Introduction to R - WordPress.com · 2021. 1. 5. · Introduction Plan 1. Introduction 2. Software installation 3. Types and basics operations with R 4. Data import 5. Data simulation

Introduction to R

PRISM - Nicolas Sutton-Charani

5 January 2021

Data science Advanced statistics 5 January 2021 1 / 28

1. Introduction

2. Software installation

3. Types and basics operations with R

4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Introduction

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Introduction

What is R ?

Programming langage and statistical computing for data analysis

GNU package (C, Fortran)

freely available under GNU General Public License

collaborative project

Comprehensive R Archive Network (CRAN)

History

1975 : J. Chambers (Bell Laboratories) → S1995 : R. Ihaka and R. Gentleman (University of Auckland) → R


Introduction

Employment

1

1. https://r4stats.com/articles/popularity/


https://r4stats.com/articles/popularity/

Introduction

R vs Python

2

2. http://r4stats.com/articles/popularity/


http://r4stats.com/articles/popularity/

Introduction

Analytic tool

3




Introduction

Analytic tool

4




Software installation

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions




R : the software

http://www.r-project.org/ → Download R→ choose one of the french mirrors→ Download R for Windows/Mac/Linux→ base→ Download R 4.0.3 for XXX

R studio : development environment

https://www.rstudio.com/ → Download→ RStudio Desktop→ choose correct installer

(windows/mac/linux)

Execute the 2 .exe files


http://www.r-project.org/

https://www.rstudio.com/

Types and basics operations with R

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions



Variables types

No type declaration ! → R-object assignment :

Vectors

Lists

Matrices

Arrays

Factors

Data frames

Data types :

logical (TRUE, FALSE)

numeric (ex : 12.3, 5, 999)

character (ex : ’a’ , ’”good”, ”TRUE”, ’23.4’) or factor when allmodalities are known



Operators

Arithmetic Comparison Logical+ addition < lesser than ! x logical NOT− subtraction > greater than x & y logical AND∗ multiplication <= lesser than or equal to x && y id./ division >= greater than or equal to x | y logical OR∧ power == equal x ‖ y id.

%% modulo ! = different xor(x, y) exclusive OR%/% integer division



Vectors

Values assignment : ’<-’ or ’=’ (ex : x <- 3 or x = 3)

Data generation

1 : 10[1] 1 2 3 4 5 6 7 8 9 10

seq(-3, +3, length = 13)[1] -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0

v <- c(4, 10, 16)v[3]

[1] 16

Functions on vectors

mean(), sum(), median()

var() and sd()

length()

summary()



Matrices

m <- matrix(data = 1 : 12, nrow = 3, ncol = 4)m

[,1] [,2] [,3] [,4][1,] 1 4 7 10[2,] 2 5 8 11[3,] 3 6 9 12

m[2, 3][1] 8

dim(m)[1] 3 4

cbind(m, v)v

[1,] 1 4 7 10 4[2,] 2 5 8 11 10[3,] 3 6 9 12 16

rbind(m, c(v, 5))[,1] [,2] [,3] [,4]

[1,] 1 4 7 10[2,] 2 5 8 11[3,] 3 6 9 12[4,] 4 10 16 5



Dataframes

special type of matrix

mixed types of data

nominal column indexing → insensitive to data reordering !

df <- data.frame(id = c(”id1”, ”id2”, ”id3”, ”id4”, ”id5”),poids = c(85, 78, 56, 102, 91),taille = c(170, 176, 155, 187, 202))

dfid poids taille

1 id1 85 1702 id2 78 1763 id3 56 1554 id4 102 1875 id5 91 202

call columns by their names : df$poids (vector) or df[’poids’](dataframe)



Dataframes

List of equal-sized vector containing different variable types→ function ’class’ :

class(df)[1] ”data.frame”

class(df$taille)[1] ”numeric”

class(df$id)[1] ”factor”

Variable names → selection, filter :

df1 <- subset(df, select = c(id, taille)) ⇔ df1 <- subset(df, select = - poids)

df2 <- df[df$poids > 80, ]


Data import

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Data import

Import

from a text or csv file :df <- read.table(file = ”file.txt”, sep = ” ;”, dec = ”,”, header = TRUE)

from a csv file : df <- read.csv(file = ”file.csv”)

from an excel file :library(xlsx)

df <- read.xlsx(”my file.xls”, sheetIndex = 2)

from a database :library(RODBC)connexion <- odbcDriverConnect(’driver = SQL Server ;

server = mysqlhost ;database = mydbname ;trusted connection = true’)

df <- sqlQuery(connexion, ’SELECT * FROM information schema.tables’)

odbcClose(channel)


Data simulation

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Data simulation

Simulations

Simple randomness : n <- sample(N, size = 7, replace = FALSE)

Random generation

Normal (Gauss) : v <- rnorm(n, mean = 0, sd = 1)

Poisson : v <- rpois(n, lambda)

Binomial : v <- rbinom(n, size, prob)

...

Probability corresponding distributions : dnorm, dpois, dbinom, ...


Plots

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Plots

Plotsx <- seq(-10, +10, length = 10000)y <- cos(x)z <- dnorm(x)

plot(x, y)

−10 −5 0 5 10

−1.

0−

0.5

0.0

0.5

1.0

x

cos(

x)

plot(x, z, main = ”Normal distribution”,cex.main = 3, font.main = 6,xlab = ”x”, ylab = ”f(x)”, pch = ”+”,

cex.axis = 1.5, cex.lab = 1.5, col = ”red”)

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−10 −5 0 5 10

0.0

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Normal distribution

x

f(x)


Packages

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Packages

Packages

Installation : install.packages(”<the package’s name>”,repos=’http ://cran.us.r-project.org’)Loading : library(”<the package’s name>”)

package name descriptionggplot advances plottingMASS statistical toolsmatlab use matlab codedplyr data manipulationdoParallel parallelisationcaret machine learninge1071 SVMshiny interfacing...

...


Useful functions

Plan

1. Introduction



4. Data import

5. Data simulation

6. Plots

7. Packages

8. Useful functions


Useful functions

Useful functions

grep :grep(”mtpl”, c(”PSG”, ”OL”, ”MSCHmtplPro”, ”GazelecAjac”))[1] 3

apply :df <- data.frame(A = c(”hello”, ”bye”, ”thanks”),+ B = 1 : 3,+ C = c(T, F, F))sapply(df, class)

A B C”factor” ”integer” ”logical”


Useful functions

Useful functions

cat, paste :n <- 10cat(paste(”run number”, n))run number 10

system.time :learCT <- system.time(

svm <- svm(target ∼ ., data = trainData)

head/tail

which


Documents

Introduction to R - WordPress.com · 2021. 1. 5. · Introduction Plan 1. Introduction 2. Software installation 3. Types and basics operations with R 4. Data import 5. Data simulation