Introduction to R

How to install R?

Useful link

To install Cran R, you must go to https://cran.r-project.org/, and download the install file for your favorite operating system, click on the .exe, .dmg, .pkg, .deb, respectively for Windows, MacOS and Linux-debian.

And click on follow… until to reach successful installation Then download and install the IDE Rstudio https://www.rstudio.com/products/rstudio/.

Everything is free to download

First commands

Where am I?

To get the current working directory

getwd()

[1] "/Users/marcgarel/OSU/MIO/2026/Projet_2026/Formation_OMIC_2026/formation-metabarcoding-long-read/scripts"

To change my working directory from the console

setwd("/path/to/my/fancy/project/")

Note

In Rstudio, we can change the working directory by navigating in folder in File panel and clicking on menu More

If you are using a Rproject, you don’t need to change your working directory.

How can I find documentation about function?

The command help() is the 911

# exemple with function read.table
help("read.table")
#other exemple of 911
example("plot")
??plot()

How to use a package?

Installation

You can install new packages by clicking directly in Rstudio or by command line (the best way for me)

install.packages("your_package") # for packages on CRAN mirror

For packages from Bioconductor (specifically for bioinformatic):

BiocManager::install("your_package")

or from github (using the package devtools)

devtools::install_github("your_package")

Using functions from a package

To use functions from a specific package you can either load the entire package:

library("your package1")
library("your package2")
library("your package3")

or call the function this way:

your_package::yourfunction()

On your R session using cloud IFB all the necessary packages are installed

Expression and affectation

Expression

An expression is directly evaluated and the result is displayed on terminal Example :

2 + 3

[1] 5

sqrt(25)

[1] 5

Affectation in an object

An assignment is an expression stored in object or variable. In this example expression, constant, array, matrix, data frame, list Example :

a=2+3
b="madame"

What happens when you execute this cell?</span

a

[1] 5

b

[1] "madame"

a=10
b="5"
somme<-a+b

Why do we have an error ? I need to know the type of the object

str(a)
str(b)

What kind of information do we get?

We can also perform mathematical operations on numerical objects.

log(a) # to obtain the logarithm of a

[1] 1.609

sqrt(a)# to obtain the square root of a

[1] 2.236

# we can compare it
log(a)>sqrt(a)

[1] FALSE

Type of objects

Vectors

Vectors are objects composed by values with the same type or (i.e, numeric, characters…)

V1 <- c(2, 6, 9) # numeric vector
V2 <- c("monday", "Tuesday", "Wednesday")# character vector
V3 <- rep(6, 3) # repetition of the same value
V4 <- seq(1, 3, 0.1)# sequence of number
V5 <- 1:100
V5

  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
 [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
 [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
 [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
 [91]  91  92  93  94  95  96  97  98  99 100

To know the number of value inside a vector

length(V1)

[1] 3

What do you think about V4?

Filter a vector according to criteria

# Example : with list of value (a vector)

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

# get element from 2 to 6

x[2:6]

[1] 3 5 3 2 1

# get elements 3 et 5 from x.

x[c(3, 5)]

[1] 5 2

# get value more than 20.

x[x > 5]

[1] 6 7

# get value of  x where x is equal to 21.

x[x == 5]

[1] 5 5

# return elements form x the the value different from 5

x[x != 5]

 [1] 1 3 3 2 1 4 6 4 7 4 3

Filter a vector according to several criteria

# 3 lists : ages, sexes et poids

age <- c(20, 30, 40,
         15, 22, 24,
         36, 38)

sexe <- c("F", "M", "F",
          "M", "F", "M",
          "F", "M")

poids <- c(75, 76, 73,
           72, 64, 76,
           73, 72)

# get value from age greater than 20 and less than 30.

age[age > 20 & age < 30]

[1] 22 24

# Recovering "poids" for those who are older than 25 and female

poids[age > 25 & sexe == "F"]

[1] 73 73

#Retrieve age values below 20 or above 30.

age[age < 20 | age > 30]

[1] 40 15 36 38

Exo1

Considering the vector a such as a <- c("lannister", "targaryen", "baratheon", "starck", "greyjoy")

1. What is the length of the vector?

2. Try doing a[1:3]. What do you get?

3. Create a new vector b containing only lannister and starck.

4. Try doing a[-1]. What do you get?

5. Sort by alphabetical order using sort()

Exo2

1. Create a vector a containing all integers from 1 to 100.

2. Add the values 200, 201, 202 to the vector a.

3. Create a vector b containing all even integers from 2 to 100 using seq()

Data frames

Data frames are objects composed by vector where the value are of different modes (i.e, numeric, characters…)

Data frame examples

Load a data frame

data(iris)

Visualise the data frame in a table

View(iris)

Display its internal structure

str(iris)

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

What can we notice?

