R and RStudio - Basics

Day 1

Jonas Vollhüter

Freie Universität Berlin @ Theoretical Ecology

January 15, 2024

Difference between R and RStudio



R is the programming language and the program that does the actual work

  • Can be used with many different programming environments

RStudio is the integrated development environment (IDE)

  • Provides an interface to R
  • Specifically built around R code
  • Execute code
  • Syntax highlighting
  • File and project management
  • …

Difference between R and RStudio



Analogy and image from ModernDive Book

Summary

You can use R without RStudio but RStudio without R would be of little use

Basic idea of writing code for data analysis

  1. Break down your process into small steps

  2. Write precise instructions in the R language for each step

  • You do this in a document called an R script

  1. Tell R to execute these instructions

  2. R will give you the results (or an error message)

1 Introduction to RStudio

Introduction to RStudio

Console pane

  • Execute R code

  • Output from R code in scripts is printed there

  • Type a command into the console and execute with Enter/Return

Tip

Use arrow keys to bring back last commands

Script pane

  • Write scripts with R code

    • Scripts are text files with R commands (file ending .R)

    • Use scripts to save commands for reuse

Script pane

  • Create a new R script:
    File -> New File -> R Script
  • Save an R script:
    File->Save (Ctrl/Cmd + S)
  • Run code line by line with Run button (Ctrl+Enter/Cmd+Return)
  • You can open multiple scripts

Summary

Use scripts for all your analysis and for commands that you want to save.
Use console for temporary commands, e.g. to test something.

Environment pane

  • Shows objects currently present in the R session

  • Is empty if you start R

Files pane

  • Similar to Explorer/Finder

  • Browse project structure and files

    • Find and open files
    • Create new folders
    • Delete files
    • Rename files
    • …

  • Practical if you don’t want to switch between File Explorer and RStudio all the time

Plot pane

  • Plots that are created with R will be shown here

Project oriented workflow

How to use RStudio to organize your projects

Project oriented workflow

  • One directory with all files relevant for project
    • Scripts, data, plots, documents, …
MyProject
|
|- data
|
|- documents
|   |
|   |- notes
|   |
|   |- reports
|
|- analysis
|   |
|   |- clean_data.R 
|   |
|   |- statistics.R
|
|

Example project structure

Project oriented workflow

  • One directory with all files relevant for project
    • Scripts, data, plots, documents, …
  • An RStudio project is just a normal directory with an .Rproj file
Project
|
|- data
|
|- documents
|   |
|   |- notes
|   |
|   |- reports
|
|- analysis
|   |
|   |- clean_data.R 
|   |
|   |- statistics.R
|
|- MyProject.RProj

Example RStudio project structure

Project oriented workflow

Advantages of using RStudio projects

  • Easy to navigate in R Studio (File pane)
  • Easy to find and access scripts and data in RStudio
  • Project root is working directory
  • Open multiple projects simultaneously in separate RStudio instances
Project
|
|- data
|
|- documents
|   |
|   |- notes
|   |
|   |- reports
|
|- analysis
|   |
|   |- clean_data.R 
|   |
|   |- statistics.R
|
|- *.RProj

Example RStudio project structure

Create an RStudio project

Create a project from scratch:

  1. File -> New Project -> New Directory -> New Project
  2. Enter a directory name (this will be the name of your project)
  3. Choose the Directory where the project should be initiated
  4. Create Project

Example RStudio project structure in the Files pane

RStudio will now create and open the project for you.

Open a project from outside RStudio

To open an RStudio project from your file explorer/finder, just double click on the .Rproj file

Open a project inside RStudio

To open an RStudio project from RStudio, click on the project symbol on the top right of R Studio and select the project from the list.

A tip before we get started

Learn the most important keyboard shortcuts of R Studio.

Find all shortcuts under Tools -> Keyboard Shortcuts Help

  • Save active file: Ctrl/Cmd + S
  • Run current line: Ctrl/Cmd + Enter
  • Create new R Script: Ctrl/Cmd + N
  • Undo: Ctrl/Cmd + Z
  • Redo: Ctrl/Cmd + Y
  • Copy/Paste: Ctrl/Cmd + C/V

R as a calculator

Arithmetic operators


Addition +
Subtraction -
Multiplication *
Division /
Modulo %%
Power ^ 
# Addition
2 + 2
# Subtraction
5.432 - 34234
# Multiplication
33 * 42
# Division
3 / 42
# Modulo (Remainder)
2 %% 2
# Power
2^2
# Combine operations
((2 + 2) * 5)^(10 %% 10)

