# Programming with dplyr

## Introduction

Most dplyr verbs use tidy evaluation in some way. Tidy evaluation is a special type of non-standard evaluation used throughout the tidyverse. There are two basic forms found in dplyr:

• arrange(), count(), filter(), group_by(), mutate(), and summarise() use data masking so that you can use data variables as if they were variables in the environment (i.e. you write my_variable not df$myvariable). • across(), relocate(), rename(), select(), and pull() use tidy selection so you can easily choose variables based on their position, name, or type (e.g. starts_with("x") or is.numeric). To determine whether a function argument uses data masking or tidy selection, look at the documentation: in the arguments list, you’ll see <data-masking> or <tidy-select>. Data masking and tidy selection make interactive data exploration fast and fluid, but they add some new challenges when you attempt to use them indirectly such as in a for loop or a function. This vignette shows you how to overcome those challenges. We’ll first go over the basics of data masking and tidy selection, talk about how to use them indirectly, and then show you a number of recipes to solve common problems. This vignette will give you the minimum knowledge you need to be an effective programmer with tidy evaluation. If you’d like to learn more about the underlying theory, or precisely how it’s different from non-standard evaluation, we recommend that you read the Metaprogramming chapters in Advanced R. library(dplyr) ## Data masking Data masking makes data manipulation faster because it requires less typing. In most (but not all1) base R functions you need to refer to variables with $, leading to code that repeats the name of the data frame many times:

starwars[starwars$homeworld == "Naboo" & starwars$species == "Human", ,]

The dplyr equivalent of this code is more concise because data masking allows you to need to type starwars once:

starwars %>% filter(homeworld == "Naboo", species == "Human")

### Data- and env-variables

The key idea behind data masking is that it blurs the line between the two different meanings of the word “variable”:

• env-variables are “programming” variables that live in an environment. They are usually created with <-.

• data-variables are “statistical” variables that live in a data frame. They usually come from data files (e.g. .csv, .xls), or are created manipulating existing variables.

To make those definitions a little more concrete, take this piece of code:

df <- data.frame(x = runif(3), y = runif(3))
df$x #> [1] 0.08075014 0.83433304 0.60076089 It creates a env-variable, df, that contains two data-variables, x and y. Then it extracts the data-variable x out of the env-variable df using $.

I think this blurring of the meaning of “variable” is a really nice feature for interactive data analysis because it allows you to refer to data-vars as is, without any prefix. And this seems to be fairly intuitive since many newer R users will attempt to write diamonds[x == 0 | y == 0, ].

Unfortunately, this benefit does not come for free. When you start to program with these tools, you’re going to have to grapple with the distinction. This will be hard because you’ve never had to think about it before, so it’ll take a while for your brain to learn these new concepts and categories. However, once you’ve teased apart the idea of “variable” into data-variable and env-variable, I think you’ll find it fairly straightforward to use.

### Indirection

The main challenge of programming with functions that use data masking arises when you introduce some indirection, i.e. when you want to get the data-variable from an env-variable instead of directly typing the data-variable’s name. There are two main cases:

• When you have the data-variable in a function argument (i.e. an env-variable that holds a promise2), you need to embrace the argument by surrounding it in doubled braces, like filter(df, {{ var }}).

The following function uses embracing to create a wrapper around summarise() that computes the minimum and maximum values of a variable, as well as the number of observations that were summarised:

var_summary <- function(data, var) {
data %>%
summarise(n = n(), min = min({{ var }}), max = max({{ var }}))
}
mtcars %>%
group_by(cyl) %>%
var_summary(mpg)
• When you have an env-variable that is a character vector, you need to index into the .data pronoun with [[, like summarise(df, mean = mean(.data[[var]])).