How to build your own data frame

date <- c("1_monday", "2_Tuesday", "3_Wednesday",
          "4_Thursday", "5_Friday", "6_Sturday",
          "7_Sunday")

is.character(date)

[1] TRUE

# temperature in deg Celsius
temperature <- c(24, 27, 25,
               22, 30, 21,
               28)

is.numeric(temperature)

[1] TRUE

# rain in mm
rain <- c(1, 0, 0,
          5, 2, 0,
          0)

is.numeric(rain)

[1] TRUE

# make data.frame
df <- data.frame(date, temperature, rain)
str(df)

'data.frame':   7 obs. of  3 variables:
 $ date       : chr  "1_monday" "2_Tuesday" "3_Wednesday" "4_Thursday" ...
 $ temperature: num  24 27 25 22 30 21 28
 $ rain       : num  1 0 0 5 2 0 0

#To select a column or vector
df$temperature

[1] 24 27 25 22 30 21 28

df[, 2]

[1] 24 27 25 22 30 21 28

# here we use list() instead of c()
# because there is multiple class in inside row

day <- list("8_monday", 29, 1)
new_def <- rbind(df, day)# add row to a data frame
new_def

         date temperature rain
1    1_monday          24    1
2   2_Tuesday          27    0
3 3_Wednesday          25    0
4  4_Thursday          22    5
5    5_Friday          30    2
6   6_Sturday          21    0
7    7_Sunday          28    0
8    8_monday          29    1

Functions

Definition

Function are a compilation of command line with different instructions inside one object to simplify code. A function is composed by arguments and options.

function(argument1, argument2, option1, … ,option10)

Usual functions for data frame

head() # to know first line of your data frame
class()# return the class of the object. Ex : data.frame, matrix, list ....
str()# return the structure of the object. Ex : numeric, factor, character....
names()# to get or set the names of an object
sum() # for addition
min() # return the minimum of the vector
max() # return the minimum of the vector
row.names() # attribute names for lines of the data frame
colnames() # attribute names for column of the data frame
apply() # Returns a vector or array or list of values obtained by applying a function to margins of an array or matrix.

Example function row.names, class and str

df2 <- data.frame(x = c(TRUE, FALSE, NA, NA), y = c(12, 34, 56, 78))
df2

      x  y
1  TRUE 12
2 FALSE 34
3    NA 56
4    NA 78

row.names(df2) <- paste("row", 1 : 4, sep = "_")
df2 # what do you see

          x  y
row_1  TRUE 12
row_2 FALSE 34
row_3    NA 56
row_4    NA 78

class(df2)

[1] "data.frame"

str(df2)

'data.frame':   4 obs. of  2 variables:
 $ x: logi  TRUE FALSE NA NA
 $ y: num  12 34 56 78

Example function apply

head(df)

         date temperature rain
1    1_monday          24    1
2   2_Tuesday          27    0
3 3_Wednesday          25    0
4  4_Thursday          22    5
5    5_Friday          30    2
6   6_Sturday          21    0

class(df)

[1] "data.frame"

str(df)

'data.frame':   7 obs. of  3 variables:
 $ date       : chr  "1_monday" "2_Tuesday" "3_Wednesday" "4_Thursday" ...
 $ temperature: num  24 27 25 22 30 21 28
 $ rain       : num  1 0 0 5 2 0 0

# return mean for the numerical column of the data.frame. apply(data,margin,fun). 
# For margin parameter the value 1 return mean for each row, 
# for margin=2 return mean for each selected column.
apply(df[, 2:3], 2, mean)

temperature        rain 
     25.286       1.143 

Exo 3

1. Create a vector month name named “month” using function month.name (don’t forget to use help if you need)
2. Create a numeric vector of 12 random number ranged between 20 and 50 named “wind” using function runif. Clue (runif(n, min=x, max=y)).
3. Create a data.frame with vectors “month” and “wind”.
4. Give me the class.
5. Give me the structure.
6. Select only vectore “wind”.
7. Select the 8th element inside vector “winb”.

Exo 4

From data set iris in package datasets** Load package datasets and load data set iris using data()

1. Give the class Sepal.Width and Species vectors
2. What is the minimum / maximum / average sepal length of these irises?
3. What are the values of the first 10 irises?
4. Calculate standard deviation for every numeric vector (function : sd())
5. Calculate mean for every numeric vector
6. Create a data frame with mean and sd as line and give a name for each line
7. An error of 0.5cm was made in the measurement of the length of the sepal of the 41st iris: add 0.5cm to this measurement

Use dplyr to select, filter a data frame

dplyr is part of the library set named tidyverse (contraction of “tidy” and “universe”, it’s a tidy universe). tidyverse packages are designed to work together and thus follow the same code logic and a common grammar.

The pipe, %>%, is one of the useful elements of the tidyverse. It allows to structure sequences of operations by minimizing the creation of intermediate objects and by facilitating the addition of a step anywhere in this sequence. Note that from R 4.1 you can use a new pipe, |> without the need of loading any library.

