Welcome! Who are you?

• What part of your education are you in? (Bachelor, PhD, prof…)

• What is your faculty/background? (Economics, Medicine, Biology…)

• What is your motivation for learning R?

• What is your experience with R?

This morning’s schedule

•   9:30   Introductions

• 10:00   Base R + Exercises 1- 6

• 11:25   Recap & questions

• 11:30   Coffee break

• 11:45   Programming + Exercises 7-9

• 12:45   Lunch break

• 13:30   Reconvene for afternoon program

What is R

We will work in Rstudio

• Integrated Development Environment (IDE) for R
• Founded by J.J. Allaire, available since 2010
• Bloody useful! Let’s take a look: please open RStudio!

The Rstudio interface

Course materials

All course materials, videos, information & resources are at tinyurl.com/introRDatasite.

1. Download the course materials.

2. Store them in a local (i.e. not on a mounted drive), accessible location.

3. Unzip the download to create a single folder. What animal is displayed on animal.png?

4. Double-click the course-materials.Rproj file. Or: Go to File > Open Project > select course-materials.Rproj > Open

5. From the ‘Files’ menu (bottom right), click baseR_exercises.Rmd.

Running R code

In the baseR_exercises.Rmd:

You can execute Exercise 0 chunk as a whole with the green triangle:

In a script/Rmarkdown document

• Place your cursor in the line of code you want to execute
• Press or ctrl + enter
• When running multiple lines: select all lines, then press ‘Run’ or ctrl + enter

Variable assignment

x <- 6
x <- 'apple'
x <- "hello world"

Do you expect an answer?

Saving information as an R object:

x <- 1

Asking for information to be returned:

x

[1] 1

Note the difference in syntax:

• <- operator: storing information = no immediate ‘answer’
• calling up an object, or making calculations: R shows you the answer

Functions

Functions: code that performs a specific task based on the arguments provided.

Examples:

• mean(x): calculate the mean of x
• mean(x, na.rm = TRUE): calculate the mean of x by leaving out the NAs
• getwd(): print the working directory to the screen (requires no arguments)

?mean

Maths functions

You can perform math with your variables:

x * 3

[1] 3

and store the results as new variables:

y <- x + 2

log2(y)

[1] 1.584963

Logicals

A logical is TRUE or FALSE, and can also be written as T or F.

Logicals are mostly used as tests:

For example:

x == 6

[1] FALSE

x != 10

[1] TRUE

Answers to exercise 1

1. Do the following calculation in R: 1 plus 5, divided by 9

(1+5)/9

[1] 0.6666667

x <- (1+5)/9

x > 1

[1] FALSE

round(x, 1)

[1] 0.7

Combining data: creating vectors

Vectors are created with the function c()

A numeric vector:

c(1,2,3)

[1] 1 2 3

c("a","b","c")

[1] "a" "b" "c"

c(T, TRUE, F)

[1]  TRUE  TRUE FALSE

Combining data: creating vectors

What is this vector?

c(TRUE, "a", 3)

[1] "TRUE" "a"    "3"   

Vector functions

Vectors can be used in mathematical operations

p <- 1:5
p

[1] 1 2 3 4 5

mean(p)

[1] 3

p * 2

[1]  2  4  6  8 10

Vector functions

Operations with multiple vectors are performed by aligning the index

q <- 5:1
q

[1] 5 4 3 2 1

p * q

[1] 5 8 9 8 5

Answers to exercise 2

1. Meet Ann, Bob, Chloe, and Dan. Create a character vector called “name” and add these names to the vector using the c() function.

name <- c("Ann", "Bob", "Chloe", "Dan")

age <- c(35, 22, 50, 51)

class(name)

[1] "character"

class(age)

[1] "numeric"

Answers to exercise 2 (continued)

4. What is their average age? Use a function in R to calculate this. Tip: use the Maths Functions section of the Base R cheat sheet!

mean(age)

[1] 39.5

Data structures: vector

We have two vectors: name and age

name

[1] "Ann"   "Bob"   "Chloe" "Dan"  

age

[1] 35 22 50 51

How do we combine them?

c(name,age)

[1] "Ann"   "Bob"   "Chloe" "Dan"   "35"    "22"    "50"    "51"   

Data structures: data frame

How about combining name and age in a two-dimensional table structure?

data.frame(name, age)

   name age
1   Ann  35
2   Bob  22
3 Chloe  50
4   Dan  51

Data structures: list

Or: in a multi-dimensional list.

list(name, age)

[[1]]
[1] "Ann"   "Bob"   "Chloe" "Dan"  

[[2]]
[1] 35 22 50 51

Data structures: summary

NB: dataframes and lists appear under Data in the Environment (top right panel in RStudio), vectors under Values.

Factors

Special type of vector, defined by levels. Usually as categorical variable in a data frame.

