block; margin: auto;" /> +<img src="img/script3.png" width="70%" style="display: block; margin: auto;" /> --- # Getting started - RStudio interface -<img src="img/scritpt4.png" width="70%" style="display: block; margin: auto;" /> +<img src="img/script4.png" width="70%" style="display: block; margin: auto;" /> --- # Getting started - Importing data -Let's start by loading the data set we'll be using: +Let's start by loading the data we'll be using: -## Exercise 1: Import data manually <font size="5">( 3 min)</font> +## Exercise 1: Import data manually <font size="5">( 3 min)</font> -1. Go to the OSF page of the course (https://osf.io/86g3b/) and download the file located in `R for Stata Users - 2022 May` > `Data` > `whr_panel.csv` +1. Go to the OSF page of the course (https://osf.io/86g3b/) and download the file located in `R for Stata Users - 2023 March` > `Data` > `whr_panel.csv` 2. In RStudio, go to `File` > `Import Dataset` > `From Text (base)` and open the `whr_panel.csv` file. + Depending on your Rstudio version, it might be `File` > `Import Dataset` > `From CSV` -3. Assign the name `whr` to the dataset on the import window. +3. Assign the name `whr` to the dataframe on the import window. + + If you solved the exercise correctly, you'll see that RStudio opens a tab with a viewer of the dataframe --- @@ -227,11 +228,11 @@ ## In R: -__R__ works in a completely different way: +Datasets are called __dataframes__. R works with them in a different way: -* You can load __as many datasets as you wish__ or your computer's memory allows +* You can load __as many dataframes as you wish__ or your computer's memory allows -* Operations will have lasting effects __only if you store them__ +* Operations will have lasting effects __only if you store their results__ --- @@ -239,7 +240,7 @@ ## In R: -* Everything that exists in R's memory -- variables, datasets, functions -- __is an object__ +* Everything that exists in R's memory -variables, dataframes, functions- __is an object__ * You could think of an object like a chunk of data with some properties that has a name by which you call it @@ -262,7 +263,7 @@ View(whr) # <--- Note that the first letter is uppercase ``` -<img src="img/View.png" width="45%" style="display: block; margin: auto;" /> +<img src="img/View.png" width="40%" style="display: block; margin: auto;" /> --- @@ -278,9 +279,9 @@ Now, let's try some simple manipulations. First, assume we're only interested in data of the year 2016. -## Exercise 2: Subset the data <font size="5">( 1 min)</font> +## Exercise 2: Subset the data <font size="5">( 1 min)</font> -- Subset the data set, keeping only observations where variable `year` equals `2016`. +- Subset the dataframe, keeping only observations where variable `year` equals `2016`. ```r @@ -349,9 +350,9 @@ # Data in R -## Exercise 3: Create an object <font size="5">( 1 min)</font> +## Exercise 3: Create an object <font size="5">( 1 min)</font> -Create a new dataset, called `whr2016`, that is a subset of the `whr` data set containing only data from the year 2016. +Create a new dataframe, called `whr2016`, that is a subset of the `whr` dataframe containing only data from the year 2016. ```r @@ -361,7 +362,7 @@ # Display the 6 first obs. of the new data head(whr2016) -# Notice that we still have the original data set intact +# Notice that we still have the original dataframe intact head(whr) ``` @@ -369,19 +370,6 @@ # Data in R - -```r -whr2016 <- subset(whr, year == 2016) -head(whr2016) -head(whr) -``` - -<iframe src="https://rrmaximiliano.shinyapps.io/learnr-app/?showcase=0" width="100%" height="400px" data-external="1"></iframe> - ---- - -# Data in R - You can also see that your environment panel now has two `Data` objects: <img src="img/environment_2vars_2021.png" width="60%" style="display: block; margin: auto;" /> @@ -468,7 +456,7 @@ * Usually the first argument is the object you want to use the function on, e.g. `subset(whr, ...)` -* Functions usually return values that you can store in an object, print or use directly as an argument of another function. +* Functions usually return values that you can store in an object, print or use directly as an argument of another function. __They rarely modify an object in-place__ We will explore these ideas in depth in a later session. @@ -499,7 +487,7 @@ * An object is like a global or local in Stata, it's __something you can refer to later__ in your code to get a value -* But while you can only put a number or a string in a global, __you can put anything into an object__: scalars, strings, datasets, vectors, plots, functions, etc. +* But while you can only put a number or a string in a global, __you can put anything into an object__: scalars, strings, dataframes, vectors, plots, functions * Objects also have attributes that can be used to manipulate them @@ -513,7 +501,7 @@ * __Vectors:__ an uni-dimensional object that __stores a sequence of values of the same class__ -* __Data frames:__ a combination of different vectors of the same length (the same as your dataset in Stata) +* __Dataframes:__ a combination of different vectors of the same length (the same as a dataset in Stata) * __Lists:__ a multidimensional object that can store several objects __of different classes and dimensions__ @@ -562,52 +550,13 @@ --- -# R objects - Vectors - -## Vectors - -To R, each of the columns of the object `whr` is a vector. - -### Calling a vector from a `data.frame` column: - -We use the `$` character (operator) to extract vectors (variables) by their names in a `data.frame` - -For example: - - -```r -# Create a vector with the values of the "year" variable -year_vector <- whr$year - -# See the 3 first elements of the year column -whr$year[1:3] -``` - -``` -## [1] 2015 2015 2015 -``` - ---- - -# R objects - Vectors - - -```r -year_vector <- whr$year # creates a vector with the values of the "year" variable -whr$year[1:3] # see the 3 first elements of the year column -``` - -<iframe src="https://rrmaximiliano.shinyapps.io/learnr-app/?showcase=0" width="100%" height="400px" data-external="1"></iframe> - ---- - -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes -The `whr` and `whr2016` objects are both data frames. You can also construct a new data frame from scratch by __combining vectors with the same number of elements__. +The `whr` and `whr2016` objects are both dataframes. You can also construct a new dataframe from scratch by __combining vectors with the same number of elements__ with the command `data.frame()`. -#### Now, type the following code to create a new data frame +#### Now, type the following code to create a new dataframe ```r # Dataframe created by biding vectors @@ -626,7 +575,7 @@ --- -# R objects - Data frames +# R objects - Dataframes ```r @@ -638,11 +587,11 @@ --- -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes -Since a data frame has two dimensions, you can use indices for both. The first index indicates the row selection and the second indicates the column. +Since a dataframe has two dimensions, you can use indices for both. The first index indicates the row selection and the second indicates the column. ### Numeric indexing @@ -659,7 +608,7 @@ --- -# R objects - Data frames +# R objects - Dataframes ```r @@ -672,9 +621,9 @@ --- -# R objects - Data frames +# R objects - Dataframes -## Data frames +## Dataframes Alternatively, you can use the column names for indexing, which is the same as using the `$` sign. @@ -694,7 +643,7 @@ --- -# R objects - Data frames +# R objects - Dataframes ```r @@ -706,15 +655,54 @@ --- +# R objects - Dataframes + +## Vectors in dataframes + +To R, each of the columns of the object `whr` is a vector. + +### Calling a vector from a dataframe: + +We use the `$` character to extract vectors (variables) by their names in a dataframe + +For example: + + +```r +# Create a vector with the values of the "year" variable +year_vector <- whr$year + +# See the 3 first elements of the year column +whr$year[1:3] +``` + +``` +## [1] 2015 2015 2015 +``` + +--- + +# R objects - Dataframes + + +```r +year_vector <- whr$year # creates a vector with the values of the "year" variable +whr$year[1:3] # see the 3 first elements of the year column +``` + +<iframe src="https://rrmaximiliano.shinyapps.io/learnr-app/?showcase=0" width="100%" height="400px" data-external="1"></iframe> + +--- + # R objects - Lists ## Lists Lists are more complex objects that can contain many objects of __different classes and dimensions__. -The outputs of many functions, a regression for example, are similar to lists. +The outputs of many functions, a regression for example, are similar to lists (more on this in a later session). -It would be beyond the scope of this introduction to go deep into them, but here's a quick example: +Here's a quick example: ### Combine several objects of different types in a list @@ -788,7 +776,7 @@ Now we'll use string data to practice some basic object manipulations in R. -### Exercise 4: Create a vector of strings <font size="5">( 2 min)</font> +### Exercise 4: Create a vector of strings <font size="5">( 2 min)</font> Create a string vector containing the names of commonly used statistical software: ```r @@ -814,7 +802,7 @@ # Basic types of data - Strings -### Exercise 5: Concatenate strings <font size="5">( 3 min)</font> +### Exercise 5: Concatenate strings <font size="5">( 3 min)</font> 1. Create a scalar (a vector of one element) containing the phrase "can be an option to" and call it `str_scalar`. Your code will be similar to this: `str_scalar <- "can be an option to"` @@ -932,7 +920,7 @@ ## Booleans -### Exercise 6 <font size="5">( 3 min)</font> +### Exercise 6 <font size="5">( 3 min)</font> Create a boolean vector with the condition of annual income below average: @@ -1019,9 +1007,9 @@ ## Booleans -Now let's use the boolean vector `inc_below_avg` to add a dummy variable in the `whr` data set for the same condition. +Now let's use the boolean vector `inc_below_avg` to add a dummy variable in the `whr` dataframe for the same condition. -### Exercise 7 <font size="5">( 3 min)</font> +### Exercise 7 <font size="5">( 3 min)</font> * Create a column in `whr` containing zeros and call it `rank_low`. You can do this by typing: @@ -1141,26 +1129,26 @@ Here are some advantages of R: -* R is a free and open source software! +* R is a free and open source software, a huge advantage for open science -* It allows you to have several datasets open simultaneously. +* It allows you to have several dataframes open simultaneously + No need to use `keep`, `preserve`, `restore` -* It can run complex Geographic Information System (GIS) analyses. +* It can run complex Geographic Information System (GIS) analyses -* You can use it for web scrapping. +* You can use it for web scrapping and APIs -* You can run machine learning algorithms with it. +* You can easily run machine learning algorithms with it -* You can create complex Markdown documents. This presentation, for example, is entirely done in R. +* You can create complex Markdown documents. This presentation, for example, is entirely done in R -* You can create interactive dashboards and online applications with the Shiny package. +* You can create interactive dashboards and online applications with the Shiny package --- # Appendix - Syntax -R's syntax is a bit heavier than Stata's: +R's syntax is heavier than Stata's: * Parentheses to separate function names from its arguments. * Commas to separate arguments. diff --git a/Presentations/img/script1.png b/Presentations/img/script1.png new file mode 100644 index 0000000000000000000000000000000000000000..a67cf1a479cbb6b06e3dca88576b0e1c91663cc8 GIT binary patch literal 69668 zcmce;2~^VS*FS2fPFAMY@tn+@GArwtnwpvelG@Q6($Y@mNaaAz<%EDpWoo5q=9Cjg zre>))QqI&&|r*3Ds+m6@Nv8DrnjZAlpM=JSO-4oZ`y>ikih>M)_Y9te*P|U!