Then click on the downloaded file and follow the installation instructions.
After installation, click to open the app from the start menu:
RStudio Overview
Getting Started
RStudio will open with 4 sections (called panes):
1. Source editor pane
You will write your R code/script here and it will be run in the console.
To create a new R script you can either go to File -> New -> R Script, or click on the icon with the + sign and select R Script, or simply press Ctrl+Shift+N.
Make sure to save the script.
2. Console pane
Interactively run R commands
3. Environment/history pane
Environment: view objects in the global environment
History: search and view command history
4. Files/Plots/Packages/Help pane
Files: navigate directories and
Plots: view generated plots.
Packages: manage packages (install or update)
Help: View help documentations for any package/function
The RStudio panes
By default, RStudio is arranged into four window panes.
If you only see three panes, open a new script with File > New File > R Script . This should reveal one more pane.
The RStudio panes
First, open a new script under the File menu if one is not yet open: File > New File > R Script. In the script, type the following:
Code
print("excited for R!")
To run code, place your cursor anywhere in the line of code, then press Ctrl + Enter (on Windows/Linux) or Cmd + Enter (on Mac).
This should send the code to the Console and run it.
You can also run multiple lines at once.
Code
print("excited for R!")print("and RStudio!")
Now drag your cursor to highlight both lines and press Control + Enter.
To run the entire script, you can use Control + A to select all code, then press `Control + Enter.
Next, save the script. Hit Control + S to bring up the Save dialog box.
Console
The console, at the bottom left, is where code is executed. You can type code directly here, but it will not be saved.
Type a random piece of code (maybe a calculation like 3 + 3) and press ‘Enter’.
If you place your cursor on the last line of the console, and you press the up arrow, you can go back to the last code that was run. Keep pressing it to scroll the previous commands.
To run any of these previous commands, press Enter.
Environment
At the top right of the RStudio Window, you should see the Environment tab.
The Environment tab shows datasets and other objects that are loaded into R’s working memory, or “workspace”.
To explore this tab, let’s import a dataset into your environment from R.
Type the code below into your script and run it:
Code
data <- iris
You have now imported the dataset and stored it in an object named data. (You could have named the object anything you want.)
Environment
Now that the dataset is stored by R, you should be able to see it in the Environment pane.
If you click on the blue drop-down icon beside the object’s name in the Environment tab to reveal a summary.
Try clicking directly on the data dataset from the Environment tab. This opens it in a ‘View’ tab.
Environment
The broom icon, at the top of the Environment pane is used to clear your workspace.
You can also remove an object from the workspace with the rm() function.
Type and run the following in a new line on your R script.
Code
rm(data)
Notice that the data object no longer shows up in your environment after having run that code.
History
Next, the History tab shows previous commands you have run.
You can click a line to highlight it, then send it to the console or to your script with the “To Console” and “To Source” icons at the top of this tab.
To select multiple lines, use the “Shift-click” method: click the first item you want to select, then hold down the “Shift” key and click the last item you want to select.
Finally, notice that there is a search bar at the top right of the History pane where you can search for past commands that you have run.
Files
Next, the Files tab. This shows the files and folders in the folder you are working in.
The tab allows you to interact with your computer’s file system.
Try playing with some of the buttons here, to see what they do. You should try at least the following:
Make a new folder
Delete that folder
Make a new R Script
Rename that script
Plots
Next, the Plots tab. This is where figures that are generated by R will show up.
Try creating a simple plot with the following code:
Code
plot(AirPassengers)
That code creates a time series plot of air line passengers in the AirPassengers dataset.
You should see this figure in the Plots tab.
Now, test out the buttons at the top of this tab to explore what they do.
In particular, try to export a plot to your computer.
Packages
Next, let’s look at the Packages tab.
Packages are collections of R functions, data, and code that extend R’s capabilities.
Think of them as apps for your phone: you first install them, then you open (load) them to use them.
Packages need to be installed only once, but must be loaded in each new R session.
All the package names you see (in blue font) are packages that are installed on your system. And packages with a check mark are packages which are loaded in the current session.
Updating R and RStudio
Updating R
Go to CRAN and download new version
On Windows: A more efficient way is to use the installr package. install and load it, then run the updateR() function.
Updates R and Optionally updates all packages
May be better to do this in basic Rgui
Version should update automatically in RStudio
Check/change R version under Tools>Global Options>R version
On Mac: It’s generally recommended to download the new version directly from the CRAN website.
Then update the R packages with the code:
Code
update.packages(ask =FALSE, checkBuilt =TRUE)
To update RStudio: Go to RStudio and download new version
Click on Help>Check for Updates, follow menu prompts
Help
The Help tab shows the documentation for different R objects. Try typing out and running each line below to see what this documentation looks like.
Code
?t.test?AirPassengers?read.csv
Help files are not always very easy to understand for beginners, but with time they will become more useful.
Viewer
This tab allows you to preview HTML files and interactive objects.
RStudio Options for a Better Workflow
Go to Tools > Global Options to customize RStudio.
Appearance
Under the Appearance tab, you can choose a different Editor theme to change the look of your code. Many people find a dark theme easier on the eyes.
RStudio options
Code Settings
Under Code > Display, check these two boxes:
*Highlight R function calls**: Makes functions a different color so they stand out.
*Rainbow parentheses**: Assigns matching colors to nested parentheses, making them easier to track.
RStudio options
Workspace (Very Important!)
Finally, under General > Basic, apply these critical settings for reproducibility:
Uncheck the box for “Restore .RData into workspace at startup”.
Set “Save workspace to .RData on exit” to Never.
This ensures you start with a clean slate every time, which prevents many common errors.
