Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Apply a function to each row of a data frame

Description

by_row() and invoke_rows() apply ..f to each row of .d. If ..f's output is not a data frame nor an atomic vector, a list-column is created. In all cases, by_row() and invoke_rows() create a data frame in tidy format.

Usage

by_row(
  .d,
  ..f,
  ...,
  .collate = c("list", "rows", "cols"),
  .to = ".out",
  .labels = TRUE
)

invoke_rows(
  .f,
  .d,
  ...,
  .collate = c("list", "rows", "cols"),
  .to = ".out",
  .labels = TRUE
)

Arguments

Details

By default, the whole row is appended to the result to serve as identifier (set .labels to FALSE to prevent this). In addition, if ..f returns a multi-rows data frame or a non-scalar atomic vector, a .row column is appended to identify the row number in the original data frame.

invoke_rows() is intended to provide a version of pmap() for data frames. Its default collation method is "cols", which makes it equivalent to mdply() from the plyr package. Note that invoke_rows() follows the signature pattern of the invoke family of functions and takes .f as its first argument.

The distinction between by_row() and invoke_rows() is that the former passes a data frame to ..f while the latter maps the columns to its function call. This is essentially like using invoke() with each row. Another way to view this is that invoke_rows() is equivalent to using by_row() with a function lifted to accept dots (see lift()).

Value

A data frame.

See Also

by_slice()

Examples

# ..f should be able to work with a list or a data frame. As it
# happens, sum() handles data frame so the following works:
mtcars %>% by_row(sum)

# Other functions such as mean() may need to be adjusted with one
# of the lift_xy() helpers:
mtcars %>% by_row(purrr::lift_vl(mean))

# To run a function with invoke_rows(), make sure it is variadic (that
# it accepts dots) or that .f's signature is compatible with the
# column names
mtcars %>% invoke_rows(.f = sum)
mtcars %>% invoke_rows(.f = purrr::lift_vd(mean))

# invoke_rows() with cols collation is equivalent to plyr::mdply()
p <- expand.grid(mean = 1:5, sd = seq(0, 1, length = 10))
p %>% invoke_rows(.f = rnorm, n = 5, .collate = "cols")
## Not run: 
p %>% plyr::mdply(rnorm, n = 5) %>% dplyr::tbl_df()

## End(Not run)

# To integrate the result as part of the data frame, use rows or
# cols collation:
mtcars[1:2] %>% by_row(function(x) 1:5)
mtcars[1:2] %>% by_row(function(x) 1:5, .collate = "rows")
mtcars[1:2] %>% by_row(function(x) 1:5, .collate = "cols")

Apply a function to slices of a data frame

Description

by_slice() applies ..f on each group of a data frame. Groups should be set with slice_rows() or dplyr::group_by().

Usage

by_slice(
  .d,
  ..f,
  ...,
  .collate = c("list", "rows", "cols"),
  .to = ".out",
  .labels = TRUE
)

Arguments

Details

by_slice() provides equivalent functionality to dplyr's dplyr::do() function. In combination with map(), by_slice() is equivalent to dplyr::summarise_each() and dplyr::mutate_each(). The distinction between mutating and summarising operations is not as important as in dplyr because we do not act on the columns separately. The only constraint is that the mapped function must return the same number of rows for each variable mapped on.

Value

A data frame.

See Also

by_row(), slice_rows(), dmap()

Examples

# Here we fit a regression model inside each slice defined by the
# unique values of the column "cyl". The fitted models are returned
# in a list-column.
mtcars %>%
  slice_rows("cyl") %>%
  by_slice(purrr::partial(lm, mpg ~ disp))

# by_slice() is especially useful in combination with map().

# To modify the contents of a data frame, use rows collation. Note
# that unlike dplyr, Mutating and summarising operations can be
# used indistinctly.

# Mutating operation:
df <- mtcars %>% slice_rows(c("cyl", "am"))
df %>% by_slice(dmap, ~ .x / sum(.x), .collate = "rows")

# Summarising operation:
df %>% by_slice(dmap, mean, .collate = "rows")

# Note that mapping columns within slices is best handled by dmap():
df %>% dmap(~ .x / sum(.x))
df %>% dmap(mean)

# If you don't need the slicing variables as identifiers, switch
# .labels to FALSE:
mtcars %>%
  slice_rows("cyl") %>%
  by_slice(purrr::partial(lm, mpg ~ disp), .labels = FALSE) %>%
  purrr::flatten() %>%
  purrr::map(coef)

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

Details

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

Examples

# 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")

Slice a data frame into groups of rows

Description

slice_rows() is equivalent to dplyr's dplyr::group_by() command but it takes a vector of column names or positions instead of capturing column names with special evaluation. unslice() removes the slicing attributes.

Usage

slice_rows(.d, .cols = NULL)

unslice(.d)

Arguments

Value

A sliced or unsliced data frame.

See Also

by_slice() and dplyr::group_by()