~Ov5m!GqbFCO+u{hWxT|NaQ|@1*?AQTDE% zv->Ch`huo?ng}TNlgW@?1(s~~Vh{b%j+&}ZZ!W90!;(oCGmATOgJymWHenI?v{-EInBII(B-+!-h z3IL5m#c9~>f&^}u_M9)Xg}x0q<|g~^3Kx7jRoS>cfYhUO{8??qYKc;@e?NOQ_(=k= 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--panel-tabs-border-bottom: #ddd; + --panel-tabs-sideways-max-width: 25%; --panel-tab-foreground: currentColor; --panel-tab-background: unset; --panel-tab-active-foreground: currentColor; @@ -67,6 +68,8 @@ color: currentColor; text-decoration: none; border: none; + width: 100%; + height: 100%; } .panelset .panel-tabs .panel-tab > a:focus { @@ -110,3 +113,115 @@ .panelset .panel-active { display: block; } + +/* ---- Sideways Panelset ---- */ + +@media (min-width: 480px) { + .panelset.sideways { + display: flex; + flex-direction: row; + } + + .panelset.sideways .panel-tabs { + box-shadow: none; + flex-direction: column; + align-items: start; + margin: 0; + margin-right: 1em; + border-right: 2px solid var(--panel-tabs-border-bottom); + max-width: var(--panel-tabs-sideways-max-width); + } + + .panelset.sideways .panel { + max-width: calc(100% - var(--panel-tabs-sideways-max-width) - 1em); + } + + .panelset.sideways .panel-tabs .panel-tab { + border-top: unset; + border-bottom: unset; + padding-left: 0; + } + + .panelset.sideways.right { + flex-direction: row-reverse; + justify-content: space-between; + } + + .panelset.sideways.right { + text-align: inherit; + } + + .panelset.sideways.right .panel-tabs { + align-items: end; + margin-right: 0; + margin-left: 1em; + border-right: unset; + border-left: 2px solid var(--panel-tabs-border-bottom); + } + + .panelset.sideways.right .panel-tabs .panel-tab { + padding-left: 1em; + width: 100%; + } + + .panelset.sideways.right .panel-tabs .panel-tab a { + text-align: right; + } +} + +/* + This next part repeats the same CSS inside the @media query above but with + remarkjs-specific classes to ensure that sideways panelsets are always used. + In the future, we could use @container queries instead once they're availble. +*/ + +.remark-container .panelset.sideways { + display: flex; + flex-direction: row; +} + +.remark-container .panelset.sideways .panel-tabs { + box-shadow: none; + flex-direction: column; + align-items: start; + margin: 0; + margin-right: 1em; + border-right: 2px solid var(--panel-tabs-border-bottom); + max-width: var(--panel-tabs-sideways-max-width); +} + +.remark-container .panelset.sideways .panel { + max-width: calc(100% - var(--panel-tabs-sideways-max-width) - 1em); +} + +.remark-container .panelset.sideways .panel-tabs .panel-tab { + border-top: unset; + border-bottom: unset; + padding-left: 0; +} + +.remark-container .panelset.sideways.right { + flex-direction: row-reverse; + justify-content: space-between; +} + +.remark-container .panelset.sideways.right { + text-align: inherit; +} + +.remark-container .panelset.sideways.right .panel-tabs { + align-items: end; + margin-right: 0; + margin-left: 1em; + border-right: unset; + border-left: 2px solid var(--panel-tabs-border-bottom); +} + +.remark-container .panelset.sideways.right .panel-tabs .panel-tab { + padding-left: 1em; + width: 100%; +} + +.remark-container .panelset.sideways.right .panel-tabs .panel-tab a { + text-align: right; +} diff --git a/Presentations/libs/panelset/panelset.js b/Presentations/libs/panelset/panelset.js index 80e773c..730d54d 100644 --- a/Presentations/libs/panelset/panelset.js +++ b/Presentations/libs/panelset/panelset.js @@ -235,6 +235,7 @@ const contents = panels.map(processPanelItem).filter(o => o !== null) const newPanelSet = reflowPanelSet(contents, idx) + newPanelSet.classList = panelset.classList panelset.parentNode.insertBefore(newPanelSet, panelset) panelset.parentNode.removeChild(panelset) From 81ef8652b8f3bcd6f08e1a2c08c67346b2b5c58c Mon Sep 17 00:00:00 2001 From: luisesanmartin Date: Tue, 14 Mar 2023 19:59:47 -0400 Subject: [PATCH 2/7] uploaded main script example, for reference only we take captures from it in session 1 --- DataWork/Code/main.R | 90 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 DataWork/Code/main.R diff --git a/DataWork/Code/main.R b/DataWork/Code/main.R new file mode 100644 index 0000000..5efcd70 --- /dev/null +++ b/DataWork/Code/main.R @@ -0,0 +1,90 @@ +# ------------------------------------------------------------------------------ # +# # +# DIME # +# Introduction to R for Stata users # +# MAIN SCRIPT # +# # +# ------------------------------------------------------------------------------ # + +# PURPOSE: Set-up configurations and run scripts + +# NOTES: Version 2 + +# WRITTEN BY: Luiza Cardoso de Andrade, Leonardo Viotti + +# Last modified in Mar 2023 + +# PART 1: Select sections to run ---------------------------------------------- + +Lab2 <- 0 +Lab3 <- 0 +Lab4 <- 0 +Lab5 <- 0 +Lab6 <- 0 + +# PART 2: Load packages ----------------------------------------------------- + +packages <- c("readstata13","foreign", + "doBy", "broom", "dplyr", + "stargazer", + "ggplot2", "plotly", "ggrepel", + "RColorBrewer", "wesanderson", + "sp", "rgdal", "rgeos", "raster", "velox", + "ggmap", "rasterVis", "leaflet", + "htmlwidgets", "geosphere") + +# If you selected the option to install packages, install them +sapply(packages, function(x) { + if (!