Wrapping up
Of course, you have only scratched the surface of RStudio functionality and you can find more on the cheatsheet below:
Getting Set Up: RStudio Projects
Organize your projects into self-contained folders from the start.
A well-organized project is easier to navigate, reproducible, and shareable.
Use sub-folders to keep things tidy.
data/ (raw/cleaned data)
scripts/ (R code)
figures/ (output visuals)
documents/ (notes)
Workshop Folder Structure
We will use a basic structure that is a good starting point and can be extended as needed:
Basic-R-training/
├── scripts/ # All R analysis scripts
├── data/ # Raw and cleaned datasets
├── figures/ # Figures, plots, and visualizations
└── documents/ # Notes, manuscripts, metadata
Key Benefits of RStudio Projects:
Automatically sets your working directory to your project folder
Makes file paths simple and relative (e.g., "data/my_data.csv").
Enhances reproducibility and collaboration.
Creating a New RStudio Project
Open RStudio.
In the top right, click the blue 3D cube labeled “Project: (None)”.
Select New Project > New Directory > New Project.
Enter a project name (e.g., Basic-R-training).
Choose a location (e.g., Desktop).
Click Create Project — your new project opens!
To open the project later, simply double-click the .Rproj file in your project folder. Or Use the 3D cube icon in RStudio.
Objects in R
R is an object-oriented language. This means everything you create and manipulate in R—like numbers, text, datasets, and plots—is considered an object.
During an R session, objects are created and stored by name.
Results of calculations can be stored in objects using the assignment operators:
An arrow (<-) formed by a less than character and a hyphen without a space!. In RStudio (Alt + -)
The equal character (=).
Objects in R
There are some restrictions when giving an object a name:
Use summary() to see basic statistics for each variables
Subsetting
Using iris data, a built-in data frame with 150 rows and 5 columns.
Code
iris # the whole data frame iris[1, 1] # 1st element in 1st column iris[1, 6] # 1st element in the 6th column iris[, 1] # first column in the data frame iris[1] # first column in the data frame iris[1:3, 3] iris[3, ] # the 3rd row iris[1:6, ] # the 1st to 6th rowsiris[c(1,4), ] # rows 1 and 4 only iris[c(1,4), c(1,3) ] iris[, -1] # the whole except first columniris$Sepal.Length # Also extracts a column 'Sepal.Length'iris[,c("Sepal.Width", "Petal.Width")]# extract by name of column
Installing and Loading Packages
Packages are collections of R functions, data, and compiled code in a well-defined format.
There are three categories of packages.
1. Base Packages: Providing the basic functionality, maintained by the R Core Development group. Currently, there are 14 packages, these are
Installing a package is a one-time setup to download it onto your computer.
Loading a package with library() is required in each new R session to use its functions.
Code
# LOAD (do this every time you start a new script)library(tidyverse)library(readxl)library(writexl)library(labelled)
Note
If you get an error like "there is no package called 'dplyr'", you need to install it first
Installation downloads the package; loading makes it available for use
Only need to install once, but must load in every new session or script
Reading and Writing data
Importing data is rather easy in R but that may also depend on the nature of the data to be imported and from what format.
Most data are in tabular form such as a spreadsheet or a comma-separated file (.csv).
Base R has a series of read functions to import tabular data from plain text files with columns delimited by: space, tab, and comma, with or without a header containing the column names.
With an added package it is also possible to import directly from a Microsoft Excel spreadsheet format or other foreign formats from various sources.
Importing from local files
In base R the standard commands to read text files are based on the read.table()function.
The following table lists the collection of the base R read functions.
For more details use the help command help(read.table) that will display help for all.
Details of dataset readings
Function name
Assumes header
Separator
Decimal
File type
read.table()
No
” ”
.
.text
read.csv()
Yes
“,”
.
.csv
read.csv2()
Yes
“;”
,
.csv
read.delim()
Yes
“tab”
.
.text
read.delim2()
Yes
“tab”
,
.text
Reading raw data from other sources
Import data
There are many ways to get data into R and out of R.
Import text file data using read.table() and comma separated files using read.csv() functions.
Code
# syntax: read.table("file name with full path", arguments)
Code
# Examples:# Creates a data frame named myData mydata<-read.table(file ="datafile.txt",sep=" ", header=TRUE) mydata<-read.csv(file ="datafile.csv")
File names are specified in the same way as file.choose() function can be used to select the file interactively. i.e.
The haven package is the modern standard for importing data from other statistical software. It handles all recent versions of Stata, SPSS (.sav), and SAS (.sas7bdat).
Code
# Recommended for Stata, SPSS, and SAS fileslibrary(haven)stata_data <-read_dta("filename.dta") spss_data <-read_sav("filename.sav")sas_data <-read_sas("filename.sas7bdat")
Example: Import the Facility Characteristics data from the Ethiopia Service Provision Assessment (ESPA) 2021-22
Code
# Import the Facility Characteristics data from an ESPA Stata filelibrary(haven)facility_data <-read_dta("data/ESPA_2021/ETFC81FLSP.DTA")library(labelled)# Convert all labelled variables (like categoricals) to R factors with value labelsfacility_data <-to_factor(facility_data)
Data import wizard
The data import wizard is a quick and easy way to import your data
Inside the data wizard, you can copy the code from the code-preview window, then paste the code into the code chunk of your r script or quarto document.
Composing the data import code…
Writing the import data function can be tricky. Try the import wizard pictured above. THEN, paste the code from the Code Preview section into your script.
Easily write import data function
Excel, SPSS, SAS, etc.
The data import wizard will help you find the proper package for importing your data. For example, use…
