Fundamentals of Data Science for EESS

R session 01 - Introduction to R

Daniel Vaulot

2021-01-26

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Fundamentals of Data Science for EESS

R sessions

1 - Introduction to R
2 - R markdown
3 - Git
4 - Data wrangling
5 - Data visualisation
6 - Making maps

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Outline

What is R and why use R ?
Resources
Get started
Fundamentals of R
- Data objects
- Vectors
- Operators
- Functions
- Packages

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Wooclap - Quizz on previous sessions

https://www.wooclap.com/R01

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IntroductionFor those who are experts in R
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IntroductionFor those who are experts in Rplease refrain to answer during this session...
help your neighbor...

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Introduction

For those who are experts in R
- please refrain to answer during this session...
- help your neighbor...

Two special slide formatting

Your turn...

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Introduction

For those who are experts in R
- please refrain to answer during this session...
- help your neighbor...

Two special slide formatting

Your turn...

Warning

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IntroductionComputer languages6 / 57

Introduction

History of R

Mid 1970s - S Language for Statistical Computing conceived by John Chambers, Rick Becker, Trevor Hastie, Allan Wilks and others at Bell Labs
Early 1990's - R was first implemented in the early 1990’s by Robert Gentleman and Ross Ihaka, both faculty members at the University of Auckland.
1995 - Open Source Project
1997 - Managed by the R Core Group
2000 - First release of R
2011 - First release of R studio
Historical notes - Paper from 1998

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IntroductionWhy use R ?Script vs. Menu driven software (e.g. Excel)Can be re-rerun with new data
Reproducible workflow

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IntroductionWhy use R ?Script vs. Menu driven software (e.g. Excel)Can be re-rerun with new data
Reproducible workflow

Open sourceHuge number of libraries
Tidy "universe" : tidyverse and ggplot2Very easy to manipulate tables (select columns, create new variables)

High quality graphics


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IntroductionWhy use R ?Script vs. Menu driven software (e.g. Excel)Can be re-rerun with new data
Reproducible workflow

Open sourceHuge number of libraries
Tidy "universe" : tidyverse and ggplot2Very easy to manipulate tables (select columns, create new variables)

High quality graphics

Work environmentR studio

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IntroductionWhy use R ?Script vs. Menu driven software (e.g. Excel)Can be re-rerun with new data
Reproducible workflow

Open sourceHuge number of libraries
Tidy "universe" : tidyverse and ggplot2Very easy to manipulate tables (select columns, create new variables)

High quality graphics


Work environmentR studio

Document your data processingR markdown
Create HTML, pdf, presentations

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IntroductionWhy use R ?Script vs. Menu driven software (e.g. Excel)Can be re-rerun with new data
Reproducible workflow

Open sourceHuge number of libraries
Tidy "universe" : tidyverse and ggplot2Very easy to manipulate tables (select columns, create new variables)

High quality graphics

Work environmentR studio

Document your data processingR markdown
Create HTML, pdf, presentations

Share your data and workflowGitHub

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IntroductionWhat can you do with R ?9 / 57

IntroductionWhat can you do with R ?ScienceStatistics of course...
Data processing
Graphics
Time series analyses
Maps
Bioinformatics


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IntroductionWhat can you do with R ?ScienceStatistics of course...
Data processing
Graphics
Time series analyses
Maps
Bioinformatics


But alsoTeach
Do a presentation
Write your CV
Build a web site
Write a book
Much more...


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Introduction

What can you do with R ?

Science
- Statistics of course...
- Data processing
- Graphics
- Time series analyses
- Maps
- Bioinformatics

But also
- Teach
- Do a presentation
- Write your CV
- Build a web site
- Write a book
- Much more...

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Resources

Books and Manuals

Applied Statistics with R : Quite simple introduction with emphasis on Stats
R intro : Very good introduction to R, short and clear
R in a nutshell : Many many receipes to solve all your questions
R graphics cook book : very good for graphics

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Resources

On line courses and web sites

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Resources

Cheat sheets

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Resources

Forum

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Let's get started

Setup

Install R
Install R studio

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Let's get startedThe R studio interfaceBottom leftConsole

Top leftFile editor for .R and .Rmd files
Data frame visualization

Top rightEnvironment (i.e. R objects)
History

Bottom rightFiles
Plots
Packages
Help


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Let's get startedCreate a new projectOpen R studio
Create new project for the course in a new directorye.g. Experimental design course

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Let's get started

Your first script

print("Hello world")

[1] "Hello world"

Two ways to proceed

Type directly in command window

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Let's get started

Your first script

print("Hello world")

[1] "Hello world"

Two ways to proceed

Type directly in command window
Create a new script

Type in script window, select and execute (CTRL-R)

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The R language

variables are abstracting your data

> greeting = "Hello world"
> print(greeting)

[1] "Hello world"

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The R language

variables are abstracting your data

> greeting = "Hello world"
> print(greeting)

[1] "Hello world"

> greeting = "Bonjour"
> print(greeting)

[1] "Bonjour"

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The R language

In R, variables are objects and so are function etc...

