13  Logical vectors

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.

13.1 Introduction

In this chapter, you’ll learn tools for working with logical vectors. Logical vectors are the simplest type of vector because each element can only be one of three possible values: TRUE, FALSE, and NA. It’s relatively rare to find logical vectors in your raw data, but you’ll create and manipulate in the course of almost every analysis.

We’ll begin by discussing the most common way of creating logical vectors: with numeric comparisons. Then you’ll learn about how you can use Boolean algebra to combine different logical vectors, as well as some useful summaries. We’ll finish off with some tools for making conditional changes, and a useful function for turning logical vectors into groups.

13.1.1 Prerequisites

Most of the functions you’ll learn about in this chapter are provided by base R, so we don’t need the tidyverse, but we’ll still load it so we can use mutate(), filter(), and friends to work with data frames. We’ll also continue to draw examples from the nycflights13 dataset.

However, as we start to cover more tools, there won’t always be a perfect real example. So we’ll start making up some dummy data with c():

x <- c(1, 2, 3, 5, 7, 11, 13)
x * 2
#> [1]  2  4  6 10 14 22 26

This makes it easier to explain individual functions at the cost of making it harder to see how it might apply to your data problems. Just remember that any manipulation we do to a free-floating vector, you can do to a variable inside data frame with mutate() and friends.

df <- tibble(x)
df |> 
  mutate(y = x *  2)
#> # A tibble: 7 × 2
#>       x     y
#>   <dbl> <dbl>
#> 1     1     2
#> 2     2     4
#> 3     3     6
#> 4     5    10
#> 5     7    14
#> 6    11    22
#> # … with 1 more row

13.2 Comparisons

A very common way to create a logical vector is via a numeric comparison with <, <=, >, >=, !=, and ==. So far, we’ve mostly created logical variables transiently within filter() — they are computed, used, and then thrown away. For example, the following filter finds all daytime departures that leave roughly on time:

flights |> 
  filter(dep_time > 600 & dep_time < 2000 & abs(arr_delay) < 20)
#> # A tibble: 172,286 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1      601         600       1     844     850      -6 B6     
#> 2  2013     1     1      602         610      -8     812     820      -8 DL     
#> 3  2013     1     1      602         605      -3     821     805      16 MQ     
#> 4  2013     1     1      606         610      -4     858     910     -12 AA     
#> 5  2013     1     1      606         610      -4     837     845      -8 DL     
#> 6  2013     1     1      607         607       0     858     915     -17 UA     
#> # … with 172,280 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

It’s useful to know that this is a shortcut and you can explicitly create the underlying logical variables with mutate():

flights |> 
  mutate(
    daytime = dep_time > 600 & dep_time < 2000,
    approx_ontime = abs(arr_delay) < 20,
    .keep = "used"
  )
#> # A tibble: 336,776 × 4
#>   dep_time arr_delay daytime approx_ontime
#>      <int>     <dbl> <lgl>   <lgl>        
#> 1      517        11 FALSE   TRUE         
#> 2      533        20 FALSE   FALSE        
#> 3      542        33 FALSE   FALSE        
#> 4      544       -18 FALSE   TRUE         
#> 5      554       -25 FALSE   FALSE        
#> 6      554        12 FALSE   TRUE         
#> # … with 336,770 more rows

This is particularly useful for more complicated logic because naming the intermediate steps makes it easier to both read your code and check that each step has been computed correctly.

All up, the initial filter is equivalent to:

flights |> 
  mutate(
    daytime = dep_time > 600 & dep_time < 2000,
    approx_ontime = abs(arr_delay) < 20,
  ) |> 
  filter(daytime & approx_ontime)

13.2.1 Floating point comparison

Beware of using == with numbers. For example, it looks like this vector contains the numbers 1 and 2:

x <- c(1 / 49 * 49, sqrt(2) ^ 2)
x
#> [1] 1 2

But if you test them for equality, you get FALSE:

x == c(1, 2)
#> [1] FALSE FALSE

What’s going on? Computers store numbers with a fixed number of decimal places so there’s no way to exactly represent 1/49 or sqrt(2) and subsequent computations will be very slightly off. We can see the exact values by calling print() with the the digits1 argument:

print(x, digits = 16)
#> [1] 0.9999999999999999 2.0000000000000004

You can see why R defaults to rounding these numbers; they really are very close to what you expect.

