Basic R

​

Huyha

Email: hagiahuy311@gmail.com

2025

1

Bioinformatic analysis for cancer genomics

Why we use R?

2

Contents

1. Overview and how to install R
2. Basic R for bioinformatic
3. Operator
4. Data types and Data Structures
5. Functions

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3

4

1. Overview and install R

What is R?

● A Programming/Statistical Language

● A powerful language for statistical computing and data analysis.

● Widely used in bioinformatics and life sciences

5

Why we use R?

1. Comprehensive Statistical Tools

2. Rich Ecosystem of Bioinformatics Packages

3. Data Visualization

4. Handling High-Dimensional Data

5. Open Source and Active Community

6. Reproducibility

7. Free

6

https://roelverbelen.netlify.app/resources/r/packages/

Why we use R?

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7

Adapted from mr. Duy slide

Why we use R?

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Install R

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https://cran.rstudio.com/

R and Rstudio

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

Orthers platforms for R

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Visual code

https://code.visualstudio.com/download

Orthers platforms for R

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Google colab

https://colab.research.google.com

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2. Basic R

Work directory

# First, have a look at the current working directory �getwd()

# Change to your desired directory �setwd()

# List the file in the directory �dir()

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https://www.r-bloggers.com/2020/01/rstudio-projects-and-working-directories-a-beginners-guide/

Install and load package

# Get the list of installed packages �installed.packages()

# Install package �install.packages()

# Import package �library()

# get all packages currently loaded in the R environment. �search()

# Check installed packages location�libPaths()

# Update package �update.packages()

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https://www.javatpoint.com/r-packages

Orther ways to install package

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Bioconductor

Github

Install and load package

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Search and download these packages:

● tidyverse

● readr

● ggplot2

Help and manual

# Access the help file �?mean

# If unsure of the precise name �# search doc across all installed packages �??mean

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Package tutorial

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Google

AI

Document

Workflow in R tutorial

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Workflow in R tutorial

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Loading and Saving CSV Files in R

#Standard use, small files (base R)

#Load a CSV file

data <- read.csv("gene_expression.csv", header = TRUE) head(data) # View first few rows

#Save a CSV file

write.csv(data, "output.csv", row.names = FALSE)

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header = TRUE: Treats the first row as column names.

sep = ",": (Default) Assumes comma-separated values.

Loading and Saving CSV Files in R

#Using read.table() (More Control)

#Load CSV with custom delimiter

data <- read.table("gene_expression.csv", sep = ",", header = TRUE)

#Save CSV with write.table()

write.table(data, "output.csv", sep = ",", row.names = FALSE, quote = FALSE)

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header = TRUE: Treats the first row as column names.

sep = ",": (Default) Assumes comma-separated values.

Loading and Saving CSV Files in R

#Standard use, small files

read.csv() / write.csv()

#Custom delimiters (e.g., tab-separated)

read.table() / write.table()

#Tidyverse compatibility, easy use

read_csv() / write_csv()

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Save and quit

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An R workspace image contains all the information held in the R session at the time of exit and is saved as a .RData file

# Save current workspace save.image(file="mysession.RData")

# exit R

q()

# Load workspace

load('myession.RData')

R-base overview

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https://www.geeksforgeeks.org/r-tutorial/

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3. Operators

Operator

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https://www.tutorialkart.com/r-tutori al/r-operators/#gsc.tab=0

Arithmetic Operator

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# R Arithmetic Operators Example for integers

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a <- 7.5

b <- 2

print ( a+b ) #1 Addition

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print ( a-b ) #2 Subtraction

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print ( a*b ) #3 Multiplication

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print ( a/b ) #4 Division

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print ( a%%b ) #5 Reminder

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print ( a%/%b ) #6 Quotient

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print ( a^b ) #7 Power of

$ Rscript r_op_arithmetic.R

[1] 9.5

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[2] 5.5

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[3] 15

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[4] 3.75

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[5] 1.5

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[6] 3

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[7] 56.25

Arithmetic Operator

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# R Operators - R Arithmetic Operators Example for vectors

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a <- c(8, 9, 6)

b <- c(2, 4, 5)

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print ( a+b )#1 addition

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print ( a-b ) #2 subtraction

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print ( a*b ) #3 multiplication

