An Introduction to R

By Adrian Tam on August 15, 2023 in R for Data Science 0

R is a programming language of its kind. It is a language for statistics, and its ecosystem has a lot of libraries for all kinds of statistical tasks. It is a language targeted the statisticians rather than computer scientists. Hence you will see some unorthodox patterns in the language. In this post, you will learn about R. Specifically, you will learn

Why R is important
How to install R in your computer

Let’s get started.

An Introduction to R.
Photo by Naser Tamimi. Some rights reserved.

Overview

This post is divided into three parts; they are:

What is R?
Getting Started with R
Data in R

What is R?

R is a programming language for statistical computing and graphics. As famous as the language itself, a software called RStudio works as an IDE on top of R to allow you to visualize the computation. R is a free and open-source software, and it is available for Windows, macOS, and Linux. R has been widely used by statisticians, data scientists, and machine learning engineers since the rich ecosystem in R supports virtually all kinds of statistical analysis.

The syntax of R is simple. However, it may not be intuitive to people who come from other programming languages. The syntax of R is close to mathematical formulas. One of the most famous syntax is probably the assignment:

X <- 42

X <- 42

This is to assign the value 42 to a variable X. The use of <- is to reflect how some mathematics literature denotes assignment that intentionally to avoid the confusion with “=” for equality comparison. Note, however, that using = for assignment is allowed in later versions of R but conventionally, we stay with <-.

Over the years, R has developed a lot of language features that you can consider as a general programming language. But R also amassed a lot of statisticians as their go-to language. Therefore, you will find it powerful in performing statistical analyses because of the packages and libraries available. Examples of analyses include:

Linear regression
Logistic regression
Time series analysis
Hypothesis testing
Data mining

To learn, you can refer to the R documentation.

Getting Started with R

You will need to install it on your computer to get started with R. You can download R from the R Project website:

https://www.r-project.org/

which installers for Mac and Windows are available. For Linux, usually R is available as a package in your distribution. For example, in Debian and Ubuntu, you can run the following command to install R:

sudo apt install r-base

1	sudo apt install r-base

Once you have installed R, you can open it by typing R (uppercase) in a terminal window. You can type

q()

q()

to quit R.

In addition, you can download and install RStudio from the RStudio website

https://www.rstudio.com/products/rstudio/download/

It is a GUI that allows you easier to write R code, debug R code, and visualize R data.

Screenshot of RStudio

Once you are in R, you will be at the R prompt. This is where you can type R commands. To learn more about R commands, you can type help() at the R prompt. But a simple example of an R command would be the following:

2+2

2+2

This command will add 2 and 2 and print the result, which is 4. Vectors (lists) are also native in R, so in a similar sense, you can also do vector addition:

c(2,3) + c(3,6)

1	c(2,3) + c(3,6)

which R returns

[1] 5 9

[1] 5 9

Data in R

R can work with data in a variety of formats. The most common data formats in R are:

Lists: A list is a collection of data objects.
Matrices: A matrix is a rectangular array of data.
Data frames: A data frame is a table of data with rows and columns.

For example, if you have a table of data saved in the format of a CSV file, you can use the read.csv() function in R to read the data into a data frame named df:

df <- read.csv("data.csv")

1	df <- read.csv("data.csv")

This syntax is like pandas if you learned the Python counterpart before. Note that read.csv() is a function where the dot . is just a character in the name of the function. Making dot a legitimate character for an identifier is another property of R that is different from other programming languages. As another example, below is how we create a matrix in R:

> x <- matrix(1:9, ncol=3)
> x
     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9

> x <- matrix(1:9, ncol=3)

> x

[,1] [,2] [,3]

[1,] 1 4 7

[2,] 2 5 8

[3,] 3 6 9

where 1:9 is R’s shortcut to create a list of ascending numbers, and the matrix() function above reformat the list into a matrix of three columns. With this, computing the 3×3 matrix’s determinant using R has a function built-in:

> det(x)
[1] 0

1 2	> det(x) [1] 0

You can always learn more about a function in R using help(det) or search for a particular function with help.search(keyword).

Summary

R is a powerful programming language for statistical computing and graphics. It is free and open-source software with a large community of users. R is a great language to learn if you are interested in data science.