(x %in% installed.packages())) { + install.packages(x, dependencies = TRUE) + } +} +) + +# Load all packages -- this is equivalent to using library(package) for each +# package listed before +invisible(sapply(packages, library, character.only = TRUE)) + +# PART 3: Set folder folder paths -------------------------------------------- + +#-------------# +# Root folder # +#-------------# + +# Add your username and folder path here (for Windows computers) +# To find out what your username is, type Sys.getenv("USERNAME") +if (Sys.getenv("USERNAME") == "luiza") { + + projectFolder <- "C:/Users/luiza/Documents/GitHub/dime-r-training" + +} + +# If you're using Mac, just add your folder path, without the if statement + +#--------------------# +# Project subfolders # +#--------------------# + +rawData <- file.path(projectFolder, "Data", "Raw") +finalData <- file.path(projectFolder, "Data", "Final") +Code <- file.path(projectFolder ,"Codes") +Output <- file.path(projectFolder, "Output") + + +# PART 4: Run selected sections ----------------------------------------------- + +if (Lab2 == 1) { + source(file.path(Code, "Lab 2 - Coding for Reproducible Research")) +} +if (Lab3 == 1) { + source(file.path(Code, "Lab 3 - Data Processing")) +} +if (Lab4 == 1) { + source(file.path(Code, "Lab 4 - Descriptive Analysis")) +} +if (Lab5 == 1) { + source(file.path(Code, "Lab 5 - Data Visualization")) +} +if (Lab6 == 1) { + source(file.path(Code, "Lab 6 - Spatial Data")) +} \ No newline at end of file From 8db6ad46ee3aaa65312c6a9e2cb696199760da52 Mon Sep 17 00:00:00 2001 From: luisesanmartin Date: Sun, 19 Mar 2023 16:52:15 -0400 Subject: [PATCH 3/7] session 2 edits - moved download of datawork folder to ex2 - replaced mentions of tidyverse for dplyr and purrr - moved document outline and data exploration to annex - changed toy dataset in map vs loop exercises to 50k cols --- Presentations/02-intro-to-R-programming.Rmd | 422 +++++++--------- Presentations/02-intro-to-R-programming.html | 458 ++++++++---------- .../figure-html/unnamed-chunk-54-1.png | Bin 0 -> 45830 bytes .../figure-html/unnamed-chunk-63-1.png | Bin 0 -> 45830 bytes .../figure-html/unnamed-chunk-64-1.png | Bin 0 -> 45830 bytes Presentations/img/Warning.png | Bin 8058 -> 22838 bytes Presentations/img/Warning_tidyverse.png | Bin 28979 -> 0 bytes .../img/dime-r-training-project-dir.png | Bin 0 -> 25831 bytes Presentations/img/dime-r-training-project.png | Bin 20864 -> 18312 bytes Presentations/img/empty_environment.png | Bin 15275 -> 16339 bytes Presentations/img/loop-side-effects.png | Bin 29675 -> 29401 bytes Presentations/img/rhistory.png | Bin 34837 -> 27123 bytes Presentations/img/warning_2021.png | Bin 35845 -> 0 bytes placeholder.md | 0 14 files changed, 356 insertions(+), 524 deletions(-) create mode 100644 Presentations/02-intro-to-R-programming_files/figure-html/unnamed-chunk-54-1.png create mode 100644 Presentations/02-intro-to-R-programming_files/figure-html/unnamed-chunk-63-1.png create mode 100644 Presentations/02-intro-to-R-programming_files/figure-html/unnamed-chunk-64-1.png delete mode 100644 Presentations/img/Warning_tidyverse.png create mode 100644 Presentations/img/dime-r-training-project-dir.png delete mode 100644 Presentations/img/warning_2021.png delete mode 100644 placeholder.md diff --git a/Presentations/02-intro-to-R-programming.Rmd b/Presentations/02-intro-to-R-programming.Rmd index 40800a1..aadc8c3 100644 --- a/Presentations/02-intro-to-R-programming.Rmd +++ b/Presentations/02-intro-to-R-programming.Rmd @@ -1,8 +1,8 @@ --- title: "Session 2: Introduction to R Programming" subtitle: "R for Stata Users" -author: "Luiza Andrade, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti" -date: "The World Bank | [WB Github](https://github.com/worldbank)
May 2022" +author: "Luiza Andrade, Marc-Andrea Fiorina, Rob Marty, Rony Rodriguez-Ramirez, Luis Eduardo San Martin, Leonardo Viotti" +date: "The World Bank | [WB Github](https://github.com/worldbank)
March 2023" output: xaringan::moon_reader: css: ["libs/remark-css/default.css", @@ -74,16 +74,13 @@ whr <- read_csv(here("DataWork", "DataSets", "Final", "whr_panel.csv")) # Table of contents 1. [Introduction](#introduction) -2. [Initial settings](#initial-settings) -3. [File paths](#file-paths) -4. [Exploring a dataset](#exploring-a-dataset) -5. [Creating a document outline in RStudio](#creating-a-document-outline-in-rstudio) -6. [Using packages](#using-packages) -7. [Functions inception](#functions-inception) -8. [Mapping and iterations](#mapping-and-iterations) -9. [Custom functions](#custom-functions) -10. [Indentation](#indentation) -11. [Appendix](#appendix) +1. [Initial settings](#initial-settings) +1. [File paths](#file-paths) +1. [Using packages](#using-packages) +1. [Functions inception](#functions-inception) +1. [Mapping and iterations](#mapping-and-iterations) +1. [Custom functions](#custom-functions) +1. [Appendix](#appendix) --- @@ -133,7 +130,7 @@ name: initial-settings * Notice two things: - 1. Your environment is *probably* empty (it's OK if it's not) + 1. Your environment is *probably* empty (it's okay if it's not) ```{r echo = FALSE, out.width = "60%"} knitr::include_graphics("img/empty_environment.png") @@ -263,39 +260,53 @@ setwd("your/path") * Instead, you should use RStudio projects and the `here` library -> __Important:__ We won't get into the specifics of directory organization here, but we'll assume that all the files you use for a specific project (data, scripts, and outputs) reside in the same project directory. We'll call this the __working directory__. - * RStudio