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print ( a/b ) #4 Division

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print ( a%%b )#5 Reminder

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print ( a%/%b )#6 Quotient

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print ( a^b )#7 Power of

$ Rscript r_op_arithmetic.R

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[1] 10 13 11

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[2] 6 5 1

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[3] 16 36 30

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[4] 4.00 2.25 1.20

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[5] 0 1 1

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[6] 4 2 1

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[7] 64 6561 7776

Arithmetic Operator

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Arithmetic Operator

Classwork

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Relational Operator

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# R Operators - R Relational Operators Example for Numbers

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a <- 7.5

b <- 2

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print ( a>b ) #1 greater than

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print ( a<b ) #2 less than

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print ( a==b ) #3 equal to

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print ( a<=b ) #4 less than or equal to

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print ( a>=b ) #5 greater than or equal to

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print ( a!=b ) #6 not equal to

Relational Operator

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# R Operators - R Relational Operators Example for Numbers

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a <- 7.5

b <- 2

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print ( a>b ) #1 greater than

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print ( a<b ) #2 less than

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print ( a==b ) #3 equal to

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print ( a<=b ) #4 less than or equal to

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print ( a>=b ) #5 greater than or equal to

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print ( a!=b ) #6 not equal to

$ Rscript r_op_relational.R

[1] TRUE

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[2] FALSE

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[3] FALSE

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[4] FALSE

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[5] TRUE

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[6] TRUE

Logical Operator

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# R Operators - R Logical Operators Example for basic logical elements

a <- 0 #(TRUE)

b <- 2 #(FALES)

print ( a & b ) #1 logical AND element wise

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print ( a | b ) #2 logical OR element wise

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print ( !a ) #3 logical NOT element wise

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print ( a && b ) #4 logical AND consolidated for all elements

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print ( a || b ) #5 logical OR consolidated for all elements

Logical Operator

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# R Operators - R Logical Operators Example for basic logical elements

a <- 0 #(TRUE)

b <- 2 #(FALES)

print ( a & b ) #1 logical AND element wise

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print ( a | b ) #2 logical OR element wise

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print ( !a ) #3 logical NOT element wise

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print ( a && b ) #4 logical AND consolidated for all elements

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print ( a || b ) #5 logical OR consolidated for all elements

$ Rscript r_op_logical.R

[1] FALSE

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[2] TRUE

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[3] TRUE

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[4] FALSE

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[5] TRUE

Assignment Operator

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#Assign variable

x = 'hello'

print(x)

[1] "hello"

x <- 'learn r'

print(x)

[1] "learn r"

'r programming language' -> x; print(x)

[1] "r programming language"

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​

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R Variable can be assigned a value using one of the following three operators :

1. Equal Operator =
2. Leftward Operator <-
3. Rightward Operator ->

Miscellaneous Operator

a = 23:31

print ( a )

[1] 23 24 25 26 27 28 29 30 31

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a = c(25, 27, 76)

b = 27

print ( b %in% a )

[1] TRUE

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Miscellaneous Operator

mat = matrix(c(1,2,3,4,5,6),nrow=2,ncol=3)

print (mat)

print( t(mat))

pro = mat %*% t(mat)

print(pro)

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Output :[,1] [,2] [,3] #original matrix of order 2x3

[1,] 1 3 5

[2,] 2 4 6

[,1] [,2] #transposed matrix of order 3x2

[1,] 1 2

[2,] 3 4

[3,] 5 6

[,1] [,2] #product matrix of order 2x2

[1,] 35 44

[2,] 44 56

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Special Value(Inf, NaN, NA, NULL)

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Classwork

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Classwork : Replicate all the operation codes above

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3. Data Type and Data Structure

DataType

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> x <- TRUE

> print(class(x))

[1] "logical"

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> x <- 67.54

> print(class(x))

[1] "numeric"

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x <- 63L

> print(class(x))

[1] "integer"

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> x <- 6 + 4i

> print(class(x))

[1] "complex"

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> x <- "hello"

> print(class(x))

[1] "character"

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> x <- charToRaw("hello")

> print(class(x))

[1] "raw"

Data structure

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Vector and index

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# we can use the c function to combine the values as a vector.

