dplyr <-> base R

This vignette compares dplyr functions to their base R equivalents. This helps those familiar with base R understand better what dplyr does, and shows dplyr users how you might express the same ideas in base R code. We’ll start with a rough overview of the major differences, then discuss the one table verbs in more detail, followed by the two table verbs.

Overview

  1. The code dplyr verbs input and output data frames. This contrasts with base R functions which more frequently work with individual vectors.

  2. dplyr relies heavily on “non-standard evaluation” so that you don’t need to use $ to refer to columns in the “current” data frame. This behaviour is inspired by the base functions subset() and transform().

  3. dplyr solutions tend to use a variety of single purpose verbs, while base R solutions typically tend to use [ in a variety of ways, depending on the task at hand.

  4. Multiple dplyr verbs are often strung together into a pipeline by %>%. In base R, you’ll typically save intermediate results to a variable that you either discard, or repeatedly overwrite.

  5. All dplyr verbs handle “grouped” data frames so that the code to perform a computation per-group looks very similar to code that works on a whole data frame. In base R, per-group operations tend to have varied forms.

One table verbs

The following table shows a condensed translation between dplyr verbs and their base R equivalents. The following sections describe each operation in more detail. You learn more about the dplyr verbs in their documentation and in For more vignette("one-table").

dplyr base
arrange(df, x) df[order(x), , drop = FALSE]
distinct(df, x) df[!duplicated(x), , drop = FALSE], unique()
filter(df, x) df[which(x), , drop = FALSE], subset()
mutate(df, z = x + y) df$z <- df$x + df$y, transform()
pull(df, 1) df[[1]]
pull(df, x) df$x
rename(df, y = x) names(df)[names(df) == "x"] <- "y"
relocate(df, y) df[union("y", names(df))]
select(df, x, y) df[c("x", "y")], subset()
select(df, starts_with("x") df[grepl(names(df), "^x")]
summarise(df, mean(x)) mean(df$x), tapply(), aggregate(), by()
slice(df, c(1, 2, 5)) df[c(1, 2, 5), , drop = FALSE]

To begin, we’ll load dplyr and convert mtcars and iris to tibbles so that we can easily show only abbreviated output for each operation.

library(dplyr)
mtcars <- as_tibble(mtcars)
iris <- as_tibble(iris)

arrange(): Arrange rows by variables

dplyr::arrange() orders the rows of a data frame by the values of one or more columns:

mtcars %>% arrange(cyl, disp)
#> # 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  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#> 2  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#> 3  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#> 4  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#> # … with 28 more rows

The desc() helper allows you to order selected variables in descending order:

mtcars %>% arrange(desc(cyl), desc(disp))
#> # 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  10.4     8   472   205  2.93  5.25  18.0     0     0     3     4
#> 2  10.4     8   460   215  3     5.42  17.8     0     0     3     4
#> 3  14.7     8   440   230  3.23  5.34  17.4     0     0     3     4
#> 4  19.2     8   400   175  3.08  3.84  17.0     0     0     3     2
#> # … with 28 more rows

We can replicate in base R by using [ with order():

mtcars[order(mtcars$cyl, mtcars$disp), , drop = FALSE]
#> # 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  33.9     4  71.1    65  4.22  1.84  19.9     1     1     4     1
#> 2  30.4     4  75.7    52  4.93  1.62  18.5     1     1     4     2
#> 3  32.4     4  78.7    66  4.08  2.2   19.5     1     1     4     1
#> 4  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1
#> # … with 28 more rows

Note the use of drop = FALSE. If you forget this, and the input is a data frame with a single column, the output will be a vector, not a data frame. This is a source of subtle bugs.

Base R does not provide a convenient and general way to sort individual variables in descending order, so you have two options:

mtcars[order(mtcars$cyl, mtcars$disp, decreasing = TRUE), , drop = FALSE]
mtcars[order(-mtcars$cyl, -mtcars$disp), , drop = FALSE]

distinct(): Select distinct/unique rows

dplyr::distinct() selects unique rows:

df <- tibble(
  x = sample(10, 100, rep = TRUE),
  y = sample(10, 100, rep = TRUE)
)

df %>% distinct(x) # selected columns
#> # A tibble: 10 x 1
#>       x
#>   <int>
#> 1     9
#> 2     7
#> 3     2
#> 4     4
#> # … with 6 more rows
df %>% distinct(x, .keep_all = TRUE) # whole data frame
#> # A tibble: 10 x 2
#>       x     y
#>   <int> <int>
#> 1     9     8
#> 2     7     8
#> 3     2    10
#> 4     4     5
#> # … with 6 more rows