Now that you’ve seen why == is failing, what can you do about it? One option is to use dplyr::near() which ignores small differences:

near(x, c(1, 2))
#> [1] TRUE TRUE

13.2.2 Missing values

Missing values represent the unknown so they are “contagious”: almost any operation involving an unknown value will also be unknown:

NA > 5
#> [1] NA
10 == NA
#> [1] NA

The most confusing result is this one:

NA == NA
#> [1] NA

It’s easiest to understand why this is true if we artificially supply a little more context:

# Let x be Mary's age. We don't know how old she is.
x <- NA

# Let y be John's age. We don't know how old he is.
y <- NA

# Are John and Mary the same age?
x == y
#> [1] NA
# We don't know!

So if you want to find all flights with dep_time is missing, the following code doesn’t work because dep_time == NA will yield a NA for every single row, and filter() automatically drops missing values:

flights |> 
  filter(dep_time == NA)
#> # A tibble: 0 × 19
#> # … with 19 variables: year <int>, month <int>, day <int>, dep_time <int>,
#> #   sched_dep_time <int>, dep_delay <dbl>, arr_time <int>,
#> #   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>

Instead we’ll need a new tool: is.na().

13.2.3 is.na()

is.na(x) works with any type of vector and returns TRUE for missing values and FALSE for everything else:

is.na(c(TRUE, NA, FALSE))
#> [1] FALSE  TRUE FALSE
is.na(c(1, NA, 3))
#> [1] FALSE  TRUE FALSE
is.na(c("a", NA, "b"))
#> [1] FALSE  TRUE FALSE

We can use is.na() to find all the rows with a missing dep_time:

flights |> 
  filter(is.na(dep_time))
#> # A tibble: 8,255 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1       NA        1630      NA      NA    1815      NA EV     
#> 2  2013     1     1       NA        1935      NA      NA    2240      NA AA     
#> 3  2013     1     1       NA        1500      NA      NA    1825      NA AA     
#> 4  2013     1     1       NA         600      NA      NA     901      NA B6     
#> 5  2013     1     2       NA        1540      NA      NA    1747      NA EV     
#> 6  2013     1     2       NA        1620      NA      NA    1746      NA EV     
#> # … with 8,249 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

is.na() can also be useful in arrange(). arrange() usually puts all the missing values at the end but you can override this default by first sorting by is.na():

flights |> 
  filter(month == 1, day == 1) |> 
  arrange(dep_time)
#> # A tibble: 842 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1      517         515       2     830     819      11 UA     
#> 2  2013     1     1      533         529       4     850     830      20 UA     
#> 3  2013     1     1      542         540       2     923     850      33 AA     
#> 4  2013     1     1      544         545      -1    1004    1022     -18 B6     
#> 5  2013     1     1      554         600      -6     812     837     -25 DL     
#> 6  2013     1     1      554         558      -4     740     728      12 UA     
#> # … with 836 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

flights |> 
  filter(month == 1, day == 1) |> 
  arrange(desc(is.na(dep_time)), dep_time)
#> # A tibble: 842 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1       NA        1630      NA      NA    1815      NA EV     
#> 2  2013     1     1       NA        1935      NA      NA    2240      NA AA     
#> 3  2013     1     1       NA        1500      NA      NA    1825      NA AA     
#> 4  2013     1     1       NA         600      NA      NA     901      NA B6     
#> 5  2013     1     1      517         515       2     830     819      11 UA     
#> 6  2013     1     1      533         529       4     850     830      20 UA     
#> # … with 836 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

13.2.4 Exercises

  1. How does dplyr::near() work? Type near to see the source code.
  2. Use mutate(), is.na(), and count() together to describe how the missing values in dep_time, sched_dep_time and dep_delay are connected.

13.3 Boolean algebra

Once you have multiple logical vectors, you can combine them together using Boolean algebra. In R, & is “and”, | is “or”, and ! is “not”, and xor() is exclusive or2. Figure 13.1 shows the complete set of Boolean operations and how they work.

Six Venn diagrams, each explaining a given logical operator. The circles (sets) in each of the Venn diagrams represent x and y. 1. y & !x is y but none of x; x & y is the intersection of x and y; x & !y is x but none of y; x is all of x none of y; xor(x, y) is everything except the intersection of x and y; y is all of y and none of x; and x | y is everything.

Figure 13.1: The complete set of boolean operations. x is the left-hand circle, y is the right-hand circle, and the shaded region show which parts each operator selects.