projects let you "bind" your project files to a root directory, regardless of the path to it * This is crucial because it allows smooth interoperability between different computers where the exact path to the project root directory differs * Additionally, each RStudio project you work on keeps their own history of commands! +__Important:__ We won't get into the specifics of directory organization here, but we'll assume that all the files you use for a specific project (data, scripts, and outputs) reside in the same project directory. We'll call this the __working directory__. + --- # RStudio projects .exercise[ -### Exercise 2 `r fa("keyboard")` (`r fa("clock")` 1 min) +### Exercise 2 `r fa("keyboard")` (`r fa("clock")` 3 min) + +1. Create a folder named `dime-r-training-mar2023` in your preferred location in your computer + +1. Go to the [OSF page of the course](https://osf.io/86g3b/) and download the file in: `R for Stata Users - 2023 March` > `Data` > `DataWork.zip` + +1. Unzip `DataWork.zip` in the folder `dime-r-training-mar2023` 1. On RStudio, select `File` > `New Project...` -2. Select `New Directory` > `New Project` +2. Select `Existing Directory` -3. Assign the name: `dime-r-training-project` to the project +3. Browse to the location of `dime-r-training-mar2023` and select `Create Project` ] --- # RStudio projects -```{r echo = FALSE, out.width = "80%"} +```{r echo = FALSE, out.width = "60%"} knitr::include_graphics("img/dime-r-training-project.png") ``` --- +# RStudio projects + +```{r echo = FALSE, out.width = "60%"} +knitr::include_graphics("img/dime-r-training-project-dir.png") +``` + +--- + # The `here` library * `here` locates files relative to your project root @@ -308,7 +319,7 @@ knitr::include_graphics("img/dime-r-training-project.png") # Usage of `here` -- Install and load the `here` library: +- Install and load `here` ```{r, eval=FALSE} install.packages("here") @@ -337,13 +348,9 @@ df <- read.csv(path) ### Exercise 3 `r fa("keyboard")` (`r fa("clock")` 3 min) -1. Go to the [OSF page of the course](https://osf.io/86g3b/) and download the file in: `R for Stata Users - 2022 May` > `Data` > `DataWork.zip` - -2. Unzip the file in your RStudio project root folder. This is the folder where the file `dime-r-training-project.Rproj` sits - -3. On RStudio, go to `File` > `New File` > `R Script` and save this new empty script in `DataWork` > `Code` as `exercises-session2.R` +1. Go to `File` > `New File` > `R Script` and save this empty script in `DataWork` > `Code` as `exercises-session2.R` -5. Now let's test if that worked. Load the `here` library and read the `csv` file `DataWork/DataSets/Final/whr_panel.csv` using `here()` +1. Load `here` and read the `.csv` file in `DataWork/DataSets/Final/whr_panel.csv` using `here()` + Use the function `read.csv()` to load the file. The argument for `read.csv()` is the result of `here()` + Remember to assign the dataframe you're reading to an object. You can call it `whr` as we did yesterday @@ -363,7 +370,7 @@ whr <- read.csv(here("DataWork", "DataSets", "Final", "whr_panel.csv")) # RStudio projects and `here` -If you did the exercise correctly, you should see the `whr` data frame listed in the Environment panel +If you did the exercise correctly, you should see the `whr` dataframe listed in the Environment panel ```{r echo = FALSE, out.width = "80%"} knitr::include_graphics("img/environment_2021.png") @@ -371,151 +378,6 @@ knitr::include_graphics("img/environment_2021.png") --- -class: inverse, center, middle -name: creating-a-document-outline-in-rstudio - -# Creating a document outline in RStudio - -


- ---- - -# Document outline - -* RStudio allows you to __create an interactive index__ for your scripts - -* To add a section to your code, create a commented line with the title of your section and add at least 4 trailing dashes (`----`), pound signs (`####`) or equal signs (`====`) after it - ---- - -# Document outline - -.exercise[ - -### Exercise 4 `r fa("keyboard")` (`r fa("clock")` 1 min) - -1. In your script, add a header before the line where you used `library(here)` with the text: `# Part 1: Loading libraries----` - -2. Before `read.csv(...)`, add the following header: `Part 2: Loading data----` - - + Remember: you create a section header by adding at least 4 trailing dashes (`-`), pound (`#`) or equal (`=`) signs in a comment line - -3. Note that once you create a section header, an arrow appears right next to the row number. Click on the arrows to see what happens. -] - ---- - -# Document outline - -* The outline can be accessed by clicking on the button on the top right corner of the script window. You can use it to jump from one section to another - -* You can also use the keyboard shortcuts `Alt + L` (`Cmd + Option + L` on Mac) and `Alt + Shift + L` to collapse and expand sections - -```{r echo = FALSE, out.width = "65%"} -knitr::include_graphics("img/document-outline.png") -``` - ---- - -class: inverse, center, middle -name: exploring-a-dataset - -# Exploring a dataset - -