The most commonly used tidyverse packages are loaded in your session:

• ggplot2
• dplyr
• tidyr
• readr
• tibble
• stringr

tidyverse::tidyverse_packages()

 [1] "broom"         "conflicted"    "cli"           "dbplyr"       
 [5] "dplyr"         "dtplyr"        "forcats"       "ggplot2"      
 [9] "googledrive"   "googlesheets4" "haven"         "hms"          
[13] "httr"          "jsonlite"      "lubridate"     "magrittr"     
[17] "modelr"        "pillar"        "purrr"         "ragg"         
[21] "readr"         "readxl"        "reprex"        "rlang"        
[25] "rstudioapi"    "rvest"         "stringr"       "tibble"       
[29] "tidyr"         "xml2"          "tidyverse"    

Filter and select variable in data frame

library(tidyverse)

Warning: package 'ggplot2' was built under R version 4.5.2

Warning: package 'tibble' was built under R version 4.5.2

Warning: package 'tidyr' was built under R version 4.5.2

Warning: package 'readr' was built under R version 4.5.2

Warning: package 'purrr' was built under R version 4.5.2

Warning: package 'dplyr' was built under R version 4.5.2

Warning: package 'lubridate' was built under R version 4.5.2

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

data("iris")
#To select a variable with tidyverse
dplyr::select(iris, Species)

       Species
1       setosa
2       setosa
3       setosa
4       setosa
5       setosa
6       setosa
7       setosa
8       setosa
9       setosa
10      setosa
11      setosa
12      setosa
13      setosa
14      setosa
15      setosa
16      setosa
17      setosa
18      setosa
19      setosa
20      setosa
21      setosa
22      setosa
23      setosa
24      setosa
25      setosa
26      setosa
27      setosa
28      setosa
29      setosa
30      setosa
31      setosa
32      setosa
33      setosa
34      setosa
35      setosa
36      setosa
37      setosa
38      setosa
39      setosa
40      setosa
41      setosa
42      setosa
43      setosa
44      setosa
45      setosa
46      setosa
47      setosa
48      setosa
49      setosa
50      setosa
51  versicolor
52  versicolor
53  versicolor
54  versicolor
55  versicolor
56  versicolor
57  versicolor
58  versicolor
59  versicolor
60  versicolor
61  versicolor
62  versicolor
63  versicolor
64  versicolor
65  versicolor
66  versicolor
67  versicolor
68  versicolor
69  versicolor
70  versicolor
71  versicolor
72  versicolor
73  versicolor
74  versicolor
75  versicolor
76  versicolor
77  versicolor
78  versicolor
79  versicolor
80  versicolor
81  versicolor
82  versicolor
83  versicolor
84  versicolor
85  versicolor
86  versicolor
87  versicolor
88  versicolor
89  versicolor
90  versicolor
91  versicolor
92  versicolor
93  versicolor
94  versicolor
95  versicolor
96  versicolor
97  versicolor
98  versicolor
99  versicolor
100 versicolor
101  virginica
102  virginica
103  virginica
104  virginica
105  virginica
106  virginica
107  virginica
108  virginica
109  virginica
110  virginica
111  virginica
112  virginica
113  virginica
114  virginica
115  virginica
116  virginica
117  virginica
118  virginica
119  virginica
120  virginica
121  virginica
122  virginica
123  virginica
124  virginica
125  virginica
126  virginica
127  virginica
128  virginica
129  virginica
130  virginica
131  virginica
132  virginica
133  virginica
134  virginica
135  virginica
136  virginica
137  virginica
138  virginica
139  virginica
140  virginica
141  virginica
142  virginica
143  virginica
144  virginica
145  virginica
146  virginica
147  virginica
148  virginica
149  virginica
150  virginica

#To select several variables with tidyverse
dplyr::select(iris,Species, Sepal.Length, Sepal.Width)