As well as & and |, R also has && and ||. Don’t use them in dplyr functions! These are called short-circuiting operators and only ever return a single TRUE or FALSE. They’re important for programming and you’ll learn more about them in ?sec-conditional-execution.

13.3.1 Missing values

The rules for missing values in Boolean algebra are a little tricky to explain because they seem inconsistent at first glance:

df <- tibble(x = c(TRUE, FALSE, NA))

df |> 
  mutate(
    and = x & NA,
    or = x | NA
  )
#> # A tibble: 3 × 3
#>   x     and   or   
#>   <lgl> <lgl> <lgl>
#> 1 TRUE  NA    TRUE 
#> 2 FALSE FALSE NA   
#> 3 NA    NA    NA

To understand what’s going on, think about NA | TRUE. A missing value in a logical vector means that the value could either be TRUE or FALSE. TRUE | TRUE and FALSE | TRUE are both TRUE, so NA | TRUE must also be TRUE. Similar reasoning applies with NA & FALSE.

13.3.2 Order of operations

Note that the order of operations doesn’t work like English. Take the following code finds all flights that departed in November or December:

flights |> 
   filter(month == 11 | month == 12)

You might be tempted to write it like you’d say in English: “find all flights that departed in November or December”:

flights |> 
   filter(month == 11 | 12)
#> # A tibble: 336,776 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1      517         515       2     830     819      11 UA     
#> 2  2013     1     1      533         529       4     850     830      20 UA     
#> 3  2013     1     1      542         540       2     923     850      33 AA     
#> 4  2013     1     1      544         545      -1    1004    1022     -18 B6     
#> 5  2013     1     1      554         600      -6     812     837     -25 DL     
#> 6  2013     1     1      554         558      -4     740     728      12 UA     
#> # … with 336,770 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

This code doesn’t error but it also doesn’t seem to have worked. What’s going on? Here R first evaluates month == 11 creating a logical vector, which we call nov. It computes nov | 12. When you use a number with a logical operator it converts everything apart from 0 to TRUE, so this is equivalent to nov | TRUE which will always be TRUE, so every row will be selected:

flights |> 
  mutate(
    nov = month == 11,
    final = nov | 12,
    .keep = "used"
  )
#> # A tibble: 336,776 × 3
#>   month nov   final
#>   <int> <lgl> <lgl>
#> 1     1 FALSE TRUE 
#> 2     1 FALSE TRUE 
#> 3     1 FALSE TRUE 
#> 4     1 FALSE TRUE 
#> 5     1 FALSE TRUE 
#> 6     1 FALSE TRUE 
#> # … with 336,770 more rows

13.3.3 %in%

An easy way to avoid the problem of getting your ==s and |s in the right order is to use %in%. x %in% y returns a logical vector the same length as x that is TRUE whenever a value in x is anywhere in y .

1:12 %in% c(1, 5, 11)
#>  [1]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
letters[1:10] %in% c("a", "e", "i", "o", "u")
#>  [1]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE

So to find all flights in November and December we could write:

flights |> 
  filter(month %in% c(11, 12))

Note that %in% obeys different rules for NA to ==, as NA %in% NA is TRUE.

c(1, 2, NA) == NA
#> [1] NA NA NA
c(1, 2, NA) %in% NA
#> [1] FALSE FALSE  TRUE

This can make for a useful shortcut:

flights |> 
  filter(dep_time %in% c(NA, 0800))
#> # A tibble: 8,803 × 19
#>    year month   day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
#>   <int> <int> <int>    <int>       <int>   <dbl>   <int>   <int>   <dbl> <chr>  
#> 1  2013     1     1      800         800       0    1022    1014       8 DL     
#> 2  2013     1     1      800         810     -10     949     955      -6 MQ     
#> 3  2013     1     1       NA        1630      NA      NA    1815      NA EV     
#> 4  2013     1     1       NA        1935      NA      NA    2240      NA AA     
#> 5  2013     1     1       NA        1500      NA      NA    1825      NA AA     
#> 6  2013     1     1       NA         600      NA      NA     901      NA B6     
#> # … with 8,797 more rows, 9 more variables: flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
#> #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

13.3.4 Exercises

  1. Find all flights where arr_delay is missing but dep_delay is not. Find all flights where neither arr_time nor sched_arr_time are missing, but arr_delay is.
  2. How many flights have a missing dep_time? What other variables are missing in these rows? What might these rows represent?
  3. Assuming that a missing dep_time implies that a flight is cancelled, look at the number of cancelled flights per day. Is there a pattern? Is there a connection between the proportion of cancelled flights and average delay of non-cancelled flights?