# By default the type will be double

X<- c(61, 4, 21, 67, 89, 2)

X

[1] 61 4 21 67 89 2

# seq() function for creating

# a sequence of continuous values.

# length.out defines the length of vector.

Y<- seq(1, 10, length.out = 5)

Y

[1] 1.00 3.25 5.50 7.75 10.00

# use':' to create a vector

# of continuous values.

Z<- 2:7

Z

[1] 2 3 4 5 6 7

vector<-seq(10,100,by=10)

vector[1]

[1] 10

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vector[c(1,3)]

[1] 10 30

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Vector[7:10]

[1] 70 80 90 100

Operator in Vector

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# Numeric vector

numbers <-c(1,2,3,4,5)

​

# Character vector names<-c("Alice","Bob","Charlie")

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# Addition

result <- numbers + 2

print(result)

[1] 3 4 5 6 7

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# Multiplication

result <- numbers *2

print(result)

[1] 2 4 6 8 10

# Adding two vectors

vector1 <-c(1,2,3)

vector2 <-c(4,5,6)

result <- vector1 + vector2

print(result)

[1] 5 7 9

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Vector

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numbers<-c(1,2,3,4,5,6,7,8)

# Access the second element second_element <- numbers[2] print(second_element)

[1] 2

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numbers[3]<-5

numbers

[1] 1 2 -5 4 5 6 7 8

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numbers<-numbers[-3]

number [1] 1 2 4 5 6 7 8

# Get elements greater than 3 (logical subset)

gt_than_3 <- numbers[numbers >3] print(greater_than_three) [1] 4 5

# Get a subset of the first three elements

subset_vector <- numbers[1:3] print(subset_vector) [1] 1 2 3

Accessing Elements:

Subset Vector

Logical Subset Vector

Vector

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# Name the elements of the vector names(numbers)<-c("First","Second","Third","Fourth","Fifth")

print(numbers)

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[1] First Second Third Fourth Fifth

# 1 2 3 4 5

# Get the type of the vector

vector_type <- typeof(numbers) print(vector_type)

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[1] "double"

# Combine vectors

vector1<-c(1,2,3)

vector2<-c(4,5,6)

combined_vector <-c(vector1, vector2)

print(combined_vector)

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[1] 1 2 3 4 5 6

Vector Naming

Vector factor

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# Creating a factor from a character vector

colors <-c("red","green","blue","red","green")

color_factor <- factor(colors)

print(color_factor)

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[1] red green blue red green

Levels: blue green red

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# Specifying the order of levels

ordered_factor <- factor(colors, levels =c("red","green","blue"))

print(ordered_factor)

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[1] red green blue red green

Levels: red green blue

Recycle rule

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# Shorter vector is recycled to match the length of the longer vector short_vector

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short_vector <-c(1,2)

long_vector <-c(10,20,30,40)

result <- long_vector + short_vector

print(result)

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[1] 11 22 31 42

The Recycling Rule

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→ How R handles operations between vectors of unequal lengths.

→ R will "recycle" the shorter vector by repeating its elements until it matches the length of the longer vector.

https://www.gastonsanchez.com/R-coding-basics/vectors4.html#recycling

Vector functions

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# Sequences with seq()

> seq(from=3, to=27, by=3)

[1] 3 6 9 12 15 18 21 24 27

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# Repetition with rep()

> rep(x=1,times=4) [1] 1 1 1 1

> rep(x=c(3,62,8.3),times=3)

[1] 3.0 62.0 8.3 3.0 62.0 8.3 3.0 62.0 8.3

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# Sorting with sort()

> sort(x=c(2.5,-1,-10,3.44),decreasing=FALSE)

[1] -10.00 -1.00 2.50 3.44

> sort(x=c(2.5,-1,-10,3.44),decreasing=TRUE)

[1] 3.44 2.50 -1.00 -10.00

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# Finding a Vector length with length()

> length(x=c(3,2,8,1))

[1] 4

Data frame

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Definition:

A data frame is a table or a 2-dimensional array-like structure in R, where each column can contain different types of data (numeric, character, factor, etc.).

​

Structure:

Similar to a spreadsheet or SQL table, with rows representing observations and columns representing variables.

Creating Data frame

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# Create a data frame with three columns

df <- data.frame(ID =1:4,

Name=c("Alice","Bob","Charlie","Diana"),

Score =c(85,92,88,76))

print(df)

ID Name Score

1 1 Alice 85

2 2 Bob 92

3 3 Charlie 88

4 4 Diana 76

Accessing data in Data frame

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# Access the 'Name' Column

names<- df$Name

print(names)

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[1] "Alice" "Bob" "Charlie" "Diana"

Using $ to Access Columns:

# Access the element in the 2nd row, 3rd column

element <- df[2,3]

print(element)

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[1] 92

Using Indexing

Data frame

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# Add a new column 'Passed'

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df$Passed <- df$Score >80

print(df)