Assignement done with <-

> x <- 1
> y <- 2
> x + y

[1] 3

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The R language

In R, variables are objects and so are function etc...

Assignement done with <-

> x <- 1
> y <- 2
> x + y

[1] 3

> z <- x + y
> z

[1] 3

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The R language

= can be used instead of <- but refrain from it (not good style)

> z = x + y

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The R language

= can be used instead of <- but refrain from it (not good style)

> z = x + y

You can view the values of the objects in R-studio environment window (top-right)

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The R language

R is case sensitive

> Z

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The R language

R is case sensitive

> Z

> Z

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

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The R languageRules for naming objectsUseletters
numbers
the dot
the underscore (not the minus sign !)

Start always with a letterMyvariable, Myvariable1, Myvariable.1,Myvariable-01 are OK
1Myvariable, My-variable, Myvariable@ are not OK

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The R language

Use consistent naming

Five conventions

alllowercase: e.g. adjustcolor
period.separated: e.g. plot.new
underscore_separated: e.g. numeric_version
lowerCamelCase: e.g. addTaskCallback
UpperCamelCase: e.g. SignatureMethod

Prefer third one, much more easy to read

Use names for objects : last_name
Use verbs for function : build_name
Think about best order
- e.g. prefer maybe name_last because then you can have name_first, name_full...
- and you identify that all these objects are related to a name...

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R objects

Data types

character: "Daniel", "This is a course in R", 'Donald'
numeric: 2, 15.5, 10e-3
integer: 2L (the L tells R to store this as an integer)
date: 2018-02-25
logical: TRUE, FALSE
complex: 1+4i (complex numbers with real and imaginary parts)

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R objects

Data types

character: "Daniel", "This is a course in R", 'Donald'
numeric: 2, 15.5, 10e-3
integer: 2L (the L tells R to store this as an integer)
date: 2018-02-25
logical: TRUE, FALSE
complex: 1+4i (complex numbers with real and imaginary parts)
No data "NA"
Not a number "NaN" (e.g. division by zero)

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R objects

Data structures

Vector
List
Matrix
Data frames
Function

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Vectors

The basic R structure is a vector: $[\begin{matrix} 10 \\ 20 \\ 30 \end{matrix}]$

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Vectors

The basic R structure is a vector: $[\begin{matrix} 10 \\ 20 \\ 30 \end{matrix}]$

A vector can contain only a single element $[\begin{matrix} 10 \end{matrix}]$

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Vectors

The basic R structure is a vector: $[\begin{matrix} 10 \\ 20 \\ 30 \end{matrix}]$

A vector can contain only a single element $[\begin{matrix} 10 \end{matrix}]$

Assign a value to a vector

x <- 10
x

[1] 10

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Vectors

Assign several elements

x <- c(10, 20, 30)
x

[1] 10 20 30

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Vectors

Assign several elements

x <- c(10, 20, 30)
x

[1] 10 20 30

Assign range

x <- 10:30
x

 [1] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

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Vectors

Assign characters

PoTU <- c("Jo", "Biden")
PoTU

[1] "Jo"    "Biden"

Assign logical

flags <- c(TRUE, FALSE, TRUE)
flags

[1]  TRUE FALSE  TRUE

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Vectors

Access specific elements of a vector

First

x[1]

[1] 10

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Vectors

Access specific elements of a vector

First

x[1]

[1] 10

Range

x[1:5]

[1] 10 11 12 13 14

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Vectors

Access specific elements of a vector

First

x[1]

[1] 10

Range

x[1:5]

[1] 10 11 12 13 14

Remove one element

x[-1]

 [1] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

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Vectors

Determine object properties

Apply functions (we will come back to functions latter)

typeof() - what is the object’s data type (low-level)?
length() - how long is it? What about two dimensional objects?

typeof(x)
length(x)

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Vectors

Determine object properties

Apply functions (we will come back to functions latter)

typeof() - what is the object’s data type (low-level)?
length() - how long is it? What about two dimensional objects?

typeof(x)
length(x)

[1] "integer"

[1] 21

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Vectors

Determine object properties

Apply functions (we will come back to functions latter)

typeof() - what is the object’s data type (low-level)?
length() - how long is it? What about two dimensional objects?

typeof(x)
length(x)

[1] "integer"

[1] 21

What is the type and length of PoTU ?

https://app.wooclap.com/R01

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OperatorsArithmetic Operators

Operator
Description


+
addition

-
subtraction

*
multiplication

/
division

^ or **
exponentiation

x %% y
modulus (x mod y) 5%%2 is 1

x %/% y
integer division 5%/%2 is 2

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Operator	Description
+	addition
-	subtraction
*	multiplication
/	division
^ or **	exponentiation
x %% y	modulus (x mod y) 5%%2 is 1
x %/% y	integer division 5%/%2 is 2

Operators

Arithmetic Operators

We are performing vector operations !

