4  Data transformation

You are reading the work-in-progress second edition of R for Data Science. This chapter should be readable but is currently undergoing final polishing. You can find the complete first edition at https://r4ds.had.co.nz.

4.1 Introduction

Visualisation is an important tool for generating insight, but it’s rare that you get the data in exactly the right form you need for it. Often you’ll need to create some new variables or summaries to see the most important patterns, or maybe you just want to rename the variables or reorder the observations to make the data a little easier to work with. You’ll learn how to do all that (and more!) in this chapter, which will introduce you to data transformation using the dplyr package and a new dataset on flights that departed New York City in 2013.

The goal of this chapter is to give you an overview of all the key tools for transforming a data frame. We’ll come back these functions in more detail in later chapters, as we start to dig into specific types of data (e.g. numbers, strings, dates).

4.1.1 Prerequisites

In this chapter we’ll focus on the dplyr package, another core member of the tidyverse. We’ll illustrate the key ideas using data from the nycflights13 package, and use ggplot2 to help us understand the data.

library(nycflights13)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✔ ggplot2 3.3.6          ✔ purrr   0.3.4     
#> ✔ tibble  3.1.7          ✔ dplyr   1.0.9     
#> ✔ tidyr   1.2.0.9000     ✔ stringr 1.4.0.9000
#> ✔ readr   2.1.2          ✔ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()

Take careful note of the conflicts message that’s printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you’ll need to use their full names: stats::filter() and stats::lag().

4.1.2 nycflights13

To explore the basic dplyr verbs, we’re going to use nycflights13::flights. This dataset contains all 336,776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in ?flights.

flights
#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

If you’ve used R before, you might notice that this data frame prints a little differently to other data frames you’ve seen. That’s because it’s a tibble, a special type of data frame used by the tidyverse to avoid some common gotchas. The most important difference is the way it prints: tibbles are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. To see everything, use View(flights) to open the dataset in the RStudio viewer. We’ll come back to other important differences in Chapter Chapter 12.

You might have noticed the short abbreviations that follow each column name. These tell you the type of each variable: <int> is short for integer, <dbl> is short for double (aka real numbers), <chr> for character (aka strings), and <dttm> for date-time. These are important because the operations you can perform on a column depend so much on its “type”, and these types are used to organize the chapters in the next section of the book.

4.1.3 dplyr basics

You’re about to learn the primary dplyr verbs which will allow you to solve the vast majority of your data manipulation challenges. But before we discuss their individual differences, it’s worth stating what they have in common:

  1. The first argument is always a data frame.

  2. The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).

  3. The result is always a new data frame.

Because the first argument is a data frame and the output is a data frame, dplyr verbs work work well with the pipe, |>. The pipe takes the thing on its left and passes it along to the function on its right so that x |> f(y) is equivalent to f(x, y), and x |> f(y) |> g(z) is equivalent to into g(f(x, y), z). The easiest way to pronounce the pipe is “then”. That makes it possible to get a sense of the following code even though you haven’t yet learnt the details:

flights |>
  filter(dest == "IAH") |> 
  group_by(year, month, day) |> 
  summarize(
    arr_delay = mean(arr_delay, na.rm = TRUE)
  )

The code starts with the flights dataset, then filters it, then groups it, then summarizes it. We’ll come back to the pipe and its alternatives in Section 7.3.

dplyr’s verbs are organised into four groups based on what they operate on: rows, columns, groups, or tables. In the following sections you’ll learn the most important verbs for rows, columns, and groups, then we’ll come back to verb that work on tables in Chapter Chapter 13. Let’s dive in!

4.2 Rows

The most important verbs that operate on rows are filter(), which changes which rows are present without changing their order, and arrange(), which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged.