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ID Name Score Passed

1 1 Alice 85 TRUE

2 2 Bob 92 TRUE

3 3 Charlie 88 TRUE

4 4 Diana 76 FALSE

# Subsetting a dataframe with condition

​

high_scores <- df[df$Score >80,] print(high_scores)

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ID Name Score Passed 1 1 Alice 85 TRUE

2 2 Bob 92 TRUE

3 3 Charlie 88 TRUE

Adding a New Column

Subsetting Data Frames:

Data frame

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# Combine data frames by adding rows

df_new <- data.frame(ID=5,

Name="Eve", Score=90)

combined_df <- rbind(df, df_new)

​

print(combined_df)

ID Name Score Passed

1 1 Alice 85 TRUE

2 2 Bob 92 TRUE

3 3 Charlie 88 TRUE

4 4 Diana 76 FALSE

5 5 Eve 90 TRUE

# Combine data frames by adding columns

extra_info<-data.frame(Age=c(23,25,22,21,24))

full_df <- cbind(combined_df,extra_info)

​

print(full_df)

ID Name Score Passed Age

1 1 Alice 85 TRUE 23

2 2 Bob 92 TRUE 25

3 3 Charlie 88 TRUE 22

4 4 Diana 76 FALSE 21

5 5 Eve 90 TRUE 24

Row Binding

Column Binding

Viewing and Inspecting Data Frames

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# Viewing data

View(df)

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# Explore the structure of the data

str(df)

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'data.frame': 4 obs. of 4 variables:

$ ID : int 1 2 3 4

$ Name : chr "Alice" "Bob" "Charlie" "Diana"

$ Score : num 85 92 88 76 $ Passed: logi TRUE TRUE TRUE FALSE

Accessing data in Data frame

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summary(df)

​

ID Name Score Passed

Min. :1.00 Length:4 Min. :76.00 Mode :logical

1st Qu.:1.75 Class :character 1st Qu.:82.75 FALSE:1

Median :2.50 Mode :character Median :86.50 TRUE :3

Mean :2.50 Mean :85.25

3rd Qu.:3.25 3rd Qu.:89.00

Max. :4.00 Max. :92.00

To get a summary of each column.

Summary Statistics

Data frame

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# Get rows where Score is greater than 80

high_scores <- df[df$Score >80,] print(high_scores)

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ID Name Score Passed

1 1 Alice 85 TRUE

2 2 Bob 92 TRUE

3 3 Charlie 88 TRUE

Subset Rows Based on Conditions

Select Specific Columns

# Select only the 'Name' and 'Score' columns

name_score <- df[,c("Name","Score")]

print(name_score)

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Name Score

1 Alice 85

2 Bob 92

3 Charlie 88

4 Diana 76

Adding and Modifying Columns

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# Add a column indicating if the score is above average

df$Above_Average <- df$Score > mean(df$Score)

print(df)

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ID Name Score Passed Above_Average

1 1 Alice 85 TRUE FALSE

2 2 Bob 92 TRUE TRUE

3 3 Charlie 88 TRUE TRUE

4 4 Diana 76 FALSE FALSE

Modify an Existing Column

# Adjust the score by adding 5 points to each student

df$Score <- df$Score + 5

print(df)

ID Name Score Passed Above_Average

1 1 Alice 90 TRUE FALSE

2 2 Bob 97 TRUE TRUE

3 3 Charlie 93 TRUE TRUE

4 4 Diana 81 FALSE FALSE

Add a New Column

Accessing data in Data frame

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# Sort the data frame by 'Score' in descending order

df_sorted <- df[order(-df$Score),] print(df_sorted)

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ID Name Score Passed Above_Average

2 2 Bob 97 TRUE TRUE

3 3 Charlie 93 TRUE TRUE

1 1 Alice 90 TRUE FALSE

4 4 Diana 81 FALSE FALSE

Sort by a Single Column

Sort by Multiple Columns Binding

# Sort by 'Passed' (descending) and then by 'Score' (ascending)

df_sorted_multi <- df[order(-df$Passed,df$Score),]

print(df_sorted_multi)

​

ID Name Score Passed Above_Average

1 1 Alice 90 TRUE FALSE

3 3 Charlie 93 TRUE TRUE

2 2 Bob 97 TRUE TRUE

4 4 Diana 81 FALSE FALSE

Accessing data in Data frame

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# Bind new data frame rows to an existing one

new_students <- data.frame (ID = 5,

Name = "Eve",

Score = 89,

Passed = TRUE,

Above_Average = FALSE)

df_combined <- rbind(df, new_students)

print(df_combined)

