library(tidyverse)
set.seed(1234)
theme_set(theme_minimal())

Writing for loops

Functions are one method of reducing duplication in your code. Another tool for reducing duplication is iteration, which lets you do the same thing to multiple inputs.

Example for loop

df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

Let’s say we want to compute the median for each column.

median(df$a)
## [1] -0.5555419
median(df$b)
## [1] -0.4941011
median(df$c)
## [1] -0.4656169
median(df$d)
## [1] -0.605349

Boo! We’ve copied-and-pasted median() three times. We don’t want to do that. Instead, we can use a for loop:

output <- vector(mode = "double", length = ncol(df))
for (i in seq_along(df)) {
  output[[i]] <- median(df[[i]])
}
output
## [1] -0.5555419 -0.4941011 -0.4656169 -0.6053490

Let’s review the three components of every for loop.

Output

output <- vector("double", length = ncol(df))

Before you start a loop, you need to create an empty vector to store the output of the loop. Preallocating space for your output is much more efficient than creating space as you move through the loop. The vector() function allows you to create an empty vector of any type. The first argument mode defines the type of vector (“logical”, “integer”, “double”, “character”, etc.) and the second argument length defines the length of the vector.

Numeric vectors are initialized to 0, logical vectors are initialized to FALSE, character vectors are initialized to "", and list elements to NULL.

vector(mode = "double", length = ncol(df))
## [1] 0 0 0 0
vector(mode = "logical", length = ncol(df))
## [1] FALSE FALSE FALSE FALSE
vector(mode = "character", length = ncol(df))
## [1] "" "" "" ""
vector(mode = "list", length = ncol(df))
## [[1]]
## NULL
## 
## [[2]]
## NULL
## 
## [[3]]
## NULL
## 
## [[4]]
## NULL

Sequence

i in seq_along(df)

This component determines what to loop over. During each iteration through the for loop, a new value will be assigned to i based on the the defined sequence. Here, the sequence is seq_along(df) which creates a numeric vector for a sequence of numbers beginning at 1 and continuing until it reaches the length of df (the length here is the number of columns in df).

seq_along(df)
## [1] 1 2 3 4

Body

output[[i]] <- median(df[[i]])

This is the code that actually performs the desired calculations. It runs multiple times for every value of i. We use [[ notation to reference each column of df and store it in the appropriate element in output.

Why we preallocate space for the output

If you don’t preallocate space for the output, each time the for loop iterates, it makes a copy of the output and appends the new value at the end. Copying data takes time and memory. If the output is preallocated space, the loop simply fills in the slots with the correct values.

x <- rnorm(1000, mean = 0, sd = 1)
str(x)
##  num [1:1000] 1.449 -1.069 -0.855 -0.281 -0.994 ...
# load microbenchmark library to time code
library(microbenchmark)

microbenchmark(
  # don't preallocate
  `No preallocation` = {
    output <- vector("numeric", 0)
    
    for (i in seq_along(x)) {
      output <- c(output, x[[i]] + 1)
    }
  },
  # preallocate
  `Preallocation` = {
    output <- vector("numeric", length(x))
    
    for (i in seq_along(x)) {
      output[[i]] <- x[[i]] + 1
    }
  }) %>%
  autoplot +
  scale_y_log10(breaks = c(2, 4, 8, 16, 32)) +
  labs(y = "Time [milliseconds]")
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

Here, preallocating space for the vector cuts the computation time by a factor of 3.

Exercise: write a for loop

Mean of columns in mtcars

Write a for loop that calculates the arithmetic mean for every column in mtcars.

as_tibble(mtcars)
## # A tibble: 32 x 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##  * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
##  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
##  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
##  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
##  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
##  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
##  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
##  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
##  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## # ... with 22 more rows

Before you write the for loop, identify the three components you need:

  • Output
  • Sequence
  • Body
Click for the solution

  • Output - a numeric vector of length 11
  • Sequence - i in seq_along(mtcars)
  • Body - calculate the mean() of the \(i\)th column, store the new value as the \(i\)th element of the vector output
output <- vector("numeric", ncol(mtcars))

for(i in seq_along(mtcars)){
  output[[i]] <- mean(mtcars[[i]], na.rm = TRUE)
}

output
##  [1]  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250
##  [7]  17.848750   0.437500   0.406250   3.687500   2.812500

