across {poorman}

Apply a function (or functions) across multiple columns

Description

across() makes it easy to apply the same transformation to multiple columns, allowing you to use select() semantics inside in "data-masking" functions like summarise() and mutate().

if_any() and if_all() are used to apply the same predicate function to a selection of columns and combine the results into a single logical vector.

across() supersedes the family of dplyr "scoped variants" like summarise_at(), summarise_if(), and summarise_all() and therefore these functions will not be implemented in poorman.

Usage

across(.cols = everything(), .fns = NULL, ..., .names = NULL)

if_any(.cols, .fns = NULL, ..., .names = NULL)

if_all(.cols, .fns = NULL, ..., .names = NULL)

Arguments

.fns

Functions to apply to each of the selected columns. Possible values are:

• NULL, to returns the columns untransformed.

• A function, e.g. mean.

• A lambda, e.g. ~ mean(.x, na.rm = TRUE)

• A list of functions/lambdas, e.g. ⁠list(mean = mean, n_miss = ~ sum(is.na(.x))⁠

Within these functions you can use cur_column() and cur_group() to access the current column and grouping keys respectively.

...

Additional arguments for the function calls in .fns.

.names

A glue specification that describes how to name the output columns. This can use {.col} to stand for the selected column name, and {.fn} to stand for the name of the function being applied. The default (NULL) is equivalent to "{.col}" for the single function case and "⁠{.col}_{.fn}⁠" for the case where a list is used for .fns.

cols, .cols

<poor-select> Columns to transform. Because across() is used within functions like summarise() and mutate(), you can't select or compute upon grouping variables.

Value

across() returns a data.frame with one column for each column in .cols and each function in .fns.

if_any() and if_all() return a logical vector.

Examples

# across() -----------------------------------------------------------------
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean))
iris %>%
  mutate(across(where(is.factor), as.character))

# Additional parameters can be passed to functions
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), mean, na.rm = TRUE))

# A named list of functions
iris %>%
  group_by(Species) %>%
  summarise(across(starts_with("Sepal"), list(mean = mean, sd = sd)))

# Use the .names argument to control the output names
iris %>%
  group_by(Species) %>%
  summarise(
    across(starts_with("Sepal"),
    mean,
    .names = c("mean_sepal_length", "mean_sepal_width"))
  )

# if_any() and if_all() ----------------------------------------------------
iris %>%
  filter(if_any(ends_with("Width"), ~ . > 4))
iris %>%
  filter(if_all(ends_with("Width"), ~ . > 2))