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R Tidyverse: Data transformation

Kasper Welbers & Wouter van Atteveldt 2018-09


The goal of this tutorial is to get you acquainted with the Tidyverse. Tidyverse is a collection of packages that have been designed around a singular and clearly defined set of principles about what data should look like and how we should work with it. It comes with a nice introduction in the R for Data Science book, for which the digital version is available for free. This tutorial deals with most of the material in chapter 5 of that book.

In this part of the tutorial, we'll focus on working with data using the tidyverse package. This package includes the dplyr (data-pliers) packages, which contains most of the tools we're using below, but it also contains functions for reading, analysing and visualising data that will be explained later.

Instaling tidyverse

As before, install.packages() is used to download and install the package (you only need to do this once on your computer) and library() is used to make the functions from this package available for use (required each session that you use the package).

install.packages('tidyverse') # only needed once

Note: don't be scared if you see a red message after calling library. RStudio doesn't see the difference between messages, warnings, and errors, so it displays all three in red. You need to read the message, and it will contain the word 'error' if there is an error, such as a misspelled package:

library(tidyvers) # this will cause an error!

Tidyverse basics

[Note: Please review R Basics Tutorial if you are uncertain about objects, values, and functions.]

As in most packages, the functionality in dplyr is offered through functions. In general, a function can be seen as a command or instruction to the computer to do something and (generally) return the result. In the tidverse package dplyr, almost all functions primarily operate on data sets, for example for filtering and sorting data.

With a data set we mean a rectangular data frame consisting of rows (often items or respondents) and columns (often measurements of or data about these items). These data sets can be R data.frames, but tidyverse has its own version of data frames called tibble, which is functionally (almost) equivalent to a data frame but is more efficient and somewhat easier to use.

As a very simply example, the following code creates a tibble containing respondents, their gender, and their height:

tibble(resp=c(1,2,3), gender=c("M","M","F"), height=c(176, 165, 172))

Reading data: read_csv

The example above manually created a data set, but in most cases you will start with data that you get from elsewhere, such as a csv file (e.g. downloaded from an online dataset or exported from excel) or an SPSS or Stata data file.

Tidyverse contains a function read_csv that allows you to read a csv file directly into a data frame. You specify the location of the file, either on your local drive or directly from the Internet!

The example below downloads an overview of gun polls from the data analytics site 538, and reads it into a tibble using the read_csv function:

url = ""
d = read_csv(url)

(Note that you can safely ignore the (red) message, they simply tell you how each column was parsed)

The shows the first ten rows of the data set, and if the columns don't fit they are not printed. The remaining rows and columns are printed at the bottom. For each column the data type is also mentioned ( stands for integer, which is a numeric value; is textual or character data). If you want to browse through your data, you can also click on the name of the data.frame (d) in the top-right window "Environment" tab or call View(d).

Subsetting with filter()

The filter function can be used to select a subset of rows. In the guns data, the Question column specifies which question was asked. We can select only those rows (polls) that asked whether the minimum purchage age for guns should be raised to 21:

age21 = filter(d, Question == 'age-21')

This call is typical for a tidyverse function: the first argument is the data to be used (d), and the remaining argument(s) contain information on what should be done to the data.

Note the use of == for comparison: In R, = means assingment and == means equals. Other comparisons are e.g. > (greather than), <= (less than or equal) and != (not equal). You can also combine multiple conditions with logical (boolean) operators: & (and), | or, and ! (not), and you can use parentheses like in mathematics.

So, we can find all surveys where support for raising the gun age was at least 80%:

filter(d, Question == 'age-21' & Support >= 80)

Note that this command did not assign the result to an object, so the result is only displayed on the screen but not remembered. This can be a great way to quickly inspect your data, but if you want to continue analysing this subset you need to assign it to an object as above.

Aside: getting help on (tidy) function

As explained earlier, to get help on a function you can type ?filter in the console or search for filter in the help pane. In both cases, you need to specify that you mean filter from the dplyr package, as there is also a filter function in other packages.

If you look at the help page, you will first see the general description. This is followed by Usage, which shows how the function should be called. In this case, it lists filter(.data, ...). The first argument (.data) makes sense, but the ... is confusing. What is means is that you can give an arbitrary number of extra arguments, that will (in this case) all be used as filters. This is explained in the Arguments: the ... arguments are 'Logical predicates defined in terms of the variables in .data'.

The remainder give extra information on what exactly the function does (Details), the output it produces (Value), and links to other useful packages, functions, and finally a number examples.

Altohugh it may seem intimidating at first, it is important to get used to style of the R documentation as it is the primary source of information on most functions and packages you will be using!

