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Programming with R

Data Structures

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Overview

Teaching: 15 min
Exercises: 10 min
Questions

  • What are the basic data types in R?

  • How does R handle operations on different data types?

Objectives

  • To be aware of the different types of data.

  • To be able to ask questions from R about the type, class, and structure of an object.

Data Types in R

In the previous lesson, we discovered how to perform operations on variables through operators such as + and *. Let’s review that now, using the variables x and z that we assigned during our last lesson:

x + z

[1] 101.025

Our output was just as we expected. Let’s try adding together x and y:

x + y

Error in x + y : non-numeric argument to binary operator

As you might have expected, R gave us an error. It does not know how to add the values 101 and “green”. This is because these are not the correct data types. R has 5 main data types: double, integer, complex, logical, and character.

typeof(3.14)

[1] "double"

A double, also referred to as a floating point number, is how R stores numeric values by default.

typeof(1L)

[1] "integer"

To use an integer value in R, we use the L to tell R that this value is an integer value. Without the L, R would store this value as a double.

typeof(1+1i)

[1] "complex"

R can also support complex values as well. Unless you are doing mathematical analyses or complicated transformations, chances are you will not encounter this data type very often.

typeof(TRUE)

[1] "logical"

Logical data types are particularly helpful in subsetting data frames and other types of data manipulation. We will explore this concept more later.

typeof('banana')

[1] "character"

Lastly, R stores strings as the character type.

No matter how complicated our analyses become, all data in R is interpreted as one of these basic data types.

Vectors

Remember previously when R returned a value, it prepended the output with [1]? This is because R never actually works with just one value, but always a series of values called a vector. All of our previous output were vectors with a length of 1. As we saw in our last challenge, we can create vectors of longer length by using the c function.

x <- c(2, 4, 6, 8, 10, 12, 14, 16)
x

[1]  2  4  6  8 10 12 14 16

Additionally, we can also use the colon operator to quickly create sequential vectors:

y <- 1:8
y

[1] 1 2 3 4 5 6 7 8

Using the colon operator, we can specify whatever start and stop point we want and R will automatically create an integer vector containing these and all numbers between for us.

-4:7

[1] -4 -3 -2 -1  0  1  2  3  4  5  6  7

R is vectorized. This means that we can perform operations on the entire vector just like we did for single values previously. When operations are applied to a vector, R returns a new vector with the results.

y + 10

[1] 11 12 13 14 15 16 17 18

x * 2

[1]  4  8 12 16 20 24 28 32

x + y

[1]  3  6  9 12 15 18 21 24

Notice that when we add two different vectors together, R performs the operation element by element:

x:  2  4  6  8 10 12 14 16
    +  +  +  +  +  +  +  +
y:  1  2  3  4  5  6  7  8
--------------------------
    3  6  9 12 15 18 21 24

Vectors can be made up of any of the basic data types.

Character Vectors:

a <- c("one", "two", "three", "four")
a

[1] "one"   "two"   "three" "four"

Just like we used the typeof command earlier, the str command will also tell us the data type of our object. Additionally, it gives us a compact view of some basic information about our object.

str(a)

 chr [1:4] "one" "two" "three" "four"

We can see from the first three letters chr that this is a character vector. The numbers in the brackets indicates the dimensions of our vector. And then it will list the first few elements of the vector.

Logical Vectors:

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

[1]  TRUE  TRUE FALSE  TRUE

In addition to using the c command to create vectors, we can use it to add elements to an existing vector:

c(x, 20, 25)

 [1]  2  4  6  8 10 12 14 16 20 25

These changes won’t take place until we use the assignment arrow to store the new value. Let’s modify our vector y so that it contains all numbers up to 20. We can use the colon operator that we talked about earlier:

y <- c(y, 9:20)
y

 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

This is an example of a nested operation. Remember from the order of operations previously, R will do any operations inside the parenthesis first. In this example, R created the sequence from 9 to 20, and then it combined it with what we already had stored in y.

Another helpful function for vectors is length. We can use this command to quickly return the length of a vector.

length(y)

[1] 20

Challenge 1

Predict what will happen if we perform an operation between two vectors of different size?

Test your guess by creating two vectors of different lengths using the colon operator and adding or multiplying them together.

Solution to Challenge 1

a <- 1:10
b <- 1:5
a * b

[1]  1  4  9 16 25  6 14 24 36 50

Notice how R repeated the shorter vector until it had finished operating on every element of the larger vector. This is known as vector recycling.

If your shorter vector is not a even multiple of the larger one, R will still perform the operation but it will give you the following error message:

Warning message:
In a * b : longer object length is not a multiple of shorter object length

Challenge 2

What happens when we create a vector that combines data types?

Try creating a vector named my_vector containing the elements 1, “four”, and TRUE. What does the vector look like?

Use the str command to determine what data type is in your vector?

Solution to Challenge 2

my_vector <- c(1, "four", TRUE)
my_vector

[1] "1"    "four" "TRUE"

str(my_vector)

 chr [1:3] "1" "four" "TRUE"

See the quotes around each element of my_vector? R turned every element of the vector into a character. Since all elements of the vector must be of the same data type, R picked the best one based on the data we gave it. This is called type coercion. Type coercion can cause problems if your data is not consistant or if it is assigned to an unexpected data type, so we need to watch for it as we work with data in R.

We can manually coerce the data by using commands such as as.numeric or as.character. For more information on how these commands work, you can read their help documentation by typing ?as.numeric or ?as.character.

Challenge 3

R also vectorizes functions on character vectors as well.

Use the c function to create a character vector named colors with the values: “red”, “yellow” and “blue”. Use the paste function to combine "My ball is" with each element of your vector.

Solution to Challenge 3

colors <- c("red", "yellow", "blue")
paste("My ball is", colors)

[1] "My ball is red"    "My ball is yellow"
[3] "My ball is blue"

There are other data structures in R called lists and matrices. You can discover more about these by looking at the help files associated with their constructor functions: ?list, ?matrix or by checking out the supplemental lesson Lists and Matrices

Today, we will be working primarily with the data frame data structures. We will explore these more indepth after our break. v

Key Points

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