Microbes on Earth

R sessions

Wooclap - Past experience

Introduction

• For those who are experts in R
  • please refrain to answer during this session...
  • help your neighbor (once COVID is gone...)

• Two special slide formatting

Your turn...

Warning

Introduction

Why use R ?

• Script vs. Menu driven software (e.g. Excel)
  • Can be re-rerun with new data
  • Reproducible workflow
• Open source
  • Huge number of libraries
  • Tidy "universe" : tidyverse and ggplot2
    • Very easy to manipulate tables (select columns, create new variables)
    • High quality graphics
• Work environment
  • R studio
• Document your data processing
  • R markdown
  • Create HTML, pdf, presentations
• Share your data and workflow
  • GitHub

Introduction

What can you do with R ?

Resources

Cheat sheets

• R basics
• ggplot2
• dplyr

Resources

Forum

• https://stackoverflow.com/
• http://r-statistics.co/
• https://www.r-bloggers.com/

Let's get started

Setup

• Install R

• Install R studio

Let's get started

The R studio interface

Let's get started

Create a new project

• Open R studio
• Create new project for the course in a new directory
  • e.g. Microbes course

Let's get started

Your first script

print("Hello world")

[1] "Hello world"

Two ways to proceed

1. Type directly in command window

2. Create a new script

The R language

variables are abstracting your data

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

[1] "Hello world"

> greeting = "Bonjour"
> print(greeting)

[1] "Bonjour"

The R language

variables are objects

• Assignement done with <-

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

[1] 3

>  z <- x + y
>  z

[1] 3

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)

The R language

R is case sensitive

>  Z

>  Z

Error in eval(expr, envir, enclos): objet 'Z' introuvable

The R language

Rules for naming objects

• Use
  • letters
  • numbers
  • the dot
  • the underscore (not the minus sign !)

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

R objects

Data types

• character: "Daniel", "This is a course in R", 'Joe Biden'

• 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)

R objects

Data structures

• Vector

• List

• Matrix

• Data frames

• Function

Vectors

The basic R structure is a vector (think as a column in Excel): $$\left[\begin{array}{c}10\\ 20\\ 30\end{array}\right]$$

A vector can contain only a single element $$\left[\begin{array}{c}10\end{array}\right]$$

Assign a value to a vector

 x <- 10
 x

[1] 10

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

Vectors

Assign characters

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

[1] "Jo"    "Biden"

Assign logical

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

[1]  TRUE FALSE  TRUE

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

Operators

Arithmetic Operators

Operators

Arithmetic Operators

We are performing vector operations !

$$\left[\begin{array}{c}1\\ 2\\ 3\\ ..\end{array}\right]+\left[\begin{array}{c}1\\ 2\\ 3\\ ..\end{array}\right]=\left[\begin{array}{c}2\\ 4\\ 6\\ ..\end{array}\right]$$

Think about it as adding 2 columns in Excel.

Operators

Arithmetic Operators

Vector one element

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

[1] 3

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

Operators

Logical Operators

Operators

Logical Operators

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

[1] TRUE

[1] FALSE

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

• Can go directly to Help panel and type function name

Functions

Help

Functions

Help

Functions

Write your own function

If you write 3 times the same piece of code, then 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

Functions

Call your function

 my_sum(10, 20)

[1] 30

• better

 my_sum(a = 10, b = 20)

[1] 30

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...

• Sum

 x <- 1:100
 sum(x)

[1] 5050

• Normal distribution

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

 [1]  0.18698515 -1.90285690  1.06906481 -0.55548326  0.59540370 -0.04581134  0.38981055  0.92785608 -0.81038363 -0.08401824

Functions

Statistics

 mean(y)

[1] -0.02294331

 sd(y)

[1] 0.8903981

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

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

[1] 0.003199685

 sd(y)

[1] 1.003417

Functions

Plot

  library(graphics)
  hist(y)

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

Packages

Installing a package

Download on your computer the package you need

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

Packages

List installed packages

Next: 02 - Data wrangling