# 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’ll learn more about the dplyr verbs in their documentation and in `vignette("dplyr")`.

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("^x", names(df))]`
`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 × 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
    #> # ℹ 28 more rows

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

    mtcars %>% arrange(desc(cyl), desc(disp))
    #> # A tibble: 32 × 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
    #> # ℹ 28 more rows

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

    mtcars[order(mtcars$cyl, mtcars$disp), , drop = FALSE]
    #> # A tibble: 32 × 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
    #> # ℹ 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:

- For numeric variables, you can use `-x`.
- You can request `order()` to sort all variables in descending order.

    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 × 1
    #>       x
    #>   <int>
    #> 1     8
    #> 2     7
    #> 3    10
    #> 4     4
    #> # ℹ 6 more rows
    df %>% distinct(x, .keep_all = TRUE) # whole data frame
    #> # A tibble: 10 × 2
    #>       x     y
    #>   <int> <int>
    #> 1     8     7
    #> 2     7     3
    #> 3    10     1
    #> 4     4     2
    #> # ℹ 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 × 1
    #>       x
    #>   <int>
    #> 1     8
    #> 2     7
    #> 3    10
    #> 4     4
    #> # ℹ 6 more rows
    df[!duplicated(df$x), , drop = FALSE] # whole data frame
    #> # A tibble: 10 × 2
    #>       x     y
    #>   <int> <int>
    #> 1     8     7
    #> 2     7     3
    #> 3    10     1
    #> 4     4     2
    #> # ℹ 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 × 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 Sky…    172    77 blond      fair       blue            19   male  mascu…
    #> 2 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
    #> 3 Leia Org…    150    49 brown      light      brown           19   fema… femin…
    #> 4 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
    #> # ℹ 31 more rows
    #> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
    #> #   vehicles <list>, starships <list>
    starwars %>% filter(mass > 1000)
    #> # A tibble: 1 × 14
    #>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
    #>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>
    #> 1 Jabba De…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
    #> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
    #> #   vehicles <list>, starships <list>
    starwars %>% filter(hair_color == "none" & eye_color == "black")
    #> # A tibble: 9 × 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 Nunb    160    68 none       grey       black             NA male  mascu…
    #> 2 Gasgano      122    NA none       white, bl… black             NA male  mascu…
    #> 3 Kit Fisto    196    87 none       green      black             NA male  mascu…
    #> 4 Plo Koon     188    80 none       orange     black             22 male  mascu…
    #> # ℹ 5 more rows
    #> # ℹ 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 × 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 Sky…    172    77 blond      fair       blue            19   male  mascu…
    #> 2 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
    #> 3 Leia Org…    150    49 brown      light      brown           19   fema… femin…
    #> 4 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
    #> # ℹ 31 more rows
    #> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
    #> #   vehicles <list>, starships <list>
    subset(starwars, mass > 1000)
    #> # A tibble: 1 × 14
    #>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
    #>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>
    #> 1 Jabba De…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
    #> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
    #> #   vehicles <list>, starships <list>
    subset(starwars, hair_color == "none" & eye_color == "black")
    #> # A tibble: 9 × 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 Nunb    160    68 none       grey       black             NA male  mascu…
    #> 2 Gasgano      122    NA none       white, bl… black             NA male  mascu…
    #> 3 Kit Fisto    196    87 none       green      black             NA male  mascu…
    #> 4 Plo Koon     188    80 none       orange     black             22 male  mascu…
    #> # ℹ 5 more rows
    #> # ℹ 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 `NA`s:

    starwars[which(starwars$species == "Human"), , drop = FALSE]
    #> # A tibble: 35 × 14
    #>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
    #>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>
    #> 1 Luke Sky…    172    77 blond      fair       blue            19   male  mascu…
    #> 2 Darth Va…    202   136 none       white      yellow          41.9 male  mascu…
    #> 3 Leia Org…    150    49 brown      light      brown           19   fema… femin…
    #> 4 Owen Lars    178   120 brown, gr… light      blue            52   male  mascu…
    #> # ℹ 31 more rows
    #> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
    #> #   vehicles <list>, starships <list>
    starwars[which(starwars$mass > 1000), , drop = FALSE]
    #> # A tibble: 1 × 14
    #>   name      height  mass hair_color skin_color eye_color birth_year sex   gender
    #>   <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> <chr>
    #> 1 Jabba De…    175  1358 <NA>       green-tan… orange           600 herm… mascu…
    #> # ℹ 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 × 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 Nunb    160    68 none       grey       black             NA male  mascu…
    #> 2 Gasgano      122    NA none       white, bl… black             NA male  mascu…
    #> 3 Kit Fisto    196    87 none       green      black             NA male  mascu…
    #> 4 Plo Koon     188    80 none       orange     black             22 male  mascu…
    #> # ℹ 5 more rows
    #> # ℹ 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 × 4
    #>       x     y     z    z2
    #>   <int> <int> <int> <dbl>
    #> 1     8     7    15   225
    #> 2     8     9    17   289
    #> 3     7     3    10   100
    #> 4    10     1    11   121
    #> # ℹ 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  8 7 15 225
    #> 2  8 9 17 289
    #> 3  7 3 10 100
    #> 4 10 1 11 121
    #> 5  4 2  6  36
    #> 6  2 1  3   9

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 × 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 × 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
    #> # ℹ 28 more rows

    # to back
    mtcars %>% relocate(mpg, cyl, .after = last_col())
    #> # A tibble: 32 × 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
    #> # ℹ 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 × 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
    #> # ℹ 28 more rows

    to_back <- c("mpg", "cyl")
    mtcars[c(setdiff(names(mtcars), to_back), to_back)]
    #> # A tibble: 32 × 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
    #> # ℹ 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 × 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
    #> # ℹ 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 × 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
    #> # ℹ 146 more rows

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

    setNames(iris, toupper(names(iris)))
    #> # A tibble: 150 × 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
    #> # ℹ 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 × 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
    #> # ℹ 146 more rows
    iris %>% select(Species, Sepal.Length)
    #> # A tibble: 150 × 2
    #>   Species Sepal.Length
    #>   <fct>          <dbl>
    #> 1 setosa           5.1
    #> 2 setosa           4.9
    #> 3 setosa           4.7
    #> 4 setosa           4.6
    #> # ℹ 146 more rows
    iris %>% select(starts_with("Petal"))
    #> # A tibble: 150 × 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
    #> # ℹ 146 more rows
    iris %>% select(where(is.factor))
    #> # A tibble: 150 × 1
    #>   Species
    #>   <fct>
    #> 1 setosa
    #> 2 setosa
    #> 3 setosa
    #> 4 setosa
    #> # ℹ 146 more rows

Subsetting variables by position is straightforward in base R:

    iris[1:3] # single argument selects columns; never drops
    #> # A tibble: 150 × 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
    #> # ℹ 146 more rows
    iris[1:3, , drop = FALSE]
    #> # A tibble: 3 × 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 × 2
    #>   Species Sepal.Length
    #>   <fct>          <dbl>
    #> 1 setosa           5.1
    #> 2 setosa           4.9
    #> 3 setosa           4.7
    #> 4 setosa           4.6
    #> # ℹ 146 more rows
    subset(iris, select = c(Species, Sepal.Length))
    #> # A tibble: 150 × 2
    #>   Species Sepal.Length
    #>   <fct>          <dbl>
    #> 1 setosa           5.1
    #> 2 setosa           4.9
    #> 3 setosa           4.7
    #> 4 setosa           4.6
    #> # ℹ 146 more rows

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

    iris[grep("^Petal", names(iris))]
    #> # A tibble: 150 × 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
    #> # ℹ 146 more rows

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

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

This is straightforward to replicate with `[`:

    mtcars[25:nrow(mtcars), , drop = FALSE]
    #> # A tibble: 8 × 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
    #> # ℹ 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:

- dplyr preserves the order of the `x` data frame.
- `merge()` sorts the key columns.

### Filtering joins

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

    band_members %>% semi_join(band_instruments)
    #> Joining with `by = join_by(name)`
    #> # A tibble: 2 × 2
    #>   name  band
    #>   <chr> <chr>
    #> 1 John  Beatles
    #> 2 Paul  Beatles
    band_members %>% anti_join(band_instruments)
    #> Joining with `by = join_by(name)`
    #> # A tibble: 1 × 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 × 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 × 2
    #>   name  band
    #>   <chr> <chr>
    #> 1 Mick  Stones

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