$[\begin{matrix} 1 \\ 2 \\ 3 \\ . . \end{matrix}] + [\begin{matrix} 1 \\ 2 \\ 3 \\ . . \end{matrix}] = [\begin{matrix} 2 \\ 4 \\ 6 \\ . . \end{matrix}]$

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Operators

Arithmetic Operators

Vector one element

 x <- 1
 y <- 2
 z <- x + y
 z

[1] 3

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Operators

Arithmetic Operators

Vector several elements

# Two instructions on the same line
 x <- 1:9;  y <- 1:9
 z <- x + y
 z

[1]  2  4  6  8 10 12 14 16 18

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Operators

Arithmetic Operators

Vector several elements

# Two instructions on the same line
 x <- 1:9;  y <- 1:9
 z <- x + y
 z

[1]  2  4  6  8 10 12 14 16 18

Several instructions on same line separate by ;
The hastag # indicate a comment -> Use heavily to document your code
However, it is even better to use R markdown (see next class)

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Operators

Arithmetic Operators

Vector several elements

# Two instructions on the same line
 x <- 1:9;  y <- 1:9
 z <- x + y
 z

[1]  2  4  6  8 10 12 14 16 18

Several instructions on same line separate by ;
The hastag # indicate a comment -> Use heavily to document your code
However, it is even better to use R markdown (see next class)

Use the other operators

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Operators

Arithmetic Operators

What happens when the vectors have different number of elements ?

x <- 1:9
y <- 1
z <- x + y
z

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Operators

Arithmetic Operators

What happens when the vectors have different number of elements ?

x <- 1:9
y <- 1
z <- x + y
z

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

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Operators

Arithmetic Operators

What happens when the vectors have different number of elements ?

x <- 1:9
y <- 1
z <- x + y
z

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

Equivalent to

y <- c(1, 1, 1, 1, 1, 1, 1, 1, 1)

The recycling rule...

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Operators

Can we add logical ?

x <- TRUE
y <- FALSE
z <- x + y
z

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Operators

Can we add logical ?

x <- TRUE
y <- FALSE
z <- x + y
z

[1] 1

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Operators

Can we add logical ?

No error but...

The resulting variable is transformed to a numeric

How you would show that ?

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Operators

Can we add logical ?

No error but...

The resulting variable is transformed to a numeric

How you would show that ?

typeof(x)

[1] "logical"

typeof(z)

[1] "integer"

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OperatorsLogical Operators

Operator
Description


<
less than

<=
less than or equal to

>
greater than

>=
greater than or equal to

==
exactly equal to

!=
not equal to

!x
Not x

x | y
x OR y

x & y
x AND y

isTRUE(x)
test if X is TRUE

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Operator	Description
<	less than
<=	less than or equal to
>	greater than
>=	greater than or equal to
==	exactly equal to
!=	not equal to
!x	Not x
x \| y	x OR y
x & y	x AND y
isTRUE(x)	test if X is TRUE

Operators

Logical Operators

x <- TRUE
y <- FALSE
z1 <- x | y
z2 <- x == y

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Operators

Logical Operators

x <- TRUE
y <- FALSE
z1 <- x | y
z2 <- x == y

[1] TRUE

[1] FALSE

Do not mix

== which is logical operator
= which is assignement

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Operators

Can we add characters ?

first <- "Jo"
last <- "Biden"
full <- first + last

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Operators

Can we add characters ?

first <- "Jo"
last <- "Biden"
full <- first + last

Generates an error

Error in first + last: non-numeric argument to binary operator

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Operators

Can we add characters ?

first <- "Jo"
last <- "Biden"
full <- first + last

Generates an error

Error in first + last: non-numeric argument to binary operator

What can we do ?

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Functions

Functions perform specific task on objects

e.g. to concatanate strings we use paste()

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Functions

Functions perform specific task on objects

e.g. to concatanate strings we use paste()

paste(first, last)

[1] "Jo Biden"

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Functions

Functions perform specific task on objects

e.g. to concatanate strings we use paste()

paste(first, last)

[1] "Jo Biden"

Functions take arguments and return an object called result
To know the arguments use ?

  ? paste()  # Do not forget the parenthesis

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Functions

Functions perform specific task on objects

e.g. to concatanate strings we use paste()

paste(first, last)

[1] "Jo Biden"

Functions take arguments and return an object called result
To know the arguments use ?

