Introduction to dplyr

Source: ../../vignettes/dplyr.Rmd

When working with data you must:

  • Figure out what you want to do.

  • Describe those tasks in the form of a computer program.

  • Execute the program.

The dplyr package makes these steps fast and easy:

  • By constraining your options, it helps you think about your data manipulation challenges.

  • It provides simple “verbs”, functions that correspond to the most common data manipulation tasks, to help you translate your thoughts into code.

  • It uses efficient backends, so you spend less time waiting for the computer.

This document introduces you to dplyr’s basic set of tools, and shows you how to apply them to data frames. dplyr also supports databases via the dbplyr package, once you’ve installed, read vignette("dbplyr") to learn more.

Data: nycflights13

To explore the basic data manipulation verbs of dplyr, we’ll use nycflights13::flights. This dataset contains all 336776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in ?nycflights13

Note that nycflights13::flights is a tibble, a modern reimagining of the data frame. It’s particularly useful for large datasets because it only prints the first few rows. You can learn more about tibbles at http://tibble.tidyverse.org; in particular you can convert data frames to tibbles with as_tibble().

Single table verbs

Dplyr aims to provide a function for each basic verb of data manipulation:

Filter rows with filter()

filter() allows you to select a subset of rows in a data frame. Like all single verbs, the first argument is the tibble (or data frame). The second and subsequent arguments refer to variables within that data frame, selecting rows where the expression is TRUE.

For example, we can select all flights on January 1st with:

This is rougly equivalent to this base R code:

Arrange rows with arrange()

arrange() works similarly to filter() except that instead of filtering or selecting rows, it reorders them. It takes a data frame, and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns:

Use desc() to order a column in descending order:

Select columns with select()

Often you work with large datasets with many columns but only a few are actually of interest to you. select() allows you to rapidly zoom in on a useful subset using operations that usually only work on numeric variable positions:

There are a number of helper functions you can use within select(), like starts_with(), ends_with(), matches() and contains(). These let you quickly match larger blocks of variables that meet some criterion. See ?select for more details.

You can rename variables with select() by using named arguments:

But because select() drops all the variables not explicitly mentioned, it’s not that useful. Instead, use rename():

Add new columns with mutate()

Besides selecting sets of existing columns, it’s often useful to add new columns that are functions of existing columns. This is the job of mutate():

dplyr::mutate() is similar to the base transform(), but allows you to refer to columns that you’ve just created:

If you only want to keep the new variables, use transmute():

Summarise values with summarise()

The last verb is summarise(). It collapses a data frame to a single row.

It’s not that useful until we learn the group_by() verb below.

Commonalities

You may have noticed that the syntax and function of all these verbs are very similar:

  • The first argument is a data frame.

  • The subsequent arguments describe what to do with the data frame. You can refer to columns in the data frame directly without using $.

  • The result is a new data frame

Together these properties make it easy to chain together multiple simple steps to achieve a complex result.

These five functions provide the basis of a language of data manipulation. At the most basic level, you can only alter a tidy data frame in five useful ways: you can reorder the rows (arrange()), pick observations and variables of interest (filter() and select()), add new variables that are functions of existing variables (mutate()), or collapse many values to a summary (summarise()). The remainder of the language comes from applying the five functions to different types of data. For example, I’ll discuss how these functions work with grouped data.

Patterns of operations

The dplyr verbs can be classified by the type of operations they accomplish (we sometimes speak of their semantics, i.e., their meaning). The most important and useful distinction is between grouped and ungrouped operations. In addition, it is helpful to have a good grasp of the difference between select and mutate operations.

Grouped operations

The dplyr verbs are useful on their own, but they become even more powerful when you apply them to groups of observations within a dataset. In dplyr, you do this with the group_by() function. It breaks down a dataset into specified groups of rows. When you then apply the verbs above on the resulting object they’ll be automatically applied “by group”.

Grouping affects the verbs as follows:

In the following example, we split the complete dataset into individual planes and then summarise each plane by counting the number of flights (count = n()) and computing the average distance (dist = mean(distance, na.rm = TRUE)) and arrival delay (delay = mean(arr_delay, na.rm = TRUE)). We then use ggplot2 to display the output.

You use summarise() with aggregate functions, which take a vector of values and return a single number. There are many useful examples of such functions in base R like min(), max(), mean(), sum(), sd(), median(), and IQR(). dplyr provides a handful of others:

  • n(): the number of observations in the current group

  • n_distinct(x):the number of unique values in x.

  • first(x), last(x) and nth(x, n) - these work similarly to x[1], x[length(x)], and x[n] but give you more control over the result if the value is missing.

For example, we could use these to find the number of planes and the number of flights that go to each possible destination:

When you group by multiple variables, each summary peels off one level of the grouping. That makes it easy to progressively roll-up a dataset:

However you need to be careful when progressively rolling up summaries like this: it’s ok for sums and counts, but you need to think about weighting for means and variances (it’s not possible to do this exactly for medians).

Selecting operations

One of the appealing features of dplyr is that you can refer to columns from the tibble as if they were regular variables. However, the syntactic uniformity of referring to bare column names hides semantical differences across the verbs. A column symbol supplied to select() does not have the same meaning as the same symbol supplied to mutate().

Selecting operations expect column names and positions. Hence, when you call select() with bare variable names, they actually represent their own positions in the tibble. The following calls are completely equivalent from dplyr’s point of view:

By the same token, this means that you cannot refer to variables from the surrounding context if they have the same name as one of the columns. In the following example, year still represents 1, not 5:

year <- 5
select(flights, year)

One useful subtlety is that this only applies to bare names and to selecting calls like c(year, month, day) or year:day. In all other cases, the columns of the data frame are not put in scope. This allows you to refer to contextual variables in selection helpers:

These semantics are usually intuitive. But note the subtle difference:

In the first argument, year represents its own position 1. In the second argument, year is evaluated in the surrounding context and represents the fifth column.

