Data Analysis with RStudio: Understanding the Basics

The R programming language is extremely popular among data-centric professionals, including statisticians, data miners, and data scientists. While R has become widely popular due to the open-source environment and range of tools available, RStudio is an integrated development environment that combines certain functionalities and makes it easier to navigate than the traditional R platform.

In this article, we will explore how RStudio differs from R, its key features, and how to use RStudio in different contexts.

What is R?

R is a free, open-source statistical software environment that was specifically designed for statistical computing and graphics. Ross Ihaka and Robert Gentleman developed R in 1993 at the University of Auckland, New Zealand. Since then, it has become one of the most popular languages for statistics, data analysis, and machine learning.

The open-source nature of R means that anyone can examine, modify, and improve the software code. This has led to a strong, active community of users who contribute packages that extend R’s functionality. There are packages for virtually everything, from advanced statistical techniques to data cleaning, data visualization, text analysis and mining, and even bioinformatics and genomics tasks.

What is RStudio?

While R provides a robust environment for statistical analysis, RStudio enhances its utility by offering a more user-friendly interface (also known as a graphical user interface or GUI). RStudio is an integrated development environment (IDE) for R. It makes working with R easier and more intuitive, especially for those who are new to coding [1].

Essentially, RStudio uses R as a base and then integrates additional features onto it. This provides a powerful computing platform with a beginner-friendly format. 

RStudio contains packages, which are software applications that are suited to specific functions. Because RStudio is an open-source platform, professionals from around the world can build packages and share them with one another. This allows other users to find packages that help them create a basis for their task and then build upon existing code rather than start from scratch [1].

Panes in RStudio

RStudio operates using four main panes: the source editor, the console, the environment and history pain (also called the workspace), and the files / plots / packages / help pane [2].

Source editor: This is where you write and edit R code, also known as R scripts. You can save scripts for future use, making work reproducible [2].

Console: This is where the R code is executed. You can run a line of code from the script editor in the console by pressing "enter." However, most of the time, you will want to run code by typing it in the source editor. This is because code typed in the console won’t be saved [2].

Environment and history pane: The environment tab shows all the data vectors, data frames, matrices, and other objects created in your current R session. This pane also includes the number of observations and the details of data objects. The history tab shows all the commands that you have sent to the R console [2]. 

Files / plots / packages / help pane: This pane has four panels. The "files" panel shows the directory’s contents, and the "plots" panel displays any graphs you create. The "packages" panel allows installing, loading, and managing R packages, while the "help" panel provides a menu of R functions [2].

Features of RStudio

Several features of RStudio make it intuitive for users and easier to manage than traditional R. Some notable features include:

• Syntax highlighting

• Code completion

• Smart indentation 

• Multiple working directories management

• Easy image exporting

What is RStudio used for?

RStudio, along with R, is a comprehensive tool used across various fields and industries, such as academia, business, and research. It’s primarily known for its application in statistical analysis, data visualization, and data management. Some capabilities of RStudio within each of these domains include the following:  

Data analytics

You can use RStudio for many data analytics applications, such as:

• Univariate analysis

• Bivariate correlation

• Linear and logistic regression

• ANOVA

• Multivariate correlation and regression

• Factor analysis

• Geostatistics

• Machine learning algorithms 

Data visualization

Visualization plays a critical role in data analysis, helping people understand complex data and identify core patterns and trends within the data more effectively. Some visualization packages that are commonly used in RStudio include:

• ggplot2

• Shiny

• Plotly

• smplot

Each visualization package has its own domain. For instance, the ggplot2 package in R provides a powerful system for generating data visualizations by declaratively creating graphics. You can use this system by importing your data, then telling the ggplot2 function how to aesthetically map your variables and what type of visuals to use. From this, the ggplot2 system can create diverse and intricate graphics. Graphics produced by ggplot2 can range from simple bar plots and line charts to complex scatter plots and interactive designs [3]. 

If you are interested in web design, you might use Shiny and Plotly packages instead. The Shiny package allows users to build interactive web applications, while Plotly can create interactive web graphics. 

Data management

You can choose between many functions for basic and advanced data management within RStudio. Some basic commands you might use for data management include:

• mutate(): Adds new columns or alters existing variables

• summarize(): Returns a one-row summary of all rows

• filter(): Modifies data to only include rows that meet specific criteria

• select(): Only displays columns that you click

More advanced commands include:

• count(): Returns counts of observations for each group of values and collapses rows

• rename(): Changes the name of a variable or column

• ifelse(): Creates a new variable based on a certain condition

Disadvantages of R

While you will likely find many advantages of RStudio, you will naturally find certain disadvantages. R generally requires users to have a basic understanding of programming and syntax, which can lead to time-consuming errors in the beginning.

R is also typically less powerful for analyzing big data sets than some other popular programming languages, such as SAS. Depending on the size of your data set, you may consider alternatives to speed up data processing.

How to learn RStudio

Learning RStudio and R involves a few steps:

1. Install R.

First, you'll need to install the R software on your computer. It’s available for free download from the CRAN (Comprehensive R Archive Network) website.

2. Install RStudio.

Next, install the free version of RStudio from the RStudio website. You will open the installer and follow the guided steps.

3. Take an introductory course.

Start with an introductory course to R and RStudio. You can utilize free resources provided by RStudio Education, such as videos like A Gentle Introduction to Tidy Statistics in R, or free written resources like the R For Data Science textbook. You can also take a more formal course on learning platforms like Coursera, such as Data Analysis With R Programming by Google.

4. Try projects using R.

Apply what you’ve learned in real-world projects. This can be anything from analyzing a data set you find interesting, to creating a visualization, or even replicating a statistical analysis. You can start right away using RStudio.cloud Primers, which is a cloud-based guided learning environment. 

To meet and network with other industry professionals, you can find many RStudio developer conferences. These conferences can be a great place to meet R users and chat with other programmers, helping you build your personal and professional skill set. You can also check out R forums, where many professionals and hobbyists share their ideas and code.

Getting started with Coursera

Interested in rounding out your data analytics skill set? Consider completing the Google Data Analytics Professional Certificate on Coursera learning platform.

In under six months, you can build in-demand skills such as data cleaning, visualization, analysis, and management techniques to prepare you for an entry-level position.