There are two equivalents in base R, depending on whether you want the whole data frame, or just selected variables:

unique(df["x"]) # selected columns
#> # A tibble: 10 x 1
#>       x
#>   <int>
#> 1     9
#> 2     7
#> 3     2
#> 4     4
#> # … with 6 more rows
df[!duplicated(df$x), , drop = FALSE] # whole data frame
#> # A tibble: 10 x 2
#>       x     y
#>   <int> <int>
#> 1     9     8
#> 2     7     8
#> 3     2    10
#> 4     4     5
#> # … with 6 more rows

filter(): Return rows with matching conditions

dplyr::filter() selects rows where an expression is TRUE:

starwars %>% filter(species == "Human")
#> # A tibble: 35 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Luke…    172    77 blond      fair       blue            19   male  mascu…
#> 2 Dart…    202   136 none       white      yellow          41.9 male  mascu…
#> 3 Leia…    150    49 brown      light      brown           19   fema… femin…
#> 4 Owen…    178   120 brown, gr… light      blue            52   male  mascu…
#> # … with 31 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
starwars %>% filter(mass > 1000)
#> # A tibble: 1 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
#> # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>
starwars %>% filter(hair_color == "none" & eye_color == "black")
#> # A tibble: 9 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Nien…    160    68 none       grey       black             NA male  mascu…
#> 2 Gasg…    122    NA none       white, bl… black             NA male  mascu…
#> 3 Kit …    196    87 none       green      black             NA male  mascu…
#> 4 Plo …    188    80 none       orange     black             22 male  mascu…
#> # … with 5 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

The closest base equivalent (and the inspiration for filter()) is subset():

subset(starwars, species == "Human")
#> # A tibble: 35 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Luke…    172    77 blond      fair       blue            19   male  mascu…
#> 2 Dart…    202   136 none       white      yellow          41.9 male  mascu…
#> 3 Leia…    150    49 brown      light      brown           19   fema… femin…
#> 4 Owen…    178   120 brown, gr… light      blue            52   male  mascu…
#> # … with 31 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
subset(starwars, mass > 1000)
#> # A tibble: 1 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
#> # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>
subset(starwars, hair_color == "none" & eye_color == "black")
#> # A tibble: 9 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Nien…    160    68 none       grey       black             NA male  mascu…
#> 2 Gasg…    122    NA none       white, bl… black             NA male  mascu…
#> 3 Kit …    196    87 none       green      black             NA male  mascu…
#> 4 Plo …    188    80 none       orange     black             22 male  mascu…
#> # … with 5 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

You can also use [ but this also requires the use of which() to remove NAs:

starwars[which(starwars$species == "Human"), , drop = FALSE]
#> # A tibble: 35 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Luke…    172    77 blond      fair       blue            19   male  mascu…
#> 2 Dart…    202   136 none       white      yellow          41.9 male  mascu…
#> 3 Leia…    150    49 brown      light      brown           19   fema… femin…
#> 4 Owen…    178   120 brown, gr… light      blue            52   male  mascu…
#> # … with 31 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>
starwars[which(starwars$mass > 1000), , drop = FALSE]
#> # A tibble: 1 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Jabb…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
#> # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>
starwars[which(starwars$hair_color == "none" & starwars$eye_color == "black"), , drop = FALSE]
#> # A tibble: 9 x 14
#>   name  height  mass hair_color skin_color eye_color birth_year sex   gender
#>   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr> 
#> 1 Nien…    160    68 none       grey       black             NA male  mascu…
#> 2 Gasg…    122    NA none       white, bl… black             NA male  mascu…
#> 3 Kit …    196    87 none       green      black             NA male  mascu…
#> 4 Plo …    188    80 none       orange     black             22 male  mascu…
#> # … with 5 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> #   films <list>, vehicles <list>, starships <list>

mutate(): Create or transform variables

dplyr::mutate() creates new variables from existing variables:

df %>% mutate(z = x + y, z2 = z ^ 2)
#> # A tibble: 100 x 4
#>       x     y     z    z2
#>   <int> <int> <int> <dbl>
#> 1     9     8    17   289
#> 2     7     8    15   225
#> 3     2    10    12   144
#> 4     4     5     9    81
#> # … with 96 more rows