For a long time, select() used to only understand column positions. Counting from dplyr 0.6, it now understands column names as well. This makes it a bit easier to program with select():

Note that the code above is somewhat unsafe because you might have added a column named vars to the tibble, or you might apply the code to another data frame containing such a column. To avoid this issue, you can wrap the variable in an identity() call as we mentioned above, as this will bypass column names. However, a more explicit and general method that works in all dplyr verbs is to unquote the variable with the !! operator. This tells dplyr to bypass the data frame and to directly look in the context:

This operator is very useful when you need to use dplyr within custom functions. You can learn more about it in vignette("programming"). However it is important to understand the semantics of the verbs you are unquoting into, that is, the values they understand. As we have just seen, select() supports names and positions of columns. But that won’t be the case in other verbs like mutate() because they have different semantics.

Mutating operations

Mutate semantics are quite different from selection semantics. Whereas select() expects column names or positions, mutate() expects column vectors. Let’s create a smaller tibble for clarity:

df <- select(flights, year:dep_time)

When we use select(), the bare column names stand for ther own positions in the tibble. For mutate() on the other hand, column symbols represent the actual column vectors stored in the tibble. Consider what happens if we give a string or a number to mutate():

mutate() gets length-1 vectors that it interprets as new columns in the data frame. These vectors are recycled so they match the number of rows. That’s why it doesn’t make sense to supply expressions like "year" + 10 to mutate(). This amounts to adding 10 to a string! The correct expression is:

In the same way, you can unquote values from the context if these values represent a valid column. They must be either length 1 (they then get recycled) or have the same length as the number of rows. In the following example we create a new vector that we add to the data frame:

A case in point is group_by(). While you might think it has select semantics, it actually has mutate semantics. This is quite handy as it allows to group by a modified column:

This is why you can’t supply a column name to group_by(). This amounts to creating a new column containing the string recycled to the number of rows:

Since grouping with select semantics can be sometimes useful as well, we have added the group_by_at() variant. In dplyr, variants suffixed with _at() support selection semantics in their second argument. You just need to wrap the selection with vars():

You can read more about the _at() and _if() variants in the ?scoped help page.

Piping

The dplyr API is functional in the sense that function calls don’t have side-effects. You must always save their results. This doesn’t lead to particularly elegant code, especially if you want to do many operations at once. You either have to do it step-by-step:

a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
a3 <- summarise(a2,
  arr = mean(arr_delay, na.rm = TRUE),
  dep = mean(dep_delay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)

Or if you don’t want to name the intermediate results, you need to wrap the function calls inside each other:

This is difficult to read because the order of the operations is from inside to out. Thus, the arguments are a long way away from the function. To get around this problem, dplyr provides the %>% operator from magrittr. x %>% f(y) turns into f(x, y) so you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom:

flights %>%
  group_by(year, month, day) %>%
  select(arr_delay, dep_delay) %>%
  summarise(
    arr = mean(arr_delay, na.rm = TRUE),
    dep = mean(dep_delay, na.rm = TRUE)
  ) %>%
  filter(arr > 30 | dep > 30)

Other data sources

As well as data frames, dplyr works with data that is stored in other ways, like data tables, databases and multidimensional arrays.

Data table

dplyr also provides data table methods for all verbs through dtplyr. If you’re using data.tables already this lets you to use dplyr syntax for data manipulation, and data.table for everything else.

For multiple operations, data.table can be faster because you usually use it with multiple verbs simultaneously. For example, with data table you can do a mutate and a select in a single step. It’s smart enough to know that there’s no point in computing the new variable for rows you’re about to throw away.

The advantages of using dplyr with data tables are:

  • For common data manipulation tasks, it insulates you from the reference semantics of data.tables, and protects you from accidentally modifying your data.

  • Instead of one complex method built on the subscripting operator ([), it provides many simple methods.

Databases

dplyr also allows you to use the same verbs with a remote database. It takes care of generating the SQL for you so that you can avoid the cognitive challenge of constantly switching between languages. To use these capabilities, you’ll need to install the dbplyr package and then read vignette("dbplyr") for the details.

Multidimensional arrays / cubes

tbl_cube() provides an experimental interface to multidimensional arrays or data cubes. If you’re using this form of data in R, please get in touch so I can better understand your needs.

Comparisons

Compared to all existing options, dplyr:

  • abstracts away how your data is stored, so that you can work with data frames, data tables and remote databases using the same set of functions. This lets you focus on what you want to achieve, not on the logistics of data storage.

  • provides a thoughtful default print() method that doesn’t automatically print pages of data to the screen (this was inspired by data table’s output).

Compared to base functions:

  • dplyr is much more consistent; functions have the same interface. So once you’ve mastered one, you can easily pick up the others

  • base functions tend to be based around vectors; dplyr is based around data frames

Compared to plyr, dplyr:

  • is much much faster

  • provides a better thought out set of joins

  • only provides tools for working with data frames (e.g. most of dplyr is equivalent to ddply() + various functions, do() is equivalent to dlply())

Compared to virtual data frame approaches:

  • it doesn’t pretend that you have a data frame: if you want to run lm etc, you’ll still need to manually pull down the data

  • it doesn’t provide methods for R summary functions (e.g. mean(), or sum())