13.4 Summaries

The following sections describe some useful techniques for summarizing logical vectors. As well as functions that only work specifically with logical vectors, you can also use functions that work with numeric vectors.

13.4.1 Logical summaries

There are two main logical summaries: any() and all(). any(x) is the equivalent of |; it’ll return TRUE if there are any TRUE’s in x. all(x) is equivalent of &; it’ll return TRUE only if all values of x are TRUE’s. Like all summary functions, they’ll return NA if there are any missing values present, and as usual you can make the missing values go away with na.rm = TRUE.

For example, we could use all() to find out if there were days where every flight was delayed:

flights |> 
  group_by(year, month, day) |> 
  summarise(
    all_delayed = all(arr_delay >= 0, na.rm = TRUE),
    any_delayed = any(arr_delay >= 0, na.rm = TRUE),
    .groups = "drop"
  )
#> # A tibble: 365 × 5
#>    year month   day all_delayed any_delayed
#>   <int> <int> <int> <lgl>       <lgl>      
#> 1  2013     1     1 FALSE       TRUE       
#> 2  2013     1     2 FALSE       TRUE       
#> 3  2013     1     3 FALSE       TRUE       
#> 4  2013     1     4 FALSE       TRUE       
#> 5  2013     1     5 FALSE       TRUE       
#> 6  2013     1     6 FALSE       TRUE       
#> # … with 359 more rows

In most cases, however, any() and all() are a little too crude, and it would be nice to be able to get a little more detail about how many values are TRUE or FALSE. That leads us to the numeric summaries.

13.4.2 Numeric summaries

When you use a logical vector in a numeric context, TRUE becomes 1 and FALSE becomes 0. This makes sum() and mean() very useful with logical vectors because sum(x) will give the number of TRUEs and mean(x) the proportion of TRUEs. That lets us see the distribution of delays across the days of the year as shown in Figure 13.2.

flights |> 
  group_by(year, month, day) |> 
  summarise(
    prop_delayed = mean(arr_delay > 0, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  ggplot(aes(prop_delayed)) + 
  geom_histogram(binwidth = 0.05)

The distribution is unimodal and mildly right skewed. The distribution peaks around 30% delayed flights.

Figure 13.2: A histogram showing the proportion of delayed flights each day.

Or we could ask how many flights left before 5am, which are often flights that were delayed from the previous day:

flights |> 
  group_by(year, month, day) |> 
  summarise(
    n_early = sum(dep_time < 500, na.rm = TRUE),
    .groups = "drop"
  ) |> 
  arrange(desc(n_early))
#> # A tibble: 365 × 4
#>    year month   day n_early
#>   <int> <int> <int>   <int>
#> 1  2013     6    28      32
#> 2  2013     4    10      30
#> 3  2013     7    28      30
#> 4  2013     3    18      29
#> 5  2013     7     7      29
#> 6  2013     7    10      29
#> # … with 359 more rows

13.4.3 Logical subsetting

There’s one final use for logical vectors in summaries: you can use a logical vector to filter a single variable to a subset of interest. This makes use of the base [ (pronounced subset) operator, which you’ll learn more about this in Section 27.3.

Imagine we wanted to look at the average delay just for flights that were actually delayed. One way to do so would be to first filter the flights:

flights |> 
  filter(arr_delay > 0) |> 
  group_by(year, month, day) |> 
  summarise(
    behind = mean(arr_delay),
    n = n(),
    .groups = "drop"
  )
#> # A tibble: 365 × 5
#>    year month   day behind     n
#>   <int> <int> <int>  <dbl> <int>
#> 1  2013     1     1   32.5   461
#> 2  2013     1     2   32.0   535
#> 3  2013     1     3   27.7   460
#> 4  2013     1     4   28.3   297
#> 5  2013     1     5   22.6   238
#> 6  2013     1     6   24.4   381
#> # … with 359 more rows

This works, but what if we wanted to also compute the average delay for flights that arrived early? We’d need to perform a separate filter step, and then figure out how to combine the two data frames together3. Instead you could use [ to perform an inline filtering: arr_delay[arr_delay > 0] will yield only the positive arrival delays.