Maximum value in each column of diamonds

Write a for loop that calculates the maximum value in each column of diamonds.

diamonds
## # A tibble: 53,940 x 10
##    carat       cut color clarity depth table price     x     y     z
##    <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
##  2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
##  3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
##  4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
##  5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good     I    VVS1  62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good     H     SI1  61.9    55   337  4.07  4.11  2.53
##  9  0.22      Fair     E     VS2  65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good     H     VS1  59.4    61   338  4.00  4.05  2.39
## # ... with 53,930 more rows

Before you write the for loop, identify the three components you need:

  • Output
  • Sequence
  • Body
Click for the solution

  • Output - a vector of length 10
  • Sequence - i in seq_along(diamonds)
  • Body - get the maximum value of the \(i\)th column of the data frame diamonds, store the new value as the \(i\)th element of the list output
output <- vector("numeric", ncol(diamonds))

for(i in seq_along(diamonds)){
  output[i] <- max(diamonds[[i]])
}

output
##  [1]     5.01     5.00     7.00     8.00    79.00    95.00 18823.00
##  [8]    10.74    58.90    31.80

To preserve the name attributes from diamonds, use the names() function to extract the names of each column in diamonds and apply them as the names to the output vector:

# get the names of the columns in diamonds
names(diamonds)
##  [1] "carat"   "cut"     "color"   "clarity" "depth"   "table"   "price"  
##  [8] "x"       "y"       "z"
# assign the names of the diamonds columns to output
names(output) <- names(diamonds)
output
##    carat      cut    color  clarity    depth    table    price        x 
##     5.01     5.00     7.00     8.00    79.00    95.00 18823.00    10.74 
##        y        z 
##    58.90    31.80

Notice that all the columns have a maximum value, even the apparently text-based columns. This is because cut, color, and clarity are all stored as factor columns. Remember that factor vectors are built on top of integers, so the underlying values are numeric. As a result we can apply max() to a factor vector and still retrieve a (partially) meaningful result.

Map functions

You will frequently need to iterate over vectors or data frames, perform an operation on each element, and save the results somewhere. for loops are not the devil. At first, they may seem more intuitive to use because you are explicitly identifying each component of the process. However the downside is that they focus on a lot of non-essential stuff. You have to track the value on which you are iterating, you need to explicitly create a vector to store the output, you have to assign the output of each iteration to the appropriate element in the output vector, etc.

tidyverse is all about focusing on verbs, not nouns. That is, focus on the operation being performed (e.g. mean(), median(), max()), not all the extra code needed to make the operation work. The purrr library provides a family of functions that mirrors for loops. They:

  • Loop over a vector
  • Do something to each element
  • Save the results

There is one function for each type of output:

  • map() makes a list.
  • map_lgl() makes a logical vector.
  • map_int() makes an integer vector.
  • map_dbl() makes a double vector.
  • map_chr() makes a character vector.
map_dbl(df, mean)
##          a          b          c          d 
## -0.3831574 -0.1181707 -0.3879468 -0.7661931
map_dbl(df, median)
##          a          b          c          d 
## -0.5555419 -0.4941011 -0.4656169 -0.6053490
map_dbl(df, sd)
##         a         b         c         d 
## 0.9957875 1.0673376 0.6660013 0.8942458

Just like all of our functions in the tidyverse, the first argument is always the data object, and the second argument is the function to be applied. Additional arguments for the function to be applied can be specified like this:

map_dbl(df, mean, na.rm = TRUE)
##          a          b          c          d 
## -0.3831574 -0.1181707 -0.3879468 -0.7661931

Or data can be piped:

df %>%
  map_dbl(mean, na.rm = TRUE)
##          a          b          c          d 
## -0.3831574 -0.1181707 -0.3879468 -0.7661931

Exercise: rewrite our for loops using a map() function

Mean of columns in mtcars

Write a map() function that calculates the arithmetic mean for every column in mtcars.

as_tibble(mtcars)
## # A tibble: 32 x 11
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##  * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
##  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
##  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
##  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
##  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
##  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
##  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
##  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
##  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## # ... with 22 more rows
Click for the solution 

map_dbl(mtcars, mean)
##        mpg        cyl       disp         hp       drat         wt 
##  20.090625   6.187500 230.721875 146.687500   3.596563   3.217250 
##       qsec         vs         am       gear       carb 
##  17.848750   0.437500   0.406250   3.687500   2.812500

