Hands-on: Reviewing some R basics

In this section, we will review:

  • RStudio/POSIT software usage

  • R basics.

RStudio

What is RStudio?

  • Free and open source IDE (Integrated Development Environment) for R, Python

  • Available for Windows, Mac OS and LINUX

RStudio logo posit logo

We will use a local RStudio server running a singularity container. It uses Tidyverse Rocker image.

# Download the bash script that installs the singularity container and run it in the localserver
wget https://raw.githubusercontent.com/biocorecrg/RNAseq_coursesCRG_2026/refs/heads/master/run_rstudio.sh
bash run_rstudio.sh

In your browser, type the following url: http://localhost:8787/

Panels

When you open RStudio, you will see 3-4 panels:

  • top-left: scripts and files

  • bottom-left: R console Linux-line terminal / command-line

  • top-right: environment, history, connections, tutorial

  • bottom-right: tree of folders and files, plots/graphs window, packages, help window, viewer, presentation

panels

Shortcuts

RStudio provides a variety of shortcuts to make user interaction smoother.

Click Alt + Shift + K to display all available shortcuts.

posit logo

Examples:

  • CTRL + ENTER to send the current row or a selected block of code to the console

  • CTRL + 2 to move the cursor to the console

https://docs.posit.co/ide/user/ide/guide/code/projects.html

Tip

If you start using RStudio IDE for your work, please refer to the (great) official documentation, for example:

R Basics

Objects

What stores data - of any kind - in R is an object.

panels

Assignment operators (how to assign data to the object):

  • <- or =

  • Mostly the same but, to avoid confusions:

    • Use <- for assignments

    • Keep = for functions arguments

Assigning a value to the object B:

B <- 10

B + 10

B unchanged !!

Reassigning: modifying the content of an object:

B <- B + 10

B changed !!

You can see the objects you created in the environment panel (upper right).

Note

Naming an object in R is flexible. You should nevertheless follow a few base rules:

  • You can use:

    • Letters (note that object names case sensitive: A and a are NOT the same)

    • Numbers (exception: the object name cannot start with a number)

    • Underscores _

  • You cannot use:

    • Spaces

    • Most special characters

Data types and data structures

Data types

Each object has a data type:

  • Numeric (number - integer or double)

  • Character (text)

  • Logical (TRUE / FALSE)

  • Factors (categorical variables)

Numeric: numbers, floats

number_object <- 10
mode(number_object)
typeof(number_object)
str(number_object)

Character: text, strings of characters

text_object <- "word"
mode(text_object)
typeof(text_object)
str(text_object)

Logical: boolean values (TRUE or FALSE)

logical_object <- TRUE
mode(logical_object)
typeof(logical_object)
str(logical_object)

Factor: used to work with categorical variables. For example, in statistical modeling or graphing.

Creating a factor starts by creating an object, that is then converted to a factor.

factor_object <- factor(text_object)
mode(factor_object)
typeof(factor_object)
str(factor_object)

Data structures

The main data structures in R are:

  • Vector

  • Matrix

  • Data frame

  • List

Vectors

Vectors are one-dimensional and contain a single data type.

Create a numeric vector:

a <- c(1, 2, 3, 4, 5, 6)

# same as:
a <- 1:6

Note

shorta <- 1

same as:

shorta <- c(1)

shorta is a vector of 1-element.

Check the length of (i.e. number of elements) a vector:

length(a)

You can extract elements of a vector using the slicing operator (the square bracket) [ ]:

Extract 1st and 3rd elements of a:

a[c(1,3)]

Extract all but the first element:

a[-1]

Create a second numeric vector, and check which elements of that second vector are also present in a using operator %in%:

b <- 3:8

b[b %in% a]

Tip

Table of comparison and logical operators that can be used for data selection and filtering:

Operator

Description

<

less than

<=

less than or equal to

>

greater than

>=

greater than or equal to

==

exactly equal to

!=

not equal to

!x

not x

x|y

x OR y

x&y

x AND y

%in%

checks if an element belongs to a vector

You can replace one element of a vector by pointing to its position, e.g.:

b[2] <- 10
Matrices

Matrices are two-dimensional and can only contain one data type.