This leads to:

flights |> 
  group_by(year, month, day) |> 
  summarise(
    behind = mean(arr_delay[arr_delay > 0], na.rm = TRUE),
    ahead = mean(arr_delay[arr_delay < 0], na.rm = TRUE),
    n = n(),
    .groups = "drop"
  )
#> # A tibble: 365 × 6
#>    year month   day behind ahead     n
#>   <int> <int> <int>  <dbl> <dbl> <int>
#> 1  2013     1     1   32.5 -12.5   842
#> 2  2013     1     2   32.0 -14.3   943
#> 3  2013     1     3   27.7 -18.2   914
#> 4  2013     1     4   28.3 -17.0   915
#> 5  2013     1     5   22.6 -14.0   720
#> 6  2013     1     6   24.4 -13.6   832
#> # … with 359 more rows

Also note the difference in the group size: in the first chunk n() gives the number of delayed flights per day; in the second, n() gives the total number of flights.

13.4.4 Exercises

  1. What will sum(is.na(x)) tell you? How about mean(is.na(x))?
  2. What does prod() return when applied to a logical vector? What logical summary function is it equivalent to? What does min() return applied to a logical vector? What logical summary function is it equivalent to? Read the documentation and perform a few experiments.

13.5 Conditional transformations

One of the most powerful features of logical vectors are their use for conditional transformations, i.e. doing one thing for condition x, and something different for condition y. There are two important tools for this: if_else() and case_when().

13.5.1 if_else()

If you want to use one value when a condition is true and another value when it’s FALSE, you can use dplyr::if_else()4. You’ll always use the first three argument of if_else(). The first argument, condition, is a logical vector, the second, true, gives the output when the condition is true, and the third, false, gives the output if the condition is false.

Let’s begin with a simple example of labeling a numeric vector as either “+ve” or “-ve”:

x <- c(-3:3, NA)
if_else(x > 0, "+ve", "-ve")
#> [1] "-ve" "-ve" "-ve" "-ve" "+ve" "+ve" "+ve" NA

There’s an optional fourth argument, missing which will be used if the input is NA:

if_else(x > 0, "+ve", "-ve", "???")
#> [1] "-ve" "-ve" "-ve" "-ve" "+ve" "+ve" "+ve" "???"

You can also use vectors for the the true and false arguments. For example, this allows us to create a minimal implementation of abs():

if_else(x < 0, -x, x)
#> [1]  3  2  1  0  1  2  3 NA

So far all the arguments have used the same vectors, but you can of course mix and match. For example, you could implement a simple version of coalesce() like this:

x1 <- c(NA, 1, 2, NA)
y1 <- c(3, NA, 4, 6)
if_else(is.na(x1), y1, x1)
#> [1] 3 1 2 6

You might have noticed a small infelicity in our labeling: zero is neither positive nor negative. We could resolve this by adding an additional if_else():

if_else(x == 0, "0", if_else(x < 0, "-ve", "+ve"), "???")
#> [1] "-ve" "-ve" "-ve" "0"   "+ve" "+ve" "+ve" "???"

This is already a little hard to read, and you can imagine it would only get harder if you have more conditions. Instead, you can switch to dplyr::case_when().

13.5.2 case_when()

dplyr’s case_when() is inspired by SQL’s CASE statement and provides a flexible way of performing different computations for different computations. It has a special syntax that unfortunately looks like nothing else you’ll use in the tidyverse. It takes pairs that look like condition ~ output. condition must be a logical vector; when it’s TRUE, output will be used.

This means we could recreate our previous nested if_else() as follows:

case_when(
  x == 0   ~ "0",
  x < 0    ~ "-ve", 
  x > 0    ~ "+ve",
  is.na(x) ~ "???"
)
#> [1] "-ve" "-ve" "-ve" "0"   "+ve" "+ve" "+ve" "???"

This is more code, but it’s also more explicit.

To explain how case_when() works, lets explore some simpler cases. If none of the cases match, the output gets an NA:

case_when(
  x < 0 ~ "-ve",
  x > 0 ~ "+ve"
)
#> [1] "-ve" "-ve" "-ve" NA    "+ve" "+ve" "+ve" NA

If you want to create a “default”/catch all value, use TRUE on the left hand side:

case_when(
  x < 0 ~ "-ve",
  x > 0 ~ "+ve",
  TRUE ~ "???"
)
#> [1] "-ve" "-ve" "-ve" "???" "+ve" "+ve" "+ve" "???"

