mtcars: Motor Trend Car Road Tests

The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models)

• View the content of mtcars data set:

# 1. Loading 
data("mtcars")
# 2. Print
head(mtcars)

• It contains 32 observations and 11 variables:

# Number of rows (observations)
nrow(mtcars)

[1] 32

# Number of columns (variables)
ncol(mtcars)

[1] 11

• Description of variables:

1. mpg: Miles/(US) gallon
2. cyl: Number of cylinders
3. disp: Displacement (cu.in.)
4. hp: Gross horsepower
5. drat: Rear axle ratio
6. wt: Weight (1000 lbs)
7. qsec: 1/4 mile time
8. vs: V/S
9. am: Transmission (0 = automatic, 1 = manual)
10. gear: Number of forward gears
11. carb: Number of carburetors

If you want to learn more about mtcars, type this:

?mtcars

iris

iris data set gives the measurements in centimeters of the variables sepal length, sepal width, petal length and petal width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

data("iris")

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

ToothGrowth

ToothGrowth data set contains the result from an experiment studying the effect of vitamin C on tooth growth in 60 Guinea pigs. Each animal received one of three dose levels of vitamin C (0.5, 1, and 2 mg/day) by one of two delivery methods, (orange juice or ascorbic acid (a form of vitamin C and coded as VC).

data("ToothGrowth")
  
head(ToothGrowth)

   len supp dose
1  4.2   VC  0.5
2 11.5   VC  0.5
3  7.3   VC  0.5
4  5.8   VC  0.5
5  6.4   VC  0.5
6 10.0   VC  0.5

1. len: Tooth length
2. supp: Supplement type (VC or OJ).
3. dose: numeric Dose in milligrams/day

PlantGrowth

Results obtained from an experiment to compare yields (as measured by dried weight of plants) obtained under a control and two different treatment condition.

data("PlantGrowth")
  
head(PlantGrowth)

  weight group
1   4.17  ctrl
2   5.58  ctrl
3   5.18  ctrl
4   6.11  ctrl
5   4.50  ctrl
6   4.61  ctrl

USArrests

This data set contains statistics about violent crime rates by us state.

data("USArrests")
     
head(USArrests)

           Murder Assault UrbanPop Rape
Alabama      13.2     236       58 21.2
Alaska       10.0     263       48 44.5
Arizona       8.1     294       80 31.0
Arkansas      8.8     190       50 19.5
California    9.0     276       91 40.6
Colorado      7.9     204       78 38.7

1. Murder: Murder arrests (per 100,000)
2. Assault: Assault arrests (per 100,000)
3. UrbanPop: Percent urban population
4. Rape: Rape arrests (per 100,000)

Install a package from CRAN

The function install.packages() is used to install a package from CRAN. The syntax is as follow:

install.packages("package_name")

For example, to install the package named readr, type this:

install.packages("readr")

Note that, every time you install an R package, R may ask you to specify a CRAN mirror (or server). Choose one that’s close to your location, and R will connect to that server to download and install the package files.

It’s also possible to install multiple packages at the same time, as follow:

install.packages(c("readr", "ggplot2"))

Install a package from Bioconductor

Bioconductor contains packages for analyzing biological related data. In the following R code, we want to install the R/Bioconductor package limma, which is dedicated to analyse genomic data.

To install a package from Bioconductor, use this:

source("https://bioconductor.org/biocLite.R")
biocLite("limma")

Install a package from Github

GitHub is a repository useful for all software development and data analysis, including R packages. It makes sharing your package easy. You can read more about GitHub here: Git and GitHub, by Hadley Wickham.

To install a package from GitHub, the R package devtools (by Hadley Wickham) can be used. You should first install devtools if you don’t have it installed on your computer.