       Species Sepal.Length Sepal.Width
1       setosa          5.1         3.5
2       setosa          4.9         3.0
3       setosa          4.7         3.2
4       setosa          4.6         3.1
5       setosa          5.0         3.6
6       setosa          5.4         3.9
7       setosa          4.6         3.4
8       setosa          5.0         3.4
9       setosa          4.4         2.9
10      setosa          4.9         3.1
11      setosa          5.4         3.7
12      setosa          4.8         3.4
13      setosa          4.8         3.0
14      setosa          4.3         3.0
15      setosa          5.8         4.0
16      setosa          5.7         4.4
17      setosa          5.4         3.9
18      setosa          5.1         3.5
19      setosa          5.7         3.8
20      setosa          5.1         3.8
21      setosa          5.4         3.4
22      setosa          5.1         3.7
23      setosa          4.6         3.6
24      setosa          5.1         3.3
25      setosa          4.8         3.4
26      setosa          5.0         3.0
27      setosa          5.0         3.4
28      setosa          5.2         3.5
29      setosa          5.2         3.4
30      setosa          4.7         3.2
31      setosa          4.8         3.1
32      setosa          5.4         3.4
33      setosa          5.2         4.1
34      setosa          5.5         4.2
35      setosa          4.9         3.1
36      setosa          5.0         3.2
37      setosa          5.5         3.5
38      setosa          4.9         3.6
39      setosa          4.4         3.0
40      setosa          5.1         3.4
41      setosa          5.0         3.5
42      setosa          4.5         2.3
43      setosa          4.4         3.2
44      setosa          5.0         3.5
45      setosa          5.1         3.8
46      setosa          4.8         3.0
47      setosa          5.1         3.8
48      setosa          4.6         3.2
49      setosa          5.3         3.7
50      setosa          5.0         3.3
51  versicolor          7.0         3.2
52  versicolor          6.4         3.2
53  versicolor          6.9         3.1
54  versicolor          5.5         2.3
55  versicolor          6.5         2.8
56  versicolor          5.7         2.8
57  versicolor          6.3         3.3
58  versicolor          4.9         2.4
59  versicolor          6.6         2.9
60  versicolor          5.2         2.7
61  versicolor          5.0         2.0
62  versicolor          5.9         3.0
63  versicolor          6.0         2.2
64  versicolor          6.1         2.9
65  versicolor          5.6         2.9
66  versicolor          6.7         3.1
67  versicolor          5.6         3.0
68  versicolor          5.8         2.7
69  versicolor          6.2         2.2
70  versicolor          5.6         2.5
71  versicolor          5.9         3.2
72  versicolor          6.1         2.8
73  versicolor          6.3         2.5
74  versicolor          6.1         2.8
75  versicolor          6.4         2.9
76  versicolor          6.6         3.0
77  versicolor          6.8         2.8
78  versicolor          6.7         3.0
79  versicolor          6.0         2.9
80  versicolor          5.7         2.6
81  versicolor          5.5         2.4
82  versicolor          5.5         2.4
83  versicolor          5.8         2.7
84  versicolor          6.0         2.7
85  versicolor          5.4         3.0
86  versicolor          6.0         3.4
87  versicolor          6.7         3.1
88  versicolor          6.3         2.3
89  versicolor          5.6         3.0
90  versicolor          5.5         2.5
91  versicolor          5.5         2.6
92  versicolor          6.1         3.0
93  versicolor          5.8         2.6
94  versicolor          5.0         2.3
95  versicolor          5.6         2.7
96  versicolor          5.7         3.0
97  versicolor          5.7         2.9
98  versicolor          6.2         2.9
99  versicolor          5.1         2.5
100 versicolor          5.7         2.8
101  virginica          6.3         3.3
102  virginica          5.8         2.7
103  virginica          7.1         3.0
104  virginica          6.3         2.9
105  virginica          6.5         3.0
106  virginica          7.6         3.0
107  virginica          4.9         2.5
108  virginica          7.3         2.9
109  virginica          6.7         2.5
110  virginica          7.2         3.6
111  virginica          6.5         3.2
112  virginica          6.4         2.7
113  virginica          6.8         3.0
114  virginica          5.7         2.5
115  virginica          5.8         2.8
116  virginica          6.4         3.2
117  virginica          6.5         3.0
118  virginica          7.7         3.8
119  virginica          7.7         2.6
120  virginica          6.0         2.2
121  virginica          6.9         3.2
122  virginica          5.6         2.8
123  virginica          7.7         2.8
124  virginica          6.3         2.7
125  virginica          6.7         3.3
126  virginica          7.2         3.2
127  virginica          6.2         2.8
128  virginica          6.1         3.0
129  virginica          6.4         2.8
130  virginica          7.2         3.0
131  virginica          7.4         2.8
132  virginica          7.9         3.8
133  virginica          6.4         2.8
134  virginica          6.3         2.8
135  virginica          6.1         2.6
136  virginica          7.7         3.0
137  virginica          6.3         3.4
138  virginica          6.4         3.1
139  virginica          6.0         3.0
140  virginica          6.9         3.1
141  virginica          6.7         3.1
142  virginica          6.9         3.1
143  virginica          5.8         2.7
144  virginica          6.8         3.2
145  virginica          6.7         3.3
146  virginica          6.7         3.0
147  virginica          6.3         2.5
148  virginica          6.5         3.0
149  virginica          6.2         3.4
150  virginica          5.9         3.0

# To select several lines inside data frame 
dplyr::slice(iris,22:30)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.7          1.5         0.4  setosa
2          4.6         3.6          1.0         0.2  setosa
3          5.1         3.3          1.7         0.5  setosa
4          4.8         3.4          1.9         0.2  setosa
5          5.0         3.0          1.6         0.2  setosa
6          5.0         3.4          1.6         0.4  setosa
7          5.2         3.5          1.5         0.2  setosa
8          5.2         3.4          1.4         0.2  setosa
9          4.7         3.2          1.6         0.2  setosa