- ---- - -# Exploring a dataset - -Some useful functions: - -* **`View()`:** opens the data set - -* **`class()`:** reports object type or type of data stored - -* **`dim()`:** reports the size of each one of an object's dimension - -* **`names()`:** returns the variable names of a data set - -* **`str()`:** general information about the structure of an R object - -* **`summary()`:** summary information about the variables in a data frame - -* **`head()`:** shows the first few observations in the dataset - -* **`tail()`:** shows the last few observations in the dataset - ---- - -# Exploring a dataset - -```{r, eval = F} -# View the data set (notice the uppercase "V") -View(whr) -``` - -This is the same as clicking on the object name in the environment panel. It opens a spreadsheet-style data viewer of a dataframe. - -```{r echo = FALSE, out.width = "80%"} -knitr::include_graphics("img/whr_viewer.png") -``` - ---- - -# Exploring a dataset - -```{r, eval = F} -# Object type and dimensions -class(whr) -dim(whr) -``` - -```{r echo=FALSE} -knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") -``` - ---- - -# Exploring a dataset - -```{r, eval = F} -# Object structure -str(whr) -``` - -```{r echo=FALSE} -knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") -``` - ---- - -# Exploring a dataset - -```{r, eval = F} -# Summarize a dataframe -summary(whr) -``` - -```{r echo=FALSE} -knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") -``` - ---- - -# Exploring a dataset - -```{r, eval = F} -# Printing the first rows of a dataframe -head(whr) -``` - -```{r echo=FALSE} -knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") -``` - ---- - class: inverse, center, middle name: using-packages @@ -527,21 +389,21 @@ name: using-packages # Packages -* Since there is a lot of people developing for R, it can have many different functionalities. +* Since there is a lot of people developing for R, it can have many different functionalities -* To make it simpler, these functionalities are bundled into packages. +* To make it simpler, these functionalities are bundled into packages -* A package is just __a unit of shareable code__. +* A package is just __a unit of shareable code__ --- # Packages -* It may contain new functions, but also more complex functionalities, such as a Graphic User Interface (GUI) or settings for parallel processing (similar to Stata MP). +* It may contain new functions, but also more complex functionalities, such as a Graphic User Interface (GUI) or settings for parallel processing (similar to Stata MP) -* They can be shared through R's official repository - CRAN (18,000+ packages reviewed and tested). +* They are usually shared through R's official repository - CRAN (19,000+ packages reviewed and tested) -* There are many other online sources such as GitHub, but it's important to be careful, as these probably haven't gone through a review process as rigorous as those in CRAN. +* There are many other online sources such as GitHub, but it's important to be careful, as these probably haven't gone through a review process as rigorous as those in CRAN --- @@ -551,7 +413,7 @@ name: using-packages ```{r, eval = F} # Installing a package -install.packages("tidyverse", +install.packages("dplyr", dependencies = TRUE) # the dependencies argument also installs all other packages # that it may depend upon to run @@ -565,10 +427,10 @@ install.packages("tidyverse", .exercise[ -### Exercise 5 `r fa("keyboard")` (`r fa("clock")` 1 min) +### Exercise 4 `r fa("keyboard")` (`r fa("clock")` 1 min) -1. Load the `tidyverse` meta package in part 1 of your script using `library(tidyverse)` -2. Run your script +1. Load the packages `dplyr` and `purrr` in part 1 of your script using `library(dplyr)` and `library(purrr)` +1. Run your script ] @@ -579,17 +441,17 @@ install.packages("tidyverse", What if this happens? ```{r echo = FALSE, out.width = "70%"} -knitr::include_graphics("img/warning_2021.png") +knitr::include_graphics("img/Warning.png") ``` --- # Warnings vs errors -R has two types of error messages, `warnings` and actual `errors`: +R has two types of error messages, warnings and actual errors: - * `Errors` - break your code, usually preventing it from running. - * `Warnings` - usually mean that nothing went wrong yet, but you should be careful. + * **Errors** - break your code, usually preventing it from running + * **Warnings** - your code kept running, but R wants you to be aware of something that might be a problem later RStudio's default is to print warning messages, but not to stop the code at the lines where they occur. You can configure R to stop at warnings if you want. @@ -653,7 +515,7 @@ knitr::include_graphics("img/statalog.png") * Metaprogramming is a __very powerful technique__, as you will soon see -* It's __also a common source of error__, as you can only use one function inside the other if the output of the inner function is the same as the input of the outer function +* It's __also a common source of error__, as you can only use one function inside the other if the output of the inner function can be taken as the input of the outer function * It can also get quite tricky to follow what a line of code with multiple functions inceptions is doing @@ -718,7 +580,7 @@ name: mapping-and-iterations * R, however, has a set of functions that allows users to loop through an object __in a more efficient way__, without using explicit loops -* In this training we'll introduce `map()`. It is a function part of the tidyverse meta package +* In this training we'll introduce `map()`. It is a function part of `purrr`, a package that contains tools for functional programming * Also, in case you have not noticed yet: __R is vectorized!__ this means that many operations are applied element-wise by default so you don't have to code loops to apply them to each element of a vector or dataframe @@ -726,15 +588,15 @@ name: mapping-and-iterations # Map -* To use `map()`, you need to either load the package `purrr` eithre by itself or alongside other `tidyverse` packages +* To use `map()`, you need to load the package `purrr` * The basic syntax of `map()` is: .command[ -**`map(X, function, ...)