Session 2: Introduction to R Programming

.title[
# Session 2: Introduction to R Programming
]
.subtitle[
## R for Stata Users
]
.author[
### DIME Analytics
]
.date[
### The World Bank | <a href="https://github.com/worldbank">WB Github</a> <br> March 2024
]

---

</style>

# Table of contents

1. [Introduction](#introduction)
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)

---

# Introduction

---

# What this session is about

* In the first session, you learned how to work with R

* You are probably eager to start programming in R by now

* But before you start, we recommend learning how to write R code that will be __reproducible, efficient, intelligible and easy to navigate__

* Indeed, that's what this session is about!

---

# What this session is about

* We will cover common coding practices in R so that you can make __the most efficient use__ for it

* We will also discuss some styling conventions to make your code __readable and reproducible__

* This will give you a solid foundation to write code in R and hopefully you'll be able to skip some painful steps of the "getting-your-hands-dirty" learning approach

---

# Initial settings

---

# Initial settings

* Let's start by opening RStudio or by closing and opening it again

* Notice two things:

1. Your environment is *probably* empty (it's OK if it's not)

---

# Initial settings

* Let's start by opening RStudio or by closing and opening it again

* Notice two things:

---

# Initial settings

* Let's start by opening RStudio or by closing and opening it again

* Notice two things:

1. Your environment is *probably* empty (it's OK if it's not)
  1. Go to the `Console` panel and use the up and down keys to navigate through previously executed commands. They are saved by default in a file named `.Rhistory` that you might have noticed
  
* We usually want these two things —an __empty environment__ and the __history of commands__ executed in previous sessions— to be present every time we open a new RStudio session

---

# Initial settings

Have you ever seen these lines of code before?

---

# Initial settings

Have you ever seen these lines of code before?

* We __don't need to set the memory or the maximum number of variables__ in R

* The equivalent of `set more off` is the default

* The equivalent of `clear` is not always default setting, but we'll make sure it is set in exercise 1

---

# Initial settings

### Exercise 1 <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16V384c0 8.8 7.2 16 16 16H512c8.8 0 16-7.2 16-16V128c0-8.8-7.2-16-16-16H64zM0 128C0 92.7 28.7 64 64 64H512c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128zM176 320H400c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm-72-72c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16z"/></svg> <font size="5">(<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> 1 min)</font>

After this, you'll never have to use  the equivalent of `clear all`

1. Go to `Tools` > `Global Options...`

2. In the `General` tab, make sure the following options are set:

+ Un-check *Restore .RData into workspace at startup*
  + For *Save workspace to .RData on exit*, select *Never*
  + Make sure *Always save history (even when not saving .RData)* is checked
  
3. Now restart RStudio
]

---

# Initial settings

---

# Initial settings

---

# File paths

---

# File paths

* What about working directories? We usually do something like this every time we start a new script in Stata:

* The direct equivalent to `cd` in R is this command:

```r
setwd("your/path")
```

* However, we recommend not using it unless it's absolutely necessary (never, if possible)

---

# RStudio projects

* Instead, you should use RStudio projects and the `here` library

* 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 2 <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16V384c0 8.8 7.2 16 16 16H512c8.8 0 16-7.2 16-16V128c0-8.8-7.2-16-16-16H64zM0 128C0 92.7 28.7 64 64 64H512c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128zM176 320H400c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm-72-72c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16z"/></svg> <font size="5">(<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> 3 min)</font>

1. Create a folder named `dime-r-training-202403` in your preferred location in your computer

1. Go to https://osf.io/86g3b/ and download the file in: `R for Stata Users - 2024 March` > `Data` > `DataWork.zip`

1. Unzip `DataWork.zip` in your folder `dime-r-training-202403`

1. On RStudio, select `File` > `New Project...`

1. Select `Existing Directory`

1. Browse to the location of `dime-r-training-202403` and select `Create Project`
]

---

# RStudio projects

---

# RStudio projects

---

# The `here` library

* `here` locates files relative to your project root

* It uses the root project directory to build paths to files easily

* It allows for interoperability between different computers where the absolute path to the same file is not the same

---

# Usage of `here`

- Load `here`

```r
install.packages("here") # install first if you don't have it
library(here)
```

- Now you'll be able to use `here()` to point the location of every file relative to your project root

+ For example, to load a `csv` file located in: `C:/WBG/project-root/data/raw/data-file.csv`, you should use:

```r
path <- here("data", "raw", "data-file.csv")
df   <- read.csv(path)
```

* __Notes:__

+ Your project root is the directory that contains the `.Rproj` file
  + The result of `here()` is an absolute path that points to a file or folder location in your computer

---

# File paths

### Exercise 3 <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16V384c0 8.8 7.2 16 16 16H512c8.8 0 16-7.2 16-16V128c0-8.8-7.2-16-16-16H64zM0 128C0 92.7 28.7 64 64 64H512c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128zM176 320H400c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm-72-72c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16z"/></svg> <font size="5">(<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> 3 min)</font>

1. Go to `File` > `New File` > `R Script` to open a new script

1. In the new script, 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

]

```r
library(here)
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` dataframe listed in the Environment panel

---

# Using packages

---

# Packages

- Installing R in your computer gives you access to its basic functions

- Additionally, you can also install packages. Packages are code with additional R functions that allow you to do:

+ Operations that basic R functions don't do (example: work with geographic data)
  
  + Operations that basic R functions do, but easier (example: data wrangling)

---

# Packages

In a nutshell:

---

# Packages

* You can install packages with the command `install.packages()`. packages are also commonly called "libraries" in R programming

```r
# Installing a package
install.packages("dplyr")
```

* You only have to install a package once, but you have to __load them every new session__ with `library()`

```r
# Installing a package
library(dplyr)
```

---

# Packages

- Package installation:

- Package loading:

---

# Using packages

### Exercise 4 <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 112c-8.8 0-16 7.2-16 16V384c0 8.8 7.2 16 16 16H512c8.8 0 16-7.2 16-16V128c0-8.8-7.2-16-16-16H64zM0 128C0 92.7 28.7 64 64 64H512c35.3 0 64 28.7 64 64V384c0 35.3-28.7 64-64 64H64c-35.3 0-64-28.7-64-64V128zM176 320H400c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm-72-72c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H120c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H200c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H280c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H360c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16zm64 96c0-8.8 7.2-16 16-16h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V248zm16-96h16c8.8 0 16 7.2 16 16v16c0 8.8-7.2 16-16 16H440c-8.8 0-16-7.2-16-16V168c0-8.8 7.2-16 16-16z"/></svg> <font size="5">(<svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 1 48 256a208 208 0 1 1 416 0zM0 256a256 256 0 1 0 512 0A256 256 0 1 0 0 256zM232 120V256c0 8 4 15.5 10.7 20l96 64c11 7.4 25.9 4.4 33.3-6.7s4.4-25.9-6.7-33.3L280 243.2V120c0-13.3-10.7-24-24-24s-24 10.7-24 24z"/></svg> 1 min)</font>

1. Load the packages `dplyr` and `purrr` using `library(dplyr)` and `library(purrr)`

]

__Note:__ There is no need to install `dplyr` and `purrr` as they are part of the meta-library `tidyverse`, which we asked to install before this course. If you didn't have the chance to install `tidyverse`, then install `dplyr` and `purrr` with:

```r
install.packages("dplyr")
install.packages("purrr")
```

---

# Warnings vs errors

What if this happens?

---

# Warnings vs errors

R has two types of error messages, warnings and actual errors:

* **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 prints warning messages but it doesn't stop the code excution if warnings occur.

---

# Functions inception

---