# I can affect it to an object 

subdata=dplyr::slice(iris,22:30)

#or choosing different lines
myline=c(22, 38, 120) # I build a vector containing the line that I want
dplyr::slice(iris,myline)

  Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
1          5.1         3.7          1.5         0.4    setosa
2          4.9         3.6          1.4         0.1    setosa
3          6.0         2.2          5.0         1.5 virginica

Bonus : to pipe many function serval function together

Caution

The pipe >| , or %>%, is one of the useful elements of the tidyverse. It allows to structure sequences of operations by minimizing the creation of intermediate objects and by facilitating the addition of a step anywhere in this sequence.

The useful command to manage data frame : 1) select() to select vector or variable from a data frame ; 2) filter() is used to subset a data frame, retaining all rows that satisfy your conditions ; mutate() adds new variables and preserves existing ones.

library(dplyr)
iris %>%
filter(Sepal.Length > 6) %>% # filtered using size of Sepal Length
filter(Species == "versicolor") # and the species 

   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1           7.0         3.2          4.7         1.4 versicolor
2           6.4         3.2          4.5         1.5 versicolor
3           6.9         3.1          4.9         1.5 versicolor
4           6.5         2.8          4.6         1.5 versicolor
5           6.3         3.3          4.7         1.6 versicolor
6           6.6         2.9          4.6         1.3 versicolor
7           6.1         2.9          4.7         1.4 versicolor
8           6.7         3.1          4.4         1.4 versicolor
9           6.2         2.2          4.5         1.5 versicolor
10          6.1         2.8          4.0         1.3 versicolor
11          6.3         2.5          4.9         1.5 versicolor
12          6.1         2.8          4.7         1.2 versicolor
13          6.4         2.9          4.3         1.3 versicolor
14          6.6         3.0          4.4         1.4 versicolor
15          6.8         2.8          4.8         1.4 versicolor
16          6.7         3.0          5.0         1.7 versicolor
17          6.7         3.1          4.7         1.5 versicolor
18          6.3         2.3          4.4         1.3 versicolor
19          6.1         3.0          4.6         1.4 versicolor
20          6.2         2.9          4.3         1.3 versicolor

iris %>%
 select(Sepal.Length, Species) %>%
 mutate(Sepal.Length2 = Sepal.Length * 2)%>%
 mutate(Sepal.Length2_squared = Sepal.Length2 * Sepal.Length2)

