5 Data transformation
You are reading the work-in-progress second edition of R for Data Science. This chapter is currently undergoing heavy restructuring and may be confusing or incomplete. You can find the polished first edition at https://r4ds.had.co.nz.
5.1 Introduction
Visualisation is an important tool for insight generation, but it is rare that you get the data in exactly the right form you need. Often you’ll need to create some new variables or summaries, or maybe you just want to rename the variables or reorder the observations in order 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 teach you how to transform your data using the dplyr package and a new dataset on flights departing New York City in 2013.
5.1.1 Prerequisites
In this chapter we’re going to focus on how to use 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 3.3.5 ✔ purrr 0.3.4
#> ✔ tibble 3.1.5 ✔ dplyr 1.0.7
#> ✔ tidyr 1.1.4 ✔ stringr 1.4.0.9000
#> ✔ readr 2.0.2 ✔ 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().
5.1.2 nycflights13
To explore the basic dplyr verbs, we’re going to look at nycflights13::flights.
This data frame 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 data frames you might’ve worked with in the past.
That’s because it’s a tibble, a special type of data frame designed by the tidyverse team to avoid some common data.frame gotchas.
The most important difference is the way it prints: tibbles are designed for large datasets, so only show the first few rows and only the columns that fit on one screen.
If you want to see everything you can use View(flights) to open the dataset in the RStudio viewer.
We’ll come back to other important differences in Chapter 13.
You might also have noticed the row of short abbreviations under the column names. These describe the type of each variable:
intstands for integer.dblstands for double, a vector of real numbers.chrstands for character, a vector of strings.dttmstands for date-time (a date + a time).
There are three other common types that aren’t used here but you’ll encounter later in the book:
lglstands for logical, a vector that contains onlyTRUEorFALSE.fctrstands for factor, which R uses to represent categorical variables with fixed possible values.datestands for date.
5.1.3 dplyr basics
In this chapter you are going to learn the primary dplyr verbs which will allow you to solve the vast majority of your data manipulation challenges. They are organised into four camps:
Functions that operate on rows:
filter()subsets rows based on the values of the columns,slice()and friends subsets rows based on their position, andarrange()changes the order of the rows.Functions that operate on columns:
mutate()creates new columns,select()columns,rename()changes their names, andrelocate()which changes their positions.Functions that operate on groups:
group_by()divides data up into groups for analysis, andsummarise()reduces each group to a single row.
Later, in Chapter 14, you’ll learn about other verbs that work with tables, like the join functions and the set operations.
All dplyr verbs work the same way:
The first argument is a data frame.
The subsequent arguments describe what to do with the data frame, using the variable names (without quotes).
The result is a new data frame.
Together these properties make it easy to chain together multiple simple steps to achieve a complex result. Let’s dive in and see how these verbs work.
5.2 Rows
These functions affect the rows (the observations), leaving the columns (the variables) unchanged.
filter() changes which rows are included without changing the order, arrange() changes the order without changing the membership.
5.2.1 filter()
filter() allows you to choose rows based on their values1.
The first argument is the name of the data frame.
The second and subsequent arguments are the expressions that filter the data frame.
For example, we can select all flights on January 1st with:
filter(flights, 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>When you run that line of code, dplyr executes the filtering operation and returns a new data frame.
dplyr functions never modify their inputs, so if you want to save the result, you’ll need to use the assignment operator, <-:
jan1 <- filter(flights, month == 1, day == 1)To use filtering effectively, you have to know how to select the observations that you want using the comparison operators.
R provides the standard suite: >, >=, <, <=, != (not equal), and == (equal).
It also provides %in%: filter(df, x %in% c(a, b, c)) will return all rows where x is a, b, or c.
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:
filter(flights, month = 1)
#> Error: Problem with `filter()` input `..1`.
#> ✖ Input `..1` is named.
#> ℹ This usually means that you've used `=` instead of `==`.
#> ℹ Did you mean `month == 1`?
5.2.2 arrange()
arrange() works similarly to filter() except that instead of selecting rows, it changes their order.
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:
arrange(flights, year, month, day)
#> # 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:
arrange(flights, 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>5.2.3 Exercises
-
Find all flights that
- Had an arrival delay of two or more hours
- Flew to Houston (
IAHorHOU) - Were operated by United, American, or Delta
- Departed in summer (July, August, and September)
- Arrived more than two hours late, but didn’t leave late
- Were delayed by at least an hour, but made up over 30 minutes in flight
- Departed between midnight and 6am (inclusive)
Sort
flightsto find the flights with longest departure delays. Find the flights that left earliest.Sort
flightsto find the fastest (highest speed) flights. (Hint: try sorting by a calculation).Which flights travelled the farthest? Which travelled the shortest?
