R is:

• One of the leading programming languages for statistics and data science
• Useful for basic and advanced data analyses
• Prerequisite for job positions in the life sciences

Why?

• Its is free
• It supports reproducible analysis
• It provides packages for data analysis

We will learn the basics of R programming through lectures and hands-on computer exercises using Rstudio:

• variables, data types, and data structures
• data manipulation and visualization
• operations
• reading and writing files
• running and repeating tasks using functions and control statements.

Disclaimer: this is not a course in statistics or bioinformatics.

Course material and info: https://github.com/FFinotello/Rcourse

Note: to download a text file, right-click on “Raw” and select “Save link as”.

Evaluation: based on a data analysis project to be described in a short report and sent by e-mail.

How to ask questions

• Chat
• Collaborative Google Doc
• E-mail

Install first R and then Rstudio

To install R (version ≥ 4):

https://www.r-project.org

See “To download R, please choose your preferred CRAN mirror”.

To install RStudio Desktop:

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

RStudio is an integrated development environment (IDE) for R.
It has four main panels with adjustable sizes:

From the toolbar: View > Panes > Show All Panes

In the console you can type commands and see outputs.

( 12^2 - 20 * 2 + 1) / 5

## [1] 21

log10(100) - sqrt(9) / 3

## [1] 1

# Any text preceded by a hashtag is not evaluated (= comment!)

Variables: symbolic names used to store data that can be manipulated in a computer program

In R, variables are assigned with <-

height <- 1.90

weight <- 80

• Should be short but descriptive
• Must start with a letter
• Must not contain special characters
• Cannot be reserved words

Reserved words: words with special meaning in R that cannot be used as identifiers. Examples: if, break, Inf, TRUE

👍 cellFrac, cell_frac, cell.frac, cellfrac_1

👎 cell-frac, 1cellfrac, TRUE

(T <- 5 * 2 )

## [1] 10

R is case-sensitive

# Body mass index (BMI)
# defined as: weight/height^2
BMI <- 80/1.90^2

BMI

## [1] 22.16066

bmi

## Error in eval(expr, envir, enclos): object 'bmi' not found

Once variables are assigned, they can be used to perform operations

height <- 1.90
weight <- 80
( BMI <- weight/height^2 )

## [1] 22.16066

x <- 6
y <- 2
x <- 4
x + y

## [1] 6

In R, there are several types of atomic vectors (also called classes):

• Numeric
• Character
• Logical
• Complex
• Integer

Name <- "Maria"
class(Name)

## [1] "character"

PhD <- TRUE
class(PhD)

## [1] "logical"

yearsSincePhD <- 5
class(yearsSincePhD)

## [1] "numeric"

In R, missing values are usually coded with NA

Name <- "Michael"
PhD <- FALSE
yearsSincePhD <- NA

is.na(Name)

## [1] FALSE

is.na(yearsSincePhD)

## [1] TRUE

Note: NA are not strings (no quotation marks)

Not a number: NaN

var1 <- 0/0
var1

## [1] NaN

Infinite: Inf

( var2 <- 9/0 )

## [1] Inf

( var3 <- -10/0 )

## [1] -Inf

Class conversion is possible, but it must be handled with care

var1 <- 1
( var2 <- as.character(var1) )

## [1] "1"

class(var2)

## [1] "character"

( var3 <- as.logical(var1) )

## [1] TRUE

Source: https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf

In R, there are different data structures, including:

Vectors can be build with the c function and their length can be assessed with the length function

( x <- c(1, 2, 3, 4) )

## [1] 1 2 3 4

( y <- c("a", "b", "c") )

## [1] "a" "b" "c"

length(y)

## [1] 3

Vectors can contain a single data type only, so beware of conversions

( z <- c("a", "b", 1, 2) )

## [1] "a" "b" "1" "2"

( v <- c(x, y) )

## [1] "1" "2" "3" "4" "a" "b" "c"

What are the classes of z and v?

j <- 1:4  # Seq. of integers
j

## [1] 1 2 3 4

x <- seq(0, 1, 0.1) # Seq. of numbers (from, to, step)
x

##  [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

y <- rep("A", 5) # Seq. of repeated numbers or characters
y

## [1] "A" "A" "A" "A" "A"

z <- c( rep("A", 3), rep("B", 5) )  # Vector combination
z

## [1] "A" "A" "A" "B" "B" "B" "B" "B"

( age <- c(19, 30, 20) )

## [1] 19 30 20

names(age) <- c("Mary", "John", "Lisa") # Assign

age

## Mary John Lisa 
##   19   30   20

names(age) # Query

## [1] "Mary" "John" "Lisa"

You can access the elements of a vector by using indexes between square brackets. Note: Round brackets are for functions.

age <- c(11, 12, 18, 20, 45, 2, 33)

age[1]

## [1] 11

age(1)

## Error in age(1): could not find function "age"

mean(age)

## [1] 20.14286

You can access the elements of a vector by using logical or numeric indexes (positive and negative)

days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")

days[c(6,7)] # Format: days[ INDEX ]

## [1] "Sat" "Sun"

weekend <- days[c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE)]

weekend <- days[-seq(1,5)] # Negative ind. of elements to be removed

Vector elements can be also accessed using their names, when initialized

age <- c(11, 12, 18, 20)
names(age) <- c("John", "Lisa", "Maria", "Markus")

age

##   John   Lisa  Maria Markus 
##     11     12     18     20

age[c("John", "Maria")]

##  John Maria 
##    11    18

You can extract the elements of a vector that satisfy a certain condition:

age <- c(11, 12, 18, 20, 45, 2, 33)

age[age>=18] # Logical index

## [1] 18 20 45 33

age[which(age>=18)] # Numerical index

## [1] 18 20 45 33

You can change the elements of a vector:

x <- c(11, 12, -18, 20, -45, -2, 33)

x[1] <- 35 # Subsitute the first element

x

## [1]  35  12 -18  20 -45  -2  33

x[x<0] <- 0 # Set to 0 all negative elements

x

## [1] 35 12  0 20  0  0 33

A factor is categorical variable that can be built with the function factor

strain <- factor(c("WildType", "WildType", "Mutant", "Mutant"))
strain

## [1] WildType WildType Mutant   Mutant  
## Levels: Mutant WildType

length(strain)

## [1] 4

levels(strain)

## [1] "Mutant"   "WildType"

( x <- factor(c(10, 2, 2, 3)))

## [1] 10 2  2  3 
## Levels: 2 3 10

( y <- as.numeric(x) ) # This does NOT work

## [1] 3 1 1 2

( z <- as.numeric(as.character(x)) ) # This works

## [1] 10  2  2  3