`:** applies `function` to each of the elements of `X`. If `X` is a data frame then `function` is applied column-wise while if it's a vector or a list it is applied item-wise. The output of `map()` is always a list with the results. +**`map(X, function, ...)`:** applies `function` to each of the elements of `X`. If `X` is a dataframe then `function` is applied column-wise while if it's a vector or a list it is applied item-wise. The output of `map()` is always a list with the results. - + **X:** a data frame, matrix or vector the function will be applied to + + **X:** a dataframe, matrix or vector the function will be applied to + **function:** the name of the function you want to apply to each of the elements of `X` ] @@ -783,12 +645,12 @@ knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") .exercise[ -### Exercise 6: Looping over a dataframe (`r fa("clock")` 3 min) +### Exercise 5: Looping over a dataframe (`r fa("clock")` 3 min) -- Create a toy dataframe of 70,000 columns and 400 observations using this code +- Create a toy dataframe of 50,000 columns and 400 observations using this code ```{r, eval = FALSE} -df <- data.frame(replicate(70000, sample(1:100, 400, replace=TRUE))) +df <- data.frame(replicate(50000, sample(1:100, 400, replace=TRUE))) ``` - Create an empty vector named `col_means_loop` where you will store column means with this code: `col_means_loop <- c()` @@ -814,12 +676,12 @@ for (column in df) { The solution is this: ```{r, eval=FALSE} -df <- data.frame(replicate(70000, sample(1:100, 400, replace=TRUE))) +df <- data.frame(replicate(50000, sample(1:100, 400, replace=TRUE))) col_means_loop <- c() -for (col in df){ - col_means_loop <- append(col_means_loop, mean(col)) +for (column in df){ + col_means_loop <- append(col_means_loop, mean(column)) } ``` @@ -829,16 +691,17 @@ for (col in df){ .exercise[ -### Exercise 7: Now use `map()` `r fa("keyboard")` (`r fa("clock")` 1 min) +### Exercise 6: Now use `map()` `r fa("keyboard")` (`r fa("clock")` 1 min) -- Use `map()` to produce a list with the means of the columns of `df` -- Store the result in a list named `col_means_map` +1. Use `map()` to produce a list with the means of the columns of `df` +1. Store the result in a list named `col_means_map` -> Hint: - + Remember the syntax of `map()`: `map(X, function_name)` - ] +Hints: + + Remember the syntax of `map()`: `map(X, function_name)` + + The function name inside `map()` shouldn't have parentheses next to it (i.e.: `mean` instead of `mean()`) + --- # Map vs looping @@ -863,7 +726,7 @@ Do you remember which one ran faster? # Map vs looping -Last but not least, remember we said that loops produce side effects? +Also, remember we said that loops produce side effects? ```{r echo = FALSE, out.width = "50%"} knitr::include_graphics("img/loop-side-effects.png") @@ -921,7 +784,7 @@ knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") .exercise[ -### Exercise 8 `r fa("keyboard")` (`r fa("clock")` 2 min) +### Exercise 7 `r fa("keyboard")` (`r fa("clock")` 2 min) Create a function named `zscore` that standardizes the values of a vector. @@ -973,16 +836,15 @@ knitr::include_app("https://luizaandrade.shinyapps.io/learnr/") 1. Subselect the columns `health_life_expectancy` and `freedom` in `whr` - + Use tidyverse's `select()` for this, as in: `whr %>% select(freedom, happiness_score)` + + Use dplyr's `select()` for this, as in: `whr %>% select(freedom, happiness_score)` 2. Use `map()` combined with the `zscore` function to get the z-score of these two columns and assign the resulting list to an object named `z_scores` 3. Use list indexing on `z_scores` to generate two new columns in `whr` with the standardized values of `health_life_expectancy` and `freedom` -> **Hints:** -* Use the pipes (`%>%`) successively -* Remember that we don't use parenthesis next to the function name we're using `map()` with -* Remember that we use double brackets instead of single brackets to index the actual elements of a list + **Hints:** +* Don't use parenthesis next to the function name we're using `map()` with +* Use double brackets instead of single brackets or the symbol `$` to index the elements of a list ] @@ -1023,11 +885,11 @@ name: appendix * `.RData` stores the objects in your environment only if you save your workspace, and loads them again in the next RStudio session -* Both files are relative to the working directory where your RStudio session started +* Both files are stored in the working directory where your RStudio session started --- -# Appendix - Using packages +# Appendix - More on packages Once a package is loaded, you can use its features and functions. Here's a list of some useful packages: @@ -1037,21 +899,13 @@ Once a package is loaded, you can use its features and functions. Here's a list * `stargazer` - awesome latex regression and summary statistics tables * `foreign` - reads `.dta` and other formats from inferior statistical software * `zoo` - time series and panel data manipulation useful functions -* `data.table` - some functions to deal with huge data sets +* `data.table` - some functions to deal with huge dataframes * `sp` and `rgeos` - spatial analysis * `multiwayvcov` and `sandwich` - clustered and robust standard errors * `RODBC`, `RMySQL`, `RPostgresSQL`, `RSQLite` - For relational databases and using SQL in R. --- -# Appendix - Resources - -* A discussion of folder structure and data managament can be found here: https://dimewiki.worldbank.org/wiki/DataWork_Folder - -* For a broader discussion of data management, go to https://dimewiki.worldbank.org/wiki/Data_Management - ---- - # Appendix - Git Git is a version-control system for tracking changes in code and other text files. It is a great resource to include in your work flow. @@ -1071,41 +925,11 @@ https://r4ds.had.co.nz/workflow-projects.html --- -# Appendix - Commenting - -* To comment a line, write `#` as its first character - -```{r, eval=FALSE} -# This is a comment -print("But this part is not") -``` - -* You can also add `#` halfway through a line to comment whatever comes after it - -```{r, eval=FALSE} -print("This part is not a comment") # And this is a comment -``` - -* In Stata, you can use `/*` and `*/` to comment in the middle of a line's code. That is not possible in R: everything that comes after `#` will always be a comment +# Appendix - More on folder management -* To comment a selection of lines, press `Ctrl` + `Shift` + `C` - ---- - -# Appendix - Assignment 1 - -.exercise[ - -### Exercise `r fa("keyboard")` - -1. In your script panel, select all the lines of your script - -2. Use the keyboard shortcut to comment these lines. - + Shortcut: `Ctrl` + `Shift` + `C` - -3. Use the keyboard shortcut to comment these lines again. What happened? +* A discussion of folder structure and data managament can be found here: https://dimewiki.worldbank.org/wiki/DataWork_Folder -] +* For a broader discussion of data management, go to https://dimewiki.worldbank.org/wiki/Data_Management --- @@ -1162,7 +986,7 @@ for (col in colnames(whr)) { # Appendix - Apply -* Apart from tidyverse's `map()`, base R also has a set of functions that allows users to apply a function to a number of objects without using explicit loops +* Apart from purrr's `map()`, base R also has a set of functions that allows users to apply a function to a number of objects without using explicit loops * They're called `apply` and there are many of them, with different use cases @@ -1182,10 +1006,10 @@ sapply(X, FUN, ...) * Its main arguments are: - + **X:** a data frame, matrix or vector the function will be applied to + + **X:** a dataframe, matrix or vector the function will be applied to + **FUN:** the function you want to apply -* `sapply()` applies the function (`FUN`) to all the elements of `X`. If `X` is a data frame then the function is applied column-wise, while if it's a vector or a list it is applied item-wise +* `sapply()` applies the function (`FUN`) to all the elements of `X`. If `X` is a dataframe then the function is applied column-wise, while if it's a vector or a list it is applied item-wise * The output of `sapply()` is usually a vector with the results, but it can be a matrix if the results have more than one dimension @@ -1228,7 +1052,7 @@ apply(X, MARGIN, FUN, ...) * Arguments: - + **X:** a data frame (or matrix) the function will be applied to + + **X:** a dataframe (or matrix) the function will be applied to + **MARGIN:** 1 to apply the function to all rows or 2 to apply the function to all columns + **FUN:** the function you want to apply @@ -1246,7 +1070,7 @@ apply(matrix, 2, mean) # column means --- -# Appendix - Assignment 2 +# Appendix - Assignment 1 ### Exercise: Get the row max @@ -1273,6 +1097,64 @@ whr %>% --- +# Appendix - Commenting + +* To comment a line, write `#` as its first character + +```{r, eval=FALSE} +# This is a comment +print("But this part is not") +``` + +* You can also add `#` halfway through a line to comment whatever comes after it + +```{r, eval=FALSE} +print("This part is not a comment") # And this is a comment +``` + +* In Stata, you can use `/*` and `*/` to comment in the middle of a line's code. That is not possible in R: everything that comes after `#` will always be a comment + +* To comment a selection of lines, press `Ctrl` + `Shift` + `C` + +--- + +# Appendix - Assignment 2 + +.exercise[ + +### Exercise `r fa("keyboard")` + +1. In your script panel, select all the lines of your script + +2. Use the keyboard shortcut to comment these lines. + + Shortcut: `Ctrl` + `Shift` + `C` + +3. Use the keyboard shortcut to comment these lines again. What happened? + +] + +--- + +# Appendix - Document outline + +* RStudio allows you to __create an interactive index__ for your scripts + +* To add a section to your code, create a commented line with the title of your section and add at least 4 trailing dashes (`----`), pound signs (`####`) or equal signs (`====`) after it + +--- + +# Appendix - Document outline + +* The outline can be accessed by clicking on the button on the top right corner of the script window. You can use it to jump from one section to another + +* You can also use the keyboard shortcuts `Alt + L` (`Cmd + Option + L` on Mac) and `Alt + Shift + L` to collapse and expand sections + +```{r echo = FALSE, out.width = "65%"} +knitr::include_graphics("img/document-outline.png") +``` + +--- + # Appendix - Indentation ```{r, eval = F} @@ -1376,6 +1258,28 @@ map(c(1.2, round) ``` +--- + +# Appendix - Exploring a dataframe + +Some useful functions: + +* **`View()`:** opens a visualization of the dataframe + +* **`class()`:** reports object type or type of data stored + +* **`dim()`:** reports the size of each one of an object's dimension + +* **`names()`:** returns the variable names of a dataframe + +* **`str()`:** general information about the structure of an R object + +* **`summary()`:** summary information about the variables in a dataframe + +* **`head()`:** shows the first few observations in the dataframe + +* **`tail()`:** shows the last few observations in the dataframe + --- exclude: true diff --git a/Presentations/02-intro-to-R-programming.html b/Presentations/02-intro-to-R-programming.html index 65402f9..273e95d 100644 --- a/Presentations/02-intro-to-R-programming.html +++ b/Presentations/02-intro-to-R-programming.html @@ -3,7 +3,7 @@ Session 2: Introduction to R Programming - + @@ -25,10 +25,18 @@