Maximum value in each column of diamonds

Write a map() function that calculates the maximum value in each column of diamonds.

diamonds
## # A tibble: 53,940 x 10
##    carat       cut color clarity depth table price     x     y     z
##    <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
##  2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
##  3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
##  4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
##  5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good     I    VVS1  62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good     H     SI1  61.9    55   337  4.07  4.11  2.53
##  9  0.22      Fair     E     VS2  65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good     H     VS1  59.4    61   338  4.00  4.05  2.39
## # ... with 53,930 more rows
Click for the solution 

map_dbl(diamonds, max)
##    carat      cut    color  clarity    depth    table    price        x 
##     5.01     5.00     7.00     8.00    79.00    95.00 18823.00    10.74 
##        y        z 
##    58.90    31.80

Scoped verbs

Frequently when working with data frames you may wish to apply a specific function to multiple columns. For instance, calculating the average value of each column in mtcars. If we wanted to calculate the average of a single column, it would be pretty simple using just tidyverse functions:

mtcars %>%
  summarize(mpg = mean(mpg))
##        mpg
## 1 20.09062

If we want to calculate the mean for all of them, we’d have to duplicate this code many times over:

mtcars %>%
  summarize(mpg = mean(mpg),
            cyl = mean(cyl),
            disp = mean(disp),
            hp = mean(hp),
            drat = mean(drat),
            wt = mean(wt),
            qsec = mean(qsec),
            vs = mean(vs),
            am = mean(am),
            gear = mean(gear),
            carb = mean(carb))
##        mpg    cyl     disp       hp     drat      wt     qsec     vs
## 1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375
##        am   gear   carb
## 1 0.40625 3.6875 2.8125

But this is very repetitive and prone to mistakes - I cannot tell you how many typos I originally had in this code when I first wrote it. We’ve seen how to use loops and map() functions to solve this task - let’s check out one final method: scoped verbs.

Scoped verbs allow you to use standard verbs (or functions) in dplyr that affect multiple variables at once, combining both elements of repetition and (in some cases) conditional expressions:

  • _if allows you to pick variables based on a predicate function like is.numeric() or is.character()
  • _at allows you to pick variables using the same syntax as select()
  • _all operates on all variables

summarize

summarize_all()

summarize_all() takes a tibble and a function and applies that function to each column:

summarize_all(mtcars, .funs = mean)
##        mpg    cyl     disp       hp     drat      wt     qsec     vs
## 1 20.09062 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375
##        am   gear   carb
## 1 0.40625 3.6875 2.8125

If you want to apply multiple summaries, use the funs() helper:

summarize_all(mtcars, .funs = funs(min, max))
##   mpg_min cyl_min disp_min hp_min drat_min wt_min qsec_min vs_min am_min
## 1    10.4       4     71.1     52     2.76  1.513     14.5      0      0
##   gear_min carb_min mpg_max cyl_max disp_max hp_max drat_max wt_max
## 1        3        1    33.9       8      472    335     4.93  5.424
##   qsec_max vs_max am_max gear_max carb_max
## 1     22.9      1      1        5        8

And as always, you could combine this with group_by() to calculate group-level summary statistics:

mtcars %>%
  group_by(gear) %>%
  summarize_all(.funs = mean)
## # A tibble: 3 x 11
##    gear   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  carb
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1    3.  16.1  7.47  326. 176.   3.13  3.89  17.7 0.200 0.     2.67
## 2    4.  24.5  4.67  123.  89.5  4.04  2.62  19.0 0.833 0.667  2.33
## 3    5.  21.4  6.00  202. 196.   3.92  2.63  15.6 0.200 1.00   4.40

summarize_at()

summarize_at() allows you to pick columns in the same way as select(), that is, based on their names. There is one small difference: you need to wrap the complete selection with the vars() helper (this avoids ambiguity).

summarize_at(mtcars, .vars = vars(-mpg), .funs = mean)
##      cyl     disp       hp     drat      wt     qsec     vs      am   gear
## 1 6.1875 230.7219 146.6875 3.596563 3.21725 17.84875 0.4375 0.40625 3.6875
##     carb
## 1 2.8125