Create a numeric matrix:

# define number of rows
mat <- matrix(1:100, nrow=4)

# define number of columns
mat <- matrix(1:100, ncol=4)

Check dimensions (i.e. number of rows and number of columns) of a matrix:

# Number of rows
nrow(mat)

# Number of columns
ncol(mat)

# Dimensions (first element is the number of rows, second element is the number of columns)
dim(mat)

Display the first or last rows with head or tail:

# first 6 rows (default)
head(mat)

# first 10 rows
head(mat, n=10)

# last 6 rows
tail(mat)

You can extract rows and columns of a matrix using the slicing operator (square bracket [ ] and their position/index:

# first row
mat[1,]

# row 1 and 3
mat[c(1,3),]

# first column
mat[,1]

# column 2 and 3
mat[,c(2, 3)]

# or mat[,2:3]

Note

The left item of the square bracket always corresponds to the row, while the right item always corresponds to the column:

mat[row_index, colum_index]

Data frames

Data frames are two-dimensional and can contain several data types (column-wise: one column will have a single data type).

Create a three-column data frame :

  • Name: character column

  • Age: numeric column

  • Vegetarian: logical column

# create data frame
df <- data.frame(c("Maria", "Juan", "Alba", "Xavier", "Lara", "Max"), 
        c(23, 25, 31, 28, 36, 34),
        c(TRUE, TRUE, FALSE, FALSE, TRUE, FALSE))
        
# add column names
colnames(df) <- c("Name", "Age", "Vegetarian")

# do both steps at once
df <- data.frame(Name=c("Maria", "Juan", "Alba", "Xavier", "Lara", "Max"), 
        Age=c(23, 25, 31, 28, 36, 34),
        Vegetarian=c(TRUE, TRUE, FALSE, FALSE, TRUE, FALSE))

Check dimensions (i.e. number of rows and number of columns) of a dataframe:

# Number of rows
nrow(df)

# Number of columns
ncol(df)

# Dimensions (first element is the number of rows, second element is the number of columns)
dim(df)

Check column names or row names:

colnames(df)

rownames(df)

You can extract rows - as with matrices - using the slicing operator: the square bracket [ ] df[1,]

You can extract columns of a data frame with:

  • Slicing operator []

    • Access using the column name: df[,"Age"]

    • Access using the column index (i.e. position): df[,2]

  • Dollar sign $: df$Age

Select rows of the data frame if the Age column is greater than 24:

df[df$Age > 24,]

Select rows of the data frame based on multiple conditions, for example, if the Age column is greater than 24 AND if Vegetarian is TRUE :

df[df$Age > 24 & df$Vegetarian == TRUE,]

Finally, select only columns of interest for your selection: in the following example, we extract the name of vegetarian people older than 24:

df[df$Age > 24 & df$Vegetarian == TRUE, "Name"]
Lists

Lists are one-dimensional: each element of a list can contain a different data structure!

mylist <- list(my_df=df,
              my_vector=b,
              my_matrix=mat)

The length of a list gives you the number of elements.

length(mylist)

You can extract elements of a list (and apply functions on them) using the double square brackets [[ ]].

# extract third element of the list with the index...
mylist[[3]]
# or the name
mylist[["my_matrix"]]
mylist$my_matrix

# check dimensions of the third element
dim(mylist[[3]])

Paths and directories

Get/show the path of the current directory (i.e. working directory) with getwd (get working directory):

getwd()

Change working directory with setwd (set working directory).