4.2.1 filter()

filter() allows you to keep rows based on the values of the columns1. The first argument is the data frame. The second and subsequent arguments are the conditions that must be true to keep the row. For example, we could find all flights that arrived more than 120 minutes (two hours) late:

flights |> 
  filter(arr_delay > 120)
#> # A tibble: 10,034 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      811            630       101     1047            830
#> 2  2013     1     1      848           1835       853     1001           1950
#> 3  2013     1     1      957            733       144     1056            853
#> 4  2013     1     1     1114            900       134     1447           1222
#> 5  2013     1     1     1505           1310       115     1638           1431
#> 6  2013     1     1     1525           1340       105     1831           1626
#> # … with 10,028 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

As well as > (greater than), you can use >= (greater than or equal to), < (less than), <= (less than or equal to), == (equal to), and != (not equal to). You can also use & (and) or | (or) to combine multiple conditions:

# Flights that departed on January 1
flights |> 
  filter(month == 1 & day == 1)
#> # A tibble: 842 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 836 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
#> #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

# Flights that departed in January or February
flights |> 
  filter(month == 1 | month == 2)
#> # A tibble: 51,955 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 51,949 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

There’s a useful shortcut when you’re combining | and ==: %in%. It keeps rows where the variable equals one of the values on the right:

# A shorter way to select flights that departed in January or February
flights |> 
  filter(month %in% c(1, 2))
#> # A tibble: 51,955 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 51,949 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

We’ll come back to these comparisons and logical operators in more detail in Chapter Chapter 14.

When you run filter() dplyr executes the filtering operation, creating a new data frame, and then prints it. It doesn’t modify the existing flights dataset because dplyr functions never modify their inputs. To save the result, you need to use the assignment operator, <-:

jan1 <- flights |> 
  filter(month == 1 & day == 1)

4.2.2 Common mistakes

When you’re starting out with R, the easiest mistake to make is to use = instead of == when testing for equality. filter() will let you know when this happens:

flights |> 
  filter(month = 1)
#> Error in `filter()`:
#> ! We detected a named input.
#> ℹ This usually means that you've used `=` instead of `==`.
#> ℹ Did you mean `month == 1`?

Another mistakes is you write “or” statements like you would in English:

flights |> 
  filter(month == 1 | 2)

This works, in the sense that it doesn’t throw an error, but it doesn’t do what you want. We’ll come back to what it does and why in Section 17.3.2.

4.2.3 arrange()

arrange() changes the order of the rows based on the value of the columns. 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. For example, the following code sorts by the departure time, which is spread over four columns.

flights |> 
  arrange(year, month, day, dep_time)
#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

You can use desc() to re-order by a column in descending order. For example, this code shows the most delayed flights:

flights |> 
  arrange(desc(dep_delay))
#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     9      641            900      1301     1242           1530
#> 2  2013     6    15     1432           1935      1137     1607           2120
#> 3  2013     1    10     1121           1635      1126     1239           1810
#> 4  2013     9    20     1139           1845      1014     1457           2210
#> 5  2013     7    22      845           1600      1005     1044           1815
#> 6  2013     4    10     1100           1900       960     1342           2211
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

You can combine arrange() and filter() to solve more complex problems. For example, we could look for the flights that were most delayed on arrival that left on roughly on time:

flights |> 
  filter(dep_delay <= 10 & dep_delay >= -10) |> 
  arrange(desc(arr_delay))
#> # A tibble: 239,109 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013    11     1      658            700        -2     1329           1015
#> 2  2013     4    18      558            600        -2     1149            850
#> 3  2013     7     7     1659           1700        -1     2050           1823
#> 4  2013     7    22     1606           1615        -9     2056           1831
#> 5  2013     9    19      648            641         7     1035            810
#> 6  2013     4    18      655            700        -5     1213            950
#> # … with 239,103 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

4.2.4 Exercises

  1. Find all flights that

    1. Had an arrival delay of two or more hours
    2. Flew to Houston (IAH or HOU)
    3. Were operated by United, American, or Delta
    4. Departed in summer (July, August, and September)
    5. Arrived more than two hours late, but didn’t leave late
    6. Were delayed by at least an hour, but made up over 30 minutes in flight
  2. Sort flights to find the flights with longest departure delays. Find the flights that left earliest in the morning.

  3. Sort flights to find the fastest flights (Hint: try sorting by a calculation).

  4. Which flights traveled the farthest? Which traveled the shortest?

  5. Does it matter what order you used filter() and arrange() in if you’re using both? Why/why not? Think about the results and how much work the functions would have to do.

4.3 Columns

There are four important verbs that affect the columns without changing the rows: mutate(), select(), rename(), and relocate(). mutate() creates new columns that are functions of the existing columns; select(), rename(), and relocate() change which columns are present, their names, or their positions.