By default, the newly created columns have the shortest names needed to uniquely identify the output.

summarize_at(mtcars, .vars = vars(mpg), .funs = funs(min, max))
##    min  max
## 1 10.4 33.9
summarize_at(mtcars, .vars = vars(mpg, wt), .funs = min)
##    mpg    wt
## 1 10.4 1.513
summarize_at(mtcars, .vars = vars(-mpg), .funs = funs(min, max))
##   cyl_min disp_min hp_min drat_min wt_min qsec_min vs_min am_min gear_min
## 1       4     71.1     52     2.76  1.513     14.5      0      0        3
##   carb_min cyl_max disp_max hp_max drat_max wt_max qsec_max vs_max am_max
## 1        1       8      472    335     4.93  5.424     22.9      1      1
##   gear_max carb_max
## 1        5        8

summarize_if()

summarize_at() allows you to pick variables to summarize based on their name, whereas summarize_if() allows you to pick variables to summarize based on some property of the column. Typically this is their type because you want to (e.g.) apply a numeric summary function only to numeric columns:

starwars
## # A tibble: 87 x 13
##    name     height  mass hair_color skin_color eye_color birth_year gender
##    <chr>     <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
##  1 Luke Sk…    172   77. blond      fair       blue            19.0 male  
##  2 C-3PO       167   75. <NA>       gold       yellow         112.  <NA>  
##  3 R2-D2        96   32. <NA>       white, bl… red             33.0 <NA>  
##  4 Darth V…    202  136. none       white      yellow          41.9 male  
##  5 Leia Or…    150   49. brown      light      brown           19.0 female
##  6 Owen La…    178  120. brown, gr… light      blue            52.0 male  
##  7 Beru Wh…    165   75. brown      light      blue            47.0 female
##  8 R5-D4        97   32. <NA>       white, red red             NA   <NA>  
##  9 Biggs D…    183   84. black      light      brown           24.0 male  
## 10 Obi-Wan…    182   77. auburn, w… fair       blue-gray       57.0 male  
## # ... with 77 more rows, and 5 more variables: homeworld <chr>,
## #   species <chr>, films <list>, vehicles <list>, starships <list>
starwars %>%
  group_by(species) %>%
  summarize_if(.predicate = is.numeric, .funs = mean, na.rm = TRUE)
## # A tibble: 38 x 4
##    species   height  mass birth_year
##    <chr>      <dbl> <dbl>      <dbl>
##  1 Aleena      79.0  15.0     NaN   
##  2 Besalisk   198.  102.      NaN   
##  3 Cerean     198.   82.0      92.0 
##  4 Chagrian   196.  NaN       NaN   
##  5 Clawdite   168.   55.0     NaN   
##  6 Droid      140.   69.8      53.3 
##  7 Dug        112.   40.0     NaN   
##  8 Ewok        88.0  20.0       8.00
##  9 Geonosian  183.   80.0     NaN   
## 10 Gungan     209.   74.0      52.0 
## # ... with 28 more rows

(Note that na.rm = TRUE is passed on to mean() in the same way as in purrr::map().)

Mutate

mutate_all(), mutate_if() and mutate_at() work in a similar way to their summarize equivalents.

