previous episode

Statistics Denmark API using R

next episode

Introduction to R

Overview

Teaching: 50 min
Exercises: 30 min
Questions

  • What data types are available in R?

  • What is an object?

  • How can values be initially assigned to variables of different data types?

  • What arithmetic and logical operators can be used?

  • How can subsets be extracted from vectors?

  • How does R treat missing values?

  • How can we deal with missing values in R?

Objectives

  • A quick recap of the following concepts:

  • Define the following terms as they relate to R: object, assign, call, function, arguments, options.

  • Assign values to objects in R.

  • Learn how to name objects.

  • Use comments to inform script.

  • Solve simple arithmetic operations in R.

  • Call functions and use arguments to change their default options.

  • Inspect the content of vectors and manipulate their content.

  • Subset and extract values from vectors.

  • Analyze vectors with missing data.

A very short refresher on R

You can get output from R simply by typing math in the console:

3 + 5

[1] 8

12 / 7

[1] 1.714286

We can assign values to variables:

area_hectares <- 1.0

<- is the assignment operator. It assigns values on the right to objects on the left. So, after executing x <- 3, the value of x is 3. The arrow can be read as 3 goes into x.

Now that R has area_hectares in memory, we can do arithmetic with it. For instance, we may want to convert this area into acres (area in acres is 2.47 times the area in hectares):

2.47 * area_hectares

[1] 2.47

We can also change an object’s value by assigning it a new one:

area_hectares <- 2.5
2.47 * area_hectares

[1] 6.175

Comments

All programming languages allow the programmer to include comments in their code. To do this in R we use the # character. Anything to the right of the # sign and up to the end of the line is treated as a comment and is ignored by R. You can start lines with comments or include them after any code on the line.

area_hectares <- 1.0			# land area in hectares
area_acres <- area_hectares * 2.47	# convert to acres
area_acres				# print land area in acres.

[1] 2.47

Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc. Many functions are predefined, or can be made available by importing R packages (more on that later). A function usually gets one or more inputs called arguments. Functions often (but not always) return a value. A typical example would be the function sqrt(). The input (the argument) must be a number, and the return value (in fact, the output) is the square root of that number. Executing a function (‘running it’) is called calling the function. An example of a function call is:

b <- sqrt(a)

Let’s try a function that can take multiple arguments: round().

round(3.14159)

[1] 3

Here, we’ve called round() with just one argument, 3.14159, and it has returned the value 3.

We can get information on how a function works, with the help function:

?round

We see that if we want a different number of digits, we can type digits=2 or however many we want.

round(3.14159, digits = 2)

[1] 3.14

Vectors and data types

A vector is the most common and basic data type in R, and is pretty much the workhorse of R. A vector is composed by a series of values, which can be either numbers or characters. We can assign a series of values to a vector using the c() function. For example we can create a vector of the number of household members for the households we’ve interviewed and assign it to a new object hh_members:

hh_members <- c(3, 7, 10, 6)
hh_members

[1]  3  7 10  6

A vector can also contain characters. For example, we can have a vector of the building material used to construct our interview respondents’ walls (respondent_wall_type):

respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type

[1] "muddaub"     "burntbricks" "sunbricks"

The quotes around “muddaub”, etc. are essential here. Without the quotes R will assume there are objects called muddaub, burntbricks and sunbricks. As these objects don’t exist in R’s memory, there will be an error message.