4.3.1 mutate()

The job of mutate() is to add new columns that are calculated from the existing columns. In the transform chapters, you’ll learn a large set of functions that you can use to manipulate different types of variables. For now, we’ll stick with basic algebra, which allows us to compute the gain, how much time a delayed flight made up in the air, and the speed in miles per hour:

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    speed = distance / air_time * 60
  )
#> # A tibble: 336,776 × 21
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 13 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>,
#> #   gain <dbl>, speed <dbl>

By default, mutate() adds new columns on the right hand side of your dataset, which makes it difficult to see what’s happening here. We can use the .before argument to instead add the variables to the left hand side2:

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    speed = distance / air_time * 60,
    .before = 1
  )
#> # A tibble: 336,776 × 21
#>    gain speed  year month   day dep_time sched_dep_time dep_delay arr_time
#>   <dbl> <dbl> <int> <int> <int>    <int>          <int>     <dbl>    <int>
#> 1    -9  370.  2013     1     1      517            515         2      830
#> 2   -16  374.  2013     1     1      533            529         4      850
#> 3   -31  408.  2013     1     1      542            540         2      923
#> 4    17  517.  2013     1     1      544            545        -1     1004
#> 5    19  394.  2013     1     1      554            600        -6      812
#> 6   -16  288.  2013     1     1      554            558        -4      740
#> # … with 336,770 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, origin <chr>,
#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#> #   time_hour <dttm>

The . is a sign that .before is an argument to the function, not the name of a new variable. You can also use .after to add after a variable, and in both .before and .after you can the name of a variable name instead of a position. For example, we could add the new variables after day:

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    speed = distance / air_time * 60,
    .after = day
  )
#> # A tibble: 336,776 × 21
#>    year month   day  gain speed dep_time sched_dep_time dep_delay arr_time
#>   <int> <int> <int> <dbl> <dbl>    <int>          <int>     <dbl>    <int>
#> 1  2013     1     1    -9  370.      517            515         2      830
#> 2  2013     1     1   -16  374.      533            529         4      850
#> 3  2013     1     1   -31  408.      542            540         2      923
#> 4  2013     1     1    17  517.      544            545        -1     1004
#> 5  2013     1     1    19  394.      554            600        -6      812
#> 6  2013     1     1   -16  288.      554            558        -4      740
#> # … with 336,770 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>, origin <chr>,
#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#> #   time_hour <dttm>

Alternatively, you can control which variables are kept with the .keep argument. A particularly useful argument is "used" which allows you to see the inputs and outputs from your calculations:

flights |> 
  mutate(,
    gain = dep_delay - arr_delay,
    hours = air_time / 60,
    gain_per_hour = gain / hours,
    .keep = "used"
  )
#> # A tibble: 336,776 × 6
#>   dep_delay arr_delay air_time  gain hours gain_per_hour
#>       <dbl>     <dbl>    <dbl> <dbl> <dbl>         <dbl>
#> 1         2        11      227    -9  3.78         -2.38
#> 2         4        20      227   -16  3.78         -4.23
#> 3         2        33      160   -31  2.67        -11.6 
#> 4        -1       -18      183    17  3.05          5.57
#> 5        -6       -25      116    19  1.93          9.83
#> 6        -4        12      150   -16  2.5          -6.4 
#> # … with 336,770 more rows

4.3.2 select()

It’s not uncommon to get datasets with hundreds or even thousands of variables. In this situation, the first challenge is often just focusing on the variables you’re interested in. select() allows you to rapidly zoom in on a useful subset using operations based on the names of the variables. select() is not terribly useful with the flights data because we only have 19 variables, but you can still get the general idea of how it works:

# Select columns by name
flights |> 
  select(year, month, day)
#> # A tibble: 336,776 × 3
#>    year month   day
#>   <int> <int> <int>
#> 1  2013     1     1
#> 2  2013     1     1
#> 3  2013     1     1
#> 4  2013     1     1
#> 5  2013     1     1
#> 6  2013     1     1
#> # … with 336,770 more rows

# Select all columns between year and day (inclusive)
flights |> 
  select(year:day)
#> # A tibble: 336,776 × 3
#>    year month   day
#>   <int> <int> <int>
#> 1  2013     1     1
#> 2  2013     1     1
#> 3  2013     1     1
#> 4  2013     1     1
#> 5  2013     1     1
#> 6  2013     1     1
#> # … with 336,770 more rows