mutate_all(mtcars, .funs = log10)
##         mpg       cyl     disp       hp      drat        wt     qsec   vs
## 1  1.322219 0.7781513 2.204120 2.041393 0.5910646 0.4183013 1.216430 -Inf
## 2  1.322219 0.7781513 2.204120 2.041393 0.5910646 0.4586378 1.230960 -Inf
## 3  1.357935 0.6020600 2.033424 1.968483 0.5854607 0.3654880 1.269746    0
## 4  1.330414 0.7781513 2.411620 2.041393 0.4885507 0.5071810 1.288696    0
## 5  1.271842 0.9030900 2.556303 2.243038 0.4983106 0.5365584 1.230960 -Inf
## 6  1.257679 0.7781513 2.352183 2.021189 0.4409091 0.5390761 1.305781    0
## 7  1.155336 0.9030900 2.556303 2.389166 0.5065050 0.5526682 1.199755 -Inf
## 8  1.387390 0.6020600 2.166430 1.792392 0.5670264 0.5037907 1.301030    0
## 9  1.357935 0.6020600 2.148603 1.977724 0.5932861 0.4983106 1.359835    0
## 10 1.283301 0.7781513 2.224274 2.089905 0.5932861 0.5365584 1.262451    0
## 11 1.250420 0.7781513 2.224274 2.089905 0.5932861 0.5365584 1.276462    0
## 12 1.214844 0.9030900 2.440594 2.255273 0.4871384 0.6095944 1.240549 -Inf
## 13 1.238046 0.9030900 2.440594 2.255273 0.4871384 0.5717088 1.245513 -Inf
## 14 1.181844 0.9030900 2.440594 2.255273 0.4871384 0.5774918 1.255273 -Inf
## 15 1.017033 0.9030900 2.673942 2.311754 0.4668676 0.7201593 1.254790 -Inf
## 16 1.017033 0.9030900 2.662758 2.332438 0.4771213 0.7343197 1.250908 -Inf
## 17 1.167317 0.9030900 2.643453 2.361728 0.5092025 0.7279477 1.241048 -Inf
## 18 1.510545 0.6020600 1.895975 1.819544 0.6106602 0.3424227 1.289366    0
## 19 1.482874 0.6020600 1.879096 1.716003 0.6928469 0.2081725 1.267641    0
## 20 1.530200 0.6020600 1.851870 1.812913 0.6253125 0.2636361 1.298853    0
## 21 1.332438 0.6020600 2.079543 1.986772 0.5682017 0.3918169 1.301247    0
## 22 1.190332 0.9030900 2.502427 2.176091 0.4409091 0.5465427 1.227115 -Inf
## 23 1.181844 0.9030900 2.482874 2.176091 0.4983106 0.5359267 1.238046 -Inf
## 24 1.123852 0.9030900 2.544068 2.389166 0.5717088 0.5843312 1.187803 -Inf
## 25 1.283301 0.9030900 2.602060 2.243038 0.4885507 0.5848963 1.231724 -Inf
## 26 1.436163 0.6020600 1.897627 1.819544 0.6106602 0.2866810 1.276462    0
## 27 1.414973 0.6020600 2.080266 1.959041 0.6464037 0.3304138 1.222716 -Inf
## 28 1.482874 0.6020600 1.978181 2.053078 0.5763414 0.1798389 1.227887    0
## 29 1.198657 0.9030900 2.545307 2.421604 0.6253125 0.5010593 1.161368 -Inf
## 30 1.294466 0.7781513 2.161368 2.243038 0.5587086 0.4424798 1.190332 -Inf
## 31 1.176091 0.9030900 2.478566 2.525045 0.5490033 0.5526682 1.164353 -Inf
## 32 1.330414 0.6020600 2.082785 2.037426 0.6138418 0.4440448 1.269513    0
##      am      gear      carb
## 1     0 0.6020600 0.6020600
## 2     0 0.6020600 0.6020600
## 3     0 0.6020600 0.0000000
## 4  -Inf 0.4771213 0.0000000
## 5  -Inf 0.4771213 0.3010300
## 6  -Inf 0.4771213 0.0000000
## 7  -Inf 0.4771213 0.6020600
## 8  -Inf 0.6020600 0.3010300
## 9  -Inf 0.6020600 0.3010300
## 10 -Inf 0.6020600 0.6020600
## 11 -Inf 0.6020600 0.6020600
## 12 -Inf 0.4771213 0.4771213
## 13 -Inf 0.4771213 0.4771213
## 14 -Inf 0.4771213 0.4771213
## 15 -Inf 0.4771213 0.6020600
## 16 -Inf 0.4771213 0.6020600
## 17 -Inf 0.4771213 0.6020600
## 18    0 0.6020600 0.0000000
## 19    0 0.6020600 0.3010300
## 20    0 0.6020600 0.0000000
## 21 -Inf 0.4771213 0.0000000
## 22 -Inf 0.4771213 0.3010300
## 23 -Inf 0.4771213 0.3010300
## 24 -Inf 0.4771213 0.6020600
## 25 -Inf 0.4771213 0.3010300
## 26    0 0.6020600 0.0000000
## 27    0 0.6989700 0.3010300
## 28    0 0.6989700 0.3010300
## 29    0 0.6989700 0.6020600
## 30    0 0.6989700 0.7781513
## 31    0 0.6989700 0.9030900
## 32    0 0.6020600 0.3010300

Filter

filter_all() is the most useful of the three filter() variants. You use it conjunction with all_vars() or any_vars() depending on whether or not you want rows where all variables meet the criterion, or where just one variable meets it.