For example, the following R code installs the latest version of survminer R package developed by A. Kassambara (https://github.com/kassambara/survminer).

install.packages("devtools")
devtools::install_github("kassambara/survminer")

View the list of installed packages

To view the list of the already installed packages on your computer, type :

installed.packages()

Note that, in RStudio, the list of installed packages are available in the lower right window under Packages tab (see the image below).

Folder containing installed packages

R packages are installed in a directory called library. The R function .libPaths() can be used to get the path to the library.

.libPaths()

[1] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library"

Create a vector

A vector is created using the function c() (for concatenate), as follow:

# Store your friends'age in a numeric vector
friend_ages <- c(27, 25, 29, 26) # Create
friend_ages # Print

[1] 27 25 29 26

# Store your friend names in a character vector
my_friends <- c("Nicolas", "Thierry", "Bernard", "Jerome")
my_friends 

[1] "Nicolas" "Thierry" "Bernard" "Jerome" 

# Store your friends marital status in a logical vector
# Are they married? (yes/no <=> TRUE/FALSE)
are_married <- c(TRUE, FALSE, TRUE, TRUE)
are_married

[1]  TRUE FALSE  TRUE  TRUE

It’s possible to give a name to the elements of a vector using the function names().

# Vector without element names
friend_ages

[1] 27 25 29 26

# Vector with element names
names(friend_ages) <- c("Nicolas", "Thierry", "Bernard", "Jerome")
friend_ages

Nicolas Thierry Bernard  Jerome 
     27      25      29      26 

# You can also create a named vector as follow
friend_ages <- c(Nicolas = 27, Thierry = 25, 
                 Bernard = 29, Jerome = 26)
friend_ages

Nicolas Thierry Bernard  Jerome 
     27      25      29      26 

Note that a vector can only hold elements of the same type. For example, you cannot have a vector that contains both characters and numeric values.

• Find the length of a vector (i.e., the number of elements in a vector)

# Number of friends
length(my_friends)

[1] 4

Case of missing values

I know that some of my friends (Nicolas and Thierry) have 2 child. But this information is not available (NA) for the remaining friends (Bernard and Jerome).

In R missing values (or missing information) are represented by NA:

have_child <- c(Nicolas = "yes", Thierry = "yes", 
                Bernard = NA, Jerome = NA)
have_child

Nicolas Thierry Bernard  Jerome 
  "yes"   "yes"      NA      NA 

It’s possible to use the function is.na() to check whether a data contains missing value. The result of the function is.na() is a logical vector in which, the value TRUE specifies that the corresponding element in x is NA.

# Check if have_child contains missing values
is.na(have_child)

Nicolas Thierry Bernard  Jerome 
  FALSE   FALSE    TRUE    TRUE 

Note that, there is a second type of missing values named NaN (“Not a Number”). This is produced in a situation where mathematical function won’t work properly, for example 0/0 = NaN.

Note also that, the function is.na() is TRUE for both NA and NaN values. To differentiate these, the function is.nan() is only TRUE for NaNs.

Get a subset of a vector

Subsetting a vector consists of selecting a part of your vector.

• Selection by positive indexing: select an element of a vector by its position (index) in square brackets

# Select my friend number 2
my_friends[2]

[1] "Thierry"

# Select my friends number 2 and 4 
my_friends[c(2, 4)]

[1] "Thierry" "Jerome" 

# Select my friends number 1 to 3
my_friends[1:3]

[1] "Nicolas" "Thierry" "Bernard"

Note that, R indexes from 1, NOT 0. So your first column is at [1] and not [0].

If you have a named vector, it’s also possible to use the name for selecting an element:

friend_ages["Bernard"]

Bernard 
     29 

• Selection by negative indexing: Exclude an element

# Exclude my friend number 2
my_friends[-2]

[1] "Nicolas" "Bernard" "Jerome" 

# Exclude my friends number 2 and 4
my_friends[-c(2, 4)]

[1] "Nicolas" "Bernard"

# Exclude my friends number 1 to 3
my_friends[-(1:3)]

[1] "Jerome"

• Selection by logical vector: Only, the elements for which the corresponding value in the selecting vector is TRUE, will be kept in the subset.