# Select all columns except those from year to day (inclusive)
flights |> 
  select(!year:day)
#> # A tibble: 336,776 × 16
#>   dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
#>      <int>          <int>     <dbl>    <int>          <int>     <dbl> <chr>  
#> 1      517            515         2      830            819        11 UA     
#> 2      533            529         4      850            830        20 UA     
#> 3      542            540         2      923            850        33 AA     
#> 4      544            545        -1     1004           1022       -18 B6     
#> 5      554            600        -6      812            837       -25 DL     
#> 6      554            558        -4      740            728        12 UA     
#> # … with 336,770 more rows, and 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

# Select all columns that are characters
flights |> 
  select(where(is.character))
#> # A tibble: 336,776 × 4
#>   carrier tailnum origin dest 
#>   <chr>   <chr>   <chr>  <chr>
#> 1 UA      N14228  EWR    IAH  
#> 2 UA      N24211  LGA    IAH  
#> 3 AA      N619AA  JFK    MIA  
#> 4 B6      N804JB  JFK    BQN  
#> 5 DL      N668DN  LGA    ATL  
#> 6 UA      N39463  EWR    ORD  
#> # … with 336,770 more rows

There are a number of helper functions you can use within select():

  • starts_with("abc"): matches names that begin with “abc”.
  • ends_with("xyz"): matches names that end with “xyz”.
  • contains("ijk"): matches names that contain “ijk”.
  • num_range("x", 1:3): matches x1, x2 and x3.

See ?select for more details. Once you know regular expressions (the topic of Chapter Chapter 17) you’ll also be use matches() to select variables that match a pattern.

You can rename variables as you select() them by using =. The new name appears on the left hand side of the =, and the old variable appears on the right hand side:

flights |> 
  select(tail_num = tailnum)
#> # A tibble: 336,776 × 1
#>   tail_num
#>   <chr>   
#> 1 N14228  
#> 2 N24211  
#> 3 N619AA  
#> 4 N804JB  
#> 5 N668DN  
#> 6 N39463  
#> # … with 336,770 more rows

4.3.3 rename()

If you just want to keep all the existing variables and just want to rename a few, you can use rename() instead of select():

flights |> 
  rename(tail_num = tailnum)
#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tail_num <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

It works exactly the same way as select(), but keeps all the variables that aren’t explicitly selected.

If you have a bunch of inconsistently named columns and it would be painful to fix them all by hand, check out janitor::clean_names() which provides some useful automated cleaning.

4.3.4 relocate()

You can move variables around with relocate(). By default it moves variables to the front:

flights |> 
  relocate(time_hour, air_time)
#> # A tibble: 336,776 × 19
#>   time_hour           air_time  year month   day dep_time sched_dep_time
#>   <dttm>                 <dbl> <int> <int> <int>    <int>          <int>
#> 1 2013-01-01 05:00:00      227  2013     1     1      517            515
#> 2 2013-01-01 05:00:00      227  2013     1     1      533            529
#> 3 2013-01-01 05:00:00      160  2013     1     1      542            540
#> 4 2013-01-01 05:00:00      183  2013     1     1      544            545
#> 5 2013-01-01 06:00:00      116  2013     1     1      554            600
#> 6 2013-01-01 05:00:00      150  2013     1     1      554            558
#> # … with 336,770 more rows, and 12 more variables: dep_delay <dbl>,
#> #   arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
#> #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
#> #   hour <dbl>, minute <dbl>

But you can use the same .before and .after arguments as mutate() to choose where to put them:

flights |> 
  relocate(year:dep_time, .after = time_hour)
#> # A tibble: 336,776 × 19
#>   sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight
#>            <int>     <dbl>    <int>          <int>     <dbl> <chr>    <int>
#> 1            515         2      830            819        11 UA        1545
#> 2            529         4      850            830        20 UA        1714
#> 3            540         2      923            850        33 AA        1141
#> 4            545        -1     1004           1022       -18 B6         725
#> 5            600        -6      812            837       -25 DL         461
#> 6            558        -4      740            728        12 UA        1696
#> # … with 336,770 more rows, and 12 more variables: tailnum <chr>, origin <chr>,
#> #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
#> #   time_hour <dttm>, year <int>, month <int>, day <int>, dep_time <int>
flights |> 
  relocate(starts_with("arr"), .before = dep_time)
#> # A tibble: 336,776 × 19
#>    year month   day arr_time arr_delay dep_time sched_dep_time dep_delay
#>   <int> <int> <int>    <int>     <dbl>    <int>          <int>     <dbl>
#> 1  2013     1     1      830        11      517            515         2
#> 2  2013     1     1      850        20      533            529         4
#> 3  2013     1     1      923        33      542            540         2
#> 4  2013     1     1     1004       -18      544            545        -1
#> 5  2013     1     1      812       -25      554            600        -6
#> 6  2013     1     1      740        12      554            558        -4
#> # … with 336,770 more rows, and 11 more variables: sched_arr_time <int>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

4.3.5 Exercises

  1. Compare air_time with arr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?

  2. Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?

  3. Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.

  4. What happens if you include the name of a variable multiple times in a select() call?

  5. What does the any_of() function do? Why might it be helpful in conjunction with this vector?

    variables <- c("year", "month", "day", "dep_delay", "arr_delay")
  6. Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?

    select(flights, contains("TIME"))

4.4 Groups

So far you’ve learned about functions that work with rows and columns. dplyr gets even more powerful when you add in the ability to work with groups. In this section, we’ll focus on the most important functions: group_by(), summarize(), and the slice family of functions.

4.4.1 group_by()

Use group_by() to divide your dataset into groups meaningful for your analysis:

flights |> 
  group_by(month)
#> # A tibble: 336,776 × 19
#> # Groups:   month [12]
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

group_by() doesn’t change the data but, if you look closely at the output, you’ll notice that it’s now “grouped by” month. This means subsequent operations will now work “by month”.

4.4.2 summarize()

The most important grouped operation is a summary. It collapses each group to a single row3. Here we compute the average departure delay by month:

flights |> 
  group_by(month) |> 
  summarize(
    delay = mean(dep_delay)
  )
#> # A tibble: 12 × 2
#>   month delay
#>   <int> <dbl>
#> 1     1    NA
#> 2     2    NA
#> 3     3    NA
#> 4     4    NA
#> 5     5    NA
#> 6     6    NA
#> # … with 6 more rows

Uhoh! Something has gone wrong and all of our results are NA (pronounced “N-A”), R’s symbol for missing value. We’ll come back to discuss missing values in Chapter Chapter 20, but for now we’ll remove them by using na.rm = TRUE:

flights |> 
  group_by(month) |> 
  summarize(
    delay = mean(dep_delay, na.rm = TRUE)
  )
#> # A tibble: 12 × 2
#>   month delay
#>   <int> <dbl>
#> 1     1  10.0
#> 2     2  10.8
#> 3     3  13.2
#> 4     4  13.9
#> 5     5  13.0
#> 6     6  20.8
#> # … with 6 more rows

You can create any number of summaries in a single call to summarize(). You’ll learn various useful summaries in the upcoming chapters, but one very useful summary is n(), which returns the number of rows in each group:

flights |> 
  group_by(month) |> 
  summarize(
    delay = mean(dep_delay, na.rm = TRUE), 
    n = n()
  )
#> # A tibble: 12 × 3
#>   month delay     n
#>   <int> <dbl> <int>
#> 1     1  10.0 27004
#> 2     2  10.8 24951
#> 3     3  13.2 28834
#> 4     4  13.9 28330
#> 5     5  13.0 28796
#> 6     6  20.8 28243
#> # … with 6 more rows

Means and counts can get you a surprisingly long way in data science!

4.4.3 The slice_ functions

There are five handy functions that allow you pick off specific rows within each group:

  • df |> slice_head(n = 1) takes the first row from each group.
  • df |> slice_tail(n = 1) takes the last row in each group.
  • df |> slice_min(x, n = 1) takes the row with the smallest value of x.
  • df |> slice_max(x, n = 1) takes the row with the largest value of x.
  • df |> slice_sample(x, n = 1) takes one random row.