    Sepal.Length    Species Sepal.Length2 Sepal.Length2_squared
1            5.1     setosa          10.2                104.04
2            4.9     setosa           9.8                 96.04
3            4.7     setosa           9.4                 88.36
4            4.6     setosa           9.2                 84.64
5            5.0     setosa          10.0                100.00
6            5.4     setosa          10.8                116.64
7            4.6     setosa           9.2                 84.64
8            5.0     setosa          10.0                100.00
9            4.4     setosa           8.8                 77.44
10           4.9     setosa           9.8                 96.04
11           5.4     setosa          10.8                116.64
12           4.8     setosa           9.6                 92.16
13           4.8     setosa           9.6                 92.16
14           4.3     setosa           8.6                 73.96
15           5.8     setosa          11.6                134.56
16           5.7     setosa          11.4                129.96
17           5.4     setosa          10.8                116.64
18           5.1     setosa          10.2                104.04
19           5.7     setosa          11.4                129.96
20           5.1     setosa          10.2                104.04
21           5.4     setosa          10.8                116.64
22           5.1     setosa          10.2                104.04
23           4.6     setosa           9.2                 84.64
24           5.1     setosa          10.2                104.04
25           4.8     setosa           9.6                 92.16
26           5.0     setosa          10.0                100.00
27           5.0     setosa          10.0                100.00
28           5.2     setosa          10.4                108.16
29           5.2     setosa          10.4                108.16
30           4.7     setosa           9.4                 88.36
31           4.8     setosa           9.6                 92.16
32           5.4     setosa          10.8                116.64
33           5.2     setosa          10.4                108.16
34           5.5     setosa          11.0                121.00
35           4.9     setosa           9.8                 96.04
36           5.0     setosa          10.0                100.00
37           5.5     setosa          11.0                121.00
38           4.9     setosa           9.8                 96.04
39           4.4     setosa           8.8                 77.44
40           5.1     setosa          10.2                104.04
41           5.0     setosa          10.0                100.00
42           4.5     setosa           9.0                 81.00
43           4.4     setosa           8.8                 77.44
44           5.0     setosa          10.0                100.00
45           5.1     setosa          10.2                104.04
46           4.8     setosa           9.6                 92.16
47           5.1     setosa          10.2                104.04
48           4.6     setosa           9.2                 84.64
49           5.3     setosa          10.6                112.36
50           5.0     setosa          10.0                100.00
51           7.0 versicolor          14.0                196.00
52           6.4 versicolor          12.8                163.84
53           6.9 versicolor          13.8                190.44
54           5.5 versicolor          11.0                121.00
55           6.5 versicolor          13.0                169.00
56           5.7 versicolor          11.4                129.96
57           6.3 versicolor          12.6                158.76
58           4.9 versicolor           9.8                 96.04
59           6.6 versicolor          13.2                174.24
60           5.2 versicolor          10.4                108.16
61           5.0 versicolor          10.0                100.00
62           5.9 versicolor          11.8                139.24
63           6.0 versicolor          12.0                144.00
64           6.1 versicolor          12.2                148.84
65           5.6 versicolor          11.2                125.44
66           6.7 versicolor          13.4                179.56
67           5.6 versicolor          11.2                125.44
68           5.8 versicolor          11.6                134.56
69           6.2 versicolor          12.4                153.76
70           5.6 versicolor          11.2                125.44
71           5.9 versicolor          11.8                139.24
72           6.1 versicolor          12.2                148.84
73           6.3 versicolor          12.6                158.76
74           6.1 versicolor          12.2                148.84
75           6.4 versicolor          12.8                163.84
76           6.6 versicolor          13.2                174.24
77           6.8 versicolor          13.6                184.96
78           6.7 versicolor          13.4                179.56
79           6.0 versicolor          12.0                144.00
80           5.7 versicolor          11.4                129.96
81           5.5 versicolor          11.0                121.00
82           5.5 versicolor          11.0                121.00
83           5.8 versicolor          11.6                134.56
84           6.0 versicolor          12.0                144.00
85           5.4 versicolor          10.8                116.64
86           6.0 versicolor          12.0                144.00
87           6.7 versicolor          13.4                179.56
88           6.3 versicolor          12.6                158.76
89           5.6 versicolor          11.2                125.44
90           5.5 versicolor          11.0                121.00
91           5.5 versicolor          11.0                121.00
92           6.1 versicolor          12.2                148.84
93           5.8 versicolor          11.6                134.56
94           5.0 versicolor          10.0                100.00
95           5.6 versicolor          11.2                125.44
96           5.7 versicolor          11.4                129.96
97           5.7 versicolor          11.4                129.96
98           6.2 versicolor          12.4                153.76
99           5.1 versicolor          10.2                104.04
100          5.7 versicolor          11.4                129.96
101          6.3  virginica          12.6                158.76
102          5.8  virginica          11.6                134.56
103          7.1  virginica          14.2                201.64
104          6.3  virginica          12.6                158.76
105          6.5  virginica          13.0                169.00
106          7.6  virginica          15.2                231.04
107          4.9  virginica           9.8                 96.04
108          7.3  virginica          14.6                213.16
109          6.7  virginica          13.4                179.56
110          7.2  virginica          14.4                207.36
111          6.5  virginica          13.0                169.00
112          6.4  virginica          12.8                163.84
113          6.8  virginica          13.6                184.96
114          5.7  virginica          11.4                129.96
115          5.8  virginica          11.6                134.56
116          6.4  virginica          12.8                163.84
117          6.5  virginica          13.0                169.00
118          7.7  virginica          15.4                237.16
119          7.7  virginica          15.4                237.16
120          6.0  virginica          12.0                144.00
121          6.9  virginica          13.8                190.44
122          5.6  virginica          11.2                125.44
123          7.7  virginica          15.4                237.16
124          6.3  virginica          12.6                158.76
125          6.7  virginica          13.4                179.56
126          7.2  virginica          14.4                207.36
127          6.2  virginica          12.4                153.76
128          6.1  virginica          12.2                148.84
129          6.4  virginica          12.8                163.84
130          7.2  virginica          14.4                207.36
131          7.4  virginica          14.8                219.04
132          7.9  virginica          15.8                249.64
133          6.4  virginica          12.8                163.84
134          6.3  virginica          12.6                158.76
135          6.1  virginica          12.2                148.84
136          7.7  virginica          15.4                237.16
137          6.3  virginica          12.6                158.76
138          6.4  virginica          12.8                163.84
139          6.0  virginica          12.0                144.00
140          6.9  virginica          13.8                190.44
141          6.7  virginica          13.4                179.56
142          6.9  virginica          13.8                190.44
143          5.8  virginica          11.6                134.56
144          6.8  virginica          13.6                184.96
145          6.7  virginica          13.4                179.56
146          6.7  virginica          13.4                179.56
147          6.3  virginica          12.6                158.76
148          6.5  virginica          13.0                169.00
149          6.2  virginica          12.4                153.76
150          5.9  virginica          11.8                139.24

iris %>%
  select(Sepal.Length, Species) %>%
  mutate(Sepal.Length = Sepal.Length / mean(Sepal.Length, na.rm = TRUE))

