RStudio Tutorial

R Studio is an open-source, integrated development environment(IDE) for R programming language. IDE is a GUI, where we can write our quotes, see the results and also see the variables that are generated during the course of programming.

1. R Studio is also available as both Desktop and Server versions.
2. R Studio is available as both Open source and Commercial software.
3. R Studio is also available for various platforms such as Windows, Linux and macOS.

1. Installation

To use R language, we need the R environment to be installed on our machine and an IDE (Integrated development environment) to run the language (can also be run using CMD on Windows or Terminal on Linux). We will download RStudio from its official website.

https://posit.co/download/rstudio-desktop/

2. RStudio Interface

The RStudio interface is a virtual space that is triggered when an interpreter of a programming language is launched. Simply, the environment is a collection of all the objects, variables and functions. Or, Environment can be assumed as a top-level object that contains the set of names/variables associated with some values.

3. RStudio Environment

The RStudio environment provides a user-friendly interface that streamlines the coding process. The environment includes several key tools and panels that help us interact with R effectively:

• Console: Execute R commands and get immediate feedback.
• Script Editor: Write, edit and save R scripts with syntax highlighting and code suggestions.
• Environment Panel: View all objects (variables, data frames, functions) in the current workspace.
• History Panel: Review and reuse previously executed commands.
• Files: Browse and manage files on your computer.
• Plots: View graphical outputs like plots and charts.
• Packages: Manage, load and explore installed R packages.
• Help: Access documentation for R functions and packages.
• Workspace: Store and manage all variables, functions and datasets.
• Projects: Organize work into separate folders for different tasks.
• R Markdown: Create dynamic reports with integrated R code and results in formats like HTML or PDF.

The RStudio Environment enhances productivity by offering an organized, interactive interface for coding, data analysis and reporting.

4. Working with R Scripts

We can read external datasets and operate with them in our R environment by importing data into an R script. R offers a number of functions for importing data from various file formats.

5. Packages in R

The most common method of installing and loading packages is using the install.packages() and library() function respectively.

Syntax:

install.packages(“package_name”)

library(package_name)

Example:

install.packages("dplyr")
library(dplyr)

Alternatively: Using RStudio UI

We can also installing packages Using RStudio UI:

• Open the Packages tab in the bottom-right panel.
• Click on the Install button.
• In the dialog box, we can enter the name of the package we want to install.
• Click Install and RStudio will start the installation.
• Once installed, the package will be available for use in our R session.

6. Data Manipulation

R offers several libraries for data manipulation, such as dplyr, data.table and tidyr, which provide functions to clean, transform and analyze data. These libraries cover tasks like filtering, sorting, reshaping and aggregating data. Additionally, base R functions can also be used for data manipulation.

Example:

install.package("dplyr")
library(dplyr)

data <- data.frame(Name = c("Alice", "Bob", "Charlie", "David"), Age = c(25, 30, 35, 40))

filtered_data <- filter(data, Age > 30)
print(filtered_data)

Output:

Other libraries for data manipulation include:

• lubridate: Helps in handling date-time data
• stringr: Provides tools for string manipulation
• forcats: Used for handling categorical data (factors)

7. Data Visualization

Data visualization is a key aspect of data analysis. It provides clear insights into patterns, trends and relationships within the data. R offers multiple libraries and tools for creating a wide variety of visualizations such as ggplot2, plotly, etc. Additionally, base R also provides simple but effective plotting functions.

Example:

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)

plot(x, y, main="Scatter Plot with Line", xlab="X Axis", ylab="Y Axis", pch=19)
lines(x, y, col="blue", lwd=2)

Output:

Other libraries used for data visualization are:

• Lattice : Useful for creating multi-variable plots with fine control over plot appearance.
• highcharter: Focused on creating interactive charts, such as pie charts, bar charts and more.
• leaflet: Best suited for creating interactive maps and visualizing geographic data.

8. Working with Projects

Working with projects in RStudio can help our organize our work, manage files and maintain a clean project structure. Here are the steps to work with projects in RStudio:

8.1. Create a New Project

• Open RStudio.
• Click on "File" in the top menu.
• Select "New Project..."
• Choose a directory where we want to create our project.
• Enter a project name.
• Click "Create Project."

8.2. Project Directory Structure

• RStudio will create a new directory with the project name in our chosen location.
• Inside this directory, we can organize our R scripts, data files and other project-related files.

8.3. Working with Files

• We can create R scripts by clicking "File" > "New File" > "R Script" in RStudio.
• Save our R scripts and other files directly into our project directory.

9. Version Control

R programming language offers different functionalities through different packages available in online repositories. The official repository CRAN keeps updating these packages and frequently publishes newer versions of packages.

R’s package system is set by default to overwrite the existing versions of packages so that the user always has the latest version.

10. Customization

RStudio is highly customizable. You can change the theme, set code preferences and even create your own keyboard shortcuts. Explore the Tools > Global Options menu for customization options.