# Select only married friends
my_friends[are_married == TRUE]

[1] "Nicolas" "Bernard" "Jerome" 

# Friends with age >=27
my_friends[friend_ages >= 27]

[1] "Nicolas" "Bernard"

# Friends with age different from 27
my_friends[friend_ages != 27]

[1] "Thierry" "Bernard" "Jerome" 

If you want to remove missing data, use this:

# Data with missing values
have_child

Nicolas Thierry Bernard  Jerome 
  "yes"   "yes"      NA      NA 

# Keep only values different from NA (!is.na())
have_child[!is.na(have_child)]

Nicolas Thierry 
  "yes"   "yes" 

# Or, replace NA value by "NO" and then print
have_child[!is.na(have_child)] <- "NO"
have_child

Nicolas Thierry Bernard  Jerome 
   "NO"    "NO"      NA      NA 

Calculations with vectors

Note that, all the basic arithmetic operators (+, -, *, / and ^ ) as well as the common arithmetic functions (log, exp, sin, cos, tan, sqrt, abs, …), described in the previous sections, can be applied on a numeric vector.

If you perform an operation with vectors, the operation will be applied to each element of the vector. An example is provided below:

# My friends' salary in dollars
salaries <- c(2000, 1800, 2500, 3000)
names(salaries) <- c("Nicolas", "Thierry", "Bernard", "Jerome")
salaries

Nicolas Thierry Bernard  Jerome 
   2000    1800    2500    3000 

# Multiply salaries by 2
salaries*2

Nicolas Thierry Bernard  Jerome 
   4000    3600    5000    6000 

As you can see, R multiplies each element in the salaries vector with 2.

Now, suppose that you want to multiply the salaries by different coefficients. The following R code can be used:

# create coefs vector with the same length as salaries
coefs <- c(2, 1.5, 1, 3)

# Multiply salaries by coeff
salaries*coefs

Nicolas Thierry Bernard  Jerome 
   4000    2700    2500    9000 

Note that the calculation is done element-wise. The first element of salaries vector is multiplied by the first element of coefs vector, and so on.

Compute the square root of a numeric vector:

my_vector <- c(4, 16, 9)
sqrt(my_vector)

[1] 2 4 3

Other useful functions are:

max(x) # Get the maximum value of x
min(x) # Get the minimum value of x

# Get the range of x. Returns a vector containing
# the minimum and the maximum of x
range(x) 
   
length(x) # Get the number of elements in x
  
sum(x) # Get the total of the elements in x
  
prod(x) # Get the product of the elements in x
 
# The mean value of the elements in x
# sum(x)/length(x)
mean(x) 

sd(x) # Standard deviation of x
var(x) # Variance of x

# Sort the element of x in ascending order
sort(x)

For example, if you want to compute the total sum of salaries, type this:

sum(salaries)

[1] 9300

Compute the mean of salaries:

mean(salaries)

[1] 2325

The range (minimum, maximum) of salaries is:

range(salaries)

[1] 1800 3000

Create and naming matrix

To create easily a matrix, use the function cbind() or rbind() as follow:

# Numeric vectors
col1 <- c(5, 6, 7, 8, 9)
col2 <- c(2, 4, 5, 9, 8)
col3 <- c(7, 3, 4, 8, 7)
# Combine the vectors by column
my_data <- cbind(col1, col2, col3)
my_data

     col1 col2 col3
[1,]    5    2    7
[2,]    6    4    3
[3,]    7    5    4
[4,]    8    9    8
[5,]    9    8    7

# Change rownames
rownames(my_data) <- c("row1", "row2", "row3", "row4", "row5")
my_data

     col1 col2 col3
row1    5    2    7
row2    6    4    3
row3    7    5    4
row4    8    9    8
row5    9    8    7

If you want to transpose your data, use the function t():

t(my_data)

     row1 row2 row3 row4 row5
col1    5    6    7    8    9
col2    2    4    5    9    8
col3    7    3    4    8    7

Note that, it’s also possible to construct a matrix using the function matrix().