You can vary n to select more than one row, or instead of n =, you can use prop = 0.1 to select (e.g.) 10% of the rows in each group. For example, the following code finds the most delayed flight to each destination:

flights |> 
  group_by(dest) |> 
  slice_max(arr_delay, n = 1)
#> # A tibble: 107 × 19
#> # Groups:   dest [104]
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     7    22     2145           2007        98      132           2259
#> 2  2013     7    23     1139            800       219     1250            909
#> 3  2013     1    25      123           2000       323      229           2101
#> 4  2013     8    17     1740           1625        75     2042           2003
#> 5  2013     7    22     2257            759       898      121           1026
#> 6  2013     7    10     2056           1505       351     2347           1758
#> # … with 101 more rows, and 11 more variables: arr_delay <dbl>, carrier <chr>,
#> #   flight <int>, tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>,
#> #   distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

This is similar to computing the max delay with summarize(), but you get the whole row instead of the single summary:

flights |> 
  group_by(dest) |> 
  summarize(max_delay = max(arr_delay, na.rm = TRUE))
#> Warning in max(arr_delay, na.rm = TRUE): no non-missing arguments to max;
#> returning -Inf
#> # A tibble: 105 × 2
#>   dest  max_delay
#>   <chr>     <dbl>
#> 1 ABQ         153
#> 2 ACK         221
#> 3 ALB         328
#> 4 ANC          39
#> 5 ATL         895
#> 6 AUS         349
#> # … with 99 more rows

4.4.4 Grouping by multiple variables

You can create groups using more than one variable. For example, we could make a group for each day:

daily <- flights |>  
  group_by(year, month, day)
daily
#> # A tibble: 336,776 × 19
#> # Groups:   year, month, day [365]
#>    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
#>   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
#> 1  2013     1     1      517            515         2      830            819
#> 2  2013     1     1      533            529         4      850            830
#> 3  2013     1     1      542            540         2      923            850
#> 4  2013     1     1      544            545        -1     1004           1022
#> 5  2013     1     1      554            600        -6      812            837
#> 6  2013     1     1      554            558        -4      740            728
#> # … with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> #   carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
#> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>

When you summarize a tibble grouped by more than one variable, each summary peels off the last group. In hindsight, this wasn’t great way to make this function work, but it’s difficult to change without breaking existing code. To make it obvious what’s happening, dplyr displays a message that tells you how you can change this behavior:

daily_flights <- daily |> 
  summarize(
    n = n()
  )
#> `summarise()` has grouped output by 'year', 'month'. You can override using the
#> `.groups` argument.

If you’re happy with this behavior, you can explicitly request it in order to suppress the message:

daily_flights <- daily |> 
  summarize(
    n = n(), 
    .groups = "drop_last"
  )

Alternatively, change the default behavior by setting a different value, e.g. "drop" to drop all grouping or "keep" to preserve the same groups.

4.4.5 Ungrouping

You might also want to remove grouping outside of summarize(). You can do this with ungroup().

daily |> 
  ungroup() |>
  summarize(
    delay = mean(dep_delay, na.rm = TRUE), 
    flights = n()
  )
#> # A tibble: 1 × 2
#>   delay flights
#>   <dbl>   <int>
#> 1  12.6  336776

As you can see, when you summarize an ungrouped data frame, you get a single row back because dplyr treats all the rows in an ungrouped data frame as belonging to one group.

4.4.6 Exercises

  1. Which carrier has the worst delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights |> group_by(carrier, dest) |> summarize(n()))

  2. Find the most delayed flight to each destination.

  3. How do delays vary over the course of the day. Illustrate your answer with a plot.

  4. What happens if you supply a negative n to slice_min() and friends?

  5. Explain what count() does in terms of the dplyr verbs you just learn. What does the sort argument to count() do?

4.5 Case study: aggregates and sample size

Whenever you do any aggregation, it’s always a good idea to include a count (n()). That way, you can ensure that you’re not drawing conclusions based on very small amounts of data. For example, let’s look at the planes (identified by their tail number) that have the highest average delays:

delays <- flights |>  
  filter(!is.na(arr_delay), !is.na(tailnum)) |> 
  group_by(tailnum) |> 
  summarize(
    delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )

ggplot(delays, aes(delay)) + 
  geom_freqpoly(binwidth = 10)

A frequency histogram showing the distribution of flight delays. The distribution is unimodal, with a large spike around 0, and asymmetric: very few flights leave more than 30 minutes early, but flights are delayed up to 5 hours.