Go to a directory giving the absolute path:

setwd("~")
# the home directory is likely different for each of you, but it could be like /users/username

Note

~ is a shortcut to your home directory

Now that you are in your home directory, you can create an rnaseq_course directory (if you have not created a folder for the course yet) and an r_basics directory:

dir.create("rnaseq_course/r_basics",  recursive=TRUE)

Go to the newly created directory using the relative path:

setwd("./rnaseq_course/r_basics")

# which is equivalent to
setwd("~/rnaseq_course/r_basics")

You are now in: “~/rnaseq_course/r_basics”

Move one directory “up” the tree:

setwd("..")

# and move back to r_basics
setwd("r_basics")

You are now back to: “~/rnaseq_course/r_basics”

Missing values

NA (Not Available) is a recognized element in R.

Finding missing values in a vector:

# Create vector with a missing value
x <- c(4, 2, 7, NA)

# Find missing values in vector:
is.na(x)

# Remove missing values
na.omit(x)
x[ !is.na(x) ]

Some functions can deal with NAs, either by default, or with specific parameters:

x <- c(4, 2, 7, NA)

# default arguments: what happens?
mean(x)

# set na.rm=TRUE for the mean function to handle (in this case, ignore) missing values
mean(x, na.rm=TRUE)

In a matrix or a data frame, you can keep only rows where there are no NA values with:

# Create matrix with some NA values
mydata <- matrix(c(1:10, NA, 12:2, NA, 15:20, NA), ncol=3)

# Keep only rows without NAs
mydata[complete.cases(mydata), ]
# or
na.omit(mydata)

For additional information, you can check this R blogger post on missing/null values

Read in, write out

On vectors

Write the content of a vector in a file with write:

# create a vector
mygenes <- c("SMAD4", "DKK1", "ASXL3", "ERG", "CKLF", "TIAM1", "VHL", "BTD", "EMP1", "MALL", "PAX3")

# write to a file
write(x=mygenes, 
        file="gene_list.txt")

You can specify a full or relative path where to write down a file:

# Write to home directory
write(x=mygenes,
        file="~/rnaseq_course/r_basics/gene_list.txt")
        
# Write to one directory up
write(x=mygenes,
        file="../gene_list.txt")

Read in a file into a vector object using scan:

# Read in file
scan(file="gene_list.txt")

# Save scanned data into an object k
k <- scan(file="gene_list.txt")

By default, the function scans for “double” (numeric) elements: it fails if the input contains characters.

If reading non-numeric data, you need to specify the type of data contained in the file:

# specify the type of data to scan
scan(file="gene_list.txt", 
        what="character")

If the file is not in the current directory, you can provide a full or relative path.

For example, if the file is located in the home directory, read it as:

scan(file="~/gene_list.txt", 
        what="character")
        
# here, we can read it as:

scan(file="~/rnaseq_course/r_basics/gene_list.txt", what="character")

Data frames or matrices

Read in a file as a data frame with read.table:

a <- read.table(file="file.txt")

You can convert it as a matrix, if needed, with:

a <- as.matrix(read.table(file="file.txt"))

Write a data frame or matrix to a file with write.table:

write.table(x=a,
        file="file.txt")

Useful arguments:

writetable

Note that sep=”\t” stands for tab-delimitation; if reading a .csv file, you can change to sep=”,” (or use the dedicated write.scv function!).

Install packages

R base

A set a standard packages which are supplied with R by default.
Example: package base (write, table, rownames functions), package utils (read.table, str functions), package stats (var, na.omit, median functions).

R contrib

All other packages:

* As of March 2026

Install a CRAN package using install.packages:

install.packages('BiocManager', repos = 'http://cran.us.r-project.org', dependencies = TRUE)

Install a Bioconductor package using BiocManager::install:

library('BiocManager')
BiocManager::install('GOstats')

Exercises

Exercise 1

  • Create a numeric vector y containing numbers from 2 to 11 (both included).

  • How many elements are in y?

  • Show the 3rd and the 6th elements of y.

  • Show all elements of y that have a value inferior to 7.