The simplified format of matrix() is as follow:

matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE,
       dimnames = NULL)

In the R code below, the input data has length 6. We want to create a matrix with two columns. You don’t need to specify the number of rows (here nrow = 3). R will infer this automatically. The matrix is filled column by column when the argument byrow = FALSE. If you want to fill the matrix by rows, use byrow = TRUE.

mdat <- matrix(
           data = c(1,2,3, 11,12,13), 
           nrow = 2, byrow = TRUE,
           dimnames = list(c("row1", "row2"), c("C.1", "C.2", "C.3"))
           )
mdat

     C.1 C.2 C.3
row1   1   2   3
row2  11  12  13

Dimensions of a matrix

The R functions nrow() and ncol() return the number of rows and columns present in the data, respectively.

ncol(my_data) # Number of columns

[1] 3

nrow(my_data) # Number of rows

[1] 5

dim(my_data) # Number of rows and columns

[1] 5 3

Get a subset of a matrix

• Select rows/columns by positive indexing

rows and/or columns can be selected as follow: my_data[row, col]

# Select row number 2
my_data[2, ]

col1 col2 col3 
   6    4    3 

# Select row number 2 to 4
my_data[2:4, ]

     col1 col2 col3
row2    6    4    3
row3    7    5    4
row4    8    9    8

# Select multiple rows that aren't contiguous
# e.g.: rows 2 and 4 but not 3
my_data[c(2,4), ]

     col1 col2 col3
row2    6    4    3
row4    8    9    8

# Select column number 3
my_data[, 3]

row1 row2 row3 row4 row5 
   7    3    4    8    7 

# Select the value at row 2 and column  3
my_data[2, 3]

[1] 3

• Select by row/column names

# Select column 2
my_data[, "col2"]

row1 row2 row3 row4 row5 
   2    4    5    9    8 

# Select by index and names: row 3 and olumn 2
my_data[3, "col2"]

[1] 5

• Exclude rows/columns by negative indexing

# Exclude column 1
my_data[, -1]

     col2 col3
row1    2    7
row2    4    3
row3    5    4
row4    9    8
row5    8    7

• Selection by logical: In the R code below, we want to keep only rows where col3 >=4:

col3 <- my_data[, "col3"]
my_data[col3 >= 4, ]

     col1 col2 col3
row1    5    2    7
row3    7    5    4
row4    8    9    8
row5    9    8    7

Calculations with matrices

• It’s also possible to perform simple operations on matrice. For example, the following R code multiplies each element of the matrix by 2:

my_data*2

     col1 col2 col3
row1   10    4   14
row2   12    8    6
row3   14   10    8
row4   16   18   16
row5   18   16   14

Or, compute the log2 values:

log2(my_data)

         col1     col2     col3
row1 2.321928 1.000000 2.807355
row2 2.584963 2.000000 1.584963
row3 2.807355 2.321928 2.000000
row4 3.000000 3.169925 3.000000
row5 3.169925 3.000000 2.807355

• rowSums() and colSums() functions: Compute the total of each row and the total of each column, respectively.

# Total of each row
rowSums(my_data)

row1 row2 row3 row4 row5 
  14   13   16   25   24 

# Total of each column
colSums(my_data)

col1 col2 col3 
  35   28   29 

If you are interested in row/column means, you can use the function rowMeans() and colMeans() for computing row and column means, respectively.

Note that, it’s also possible to use the function apply() to apply any statistical functions to rows/columns of matrices.