R as a calculator

Relational operators


Equal to ==
Not equal to !=
Less than <
Greater than >
Less or equal than <=
Greater or equal than >= 
2 == 2
[1] TRUE
2 != 2
[1] FALSE
33 <= 32
[1] FALSE
20 < 20
[1] FALSE

R as a calculator

Logical operators


Not ! 
!TRUE
[1] FALSE
!(3 < 1)
[1] TRUE

R as a calculator

Logical operators


Not !
And & 
(3 < 1) & (3 == 3) # FALSE & TRUE = FALSE
[1] FALSE
(1 < 3) & (3 == 3) # TRUE & TRUE = TRUE
[1] TRUE
(3 < 1) & (3 != 3) # FALSE & FALSE = FALSE
[1] FALSE

R as a calculator

Logical operators


Not !
And &
Or | 
(3 < 1) | (3 == 3) # FALSE | TRUE = TRUE
[1] TRUE
(1 < 3) | (3 == 3) # TRUE | TRUE = TRUE
[1] TRUE
(3 < 1) | (3 != 3) # FALSE | FALSE = FALSE
[1] FALSE

Basic R Syntax

  • Whitespace does not matter
# this
data<-read_csv("data/my-data.csv")

# is the same as this

data <- 
  read_csv(    "data/my-data.csv"   )

  • There are good practice rules however -> More on that later

  • RStudio will (often) tell you if something is incorrect

    • Find on the side of your script

Comments in R

# Reading and cleaning the data -----------------

data <- read_csv("data/my-data.csv")
# clean all column headers 
# (found on https://stackoverflow.com/questions/68177507/)
data <- janitor::clean_names(data)

# Analysis --------------------------------------
  • Everything that follows a # is a comment
  • Comments are not evaluated
  • Notes that make code more readable or add information
  • Comments can be used for
    • Explanation of code (if necessary)
    • Include links, names of authors, …
    • Mark different sections of your code ( try Ctrl/Cmd + Shift + R)

2 Introduction to

Variables

  • Store values under meaningful names to reuse them
  • A variable has a name and value and is created using the assignment operator

radius   <-   5

  • Variables are available in the global environment
  • R is case sensitive: radius != Radius
  • Variables can hold any R objects, e.g. numbers, tables with data, …
  • Choose meaningful variable names
    • Make your code easier to read

Variables

# create a variable
radius <- 5
# use it in a calculation and save the result
# pi is a built-in variable that comes with R
circumference <- 2 * pi * radius
# change value of variable radius
radius <- radius + 1


# just use the name to print the value to the console
radius

Atomic data types

There are 6 so-called atomic data types in R. The 4 most important are:

Numeric: There are two numeric data types:

  • Double: can be specified in decimal (1.243 or -0.2134), scientific notation (2.32e4) or hexadecimal (0xd3f1)

  • Integer: numbers that are not represented by fraction. Must be followed by an L (1L, 2038459L, -5L)

Logical: only two possible values TRUE and FALSE (abbreviation: T or F - but better use non-abbreviated form)

Character: also called string. Sequence of characters surrounded by quotes ("hello" , "sample_1")

Vectors

Vectors are data structures that are built on top of atomic data types.

Imagine a vector as a collection of values that are all of the same data type.

Image from Advanced R book

Creating vectors

Use the function c() to combine values into a vector

lgl_var <- c(TRUE, TRUE, FALSE)
dbl_var <- c(2.5, 3.4, 4.3)
int_var <- c(1L, 45L, 234L)
chr_var <- c("These are", "just", "some strings")

The : operator creates a sequence between two numbers with an increment of (-)1

1:10 # instead of c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
 [1]  1  2  3  4  5  6  7  8  9 10

There are many more options to create vectors

  • seq() to create a sequence of numbers
  • rep() ro repeat values
  • …

Working with vectors

Let’s create some vectors to work with.

# list of 10 biggest cities in Europe
cities <- c("Istanbul", "Moscow", "London", "Saint Petersburg", "Berlin", 
            "Madrid", "Kyiv", "Rome", "Bucharest", "Paris")

population <- c(15.1e6, 12.5e6, 9e6, 5.4e6, 3.8e6, 3.2e6, 3e6, 2.8e6, 2.2e6, 2.1e6)

area_km2 <- c(2576, 2561, 1572, 1439,891,604, 839, 1285, 228, 105 )


We can check the length of a vector using the length() function:

length(cities)
[1] 10

Working with vectors

Divide population and area vector to calculate population density in each city: 

population / area_km2
 [1]  5861.801  4880.906  5725.191  3752.606  4264.871  5298.013  3575.685
 [8]  2178.988  9649.123 20000.000

The operation is performed separately for each element of the two vectors and the result is a vector.