It’s particularly useful finding missing values:

library(nycflights13)

# Rows where any value is missing
filter_all(weather, .vars_predicate = any_vars(is.na(.)))
## # A tibble: 3,109 x 15
##    origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
##    <chr>  <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
##  1 EWR    2013.    1.     1    17  39.2  28.4  64.9     270.      16.1 
##  2 EWR    2013.    1.     1    18  39.2  28.4  64.9     330.      15.0 
##  3 EWR    2013.    1.     3    16  30.9  14.0  49.0      NA        4.60
##  4 EWR    2013.    1.     6    10  33.8  30.2  86.5     210.       4.60
##  5 EWR    2013.    1.     6    12  33.8  32.0  93.0     220.       9.21
##  6 EWR    2013.    1.     6    13  35.6  32.0  86.6     240.       8.06
##  7 EWR    2013.    1.     6    14  35.6  32.0  86.6     230.       8.06
##  8 EWR    2013.    1.     6    15  37.4  30.2  75.0     250.      11.5 
##  9 EWR    2013.    1.    11    17  45.0  36.0  70.5      NA        4.60
## 10 EWR    2013.    1.    11    21  46.4  39.2  75.8      90.       4.60
## # ... with 3,099 more rows, and 5 more variables: wind_gust <dbl>,
## #   precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>
# Rows where all wind variables are missing
filter_at(weather, .vars = vars(starts_with("wind")),
          .vars_predicate = all_vars(is.na(.)))
## # A tibble: 3 x 15
##   origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
##   <chr>  <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
## 1 EWR    2013.    3.    27    21  52.0  19.0  27.0       NA         NA
## 2 JFK    2013.    7.     4    10  73.0  71.1  93.5       NA         NA
## 3 JFK    2013.    7.    20    10  81.0  71.1  71.9       NA         NA
## # ... with 5 more variables: wind_gust <dbl>, precip <dbl>,
## #   pressure <dbl>, visib <dbl>, time_hour <dttm>