  ? paste()  # Do not forget the parenthesis

What happened ?

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Functions

Functions perform specific task on objects

e.g. to concatanate strings we use paste()

paste(first, last)

[1] "Jo Biden"

Functions take arguments and return an object called result
To know the arguments use ?

  ? paste()  # Do not forget the parenthesis

What happened ?

Can go directly to Help panel and type function name

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FunctionsHelp42 / 57

FunctionsHelp43 / 57

Functions

Getting what you want

Let's apply paste :

paste(first, last)

[1] "Jo Biden"

We would like to get "Jo_Biden"
Can you read the help and suggest a change in the way we call the function ?
https://app.wooclap.com/R01

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Functions

Getting what you want

Let's apply paste :

paste(first, last)

[1] "Jo Biden"

We would like to get "Jo_Biden"
Can you read the help and suggest a change in the way we call the function ?
https://app.wooclap.com/R01

paste(first, last, sep = "_")

[1] "Jo_Biden"

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Functions

Write your own function

If you write 3 times the same piece of code write a function...

my_sum <- function(a, b) {
    c <- a + b
    return(c)
}

my_sum : function name
first_number, second_number : arguments
instructions are enclosed by braces ({})
return() : the value(s) returned

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Functions

Write your own function

If you write 3 times the same piece of code write a function...

my_sum <- function(a, b) {
    c <- a + b
    return(c)
}

my_sum : function name
first_number, second_number : arguments
instructions are enclosed by braces ({})
return() : the value(s) returned

More compact way

 my_sum <- function(a, b) {a + b}

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Functions

Call your function

my_sum(10, 20)

[1] 30

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Functions

Call your function

my_sum(10, 20)

[1] 30

better

my_sum(a = 10, b = 20)

[1] 30

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Functions

Write a function to compute a product

https://app.wooclap.com/R01

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Functions

Examples of functions

Most of the time you do not have to write functions because someone has already written one for what you want to do...

x <- 1:100
sum(x)

[1] 5050

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Functions

Examples of functions

Most of the time you do not have to write functions because someone has already written one for what you want to do...

x <- 1:100
sum(x)

[1] 5050

Normal distribution

y <- rnorm(10, mean = 0, sd = 1)
y

 [1]  0.72247971  0.20521127  0.45698723  0.08563865  0.98998049 -0.11958863
 [7]  0.22778884  1.15945271 -0.08842998  0.99219102

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Functions

Statistics

mean(y)

[1] 0.4631711

sd(y)

[1] 0.473501

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Functions

Statistics

mean(y)

[1] 0.4631711

sd(y)

[1] 0.473501

Sample more points... 10,000 instead of 100

y <- rnorm(10000, mean = 0, sd = 1)
mean(y)

[1] -0.0150854

sd(y)

[1] 1.004672

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Functions

Plot

Histogram

library(graphics)
hist(y)

What is this "library()"

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Packages

Packages are set of functions that have a common goal

They are really the strength of R

And these are only the "official"" packages. You can find more on GitHub

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Packages

Installing a package

Download on your computer the package you need

Install package stringr (to manipulate strings of characters)

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Packages

Using a package

To use functions from the package

use the syntax package::function

stringr::str_c(first, last, sep = " ")

[1] "Jo Biden"

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Packages

Using a package

To use functions from the package

use the syntax package::function

stringr::str_c(first, last, sep = " ")

[1] "Jo Biden"

load the package with the library function

library(stringr)
str_c(first, last, sep = " ")

[1] "Jo Biden"

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Packages

Using a package

To use functions from the package

use the syntax package::function

stringr::str_c(first, last, sep = " ")

[1] "Jo Biden"

load the package with the library function

library(stringr)
str_c(first, last, sep = " ")

[1] "Jo Biden"

Sometimes functions from different libraries have similar names

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PackagesList installed packages54 / 57

RecapR is case sensitive: Z !=  z
Objects: data types vs data structures
Vectors: think in vector operations
Operators: arithmetic vs. logical
Functions: try to practice

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Next class: 02 - Markdown

What you will learn :

Mix text, R code and R output in a single document
Produce documents as HTML, pdf or even Word from the same template

Please install the following packages and their dependencies
- rmarkdown (will install also knitr)
- tinytex (Latex)
Installation : https://bookdown.org/yihui/rmarkdown/installation.html
Have a look at the book https://bookdown.org/yihui/rmarkdown

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Next class: 03 - Git

Resources

Pro GIT: https://git-scm.com/book/en/v2
GitHub guide: https://guides.github.com
Happy Git with R: https://happygitwithr.com/

Software

Git: https://git-scm.com
Git desktop: https://github.com
GitHub account with your NTU email: https://desktop.github.com

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