    Sepal.Length    Species
1         0.8728     setosa
2         0.8386     setosa
3         0.8043     setosa
4         0.7872     setosa
5         0.8557     setosa
6         0.9241     setosa
7         0.7872     setosa
8         0.8557     setosa
9         0.7530     setosa
10        0.8386     setosa
11        0.9241     setosa
12        0.8214     setosa
13        0.8214     setosa
14        0.7359     setosa
15        0.9926     setosa
16        0.9755     setosa
17        0.9241     setosa
18        0.8728     setosa
19        0.9755     setosa
20        0.8728     setosa
21        0.9241     setosa
22        0.8728     setosa
23        0.7872     setosa
24        0.8728     setosa
25        0.8214     setosa
26        0.8557     setosa
27        0.8557     setosa
28        0.8899     setosa
29        0.8899     setosa
30        0.8043     setosa
31        0.8214     setosa
32        0.9241     setosa
33        0.8899     setosa
34        0.9412     setosa
35        0.8386     setosa
36        0.8557     setosa
37        0.9412     setosa
38        0.8386     setosa
39        0.7530     setosa
40        0.8728     setosa
41        0.8557     setosa
42        0.7701     setosa
43        0.7530     setosa
44        0.8557     setosa
45        0.8728     setosa
46        0.8214     setosa
47        0.8728     setosa
48        0.7872     setosa
49        0.9070     setosa
50        0.8557     setosa
51        1.1979 versicolor
52        1.0953 versicolor
53        1.1808 versicolor
54        0.9412 versicolor
55        1.1124 versicolor
56        0.9755 versicolor
57        1.0782 versicolor
58        0.8386 versicolor
59        1.1295 versicolor
60        0.8899 versicolor
61        0.8557 versicolor
62        1.0097 versicolor
63        1.0268 versicolor
64        1.0439 versicolor
65        0.9584 versicolor
66        1.1466 versicolor
67        0.9584 versicolor
68        0.9926 versicolor
69        1.0610 versicolor
70        0.9584 versicolor
71        1.0097 versicolor
72        1.0439 versicolor
73        1.0782 versicolor
74        1.0439 versicolor
75        1.0953 versicolor
76        1.1295 versicolor
77        1.1637 versicolor
78        1.1466 versicolor
79        1.0268 versicolor
80        0.9755 versicolor
81        0.9412 versicolor
82        0.9412 versicolor
83        0.9926 versicolor
84        1.0268 versicolor
85        0.9241 versicolor
86        1.0268 versicolor
87        1.1466 versicolor
88        1.0782 versicolor
89        0.9584 versicolor
90        0.9412 versicolor
91        0.9412 versicolor
92        1.0439 versicolor
93        0.9926 versicolor
94        0.8557 versicolor
95        0.9584 versicolor
96        0.9755 versicolor
97        0.9755 versicolor
98        1.0610 versicolor
99        0.8728 versicolor
100       0.9755 versicolor
101       1.0782  virginica
102       0.9926  virginica
103       1.2151  virginica
104       1.0782  virginica
105       1.1124  virginica
106       1.3006  virginica
107       0.8386  virginica
108       1.2493  virginica
109       1.1466  virginica
110       1.2322  virginica
111       1.1124  virginica
112       1.0953  virginica
113       1.1637  virginica
114       0.9755  virginica
115       0.9926  virginica
116       1.0953  virginica
117       1.1124  virginica
118       1.3177  virginica
119       1.3177  virginica
120       1.0268  virginica
121       1.1808  virginica
122       0.9584  virginica
123       1.3177  virginica
124       1.0782  virginica
125       1.1466  virginica
126       1.2322  virginica
127       1.0610  virginica
128       1.0439  virginica
129       1.0953  virginica
130       1.2322  virginica
131       1.2664  virginica
132       1.3520  virginica
133       1.0953  virginica
134       1.0782  virginica
135       1.0439  virginica
136       1.3177  virginica
137       1.0782  virginica
138       1.0953  virginica
139       1.0268  virginica
140       1.1808  virginica
141       1.1466  virginica
142       1.1808  virginica
143       0.9926  virginica
144       1.1637  virginica
145       1.1466  virginica
146       1.1466  virginica
147       1.0782  virginica
148       1.1124  virginica
149       1.0610  virginica
150       1.0097  virginica

#Sepal.Length_norm is calculated using the mean of the Sepal.Length for all data set.

iris %>%
  select(Sepal.Length, Species) %>%
  group_by(Species) %>%
  mutate(Sepal.Length_norm = Sepal.Length / mean(Sepal.Length, na.rm = TRUE))