The simplified format of apply() is as follow:

apply(X, MARGIN, FUN)

• X: your data matrix
• MARGIN: possible values are 1 (for rows) and 2 (for columns)
• FUN: the function to apply on rows/columns

Use apply() as follow:

# Compute row means
apply(my_data, 1, mean)

    row1     row2     row3     row4     row5 
4.666667 4.333333 5.333333 8.333333 8.000000 

# Compute row medians
apply(my_data, 1, median)

row1 row2 row3 row4 row5 
   5    4    5    8    8 

# Compute column means
apply(my_data, 2, mean)

col1 col2 col3 
 7.0  5.6  5.8 

Create a factor

# Create a factor variable
friend_groups <- factor(c(1, 2, 1, 2))
friend_groups

[1] 1 2 1 2
Levels: 1 2

The variable friend_groups contains two categories of friends: 1 and 2. In R terminology, categories are called factor levels.

It’s possible to access to the factor levels using the function levels():

# Get group names (or levels)
levels(friend_groups)

[1] "1" "2"

# Change levels
levels(friend_groups) <- c("best_friend", "not_best_friend")
friend_groups

[1] best_friend     not_best_friend best_friend     not_best_friend
Levels: best_friend not_best_friend

Note that, R orders factor levels alphabetically. If you want a different order in the levels, you can specify the levels argument in the factor function as follow.

# Change the order of levels
friend_groups <- factor(friend_groups, 
                      levels = c("not_best_friend", "best_friend"))
# Print
friend_groups

[1] best_friend     not_best_friend best_friend     not_best_friend
Levels: not_best_friend best_friend

# Check if friend_groups is a factor
is.factor(friend_groups)

[1] TRUE

# Check if "are_married" is a factor
is.factor(are_married)

[1] FALSE

# Convert "are_married" as a factor
as.factor(are_married)

[1] TRUE  FALSE TRUE  TRUE 
Levels: FALSE TRUE

Calculations with factors

• If you want to know the number of individuals in each levels, use the function summary():

summary(friend_groups)

not_best_friend     best_friend 
              2               2 

• In the following example, I want to compute the mean salary of my friends by groups. The function tapply() can be used to apply a function, here mean(), to each group.

# Salaries of my friends
salaries

Nicolas Thierry Bernard  Jerome 
   2000    1800    2500    3000 

# Friend groups
friend_groups

[1] best_friend     not_best_friend best_friend     not_best_friend
Levels: not_best_friend best_friend

# Compute the mean salaries by groups
mean_salaries <- tapply(salaries, friend_groups, mean)
mean_salaries

not_best_friend     best_friend 
           2400            2250 

# Compute the size/length of each group
tapply(salaries, friend_groups, length)

not_best_friend     best_friend 
              2               2 

• It’s also possible to use the function table() to create a frequency table, also known as a contingency table of the counts at each combination of factor levels.

table(friend_groups)

friend_groups
not_best_friend     best_friend 
              2               2 

# Cross-tabulation between 
# friend_groups and are_married variables
table(friend_groups, are_married)

                 are_married
friend_groups     FALSE TRUE
  not_best_friend     1    1
  best_friend         0    2

Create a data frame

A data frame can be created using the function data.frame(), as follow:

# Create a data frame
friends_data <- data.frame(
  name = my_friends,
  age = friend_ages,
  height = c(180, 170, 185, 169),
  married = are_married
)
# Print
friends_data

           name age height married
Nicolas Nicolas  27    180    TRUE
Thierry Thierry  25    170   FALSE
Bernard Bernard  29    185    TRUE
Jerome   Jerome  26    169    TRUE

To check whether a data is a data frame, use the is.data.frame() function. Returns TRUE if the data is a data frame:

is.data.frame(friends_data)

[1] TRUE

is.data.frame(my_data)

[1] FALSE

The object “friends_data” is a data frame, but not the object “my_data”. We can convert-it to a data frame using the as.data.frame() function:

# What is the class of my_data? --> matrix
class(my_data)

[1] "matrix"

# Convert it as a data frame
my_data2 <- as.data.frame(my_data)
# Now, the class is data.frame
class(my_data2)

[1] "data.frame"

As described in matrix section, you can use the function t() to transpose a data frame:

t(friends_data)

Subset a data frame

To select just certain columns from a data frame, you can either refer to the columns by name or by their location (i.e., column 1, 2, 3, etc.).