5.3 Columns
These functions affect the columns (the variables) without changing the rows (the observations).
mutate() creates new variables that are functions of the existing variables; select(), rename(), and relocate() changes which variables are present, their names, and their positions.
5.3.1 mutate()
The job of mutate() is to add new columns that are functions of existing column.
In the later chapters, you’ll learn the full set of functions that you can use to manipulate different types of variables.
For now, we’ll stick with basic mathematical operators, 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:
mutate(flights,
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 at the right hand side of your dataset, which makes it hard to see what’s happening here.
We can use the .before argument to instead add the variables to the left hand side2:
mutate(flights,
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 leading . is a sign that .before is an argument to the function, not a new variable being created.
You can also use .after to add after a variable, and use a variable name instead of a position:
mutate(flights,
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, can control which variables are kept with the .keep argument:
mutate(flights,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours,
.keep = "none"
)
#> # A tibble: 336,776 × 3
#> gain hours gain_per_hour
#> <dbl> <dbl> <dbl>
#> 1 -9 3.78 -2.38
#> 2 -16 3.78 -4.23
#> 3 -31 2.67 -11.6
#> 4 17 3.05 5.57
#> 5 19 1.93 9.83
#> 6 -16 2.5 -6.4
#> # … with 336,770 more rows
5.3.2 select()
It’s not uncommon to get datasets with hundreds or even thousands of variables.
In this case, the first challenge is often narrowing in on the variables you’re actually 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
select(flights, 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)
select(flights, 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)
select(flights, -(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>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): matchesx1,x2andx3.
See ?select for more details.
Once you know regular expressions (the topic of Chapter 19) you’ll also be use matches() to select variables that match a regexp.
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:
select(flights, 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
5.3.3 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():
rename(flights, 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.
5.3.4 relocate()
You can move variables around with relocate.
By default it moves variables to the front:
relocate(flights, 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 .before and .after arguments to choose where to place them:
relocate(flights, 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>
relocate(flights, 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>These work the same way as the .before and .after arguments to mutate() — they can be a numeric position, the name of a variable, or any of the other functions that you can use with select().
5.3.5 Exercises
Currently
dep_timeandsched_dep_timeare convenient to look at, but hard to compute with because they’re not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.Compare
air_timewitharr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?Compare
dep_time,sched_dep_time, anddep_delay. How would you expect those three numbers to be related?Brainstorm as many ways as possible to select
dep_time,dep_delay,arr_time, andarr_delayfromflights.What happens if you include the name of a variable multiple times in a
select()call?-
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") -
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?
5.4 Groups
The real power of dplyr comes when you add grouping into the mix.
The two key functions are group_by() and summarise(), but as you’ll learn group_by() affects many other dplyr verbs in interesting ways.
5.4.1 group_by()
Use group_by() to divide your dataset into groups meaningful for your analysis:
by_month <- group_by(flights, month)
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, you’ll notice that it’s now “grouped by” month.
The reason to group your data is because it changes the operation of subsequent verbs.
5.4.2 summarise()
The most important operation that you might apply to grouped data is a summary. It collapses each group to a single row3. Here we compute the average departure delay by month:
summarise(by_month, 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 rowsYou can create any number of summaries in a single call to summarise().
You’ll learn various useful summaries in the upcoming chapters on individual data types, but one very useful summary is n(), which returns the number of rows in each group:
summarise(by_month, 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(In fact, count(), which we’ve used a bunch in previous chapters, is just shorthand for group_by() + summarise(n = n()).)
We’ll come back to discuss missing values in Chapter 17.
For now, know you can drop them in summary functions by using na.rm = TRUE or remove them with a filter by using !is.na():
5.4.3 Combining multiple operations
This code is starting to get a little frustrating to write because each intermediate data frame has to be given a name, even though we don’t care about them.
Naming things is hard, so this slows down our analysis.
There’s another way to tackle the same problem with the pipe, %>%:
flights %>%
filter(!is.na(dep_delay)) %>%
group_by(month) %>%
summarise(delay = mean(dep_delay), n = n())
#> # A tibble: 12 × 3
#> month delay n
#> <int> <dbl> <int>
#> 1 1 10.0 26483
#> 2 2 10.8 23690
#> 3 3 13.2 27973
#> 4 4 13.9 27662
#> 5 5 13.0 28233
#> 6 6 20.8 27234
#> # … with 6 more rowsWhen you see %>% in code, a good way to “pronounce” it in your head is as “then”.
That way you can read this code as a series of imperative statements: take the flights dataset, then filter it to remove rows with missing dep_delay, then group it by month, then summarise it with the average dep_delay and the number of observations.
Behind the scenes, x %>% f(y) turns into f(x, y), and x %>% f(y) %>% g(z) turns into g(f(x, y), z) and so on.
