dmap: Map over the columns of a data frame

Description

dmap() is just like purrr::map() but always returns a data frame. In addition, it handles grouped or sliced data frames.

Usage

dmap(.d, .f, ...)
dmap_at(.d, .at, .f, ...)
dmap_if(.d, .p, .f, ...)

Arguments

A data frame.

A function, formula, or vector (not necessarily atomic).

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

For a single argument function, use .
For a two argument function, use .x and .y
For more arguments, use ..1, ..2, ..3 etc

This syntax allows you to create very compact anonymous functions.

If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. If a component is not present, the value of .default will be returned.

...

Additional arguments passed on to the mapped function.

.at

A character vector of names, positive numeric vector of positions to include, or a negative numeric vector of positions to exlude. Only those elements corresponding to .at will be modified. If the tidyselect package is installed, you can use vars() and the tidyselect helpers to select elements.

A single predicate function, a formula describing such a predicate function, or a logical vector of the same length as .x. Alternatively, if the elements of .x are themselves lists of objects, a string indicating the name of a logical element in the inner lists. Only those elements where .p evaluates to TRUE will be modified.

Details

dmap_at() and dmap_if() recycle length 1 vectors to the group sizes.

Examples

Run this code

# NOT RUN {
# dmap() always returns a data frame:
dmap(mtcars, summary)

# dmap() also supports sliced data frames:
sliced_df <- mtcars[1:5] %>% slice_rows("cyl")
sliced_df %>% dmap(mean)
sliced_df %>% dmap(~ .x / max(.x))

# This is equivalent to the combination of by_slice() and dmap()
# with 'rows' collation of results:
sliced_df %>% by_slice(dmap, mean, .collate = "rows")
# }

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