Data Structures

Last updated on 2024-09-17 | Edit this page

Estimated time: 12 minutes

Overview

Questions

  • What are the basic data types in R?
  • How do I represent categorical information in R?

Objectives

After completing this episode, participants should be able to…

  • To be aware of the different types of data.
  • To begin exploring data frames, and understand how they are related to vectors, factors and lists.
  • To be able to ask questions from R about the type, class, and structure of an object.

Vectors

So far we’ve looked on individual values. Now we will move to a data structure called vectors. Vectors are arrays of values of the same data type.

Data types

Data type refers to a type of information that is stored by a value. It can be:

  • numerical (a number)
  • integer (a number without information about decimal points)
  • logical (a boolean - are values TRUE or FALSE?)
  • character (a text/ string of characters)
  • complex (a complex number)
  • raw (raw bytes)

We won’t discuss complex or raw data type in the workshop.

Data structures

Vectors are the most common and basic data structure in R but you will come across other data structures such as data frames, lists and matrices as well. In short:

  • data.frames is a two-dimensional data structure in which columns are vectors of the same length that can have different data types. We will use this data structure in this lesson.
  • lists can have an arbitrary structure and can mix data types;
  • matrices are two-dimensional data structures containing elements of the same data type.

For a more detailed description, see Data Types and Structures.

Note that vector data in the geospatial context is different from vector data types. More about vector data in a later lesson!

You can create a vector with a c() function.

R 

# vector of numbers - numeric data type.
numeric_vector <- c(2, 6, 3) 
numeric_vector

OUTPUT 

[1] 2 6 3

R 

# vector of words - or strings of characters- character data type
character_vector <- c('Amsterdam', 'London', 'Delft') 
character_vector

OUTPUT 

[1] "Amsterdam" "London"    "Delft"

R 

# vector of logical values (is something true or false?)- logical data type.
logical_vector <- c(TRUE, FALSE, TRUE) 
logical_vector

OUTPUT 

[1]  TRUE FALSE  TRUE

Combining vectors

The combine function, c(), will also append things to an existing vector:

R 

ab_vector <- c('a', 'b')
ab_vector

OUTPUT 

[1] "a" "b"

R 

abcd_vector <- c(ab_vector, 'c', 'd')
abcd_vector

OUTPUT 

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

Missing values

Challenge: combining vectors

Combine the abcd_vector with the numeric_vector in R. What is the data type of this new vector and why?

combined_vector <- c(abcd_vector, numeric_vector)
combined_vector

The combined vector is a character vector. Because vectors can only hold one data type and abcd_vector cannot be interpreted as numbers, the numbers in numeric_vector are coerced into characters.

A common operation you want to perform is to remove all the missing values (in R denoted as NA). Let’s have a look how to do it:

R 

with_na <- c(1, 2, 1, 1, NA, 3, NA ) # vector including missing value

First, let’s try to calculate mean for the values in this vector

R 

mean(with_na) # mean() function cannot interpret the missing values

OUTPUT 

[1] NA

R 

# You can add the argument na.rm=TRUE to calculate the result while
# ignoring the missing values.
mean(with_na, na.rm = T)

OUTPUT 

[1] 1.6

However, sometimes, you would like to have the NA permanently removed from your vector. For this you need to identify which elements of the vector hold missing values with is.na() function.

R 

is.na(with_na) # This will produce a vector of logical values,

OUTPUT 

[1] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE

R 

# stating if a statement 'This element of the vector is a missing value'
# is true or not

!is.na(with_na) # The ! operator means negation, i.e. not is.na(with_na)

OUTPUT 

[1]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE

We know which elements in the vectors are NA. Now we need to retrieve the subset of the with_na vector that is not NA. Sub-setting in R is done with square brackets[ ].

