R

R is nothing more than a programming language. At the time of writing, this language is (one of) the leading program in statistics, although not the only programming language used by statisticians.

In order to use R, we need two things:

• a text editor in which to write our code
• a place to run this code

You can download R at https://cran.r-project.org/mirrors.html. Select the CRAN mirror site closest to you, then in the box labeled “Download and Install R”, click the link corresponding to your operating system.

RStudio

This is where RStudio comes handy. RStudio is an integrated development environment (IDE) for R. This software has the advantage of offering both a powerful text editor for writing your code and a place to run the code written in this editor (and I would add that it is more user-friendly). For these reasons, I highly recommend using RStudio instead of R. (Note that RStudio requires the prior installation of the R software provided by CRAN in order to be able to function properly. Just installing RStudio on your personal computer is not enough.)

You can download RStudio at www.rstudio.com.

RStudio window

New R script in RStudio

Write and execute your code in RStudio

Run code in RStudio

Example of code executed in RStudio

Environment pane in RStudio

Calculator

Compute \(5*5\)

5 * 5
## [1] 25

Compute \(\frac{1}{\sqrt{50\pi}}\, e^{-\frac{(10 – 11)^2}{50}}\)

1 / sqrt(50 * pi) * exp(-(10 - 11)^2 / 50)
## [1] 0.07820854

As you can see, some values like \(\pi\) are stored by default so you do not need to specify its value. Note that RStudio is case sensitive, but not space sensitive. This means that pi is different than Pi but 5*5 gives the same result than 5 * 5.

Comments

To add comments in your code, use # before the code:

# A comment
# Another comment
1 + 1
## [1] 2

Store and print values

Note that in order to store a value inside an object, using = or <- is equivalent. I however recommend using <- to follow the guidelines of R programming. You can name your objects (A and B in our case) as you like. However, it is recommended to use short and concise names (as you will most likely type them several times) and avoid special characters.

A <- 5
B <- 6

When storing values, RStudio does not display it on the console. To store a value AND print it in the console, use:

(A <- 5)
## [1] 5

or:

A <- 5
A
## [1] 5

Vectors

It is also possible to store more than one value inside an object via the function c() (c stands for combine).

A <- c(1 / 2, -1, 0)
A
## [1]  0.5 -1.0  0.0

Matrices

Or create a matrix via matrix():

my_mat <- matrix(c(-1, 2, 0, 3), ncol = 2, nrow = 2)
my_mat
##      [,1] [,2]
## [1,]   -1    0
## [2,]    2    3

You can access the help of this function via ?matrix or help("matrix"). Note that inside a function, you can have multiple arguments separated by a comma. Inside matrix(), the first argument is the vector c(-1, 2, 0, 3), the second is ncol = 2 and the third is nrow = 2. For all functions in RStudio, you can specify an argument by its order inside the function or by the name of the argument. If you specify the name of the argument, the order does not matter anymore, so matrix(c(-1, 2, 0, 3), ncol = 2, nrow = 2) is equivalent to matrix(c(-1, 2, 0, 3), nrow = 2, ncol = 2):

my_mat2 <- matrix(c(-1, 2, 0, 3), nrow = 2, ncol = 2)
my_mat2
##      [,1] [,2]
## [1,]   -1    0
## [2,]    2    3
my_mat == my_mat2 # is my_mat equal to my_mat2?
##      [,1] [,2]
## [1,] TRUE TRUE
## [2,] TRUE TRUE

Generate random values

To generate 10 values based on a normal distribution with mean \(\mu = 400\) and standard deviation \(\sigma = 10\):

my_vec <- rnorm(10, mean = 400, sd = 10)
# Display only the first 5 values:
head(my_vec, 5)
## [1] 393.1760 398.1772 406.3960 391.8331 398.5543
# Display only the last 5 values:
tail(my_vec, 5)
## [1] 393.1265 402.0290 400.2321 400.0694 406.6195

You will have different values than mines due to the fact that they are randomly generated. If you want to make sure to have always the same random values, use set.seed() (with any numeric inside the brackets). For instance, with the following code, you should have the exact same values, no matter where and when you run it:

set.seed(42)
rnorm(3, mean = 10, sd = 2)
## [1] 12.741917  8.870604 10.726257

Plot

plot(my_vec,
  type = "l", # "l" stands for line
  main = "Plot title",
  ylab = "Y-axis label",
  xlab = "X-axis label"
)

This is only a very limited introduction to the possibilities of RStudio. If you want to learn more, I recommend that you read other articles related to R, starting with how to import an Excel file or how to manipulate a dataset.

Thanks for reading. I hope this article helped you to install R and RStudio. See other articles related to R here. As always, if you find a mistake/bug or if you have any questions do not hesitate to let me know in the comment section below, raise an issue on GitHub or contact me. Get updates every time a new article is published by subscribing to this blog.