# A tibble: 150 × 3
# Groups:   Species [3]
   Sepal.Length Species Sepal.Length_norm
          <dbl> <fct>               <dbl>
 1          5.1 setosa              1.02 
 2          4.9 setosa              0.979
 3          4.7 setosa              0.939
 4          4.6 setosa              0.919
 5          5   setosa              0.999
 6          5.4 setosa              1.08 
 7          4.6 setosa              0.919
 8          5   setosa              0.999
 9          4.4 setosa              0.879
10          4.9 setosa              0.979
# ℹ 140 more rows

# In this last example Sepal.Length_norm is calculated using the mean of the Sepal.Length for each species using group_by()

Important

These commands are non-persistent, no changes are made on the original iris data frame. If you want to store it, you must assign your changes to a object. How do we do ?

iris_modif<-iris %>%
  select(Sepal.Length, Species) %>%
  group_by(Species) %>%
  mutate(Sepal.Length_norm = Sepal.Length / mean(Sepal.Length, na.rm = TRUE))

How to import external data frame issue from .txt or .csv

To import data set, the function read.table() or read.csv() are commonly used.

read.table(file, header = FALSE, sep = "", dec = ".", ...)

The main parameter are :

• file : add the pathway and the name of the file
• header : a logical value (TRUE or FALSE)indicating whether the file contains the names of the variables as its first line.
• sep : the field separator character. Values on each line of the file are separated by this character. If sep = “” (the default for read.table) the separator is ‘white space’, that is one or more spaces, tabs, newlines or carriage returns.
• dec : the character used in the file for decimal points.

ds <- read.table(here::here("data","exemple_read.txt"),
                 header = TRUE,
                 sep = ";",
                 dec = ",")
head(ds)

  Temps Fatigue Traitement
1     0   7.838          O
2     0   5.588          O
3     0   7.838          O
4     0   1.838          O
5     0   2.088          O
6     0   2.088          O

Note

We use here() function include in here package to navigate into the different folder. To use it you can think to “I am here (in root of our working directory) and I am going to … here::here(”I”, “am”, “going to”))

Why I use the parameter header=TRUE ?

To export data set as .txt to read in excel

The function is similar to read.table()

write.table(x, file = "", sep = " ",na = "NA", dec = ".", ... )

• x : this is your data.frame
• file : give a name for your file
• sep : cf read.table
• dec : cf read.table
• na : give a symbole for missing data, by convention is NA

write.table(ds, "ds.txt", sep = "\t", dec = ".")

To keep our working directory tidy, we now delete ds.txt

file.remove("ds.txt")

Warning in file.remove("ds.txt"): cannot remove file 'ds.txt', reason 'No such
file or directory'

[1] FALSE

Exo 5

1. In the dataset Iris select Sepal Width, Sepal length and Species,to create a new data frame name “subset_iris”
2. Save this new data frame as text file

How to represent your data

ggplot2 is a powerfull packages to make a very smart graph “ready to use” for publication. gg means grammar and graph, a concept which describe a graph using grammar. This package belong to tidyverse according to dplyr. According to the ggplot2 concept, a graph can be divided into different basic parts:Plot = data + Aesthetics + Geometry

• data : data frame
• aesthetics : allows to indicate the x and y variables. It can also be used to control the color, size and shape of the points, etc…
• geometry : corresponds to the type of graph (histogram, box plot, line plot, …..)

library(ggplot2)
# scatter plot 
ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) + #  what I want to represent 
  geom_point() # how I represent it

# We can calorize and give a shape by month
# scatter plot
ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(aes(color = Species, shape = Species)) # shape is the same thing with classical plot on R

# You can change manually color with la fonction scale_color_manual()
ggplot(data = iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(aes(color = Species, shape = Species)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

#Boxplot
ggplot(data = iris, aes(Species, Sepal.Length)) + #  what I want to represent 
  geom_boxplot(aes(color = Species, fill = Species), alpha=0.4) + # how I represent it. Here I changed geometry using boxplot.
  labs(x ="Species", y = "Sepal.Length (mm)") +
  theme_minimal()

Save your plot

Using the function pdf()

Firstly you have to store plot inside and object

#Boxplot
g1 <- ggplot(data = iris, aes(Species, Sepal.Length)) + #  what I want to represent 
  geom_boxplot(aes(color = Species, fill = Species), alpha = 0.4) +# how I represent it. Here I changed geometry using boxplot.
  labs(x = "Species", y = "Sepal.Length (mm)") +
  theme_minimal()

myGraph <- here::here("sortie",
                             "graph")

if (!dir.exists(myGraph)) {
  dir.create(myGraph, recursive = TRUE)
}
pdf(file.path(myGraph, "boxplot1.pdf"))
g1
dev.off()

quartz_off_screen 
                2 

or ggplot2::ggsave()

ggsave(file.path(myGraph, "boxplot2.pdf"), g1)

Saving 7 x 5 in image

Bonus for the geek :)

To enhance your knowledge of R, you can also learn how to use Rmarkdown / Quarto to make a report with R code and text. You can click on this link to learn more about it with cheatsheet.