Selecting certain columns

Where filter selects specific rows, select allows you to select specific columns. Most simply, we can simply name the columns that we want to retrieve them in that particular order.

select(age21, Population, Support, Pollster)

You can also specify a range of columns, for example all columns from Support to Democratic Support:

select(age21, Support:`Democratic Support`)

Note the use of 'backticks' (reverse quotes) to specify the column name, as R does not normally allow spaces in names.

Select can also be used to rename columns when selecting them, for example to get rid of the spaces:

select(age21, Pollster, rep=`Republican Support`, dem=`Democratic Support`)

Note that select drops all columns not selected. If you only want to rename columns, you can use the rename function:

rename(age21, start_date = Start, end_date = End)

Finally, you can drop a variable by adding a minus sign in front of a name:

select(age21, -Question, -URL)

Sorting with arrange()

You can easily sort a data set with arrange: you first specify the data, and then the column(s) to sort on. To sort in descending order, put a minus in front of a variable. For example, the following orders by population and then by support (descending):

age21 = arrange(age21, Population, -Support)

Note that I assigned the result of arranging to the age21 object again, i.e. I replace the object by its sorted version. If I wouldn't assign it to anything, it would display it on screen but not remember the sorting. Assigning a result to the same name means I don't create a new object, preventing the environment from being cluttered (and saving me from the bother of thinking up yet another object name). For sorting, this should generally be fine as the sorted data should contain the same data as before. For subsetting, this means that the rows or columns are actually deleted from the dataset (in memory), so you will have to read the file again (or start from an earlier object) if you need those rows or columns later.

Adding or transforming variables with mutate()

The mutate function makes it easy to create new variables or to modify existing ones. For those more familiar with SPSS, this is what you would do with compute and recode.

If you look at the documentation page, you see that mutate works similarly to filter() and select(), in the sense that the first argument is the tibble, and then any number of additional arguments can be given to perform mutations. The mutations themselves are named arguments, in which you can provide any calculations using the existing columns.

Here we'll first create some variables and then look at the variables (using the select function to focus on the changes). Specifically, we'll make a column for the absolute difference between the support scores for republicans and democrats, as a measure of how much they disagree.

age21 = mutate(age21, party_diff = abs(`Republican Support` - `Democratic Support`))
select(age21, Question, Pollster, party_diff)
age21 = arrange(age21, Population, -Support)

To transform (recode) a variable in the same column, you can simply use an existing name in mutate() to overwrite it.

Working with Pipes

If you look at the code above, you notice that the result of each function is stored as an object, and that this object is used as the first argument for the next function. Moreover, we don't really care about this temporary object, we only care about the final summary table.

This is a very common usage pattern, and it can be seen as a pipeline of functions, where the output of each function is the input for the next function. Because this is so common, tidyverse offers a more convenient way of writing the code above using the pipeline operator %>%. In sort, whenever you write f(a, x) you can replace it by a %>% f(x). If you then want to use the output of f(a, x) for a second function, you can just add it to the pipe: a %>% f(x) %>% f2(y) is equivalent to f2(f(a,x), y), or more readable, b=f(a,x); f2(b, y)

Put simply, pipes take the output of a function, and directly use that output as the input for the .data argument in the next function. As you have seen, all the dplyr functions that we discussed have in common that the first argument is a tibble, and all functions return a tibble. This is intentional, and allows us to pipe all the functions together.

This seems a bit abstract, but consider the code below, which is a collection of statements from above:

d = read_csv(url)
age21 = filter(d, Question == 'age-21')
age21 = mutate(age21, party_diff = abs(`Republican Support` - `Democratic Support`))
age21 = select(age21, Question, Pollster, party_diff)
arrange(age21, -party_diff)

To recap, this reads the csv, filters by question, computes the difference, drops other variables, and sorts. Since the output of each function is the input of the next, we can also write this as a single pipeline:

read_csv(url) %>% filter(Question == 'age-21') %>% 
  mutate(party_diff = abs(`Republican Support` - `Democratic Support`)) %>%
  select(Question, Pollster, party_diff) %>% 

The nice thing about pipes is that it makes it really clear what you are doing. Also, it doesn't require making many intermediate objects (such as ds). If applied right, piping allows you to make nicely contained pieces of code to perform specific parts of your analysis from raw input straight to results, including statistical modeling or visualization.

Of course, you probably don't want to replace your whole script with a single pipe, and often it is nice to store intermediate values. For example, you might want to download, clean, and subset a data set before doing multiple analyses with it. In that case, you probably want to store the result of downloading, cleaning, and subsetting as a variable, and use that in your analyses.