The closest base equivalent is transform(), but note that it cannot use freshly created variables:

head(transform(df, z = x + y, z2 = (x + y) ^ 2))
#>   x  y  z  z2
#> 1 9  8 17 289
#> 2 7  8 15 225
#> 3 2 10 12 144
#> 4 4  5  9  81
#> 5 4  9 13 169
#> 6 3  8 11 121

Alternatively, you can use $<-:

mtcars$cyl2 <- mtcars$cyl * 2
mtcars$cyl4 <- mtcars$cyl2 * 2

When applied to a grouped data frame, dplyr::mutate() computes new variable once per group:

gf <- tibble(g = c(1, 1, 2, 2), x = c(0.5, 1.5, 2.5, 3.5))
gf %>% 
  group_by(g) %>% 
  mutate(x_mean = mean(x), x_rank = rank(x))
#> # A tibble: 4 x 4
#> # Groups:   g [2]
#>       g     x x_mean x_rank
#>   <dbl> <dbl>  <dbl>  <dbl>
#> 1     1   0.5      1      1
#> 2     1   1.5      1      2
#> 3     2   2.5      3      1
#> 4     2   3.5      3      2

To replicate this in base R, you can use ave():

transform(gf, 
  x_mean = ave(x, g, FUN = mean), 
  x_rank = ave(x, g, FUN = rank)
)
#>   g   x x_mean x_rank
#> 1 1 0.5      1      1
#> 2 1 1.5      1      2
#> 3 2 2.5      3      1
#> 4 2 3.5      3      2

pull(): Pull out a single variable

dplyr::pull() extracts a variable either by name or position:

mtcars %>% pull(1)
#>  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
#> [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
#> [31] 15.0 21.4
mtcars %>% pull(cyl)
#>  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

This equivalent to [[ for positions and $ for names:

mtcars[[1]]
#>  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
#> [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
#> [31] 15.0 21.4
mtcars$cyl
#>  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

relocate(): Change column order

dplyr::relocate() makes it easy to move a set of columns to a new position (by default, the front):

# to front
mtcars %>% relocate(gear, carb) 
#> # A tibble: 32 x 13
#>    gear  carb   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4     4  21       6   160   110  3.9   2.62  16.5     0     1    12    24
#> 2     4     4  21       6   160   110  3.9   2.88  17.0     0     1    12    24
#> 3     4     1  22.8     4   108    93  3.85  2.32  18.6     1     1     8    16
#> 4     3     1  21.4     6   258   110  3.08  3.22  19.4     1     0    12    24
#> # … with 28 more rows

# to back
mtcars %>% relocate(mpg, cyl, .after = last_col()) 
#> # A tibble: 32 x 13
#>    disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4   mpg   cyl
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1   160   110  3.9   2.62  16.5     0     1     4     4    12    24  21       6
#> 2   160   110  3.9   2.88  17.0     0     1     4     4    12    24  21       6
#> 3   108    93  3.85  2.32  18.6     1     1     4     1     8    16  22.8     4
#> 4   258   110  3.08  3.22  19.4     1     0     3     1    12    24  21.4     6
#> # … with 28 more rows

We can replicate this in base R with a little set manipulation:

mtcars[union(c("gear", "carb"), names(mtcars))]
#> # A tibble: 32 x 13
#>    gear  carb   mpg   cyl  disp    hp  drat    wt  qsec    vs    am  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4     4  21       6   160   110  3.9   2.62  16.5     0     1    12    24
#> 2     4     4  21       6   160   110  3.9   2.88  17.0     0     1    12    24
#> 3     4     1  22.8     4   108    93  3.85  2.32  18.6     1     1     8    16
#> 4     3     1  21.4     6   258   110  3.08  3.22  19.4     1     0    12    24
#> # … with 28 more rows

to_back <- c("mpg", "cyl")
mtcars[c(setdiff(names(mtcars), to_back), to_back)]
#> # A tibble: 32 x 13
#>    disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4   mpg   cyl
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1   160   110  3.9   2.62  16.5     0     1     4     4    12    24  21       6
#> 2   160   110  3.9   2.88  17.0     0     1     4     4    12    24  21       6
#> 3   108    93  3.85  2.32  18.6     1     1     4     1     8    16  22.8     4
#> 4   258   110  3.08  3.22  19.4     1     0     3     1    12    24  21.4     6
#> # … with 28 more rows

Moving columns to somewhere in the middle requires a little more set twiddling.