Wow, there are some planes that have an average delay of 5 hours (300 minutes)! That seems pretty surprising, so lets draw a scatterplot of number of flights vs. average delay:

ggplot(delays, aes(n, delay)) + 
  geom_point(alpha = 1/10)

A scatterplot showing number of flights versus after delay. Delays for planes with very small number of flights have very high variability (from -50 to ~300), but the variability rapidly decreases as the number of flights increases.

Not surprisingly, there is much greater variation in the average delay when there are few flights for a given plane. The shape of this plot is very characteristic: whenever you plot a mean (or other summary) vs. group size, you’ll see that the variation decreases as the sample size increases4.

When looking at this sort of plot, it’s often useful to filter out the groups with the smallest numbers of observations, so you can see more of the pattern and less of the extreme variation in the smallest groups:

delays |>  
  filter(n > 25) |> 
  ggplot(aes(n, delay)) + 
  geom_point(alpha = 1/10) + 
  geom_smooth(se = FALSE)

Now that the y-axis (average delay) is smaller (-20 to 60 minutes), we can see a more complicated story. The smooth line suggests an initial decrease in average delay from 10 minutes to 0 minutes as number of flights per plane increases from 25 to 100. This is followed by a gradual increase up to 10 minutes for 250 flights, then a gradual decrease to ~5 minutes at 500 flights.

Note the handy pattern for combining ggplot2 and dplyr. It’s a bit annoying that you have to switch from |> to +, but it’s not too much of a hassle once you get the hang of it.

There’s another common variation on this pattern that we can see in some data about baseball players. The following code uses data from the Lahman package to compare what proportion of times a player hits the ball vs. the number of attempts they take:

batters <- Lahman::Batting |> 
  group_by(playerID) |> 
  summarize(
    perf = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
    n = sum(AB, na.rm = TRUE)
  )
batters
#> # A tibble: 20,166 × 3
#>   playerID    perf     n
#>   <chr>      <dbl> <int>
#> 1 aardsda01 0          4
#> 2 aaronha01 0.305  12364
#> 3 aaronto01 0.229    944
#> 4 aasedo01  0          5
#> 5 abadan01  0.0952    21
#> 6 abadfe01  0.111      9
#> # … with 20,160 more rows

When we plot the skill of the batter (measured by the batting average, ba) against the number of opportunities to hit the ball (measured by at bat, ab), you see two patterns:

  1. As above, the variation in our aggregate decreases as we get more data points.

  2. There’s a positive correlation between skill (perf) and opportunities to hit the ball (n) because obviously teams want to give their best batters the most opportunities to hit the ball.

batters |> 
  filter(n > 100) |> 
  ggplot(aes(n, perf)) +
    geom_point(alpha = 1 / 10) + 
    geom_smooth(se = FALSE)

A scatterplot of number of batting opportunites vs batting performance overlaid with a smoothed line. Average performance increases sharply from 0.2 at when n is 1 to 0.25 when n is ~1000. Average performance continues to increase linearly at a much shallower slope reaching ~0.3 when n is ~15,000.

This also has important implications for ranking. If you naively sort on desc(ba), the people with the best batting averages are clearly lucky, not skilled:

batters |> 
  arrange(desc(perf))
#> # A tibble: 20,166 × 3
#>   playerID   perf     n
#>   <chr>     <dbl> <int>
#> 1 abramge01     1     1
#> 2 alberan01     1     1
#> 3 banisje01     1     1
#> 4 bartocl01     1     1
#> 5 bassdo01      1     1
#> 6 birasst01     1     2
#> # … with 20,160 more rows

You can find a good explanation of this problem and how to overcome it at http://varianceexplained.org/r/empirical_bayes_baseball/ and http://www.evanmiller.org/how-not-to-sort-by-average-rating.html.


  1. Later, you’ll learn about the slice_*() family which allows you to choose rows based on their positions↩︎

  2. Remember that in RStudio, the easiest way to see a dataset with many columns is View().↩︎

  3. This is a slightly simplification; later on you’ll learn how to use summarize() to produce multiple summary rows for each group.↩︎

  4. *cough* the central limit theorem *cough*↩︎