The following example uses .data to count the number of unique values in each variable of mtcars:

for (var in names(mtcars)) {
mtcars %>% count(.data[[var]]) %>% print()
}

Note that .data is not a data frame; it’s a special construct, a pronoun, that allows you to access the current variables either directly, with .data$x or indirectly with .data[[var]]. Don’t expect other functions to work with it. ## Tidy selection Data masking makes it easy to compute on values within a dataset. Tidy selection is a complementary tool that makes it easy to work with the columns of a dataset. ### The tidyselect DSL Underneath all functions that use tidy selection is the tidyselect package. It provides a miniature domain specific language that makes it easy to select columns by name, position, or type. For example: • select(df, 1) selects the first column; select(df, last_col()) selects the last column. • select(df, c(a, b, c)) selects columns a, b, and c. • select(df, starts_with("a")) selects all columns whose name starts with “a”; select(df, ends_with("z")) selects all columns whose name ends with “z”. • select(df, where(is.numeric)) selects all numeric columns. You can see more details in ?dplyr_tidy_select. ### Indirection As with data masking, tidy selection makes a common task easier at the cost of making a less common task harder. When you want to use tidy select indirectly with the column specification stored in an intermediate variable, you’ll need to learn some new tools. Again, there are two forms of indirection: • When you have the data-variable in an env-variable that is a function argument, you use the same technique as data masking: you embrace the argument by surrounding it in doubled braces. The following function summarises a data frame by computing the mean of all variables selected by the user: summarise_mean <- function(data, vars) { data %>% summarise(n = n(), across({{ vars }}, mean)) } mtcars %>% group_by(cyl) %>% summarise_mean(where(is.numeric)) • When you have an env-variable that is a character vector, you need to use all_of() or any_of() depending on whether you want the function to error if a variable is not found. The following code uses all_of() to select all of the variables found in a character vector; then ! plus all_of() to select all of the variables not found in a character vector: vars <- c("mpg", "vs") mtcars %>% select(all_of(vars)) mtcars %>% select(!all_of(vars)) ## How tos The following examples solve a grab bag of common problems. We show you the minimum amount of code so that you can get the basic idea; most real problems will require more code or combining multiple techniques. ### User-supplied data If you check the documentation, you’ll see that .data never uses data masking or tidy select. That means you don’t need to do anything special in your function: mutate_y <- function(data) { mutate(data, y = a + x) } ### Eliminating R CMD checkNOTEs If you’re writing a package and you have a function that uses data-variables: my_summary_function <- function(data) { data %>% filter(x > 0) %>% group_by(grp) %>% summarise(y = mean(y), n = n()) } You’ll get an R CMD CHECK NOTE: N checking R code for possible problems my_summary_function: no visible binding for global variable ‘x’, ‘grp’, ‘y’ Undefined global functions or variables: x grp y You can eliminate this by using .data$var and importing .data from its source in the rlang package (the underlying package that implements tidy evaluation):