There are many functions that allow you to inspect the content of a vector. length() tells you how many elements are in a particular vector:

length(hh_members)

[1] 4

length(respondent_wall_type)

[1] 3

An important feature of a vector, is that all of the elements are the same type of data. The function class() indicates the class (the type of element) of an object:

class(hh_members)

[1] "numeric"

class(respondent_wall_type)

[1] "character"

The function str() provides an overview of the structure of an object and its elements. It is a useful function when working with large and complex objects:

str(hh_members)

 num [1:4] 3 7 10 6

str(respondent_wall_type)

 chr [1:3] "muddaub" "burntbricks" "sunbricks"

You can use the c() function to add other elements to your vector:

possessions <- c("bicycle", "radio", "television")
possessions <- c("car", possessions) # add to the beginning of the vector
possessions

[1] "car"        "bicycle"    "radio"      "television"

In the first line, we take the original vector possessions, add the value "mobile_phone" to the end of it, and save the result back into possessions. Then we add the value "car" to the beginning, again saving the result back into possessions.

We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.

Vectors are one of the many data structures that R uses. Other important ones are lists (list), matrices (matrix), data frames (data.frame), factors (factor) and arrays (array).

Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

respondent_wall_type <- c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type[2]

[1] "burntbricks"

respondent_wall_type[c(3, 2)]

[1] "sunbricks"   "burntbricks"

We can also repeat the indices to create an object with more elements than the original one:

more_respondent_wall_type <- respondent_wall_type[c(1, 2, 3, 2, 1, 3)]
more_respondent_wall_type

[1] "muddaub"     "burntbricks" "sunbricks"   "burntbricks" "muddaub"    
[6] "sunbricks"

R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

Conditional subsetting

Another common way of subsetting is by using a logical vector. TRUE will select the element with the same index, while FALSE will not:

hh_members <- c(3, 7, 10, 6)
hh_members[c(TRUE, FALSE, TRUE, TRUE)]

[1]  3 10  6

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. For instance, if you wanted to select only the values above 5:

hh_members > 5    # will return logicals with TRUE for the indices that meet the condition

[1] FALSE  TRUE  TRUE  TRUE

## so we can use this to select only the values above 5
hh_members[hh_members > 5]

[1]  7 10  6

You can combine multiple tests using & (both conditions are true, AND) or | (at least one of the conditions is true, OR):

hh_members[hh_members < 4 | hh_members > 7]

[1]  3 10

hh_members[hh_members >= 4 & hh_members <= 7]

[1] 7 6

Here, < stands for “less than”, > for “greater than”, >= for “greater than or equal to”, and == for “equal to”. The double equal sign == is a test for numerical equality between the left and right hand sides, and should not be confused with the single = sign, which performs variable assignment (similar to <-).

A common task is to search for certain strings in a vector. One could use the “or” operator | to test for equality to multiple values, but this can quickly become tedious.

possessions <- c("car", "bicycle", "radio", "television", "mobile_phone")
possessions[possessions == "car" | possessions == "bicycle"] # returns both car and bicycle

[1] "car"     "bicycle"

The function %in% allows you to test if any of the elements of a search vector (on the left hand side) are found in the target vector (on the right hand side):

possessions %in% c("car", "bicycle")

[1]  TRUE  TRUE FALSE FALSE FALSE

Note that the output is the same length as the search vector on the left hand side, because %in% checks whether each element of the search vector is found somewhere in the target vector. Thus, you can use %in% to select the elements in the search vector that appear in your target vector:

possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")

[1]  TRUE  TRUE FALSE FALSE FALSE

possessions[possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")]

[1] "car"     "bicycle"

Missing data

As R was designed to analyze datasets, it includes the concept of missing data (which is uncommon in other programming languages). Missing data are represented in vectors as NA.

When doing operations on numbers, most functions will return NA if the data you are working with include missing values. This feature makes it harder to overlook the cases where you are dealing with missing data. You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.

rooms <- c(2, 1, 1, NA, 7)
mean(rooms)

[1] NA

max(rooms)

[1] NA

mean(rooms, na.rm = TRUE)

[1] 2.75

max(rooms, na.rm = TRUE)

[1] 7

If your data include missing values, you may want to become familiar with the functions is.na(), na.omit(), and complete.cases(). See below for examples.

Recall that you can use the typeof() function to find the type of your atomic vector.

Key Points

previous episode

next episode