1. Positive indexing by name and by location

# Access the data in 'name' column
# dollar sign is used
friends_data$name

[1] Nicolas Thierry Bernard Jerome 
Levels: Bernard Jerome Nicolas Thierry

# or use this
friends_data[, 'name']

[1] Nicolas Thierry Bernard Jerome 
Levels: Bernard Jerome Nicolas Thierry

# Subset columns 1 and 3
friends_data[ , c(1, 3)]

           name height
Nicolas Nicolas    180
Thierry Thierry    170
Bernard Bernard    185
Jerome   Jerome    169

2. Negative indexing

# Exclude column 1
friends_data[, -1]

        age height married
Nicolas  27    180    TRUE
Thierry  25    170   FALSE
Bernard  29    185    TRUE
Jerome   26    169    TRUE

3. Index by characteristics

We want to select all friends with age >= 27.

# Identify rows that meet the condition
friends_data$age >= 27

[1]  TRUE FALSE  TRUE FALSE

TRUE specifies that the row contains a value of age >= 27.

# Select the rows that meet the condition
friends_data[friends_data$age >= 27, ]

           name age height married
Nicolas Nicolas  27    180    TRUE
Bernard Bernard  29    185    TRUE

The R code above, tells R to get all rows from friends_data where age >= 27, and then to return all the columns.

If you don’t want to see all the column data for the selected rows but are just interested in displaying, for example, friend names and age for friends with age >= 27, you could use the following R code:

# Use column locations
friends_data[friends_data$age >= 27,  c(1, 2)]

           name age
Nicolas Nicolas  27
Bernard Bernard  29

# Or use column names
friends_data[friends_data$age >= 27, c("name", "age")]

           name age
Nicolas Nicolas  27
Bernard Bernard  29

If you’re finding that your selection statement is starting to be inconvenient, you can put your row and column selections into variables first, such as:

age27 <- friends_data$age >= 27
cols <- c("name", "age")

Then you can select the rows and columns with those variables:

friends_data[age27, cols]

           name age
Nicolas Nicolas  27
Bernard Bernard  29

It’s also possible to use the function subset() as follow.

# Select friends data with age >= 27
subset(friends_data, age >= 27)

           name age height married
Nicolas Nicolas  27    180    TRUE
Bernard Bernard  29    185    TRUE

Another option is to use the functions attach() and detach(). The function attach() takes a data frame and makes its columns accessible by simply giving their names.

The functions attach() and detach() can be used as follow:

# Attach a data frame
attach(friends_data)

# === Data manipulation ====
friends_data[age>=27, ]
# === End of data manipulation ====

# Detach the data frame
detach(friends_data)

Extend a data frame

Add new column in a data frame

# Add group column to friends_data
friends_data$group <- friend_groups
friends_data

           name age height married           group
Nicolas Nicolas  27    180    TRUE     best_friend
Thierry Thierry  25    170   FALSE not_best_friend
Bernard Bernard  29    185    TRUE     best_friend
Jerome   Jerome  26    169    TRUE not_best_friend

It’s also possible to use the functions cbind() and rbind() to extend a data frame.

cbind(friends_data, group = friend_groups)

Create a list

# Create a list
my_family <- list(
  mother = "Veronique", 
  father = "Michel",
  sisters = c("Alicia", "Monica"),
  sister_age = c(12, 22)
  )
# Print
my_family

$mother
[1] "Veronique"

$father
[1] "Michel"

$sisters
[1] "Alicia" "Monica"

$sister_age
[1] 12 22

# Names of elements in the list
names(my_family)

[1] "mother"     "father"     "sisters"    "sister_age"

# Number of elements in the list
length(my_family)

[1] 4

The list object “my_family”, contains four components, which may be individually referred to as my_family[[1]], as_family[[2]] and so on.