# Create vector country
country <- c("UK", "USA", "USA", "UK")
country

[1] "UK"  "USA" "USA" "UK" 

# Turn country into a factor
country_fac <- as.factor(country)
country_fac

[1] UK  USA USA UK 
Levels: UK USA

df <- data.frame(name, age, country_fac)
df

   name age country_fac
1   Ann  35          UK
2   Bob  22         USA
3 Chloe  50         USA
4   Dan  51          UK

summary(df)

     name                age        country_fac
 Length:4           Min.   :22.00   UK :2      
 Class :character   1st Qu.:31.75   USA:2      
 Mode  :character   Median :42.50              
                    Mean   :39.50              
                    3rd Qu.:50.25              
                    Max.   :51.00              

Answers to exercise 3

1. Create a vector called “country” containing four countries of your choice.

country <- c("UK", "US", "NL", "BE")

df <- data.frame(name, age, country)

mylist <- list(name, age, country, df)

Selecting vector elements

Answers to exercise 4

1. Return only the first number in your vector age.

age[1]

[1] 35

name[c(2, 4)]

[1] "Bob" "Dan"

age[age < 30]

[1] 22

Indexing a data frame

Indexing columns

By position:

df[,2]

[1] 35 22 50 51

df[, "age"]

[1] 35 22 50 51

df$age

[1] 35 22 50 51

Indexing rows

By position:

df[2, ]

  name age country
2  Bob  22      US

df[df$name=="Bob", ]

  name age country
2  Bob  22      US

df[df$name=="Bob", "age"]

[1] 22

Answers to exercise 5

1. From your dataframe df, return complete rows for everyone living in a country of your choice.

df[df$country=="UK", ]

  name age country
1  Ann  35      UK

df[df$age<40, "name"]

[1] "Ann" "Bob"

df[, c("name", "age")]

   name age
1   Ann  35
2   Bob  22
3 Chloe  50
4   Dan  51

Selecting from a list

mylist[1]

[[1]]
[1] "Ann"   "Bob"   "Chloe" "Dan"  

mylist[[1]]

[1] "Ann"   "Bob"   "Chloe" "Dan"  

mylist[[4]][2]

  age
1  35
2  22
3  50
4  51

mylist[[4]][1, 2]

[1] 35

Not Available (NA)

Let’s add a column to our data:

df$pet <- c("cat", "none", "", NA)

df

   name age country  pet
1   Ann  35      UK  cat
2   Bob  22      US none
3 Chloe  50      NL     
4   Dan  51      BE <NA>

Predict the answer (see Exercise 6)

5 == 5

[1] TRUE

5 == NA

[1] NA

NA == NA

[1] NA

is.na(NA)

[1] TRUE

NULL: data does not exist

Do we know about our participants’ jobs?

# Select the column "job" from df
df$job

NULL

If statement: a conditional

An if statement tests if a condition is TRUE or FALSE and exectues code depending on the outcome of that test.

If statement in R

• To build an if-statement, start with the function if():

  if()

Answers to exercise 7

Make an if statement that tests if a number is larger than 18. Assign the result to the variable age_category.

number <- 8

if(number >= 18){
  age_category <- "adult"
} else{
  age_category <- "minor"
}

print(age_category)

[1] "minor"

Functions: a sequence

Functions consist of (multiple) instruction(s) that form a cohesive unit:

mean(df$age)

[1] 39.5

mean(1:100)

[1] 50.5

Functions

Functions can also be used to make a complex line of code easier to write/read:

You write the function once:

find_bobs_age <- function(data){
  bobs_age <- data[data$name == "Bob", "age"]
  return(bobs_age)
}

Now, every time you want to find Bob’s age you use:

find_bobs_age(df)

[1] 22

Functions in R

• To make a function, use the function function():

  myFun <- function()

Using your own function

First, run the code with the function itself. It will appear in your environment:

myFun(3, 4)

[1] 12

myFun(90, 71)

[1] 6390

Answers to exercise 8

Turn the if-statement from the last exercise into a function. Let the user provide the value for number, and return the age_category.

test_age <- function(number){
  if(number >= 18){
    age_category <- "adult"
  } else{
    age_category <- "minor"
  }
  return(age_category)
}

# Test the function
test_age(20)

[1] "adult"

test_age(2)

[1] "minor"

A for-loop

• Perform the same action(s) for mutiple inputs at a time
• Input is an object with multiple similar elements (e.g., a vector, row in a dataframe, etc.) that can be iterated over

A for-loop in R

• A loop starts with the iterable object (in this case the vector 1:5), and the temporary name for each item (in this case a_number):

  for(a_number in 1:5)

Answers to exercise 9

Go over the age column in your dataframe df, and for each age: print() the age category using the test_age function from the previous exercise.

print(df$age)

[1] 35 22 50 51

for(the_age in df$age){
  test <- test_age(the_age)
  print(test)
}

[1] "adult"
[1] "adult"
[1] "adult"
[1] "adult"

Which bracket does what?

You can speak R!

What data types have you encountered so far?

logical
numeric
character

Functions

What functions have you encountered so far?

c()
data.frame()
is.na()
mean()
summary()

Help!

• How does a function work? Type in your console:
  ?mean

• Use a search engine (often useful: Stackoverflow)

• (Generative AI)

  • Upside: can write all code for you
  • Downside: you learn less, it can hallucinate, not certain what sources it uses

Help!

Help? Scroll down for examples!