And note that if multiple conditions match, only the first will be used:

case_when(
  x > 0 ~ "+ve",
  x > 3 ~ "big"
)
#> [1] NA    NA    NA    NA    "+ve" "+ve" "+ve" NA

Just like with if_else() you can use variables on both sides of the ~ and you can mix and match variables as needed for your problem. For example, we could use case_when() to provide some human readable labels for the arrival delay:

flights |> 
  mutate(
    status = case_when(
      is.na(arr_delay)      ~ "cancelled",
      arr_delay > 60        ~ "very late",
      arr_delay > 15        ~ "late",
      abs(arr_delay) <= 15  ~ "on time",
      arr_delay < -15       ~ "early",
      arr_delay < -30       ~ "very early",
    ),
    .keep = "used"
  )
#> # A tibble: 336,776 × 2
#>   arr_delay status 
#>       <dbl> <chr>  
#> 1        11 on time
#> 2        20 late   
#> 3        33 late   
#> 4       -18 early  
#> 5       -25 early  
#> 6        12 on time
#> # … with 336,770 more rows

13.6 Making groups

Before we move on to the next chapter, we want to show you one last trick that’s useful for grouping data. Sometimes you want to start a new group every time some event occurs. For example, when you’re looking at website data, it’s common to want to break up events into sessions, where a session is defined as a gap of more than x minutes since the last activity.

Here’s some made up data that illustrates the problem. So far computed the time lag between the events, and figured out if there’s a gap that’s big enough to qualify:

events <- tibble(
  time = c(0, 1, 2, 3, 5, 10, 12, 15, 17, 19, 20, 27, 28, 30)
)
events <- events |> 
  mutate(
    diff = time - lag(time, default = first(time)),
    gap = diff >= 5
  )
events
#> # A tibble: 14 × 3
#>    time  diff gap  
#>   <dbl> <dbl> <lgl>
#> 1     0     0 FALSE
#> 2     1     1 FALSE
#> 3     2     1 FALSE
#> 4     3     1 FALSE
#> 5     5     2 FALSE
#> 6    10     5 TRUE 
#> # … with 8 more rows

But how do we go from that logical vector to something that we can group_by()? consecutive_id() comes to the rescue:

events |> mutate(
  group = consecutive_id(gap)
)
#> # A tibble: 14 × 4
#>    time  diff gap   group
#>   <dbl> <dbl> <lgl> <int>
#> 1     0     0 FALSE     1
#> 2     1     1 FALSE     1
#> 3     2     1 FALSE     1
#> 4     3     1 FALSE     1
#> 5     5     2 FALSE     1
#> 6    10     5 TRUE      2
#> # … with 8 more rows

consecutive_id() starts a new group every time one of its arguments changes. That makes it useful both here, with logical vectors, and in many other place. For example, inspired by this stackoverflow question, imagine you have a data frame with a bunch of repeated values:

df <- tibble(
  x = c("a", "a", "a", "b", "c", "c", "d", "e", "a", "a", "b", "b"),
  y = c(1, 2, 3, 2, 4, 1, 3, 9, 4, 8, 10, 199)
)
df
#> # A tibble: 12 × 2
#>   x         y
#>   <chr> <dbl>
#> 1 a         1
#> 2 a         2
#> 3 a         3
#> 4 b         2
#> 5 c         4
#> 6 c         1
#> # … with 6 more rows

You want to keep the first row from each repeated x. That’s easier to express with a combination of consecutive_id() and slice_head():

df |> 
  group_by(id = consecutive_id(x)) |> 
  slice_head(n = 1)
#> # A tibble: 7 × 3
#> # Groups:   id [7]
#>   x         y    id
#>   <chr> <dbl> <int>
#> 1 a         1     1
#> 2 b         2     2
#> 3 c         4     3
#> 4 d         3     4
#> 5 e         9     5
#> 6 a         4     6
#> # … with 1 more row

  1. R normally calls print for you (i.e. x is a shortcut for print(x)), but calling it explicitly is useful if you want to provide other arguments.↩︎

  2. That is, xor(x, y) is true if x is true, or y is true, but not both. This is how we usually use “or” In English. “Both” is not usually an acceptable answer to the question “would you like ice cream or cake?”.↩︎

  3. We’ll cover this in ?sec-relational-data↩︎

  4. dplyr’s if_else() is very similar to base R’s ifelse(). There are two main advantages of if_else()over ifelse(): you can choose what should happen to missing values, and if_else() is much more likely to give you a meaningful error if you variables have incompatible types.↩︎