#' @importFrom rlang .data
my_summary_function <- function(data) {
data %>%
filter(.data$x > 0) %>% group_by(.data$grp) %>%
summarise(y = mean(.data$y), n = n()) } ### One or more user-supplied expressions If you want the user to supply an expression that’s passed onto an argument which uses data masking or tidy select, embrace the argument: my_summarise <- function(data, group_var) { data %>% group_by({{ group_var }}) %>% summarise(mean = mean(mass)) } This generalises in a straightforward way if you want to use one user-supplied expression in multiple places: my_summarise2 <- function(data, expr) { data %>% summarise( mean = mean({{ expr }}), sum = sum({{ expr }}), n = n() ) } If you want the user to provide multiple expressions, embrace each of them: my_summarise3 <- function(data, mean_var, sd_var) { data %>% summarise(mean = mean({{ mean_var }}), sd = mean({{ sd_var }})) } If you want to use the names of variables in the output, you can use glue syntax in conjunction with :=: my_summarise4 <- function(data, expr) { data %>% summarise( "mean_{{expr}}" := mean({{ expr }}), "sum_{{expr}}" := sum({{ expr }}), "n_{{expr}}" := n() ) } my_summarise5 <- function(data, mean_var, sd_var) { data %>% summarise( "mean_{{mean_var}}" := mean({{ mean_var }}), "sd_{{sd_var}}" := mean({{ sd_var }}) ) } ### Any number of user-supplied expressions If you want to take an arbitrary number of user supplied expressions, use .... This is most often useful when you want to give the user full control over a single part of the pipeline, like a group_by() or a mutate(). my_summarise <- function(.data, ...) { .data %>% group_by(...) %>% summarise(mass = mean(mass, na.rm = TRUE), height = mean(height, na.rm = TRUE)) } starwars %>% my_summarise(homeworld) #> # A tibble: 49 x 3 #> homeworld mass height #> <chr> <dbl> <dbl> #> 1 Alderaan 64 176. #> 2 Aleen Minor 15 79 #> 3 Bespin 79 175 #> 4 Bestine IV 110 180 #> # … with 45 more rows starwars %>% my_summarise(sex, gender) #> summarise() has grouped output by 'sex'. You can override using the .groups argument. #> # A tibble: 6 x 4 #> # Groups: sex [5] #> sex gender mass height #> <chr> <chr> <dbl> <dbl> #> 1 female feminine 54.7 169. #> 2 hermaphroditic masculine 1358 175 #> 3 male masculine 81.0 179. #> 4 none feminine NaN 96 #> # … with 2 more rows When you use ... in this way, make sure that any other arguments start with . to reduce the chances of argument clashes; see https://design.tidyverse.org/dots-prefix.html for more details. ### Transforming user-supplied variables If you want the user to provide a set of data-variables that are then transformed, use across(): my_summarise <- function(data, summary_vars) { data %>% summarise(across({{ summary_vars }}, ~ mean(., na.rm = TRUE))) } starwars %>% group_by(species) %>% my_summarise(c(mass, height)) #> # A tibble: 38 x 3 #> species mass height #> <chr> <dbl> <dbl> #> 1 Aleena 15 79 #> 2 Besalisk 102 198 #> 3 Cerean 82 198 #> 4 Chagrian NaN 196 #> # … with 34 more rows You can use this same idea for multiple sets of input data-variables: my_summarise <- function(data, group_var, summarise_var) { data %>% group_by(across({{ group_var }})) %>% summarise(across({{ summarise_var }}, mean)) } Use the .names argument to across() to control the names of the output. my_summarise <- function(data, group_var, summarise_var) { data %>% group_by(across({{ group_var }})) %>% summarise(across({{ summarise_var }}, mean, .names = "mean_{.col}")) } ### Loop over multiple variables If you have a character vector of variable names, and want to operate on them with a for loop, index into the special .data pronoun: for (var in names(mtcars)) { mtcars %>% count(.data[[var]]) %>% print() } This same technique works with for loop alternatives like the base R apply() family and the purrr map() family: mtcars %>% names() %>% purrr::map(~ count(mtcars, .data[[.x]])) ### Use a variable from an Shiny input Many Shiny input controls return character vectors, so you can use the same approach as above: .data[[input$var]].

library(shiny)
ui <- fluidPage(
selectInput("var", "Variable", choices = names(diamonds)),
tableOutput("output")
)
server <- function(input, output, session) {
data <- reactive(filter(diamonds, .data[[input$var]] > 0)) output$output <- renderTable(head(data()))
}

See https://mastering-shiny.org/action-tidy.html for more details and case studies.

1. dplyr’s filter() is inspired by base R’s subset(). subset() provides data masking, but not with tidy evaluation, so the techniques described in this chapter don’t apply to it.↩︎

2. In R, arguments are lazily evaluated which means that until you attempt to use, they don’t hold a value, just a promise that describes how to compute the value. You can learn more at https://adv-r.hadley.nz/functions.html#lazy-evaluation↩︎