Click to show correction

  • Create a numeric vector y containing numbers from 2 to 11 (both included).

y <- 2:11

or

y <- c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11)

  • How many elements are in y?

length(y)

  • Show the 3rd and the 6th elements of y.

y[c(3,6)]

  • Show all elements of y that have a value inferior to 7.

y[y < 7]

Exercise 2

  • Create the vector x of 1000 random numbers from the normal distribution (see rnorm function).

  • What are the mean, median, minimum and maximum values of x?

Click to show correction

  • Create the vector x of 1000 random numbers from the normal distribution (see rnorm function).

x <- rnorm(1000)

  • What are the mean, median, minimum and maximum values of x?

mean(x); median(x); min(x); max(x)

or the more straightforward:

summary(x)

Exercise 3

  • Create vector y2 as: y2 <- c(1, 11, 5, 62, NA, 18, 2, 8, NA)

  • What is the sum of all elements in y2 ?

  • Which elements of y2 are also present in y?

  • Remove NA values from y2.

Click to show correction

  • Create vector y2 as:

y2 <- c(1, 11, 5, 62,  NA, 18, 2, 8, NA)

  • What is the sum of all elements in y2 ?

sum(y2, na.rm = TRUE)

  • Which elements of y2 are also present in y?

y2[y2 %in% y]

  • Remove NA values from y2.

y2 <- na.omit(y2)

Exercise 4

  • Create the following data frame:

43

181

M

34

172

F

22

189

M

27

167

F

with :

  • row names: John, Jessica, Steve, Rachel

  • column names: Age, Height, Sex.

Then:

  • Check the structure of df with str().

  • Calculate the average age and height in df.

  • Change the row names of df so the data becomes anonymous

    • Use for example Patient1, Patient2, etc.

  • Write df to the file mydf.txt with write.table().

    • Explore parameters sep, row.names, col.names, quote.

Click to show correction

  • Create the following data frame wih

    • row names: John, Jessica, Steve, Rachel

    • column names: Age, Height, Sex.

df <- data.frame(Age=c(43, 34, 22, 27), Height=c(181, 172, 189, 167), Sex=c(“M”, “F”, “M”, “F”), row.names = c(“John”, “Jessica”, “Steve”, “Rachel”))

  • Check the structure of df with str().

str(df)

  • Calculate the average age and height in df.

mean(df$Age)

same as mean(df[,"Age"])

mean(df$Height)

same as mean(df[,"Height"])

  • Change the row names of df so the data becomes anonymous

    • Use for example Patient1, Patient2, etc.

rownames(df) <- c("Patient1", "Patient2", "Patient3", "Patient4")

  • Write df to the file mydf.txt with write.table().

    • Explore parameters sep, row.names, col.names, quote.

write.table(df, “mydf.txt”, sep=”\t”, row.names = TRUE, col.names = NA, quote = FALSE)

Exercise 5

  • Create a matrix called grades representing 4 students (rows) and 3 subjects (columns: Math, Science, English) with the following values:

    • Student 1: 85, 92, 78

    • Student 2: 70, 88, 95

    • Student 3: 99, 91, 89

    • Student 4: 60, 72, 68

  • Extract the Science grade for Student 3

  • Calculate the average score for Math across all 4 students

Click to show correction

  • Create a matrix called grades representing 4 students (rows) and 3 subjects (columns: Math, Science, English) with the following values:

    • Student1: 85, 92, 78

    • Student2: 70, 88, 95

    • Student3: 99, 91, 89

    • Student4: 60, 72, 68

grades <- matrix(c(85, 92, 78, 79, 88, 95, 99, 91, 89, 60, 72, 68),
                nrow=4,
                byrow=TRUE,
                dimnames=list(c("Student1", "Student2", "Student3", "Student4"), c("Math", "Science", "English"))
                )

  • Extract the Science grade for Student 3

grades["Student3", "Science"]

  • Calculate the average score for Math across all 4 students

mean(grades[,"Math"])