You can use the pipe to rewrite multiple operations in a way that you can read left-to-right, top-to-bottom.
We’ll use piping frequently from now on because it considerably improves the readability of code, and we’ll come back to it in more detail in Chapter 7.
5.4.4 Grouping by multiple variables
You can group a data frame by multiple variables:
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 group by multiple variables, each summary peels off one level of the grouping by default, and a message is printed that tells you how you can change this behaviour.
daily %>% summarise(n = n())
#> `summarise()` has grouped output by 'year', 'month'. You can override using the `.groups` argument.
#> # A tibble: 365 × 4
#> # Groups: year, month [12]
#> year month day n
#> <int> <int> <int> <int>
#> 1 2013 1 1 842
#> 2 2013 1 2 943
#> 3 2013 1 3 914
#> 4 2013 1 4 915
#> 5 2013 1 5 720
#> 6 2013 1 6 832
#> # … with 359 more rowsIf you’re happy with this behaviour, you can explicitly define it in order to suppress the message:
Alternatively, you can change the default behaviour by setting a different value, e.g. "drop" for dropping all levels of grouping or "keep" for same grouping structure as daily:
5.4.5 Ungrouping
You might also want to remove grouping outside of summarise().
You can do this and return to operations on ungrouped data using ungroup().
daily %>%
ungroup() %>%
summarise(
delay = mean(dep_delay, na.rm = TRUE),
flights = n()
)
#> # A tibble: 1 × 2
#> delay flights
#> <dbl> <int>
#> 1 12.6 336776For the purposes of summarising, ungrouped data is treated as if all your data was in a single group, so you get one row back.
5.4.6 Selecting rows
arrange() and filter() are mostly unaffected by grouping.
But the slice functions are super useful:
slice_head()andslice_tail()select the first or last rows in each group.slice_max()andslice_min()select the rows in each group with highest or lowest values.slice_sample()random selects rows from each group.
Each of these verbs takes either a n or prop argument depending on whether you want to select a fixed number of rows, or a number of rows proportional to the group size.
5.4.8 Exercises
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) %>% summarise(n()))What does the
sortargument tocount()do. Can you explain it in terms of the dplyr verbs you’ve learned so far?
5.5 Case study: aggregates and sample size
Whenever you do any aggregation, it’s always a good idea to include either a count (n()), or a count of non-missing values (sum(!is.na(x))).
That way you can check 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 <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)
ggplot(data = delays, mapping = aes(x = delay)) +
geom_freqpoly(binwidth = 10)
#> Warning: Removed 874 rows containing non-finite values (stat_bin).
Wow, there are some planes that have an average delay of 5 hours (300 minutes)!
The story is actually a little more nuanced. We can get more insight if we draw a scatterplot of number of flights vs. average delay:
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay),
n = n()
)
ggplot(data = delays, mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
#> Warning: Removed 874 rows containing missing values (geom_point).
Not surprisingly, there is much greater variation in the average delay when there are few flights. 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 increases.
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.
This is what the following code does, as well as showing you a handy pattern for integrating ggplot2 into dplyr flows.
It’s a bit painful that you have to switch from %>% to +, but once you get the hang of it, it’s quite convenient.
delays %>%
filter(n > 25) %>%
ggplot(mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
#> Warning: Removed 828 rows containing missing values (geom_point).
There’s another common variation of this type of pattern. Let’s look at how the average performance of batters in baseball is related to the number of times they’re at bat. Here I use data from the Lahman package to compute the batting average (number of hits / number of attempts) of every major league baseball player.
When I 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:
As above, the variation in our aggregate decreases as we get more data points.
There’s a positive correlation between skill (
ba) and opportunities to hit the ball (ab). This is because teams control who gets to play, and obviously they’ll pick their best players.
# Convert to a tibble so it prints nicely
batting <- as_tibble(Lahman::Batting)
batters <- batting %>%
group_by(playerID) %>%
summarise(
ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
ab = sum(AB, na.rm = TRUE)
)
batters %>%
filter(ab > 100) %>%
ggplot(mapping = aes(x = ab, y = ba)) +
geom_point() +
geom_smooth(se = FALSE)
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
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(ba))
#> # A tibble: 19,898 × 3
#> playerID ba ab
#> <chr> <dbl> <int>
#> 1 abramge01 1 1
#> 2 alanirj01 1 1
#> 3 alberan01 1 1
#> 4 banisje01 1 1
#> 5 bartocl01 1 1
#> 6 bassdo01 1 1
#> # … with 19,892 more rowsYou can find a good explanation of this problem at http://varianceexplained.org/r/empirical_bayes_baseball/ and http://www.evanmiller.org/how-not-to-sort-by-average-rating.html.