Introduction to R

Day 1 - Introduction to Data Analysis with R

Selina Baldauf

Freie Universität Berlin - Theoretical Ecology

March 14, 2025

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)

Variables and data types in

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


If you want to know which value stands behind a variable:

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

or have a look at the “Environment” pane.

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")

There are many more options to create vectors

  • seq() to create a sequence of numbers
  • : creates a sequence of numbers with an increment of 1 (e.g. 1:10)
  • rep() ro repeat values
  • …

Working with vectors

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

Now you

Task (30 min)

Working with vectors

Find the task description here