rename(): Rename variables by name

dplyr::rename() allows you to rename variables by name or position:

iris %>% rename(sepal_length = Sepal.Length, sepal_width = 2)
#> # A tibble: 150 x 5
#>   sepal_length sepal_width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> # … with 146 more rows

Renaming variables by position is straight forward in base R:

iris2 <- iris
names(iris2)[2] <- "sepal_width"

Renaming variables by name requires a bit more work:

names(iris2)[names(iris2) == "Sepal.Length"] <- "sepal_length"

rename_with(): Rename variables with a function

dplyr::rename_with() transform column names with a function:

iris %>% rename_with(toupper)
#> # A tibble: 150 x 5
#>   SEPAL.LENGTH SEPAL.WIDTH PETAL.LENGTH PETAL.WIDTH SPECIES
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> # … with 146 more rows

A similar effect can be achieved with setNames() in base R:

setNames(iris, toupper(names(iris)))
#> # A tibble: 150 x 5
#>   SEPAL.LENGTH SEPAL.WIDTH PETAL.LENGTH PETAL.WIDTH SPECIES
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> # … with 146 more rows

select(): Select variables by name

dplyr::select() subsets columns by position, name, function of name, or other property:

iris %>% select(1:3)
#> # A tibble: 150 x 3
#>   Sepal.Length Sepal.Width Petal.Length
#>          <dbl>       <dbl>        <dbl>
#> 1          5.1         3.5          1.4
#> 2          4.9         3            1.4
#> 3          4.7         3.2          1.3
#> 4          4.6         3.1          1.5
#> # … with 146 more rows
iris %>% select(Species, Sepal.Length)
#> # A tibble: 150 x 2
#>   Species Sepal.Length
#>   <fct>          <dbl>
#> 1 setosa           5.1
#> 2 setosa           4.9
#> 3 setosa           4.7
#> 4 setosa           4.6
#> # … with 146 more rows
iris %>% select(starts_with("Petal"))
#> # A tibble: 150 x 2
#>   Petal.Length Petal.Width
#>          <dbl>       <dbl>
#> 1          1.4         0.2
#> 2          1.4         0.2
#> 3          1.3         0.2
#> 4          1.5         0.2
#> # … with 146 more rows
iris %>% select(where(is.factor))
#> # A tibble: 150 x 1
#>   Species
#>   <fct>  
#> 1 setosa 
#> 2 setosa 
#> 3 setosa 
#> 4 setosa 
#> # … with 146 more rows

Subsetting variables by position is straightforward in base R:

iris[1:3] # single argument selects columns; never drops
#> # A tibble: 150 x 3
#>   Sepal.Length Sepal.Width Petal.Length
#>          <dbl>       <dbl>        <dbl>
#> 1          5.1         3.5          1.4
#> 2          4.9         3            1.4
#> 3          4.7         3.2          1.3
#> 4          4.6         3.1          1.5
#> # … with 146 more rows
iris[1:3, , drop = FALSE]
#> # A tibble: 3 x 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa

You have two options to subset by name:

iris[c("Species", "Sepal.Length")]
#> # A tibble: 150 x 2
#>   Species Sepal.Length
#>   <fct>          <dbl>
#> 1 setosa           5.1
#> 2 setosa           4.9
#> 3 setosa           4.7
#> 4 setosa           4.6
#> # … with 146 more rows
subset(iris, select = c(Species, Sepal.Length))
#> # A tibble: 150 x 2
#>   Species Sepal.Length
#>   <fct>          <dbl>
#> 1 setosa           5.1
#> 2 setosa           4.9
#> 3 setosa           4.7
#> 4 setosa           4.6
#> # … with 146 more rows

Subsetting by function of name requires a bit of work with grep():

iris[grep("^Petal", names(iris))]
#> # A tibble: 150 x 2
#>   Petal.Length Petal.Width
#>          <dbl>       <dbl>
#> 1          1.4         0.2
#> 2          1.4         0.2
#> 3          1.3         0.2
#> 4          1.5         0.2
#> # … with 146 more rows

And you can use Filter() to subset by type:

Filter(is.factor, iris)
#> # A tibble: 150 x 1
#>   Species
#>   <fct>  
#> 1 setosa 
#> 2 setosa 
#> 3 setosa 
#> 4 setosa 
#> # … with 146 more rows

summarise(): Reduce multiple values down to a single value

dplyr::summarise() computes one or more summaries for each group:

mtcars %>% 
  group_by(cyl) %>% 
  summarise(mean = mean(disp), n = n())
#> # A tibble: 3 x 3
#>     cyl  mean     n
#> * <dbl> <dbl> <int>
#> 1     4  105.    11
#> 2     6  183.     7
#> 3     8  353.    14