Subset a list

It’s possible to select an element, from a list, by its name or its index:

• my_family$mother is the same as my_family[[1]]
• my_family$father is the same as my_family[[2]]

# Select by name (1/2)
my_family$father

[1] "Michel"

# Select by name (2/2)
my_family[["father"]]

[1] "Michel"

# Select by index
my_family[[1]]

[1] "Veronique"

my_family[[3]]

[1] "Alicia" "Monica"

# Select a specific element of a component
# select the first ([1]) element of my_family[[3]]
my_family[[3]][1] 

[1] "Alicia"

Extend a list

Note that, it’s possible to extend an original list.

In the R code below, we want to add the components “grand_father” and “grand_mother” to my_family list object:

# Extend the list
my_family$grand_father <- "John"
my_family$grand_mother <- "Mary"
# Print
my_family

$mother
[1] "Veronique"

$father
[1] "Michel"

$sisters
[1] "Alicia" "Monica"

$sister_age
[1] 12 22

$grand_father
[1] "John"

$grand_mother
[1] "Mary"

You can also concatenate two lists as follow:

list_abc <- c(list_a, list_b, list_c)

The result is a list also, whose components are those of the argument lists joined together in sequence.

Under Windows and MAC OSX

For the first time you use R, the suggested procedure, under Windows and MAC OSX, is as follow:

1. Create a sub-directory, say R, in your “Documents” folder. This sub-folder, also known as working directory, will be used by R to read and save files.

2. Launch R by double-clicking on the icon.

3. Specify your working directory to R:
   • On Windows: File –> Change directory
   • On MAC OSX: Tools –> Change the working directory

Under Linux

1. Open the shell prompt

2. Create a working directory, named “R”, using “mkdir” command:

3. Start the R program with the command “R”:

$ R

4. To quit R program, use this:

$ q()

Launch RStudio under Windows, MAC OSX and Linux

After installing R and RStudio, launch RStudio from your computer “application folders”.

RStudio screen

RStudio is a four pane work-space for 1) creating file containing R script, 2) typing R commands, 3) viewing command histories, 4) viewing plots and more.

1. Top-left panel: Code editor allowing you to create and open a file containing R script. The R script is where you keep a record of your work. R script can be created as follow: File –> New –> R Script.

2. Bottom-left panel: R console for typing R commands

3. Top-right panel:
   • Workspace tab: shows the list of R objects you created during your R session
   • History tab: shows the history of all previous commands
4. Bottom-right panel:
   • Files tab: show files in your working directory
   • Plots tab: show the history of plots you created. From this tab, you can export a plot to a PDF or an image files
   • Packages tab: show external R packages available on your system. If checked, the package is loaded in R.

For more about RStudio read the online RStudio documentation.

Set your working directory

Recall that, the working directory is a folder where R reads and saves files.

setwd("/path/to/my/directory")

setwd("c:/Documents/my/working/directory")

Install R for windows

1. Download the latest version of R, for Windows, from CRAN at : https://cran.r-project.org/bin/windows/base/

2. Double-click on the file you just downloaded to install R

3. Cick ok –> Next –> Next –> Next …. (no need to change default installation parameters)

Install Rtools for Windows

Rtools contains tools to build your own packages on Windows, or to build R itself.

1. Download Rtools version corresponding to your R version at: https://cran.r-project.org/bin/windows/Rtools/. Use the latest release of Rtools with the latest release of R.

2. Double-click on the file you just downloaded to install Rtools (no need to change default installation parameters)

Install RStudio on Windows

• Download RStudio at : https://www.rstudio.com/products/rstudio/download/