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ID Name Score Passed Above_Average

1 1 Alice 90 TRUE FALSE

2 2 Bob 97 TRUE TRUE

3 3 Charlie 93 TRUE TRUE

4 4 Diana 81 FALSE FALSE

5 5 Eve 89 TRUE FALSE

Row Binding

Data frame

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# Add a new column for student

Age ages <- data.frame(Age = c(23,25,22,21,24))

df_with_age <- cbind(df_combined, ages)

print(df_with_age)

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ID Name Score Passed Above_Average Age

1 1 Alice 90 TRUE FALSE 23

2 2 Bob 97 TRUE TRUE 25

3 3 Charlie 93 TRUE TRUE 22

4 4 Diana 81 FALSE FALSE 21

5 5 Eve 89 TRUE FALSE 24

Column Binding

Data frame

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# Remove the 'Passed' column

df_no_passed <- df[,!(names(df)%in%"Passed")]

print(df_no_passed)

​

ID Name Score Above_Average

1 1 Alice 90 FALSE

2 2 Bob 97 TRUE

3 3 Charlie 93 TRUE

4 4 Diana 81 FALSE

Remove a Column

# Rename 'Score' to 'Final_Score' names(df)[names(df)=="Score"]<-"Final_Score"

print(df)

​

ID Name Final_Score Passed Above_Average

1 1 Alice 90 TRUE FALSE

2 2 Bob 97 TRUE TRUE

3 3 Charlie 93 TRUE TRUE

4 4 Diana 81 FALSE FALSE

Rename a Column

Data frame

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Accessing data in Data frame

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# Merge two data frames by the 'ID' column

df_info <- data.frame(ID =1:4, Gender = c("F","M","M","F"))

df_merged <- merge(df, df_info, by ="ID")

print(df_merged)

​

ID Name Score Passed Above_Average Gender

1 1 Alice 90 TRUE FALSE F

2 2 Bob 97 TRUE TRUE M

3 3 Charlie 93 TRUE TRUE M

4 4 Diana 81 FALSE FALSE F

Merging Data Frames

Key Functions in Data frame

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• nrow(df): Number of rows.
• ncol(df): Number of columns.
• dim(df): Dimensions (rows, columns).
• names(df): Column names.

Other Key Functions

Matrix and array

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Definition: A matrix is a two-dimensional (2D) data structure in R where all elements are of the same data type (numeric, character, or logical).

​

Structure: Consists of rows and columns.

Definition: An array is a multi-dimensional data structure in R that can have more than two dimensions. All elements must be of the same type.

​

Structure: Arrays can be thought of as matrices extended to more dimensions.

Matrix

Array

Matrix and array

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# Create a 3x3 numeric

matrix mat <- matrix(1:9, nrow =3, ncol =3)

print(mat)

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# Create a 3x3x2 array

arr <- array(1:18,dim=c(3,3,2))

print(arr)

Matrix

Array

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4. Functions

R function

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https://www.statmethods.net/management/functions.html

https://iqss.github.io/dss-workshops/R/Rintro/base-r-cheat-sheet.pdf

• Useful Built-in function
• Create an R function

Useful Built-in function

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Data Manipulation

● subset(): Extract subsets of data.

● merge(): Combine data frames by common columns or row names.

● apply(): Apply a function over the margins of an array or matrix.

● tapply(): Apply a function over subsets of a vector.

● reshape(): Reshape data between wide and long formats.

● cut(): Divide continuous variables into intervals.

● aggregate(): Compute summary statistics over subsets of data.

​

Statistical Analysis

● summary(): Provide a summary of an object.

● cor(): Calculate correlation between variables.

● lm(): Fit linear models.

● table(): Create a contingency table of counts.

Useful Built-in function

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Data Cleaning

● na.omit(): Remove missing values from an object.

● is.na(): Identify missing values.

● duplicated(): Identify duplicate elements.

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Data Visualization

● plot(): Generic X-Y plotting.

● hist(): Create a histogram.

● boxplot(): Create a boxplot.

● pairs(): Create a matrix of scatterplots.

Useful Built-in function

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Utility Functions

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● str(): Display the structure of an R object.

● paste(): Concatenate strings.

● seq(): Generate a sequence of numbers.

● rep(): Repeat elements of a vector.

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5. Decision Making

Decision making

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6. Control Flow

Control flow

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R cheat sheet

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https://iqss.github.io/dss-workshops/R/Rintro/base-r-cheat-sheet.pdf

Summary in R tutorial

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Thanks you