R 

without_na <- with_na[ !is.na(with_na) ] # this notation will return only
# the elements that have TRUE on their respective positions

without_na

OUTPUT 

[1] 1 2 1 1 3

Factors

Another important data structure is called a factor. Factors look like character data, but are used to represent categorical information.

Factors create a structured relation between the different levels (values) of a categorical variable, such as days of the week or responses to a question in a survey. While factors look (and often behave) like character vectors, they are actually treated as numbers by R, which is useful for computing summary statistics about their distribution, running regression analysis, etc. So you need to be very careful when treating them as strings.

Create factors

Once created, factors can only contain a pre-defined set of values, known as levels.

R 

nordic_str <- c('Norway', 'Sweden', 'Norway', 'Denmark', 'Sweden')
nordic_str # regular character vectors printed out

OUTPUT 

[1] "Norway"  "Sweden"  "Norway"  "Denmark" "Sweden"

R 

# factor() function converts a vector to factor data type
nordic_cat <- factor(nordic_str)
nordic_cat # With factors, R prints out additional information - 'Levels'

OUTPUT 

[1] Norway  Sweden  Norway  Denmark Sweden
Levels: Denmark Norway Sweden

Inspect factors

R will treat each unique value from a factor vector as a level and (silently) assign numerical values to it. This can come in handy when performing statistical analysis. You can inspect and adapt levels of the factor.

R 

levels(nordic_cat) # returns all levels of a factor vector.

OUTPUT 

[1] "Denmark" "Norway"  "Sweden"

R 

nlevels(nordic_cat) # returns number of levels in a vector

OUTPUT 

[1] 3

Reorder levels

Note that R sorts the levels in the alphabetic order, not in the order of occurrence in the vector. R assigns value of:

  • 1 to level ‘Denmark’,
  • 2 to ‘Norway’
  • 3 to ‘Sweden’.

This is important as it can affect e.g. the order in which categories are displayed in a plot or which category is taken as a baseline in a statistical model.

You can reorder the categories using factor() function. This can be useful, for instance, to select a reference category (first level) in a regression model or for ordering legend items in a plot, rather than using the default category systematically (i.e. based on alphabetical order).

R 

nordic_cat <- factor(
  nordic_cat,
  levels = c(
    "Norway",
    "Denmark",
    "Sweden"
  )
)

# now Norway will be the first category, Denmark second and Sweden third
nordic_cat

OUTPUT 

[1] Norway  Sweden  Norway  Denmark Sweden
Levels: Norway Denmark Sweden

Callout

There is more than one way to reorder factors. Later in the lesson, we will use fct_relevel() function from forcats package to do the reordering.

R 

library(forcats)

nordic_cat <- fct_relevel(
  nordic_cat,
  "Norway",
  "Denmark",
  "Sweden"
) # With this, Norway will be  first category,
# Denmark second and Sweden third

nordic_cat

OUTPUT 

[1] Norway  Sweden  Norway  Denmark Sweden
Levels: Norway Denmark Sweden

You can also inspect vectors with str() function. In factor vectors, it shows the underlying values of each category. You can also see the structure in the environment tab of RStudio.

R 

str(nordic_cat)

OUTPUT 

 Factor w/ 3 levels "Norway","Denmark",..: 1 3 1 2 3

Note of caution

Remember that once created, factors can only contain a pre-defined set of values, known as levels. It means that whenever you try to add something to the factor outside of this set, it will become an unknown/missing value detonated by R as NA.

R 

nordic_str

OUTPUT 

[1] "Norway"  "Sweden"  "Norway"  "Denmark" "Sweden"

R 

nordic_cat2 <- factor(
  nordic_str,
  levels = c("Norway", "Denmark")
)

# because we did not include Sweden in the list of
# factor levels, it has become NA.
nordic_cat2

OUTPUT 

[1] Norway  <NA>    Norway  Denmark <NA>
Levels: Norway Denmark

Key Points

  • The mostly used basic data types in R are numeric, integer, logical, and character
  • Use factors to represent categories in R.