I think the closest base R equivalent uses by(). Unfortunately by() returns a list of data frames, but you can combine them back together again with do.call() and rbind():

mtcars_by <- by(mtcars, mtcars$cyl, function(df) {
  with(df, data.frame(cyl = cyl[[1]], mean = mean(disp), n = nrow(df)))
})
do.call(rbind, mtcars_by)
#>   cyl     mean  n
#> 4   4 105.1364 11
#> 6   6 183.3143  7
#> 8   8 353.1000 14

aggregate() comes very close to providing an elegant answer:

agg <- aggregate(disp ~ cyl, mtcars, function(x) c(mean = mean(x), n = length(x)))
agg
#>   cyl disp.mean   disp.n
#> 1   4  105.1364  11.0000
#> 2   6  183.3143   7.0000
#> 3   8  353.1000  14.0000

But unfortunately while it looks like there are disp.mean and disp.n columns, it’s actually a single matrix column:

str(agg)
#> 'data.frame':    3 obs. of  2 variables:
#>  $ cyl : num  4 6 8
#>  $ disp: num [1:3, 1:2] 105 183 353 11 7 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:2] "mean" "n"

You can see a variety of other options at https://gist.github.com/hadley/c430501804349d382ce90754936ab8ec.

slice(): Choose rows by position

slice() selects rows with their location:

slice(mtcars, 25:n())
#> # A tibble: 8 x 13
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  19.2     8 400     175  3.08  3.84  17.0     0     0     3     2    16    32
#> 2  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1     8    16
#> 3  26       4 120.     91  4.43  2.14  16.7     0     1     5     2     8    16
#> 4  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2     8    16
#> # … with 4 more rows

This is straightforward to replicate with [:

mtcars[25:nrow(mtcars), , drop = FALSE]
#> # A tibble: 8 x 13
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb  cyl2  cyl4
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  19.2     8 400     175  3.08  3.84  17.0     0     0     3     2    16    32
#> 2  27.3     4  79      66  4.08  1.94  18.9     1     1     4     1     8    16
#> 3  26       4 120.     91  4.43  2.14  16.7     0     1     5     2     8    16
#> 4  30.4     4  95.1   113  3.77  1.51  16.9     1     1     5     2     8    16
#> # … with 4 more rows

Two-table verbs

When we want to merge two data frames, x and y), we have a variety of different ways to bring them together. Various base R merge() calls are replaced by a variety of dplyr join() functions.

dplyr base
inner_join(df1, df2) merge(df1, df2)
left_join(df1, df2) merge(df1, df2, all.x = TRUE)
right_join(df1, df2) merge(df1, df2, all.y = TRUE)
full_join(df1, df2) merge(df1, df2, all = TRUE)
semi_join(df1, df2) df1[df1$x %in% df2$x, , drop = FALSE]
anti_join(df1, df2) df1[!df1$x %in% df2$x, , drop = FALSE]

For more information about two-table verbs, see vignette("two-table").

Mutating joins

dplyr’s inner_join(), left_join(), right_join(), and full_join() add new columns from y to x, matching rows based on a set of “keys”, and differ only in how missing matches are handled. They are equivalent to calls to merge() with various settings of the all, all.x, and all.y arguments. The main difference is the order of the rows:

Filtering joins

dplyr’s semi_join() and anti_join() affect only the rows, not the columns:

band_members %>% semi_join(band_instruments)
#> Joining, by = "name"
#> # A tibble: 2 x 2
#>   name  band   
#>   <chr> <chr>  
#> 1 John  Beatles
#> 2 Paul  Beatles
band_members %>% anti_join(band_instruments)
#> Joining, by = "name"
#> # A tibble: 1 x 2
#>   name  band  
#>   <chr> <chr> 
#> 1 Mick  Stones

They can be replicated in base R with [ and %in%:

band_members[band_members$name %in% band_instruments$name, , drop = FALSE]
#> # A tibble: 2 x 2
#>   name  band   
#>   <chr> <chr>  
#> 1 John  Beatles
#> 2 Paul  Beatles
band_members[!band_members$name %in% band_instruments$name, , drop = FALSE]
#> # A tibble: 1 x 2
#>   name  band  
#>   <chr> <chr> 
#> 1 Mick  Stones

Semi and anti joins with multiple key variables are considerably more challenging to implement.