Same, if a vector is divided by vector of length 1 (i.e. a single number). Result is always a vector.

mean_population <- mean(population) # calculate the mean of population vector
mean_population
[1] 5910000
population / mean_population # divide population vector by the mean
 [1] 2.5549915 2.1150592 1.5228426 0.9137056 0.6429780 0.5414552 0.5076142
 [8] 0.4737733 0.3722504 0.3553299

Working with vectors

We can also work with relational and logical operators

population > mean_population
 [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

The result is a vector containing TRUE and FALSE, depending on whether the city’s population is larger than the mean population or not.


Logical and relational operators can be combined

# population larger than mean population OR population larger than 3 million
population > mean_population | population > 3e6
 [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE

Working with vectors

Check whether elements occur in a vector:

cities == "Istanbul"
 [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE


The %in% operator checks whether multiple elements occur in a vector.

# for each element of cities, checks whether that element is contained in to_check 
to_check <- c("Istanbul", "Berlin", "Madrid")
cities %in% to_check # same as cities %in% c("Istanbul", "Berlin", "Madrid")
 [1]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE


%in% always returns a vector of the same length as the vector on the left side

# for each element of to_check, check whether that element is contained in cities 
to_check %in% cities
[1] TRUE TRUE TRUE

Indexing vectors

You can use square brackets [] to access specific elements from a vector.

The basic structure is:

vector [ vector of indexes to select ]


cities[5]
[1] "Berlin"


# the three most populated cities
cities[1:3] # same as cities[c(1,2,3)]
[1] "Istanbul" "Moscow"   "London"  


# the last entry of the cities vector
cities[length(cities)] # same as cities[10]
[1] "Paris"

Indexing vectors

Change the values of a vector at specified indexes using the assignment operator <-

Imagine for example, that the population of

  • Istanbul (index 1) increased to 20 Million
  • Rome (index 8) changed but is unknown
  • Paris (index 10) decreased by 200,000
# Update Istanbul (1) and Rome(8)
population[c(1, 8)] <- c(20e6, NA) # NA means missing value
# Update Paris (10)
population[10] <- population[10] - 200000 

# Look at the result
population
 [1] 20000000 12500000  9000000  5400000  3800000  3200000  3000000       NA
 [9]  2200000  1900000

Indexing vectors

You can also index a vector using logical tests. The basic structure is:

vector [ logical vector of same length ]


mega_city <- population > mean_population
mega_city
 [1]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Which are the mega cities?

cities[mega_city] # or short: cities[population > mean_population]
[1] "Istanbul" "Moscow"   "London"

Return only the cities for which the comparison of their population against the mean population is TRUE

Summary

Introduction to R

Summary I

  • Variables have a name and a value and are created using the assignment operator <-, e.g.
radius <- 5
  • Vectors are a collection of values of the same data type:
    • character ("hello")
    • numeric: integer (23L) and double (2.23)
    • logical (TRUE and FALSE)

Summary II

Create vectors

# combine objects into vector
c(1,2,3)

# create a sequence of values
seq(from = 3, to = 6, by = 0.5)
seq(from = 3, to = 6, length.out = 10)
2:10

# repeat values from a vector
rep(c(1,2), times = 2)
rep(c("a", "b"), each = 2)

Summary III

Indexing and subsetting vectors

# By index
v[3]
v[1:4]
v[c(1,5,7)]

# Logical indexing with 1 vector
v[v > 5]
v[v != "bird" | v == "rabbit"]
v[v %in% c(1,2,3)] # same as v[v == 1 | v == 2 | v == 3]

# Logical indexing with two vectors of same length
v[y == "bird"] # return the value in v for which index y == "bird"
v[y == max(y)] # return the value in v for which y is the maximum of y

Summary IV

Working with vectors

# length
length(v)
# rounding numbers
round(v, digits = 2)
# sum
sum(v)
# mean
mean(v)
# median
median(v)
# standard deviation
sd(v)
# find the min value
min(v)
# find the max value

Introduction to R