Acknowledgments

Session Info

devtools::session_info()
## Session info -------------------------------------------------------------
##  setting  value                       
##  version  R version 3.4.3 (2017-11-30)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2018-04-18
## Packages -----------------------------------------------------------------
##  package    * version    date       source                             
##  assertthat   0.2.0      2017-04-11 CRAN (R 3.4.0)                     
##  backports    1.1.2      2017-12-13 CRAN (R 3.4.3)                     
##  base       * 3.4.3      2017-12-07 local                              
##  bindr        0.1.1      2018-03-13 CRAN (R 3.4.3)                     
##  bindrcpp     0.2.2.9000 2018-04-08 Github (krlmlr/bindrcpp@bd5ae73)   
##  broom        0.4.4      2018-03-29 CRAN (R 3.4.3)                     
##  cellranger   1.1.0      2016-07-27 CRAN (R 3.4.0)                     
##  cli          1.0.0      2017-11-05 CRAN (R 3.4.2)                     
##  colorspace   1.3-2      2016-12-14 CRAN (R 3.4.0)                     
##  compiler     3.4.3      2017-12-07 local                              
##  crayon       1.3.4      2017-10-03 Github (gaborcsardi/crayon@b5221ab)
##  datasets   * 3.4.3      2017-12-07 local                              
##  devtools     1.13.5     2018-02-18 CRAN (R 3.4.3)                     
##  digest       0.6.15     2018-01-28 CRAN (R 3.4.3)                     
##  dplyr      * 0.7.4.9003 2018-04-08 Github (tidyverse/dplyr@b7aaa95)   
##  evaluate     0.10.1     2017-06-24 CRAN (R 3.4.1)                     
##  forcats    * 0.3.0      2018-02-19 CRAN (R 3.4.3)                     
##  foreign      0.8-69     2017-06-22 CRAN (R 3.4.3)                     
##  ggplot2    * 2.2.1.9000 2018-04-10 Github (tidyverse/ggplot2@3c9c504) 
##  glue         1.2.0      2017-10-29 CRAN (R 3.4.2)                     
##  graphics   * 3.4.3      2017-12-07 local                              
##  grDevices  * 3.4.3      2017-12-07 local                              
##  grid         3.4.3      2017-12-07 local                              
##  gtable       0.2.0      2016-02-26 CRAN (R 3.4.0)                     
##  haven        1.1.1      2018-01-18 CRAN (R 3.4.3)                     
##  hms          0.4.2      2018-03-10 CRAN (R 3.4.3)                     
##  htmltools    0.3.6      2017-04-28 CRAN (R 3.4.0)                     
##  httr         1.3.1      2017-08-20 CRAN (R 3.4.1)                     
##  jsonlite     1.5        2017-06-01 CRAN (R 3.4.0)                     
##  knitr        1.20       2018-02-20 CRAN (R 3.4.3)                     
##  lattice      0.20-35    2017-03-25 CRAN (R 3.4.3)                     
##  lazyeval     0.2.1      2017-10-29 CRAN (R 3.4.2)                     
##  lubridate    1.7.4      2018-04-11 CRAN (R 3.4.3)                     
##  magrittr     1.5        2014-11-22 CRAN (R 3.4.0)                     
##  memoise      1.1.0      2017-04-21 CRAN (R 3.4.0)                     
##  methods    * 3.4.3      2017-12-07 local                              
##  mnormt       1.5-5      2016-10-15 CRAN (R 3.4.0)                     
##  modelr       0.1.1      2017-08-10 local                              
##  munsell      0.4.3      2016-02-13 CRAN (R 3.4.0)                     
##  nlme         3.1-137    2018-04-07 CRAN (R 3.4.4)                     
##  parallel     3.4.3      2017-12-07 local                              
##  pillar       1.2.1      2018-02-27 CRAN (R 3.4.3)                     
##  pkgconfig    2.0.1      2017-03-21 CRAN (R 3.4.0)                     
##  plyr         1.8.4      2016-06-08 CRAN (R 3.4.0)                     
##  psych        1.8.3.3    2018-03-30 CRAN (R 3.4.4)                     
##  purrr      * 0.2.4      2017-10-18 CRAN (R 3.4.2)                     
##  R6           2.2.2      2017-06-17 CRAN (R 3.4.0)                     
##  Rcpp         0.12.16    2018-03-13 CRAN (R 3.4.4)                     
##  readr      * 1.1.1      2017-05-16 CRAN (R 3.4.0)                     
##  readxl       1.0.0      2017-04-18 CRAN (R 3.4.0)                     
##  reshape2     1.4.3      2017-12-11 CRAN (R 3.4.3)                     
##  rlang        0.2.0.9001 2018-04-10 Github (r-lib/rlang@70d2d40)       
##  rmarkdown    1.9        2018-03-01 CRAN (R 3.4.3)                     
##  rprojroot    1.3-2      2018-01-03 CRAN (R 3.4.3)                     
##  rstudioapi   0.7        2017-09-07 CRAN (R 3.4.1)                     
##  rvest        0.3.2      2016-06-17 CRAN (R 3.4.0)                     
##  scales       0.5.0.9000 2018-04-10 Github (hadley/scales@d767915)     
##  stats      * 3.4.3      2017-12-07 local                              
##  stringi      1.1.7      2018-03-12 CRAN (R 3.4.3)                     
##  stringr    * 1.3.0      2018-02-19 CRAN (R 3.4.3)                     
##  tibble     * 1.4.2      2018-01-22 CRAN (R 3.4.3)                     
##  tidyr      * 0.8.0      2018-01-29 CRAN (R 3.4.3)                     
##  tidyselect   0.2.4      2018-02-26 CRAN (R 3.4.3)                     
##  tidyverse  * 1.2.1      2017-11-14 CRAN (R 3.4.2)                     
##  tools        3.4.3      2017-12-07 local                              
##  utils      * 3.4.3      2017-12-07 local                              
##  withr        2.1.2      2018-04-10 Github (jimhester/withr@79d7b0d)   
##  xml2         1.2.0      2018-01-24 CRAN (R 3.4.3)                     
##  yaml         2.1.18     2018-03-08 CRAN (R 3.4.4)

This work is licensed under the CC BY-NC 4.0 Creative Commons License.