fsummarise: Fast Summarise

In SebKrantz/collapse: Advanced and Fast Data Transformation

Fast Summarise

Description

fsummarise is a much faster version of dplyr::summarise, when used together with the Fast Statistical Functions.

fsummarize and fsummarise are synonyms.

Usage

fsummarise(.data, ..., keep.group_vars = TRUE, .cols = NULL)
fsummarize(.data, ..., keep.group_vars = TRUE, .cols = NULL)
smr(.data, ..., keep.group_vars = TRUE, .cols = NULL)        # Shorthand

Arguments

.data	a (grouped) data frame or named list of columns. Grouped data can be created with fgroup_by or dplyr::group_by.
...	name-value pairs of summary functions, across statements, or arbitrary expressions resulting in a list. See Examples. For fast performance use the Fast Statistical Functions.
keep.group_vars	logical. FALSE removes grouping variables after computation.
.cols	for expressions involving .data, .cols can be used to subset columns, e.g. mtcars |> gby(cyl) |> smr(mctl(cor(.data), TRUE), .cols = 5:7). Can pass column names, indices, a logical vector or a selector function (e.g. is.numericr).

Value

If .data is grouped by fgroup_by or dplyr::group_by, the result is a data frame of the same class and attributes with rows reduced to the number of groups. If .data is not grouped, the result is a data frame of the same class and attributes with 1 row.

Note

Since v1.7, fsummarise is fully featured, allowing expressions using functions and columns of the data as well as external scalar values (just like dplyr::summarise). NOTE however that once a Fast Statistical Function is used, the execution will be vectorized instead of split-apply-combine computing over groups. Please see the first Example.

See Also

across, collap, Data Frame Manipulation, Fast Statistical Functions, Collapse Overview

Examples

## Since v1.7, fsummarise supports arbitrary expressions, and expressions
## containing fast statistical functions receive vectorized execution:

# (a) This is an expression using base R functions which is executed by groups
mtcars |> fgroup_by(cyl) |> fsummarise(res = mean(mpg) + min(qsec))

# (b) Here, the use of fmean causes the whole expression to be executed
# in a vectorized way i.e. the expression is translated to something like
# fmean(mpg, g = cyl) + min(mpg) and executed, thus the result is different
# from (a), because the minimum is calculated over the entire sample
mtcars |> fgroup_by(cyl) |> fsummarise(mpg = fmean(mpg) + min(qsec))

# (c) For fully vectorized execution, use fmin. This yields the same as (a)
mtcars |> fgroup_by(cyl) |> fsummarise(mpg = fmean(mpg) + fmin(qsec))

# More advanced use: vectorized grouped regression slopes: mpg ~ carb
mtcars |>
  fgroup_by(cyl) |>
  fmutate(dm_carb = fwithin(carb)) |>
  fsummarise(beta = fsum(mpg, dm_carb) %/=% fsum(dm_carb^2))


# In across() statements it is fine to mix different functions, each will
# be executed on its own terms (i.e. vectorized for fmean and standard for sum)
mtcars |> fgroup_by(cyl) |> fsummarise(across(mpg:hp, list(fmean, sum)))

# Note that this still detects fmean as a fast function, the names of the list
# are irrelevant, but the function name must be typed or passed as a character vector,
# Otherwise functions will be executed by groups e.g. function(x) fmean(x) won't vectorize
mtcars |> fgroup_by(cyl) |> fsummarise(across(mpg:hp, list(mu = fmean, sum = sum)))

# We can force none-vectorized execution by setting .apply = TRUE
mtcars |> fgroup_by(cyl) |> fsummarise(across(mpg:hp, list(mu = fmean, sum = sum), .apply = TRUE))

# Another argument of across(): Order the result first by function, then by column
mtcars |> fgroup_by(cyl) |>
     fsummarise(across(mpg:hp, list(mu = fmean, sum = sum), .transpose = FALSE))


# Since v1.9.0, can also evaluate arbitrary expressions
mtcars |> fgroup_by(cyl, vs, am) |>
   fsummarise(mctl(cor(cbind(mpg, wt, carb)), names = TRUE))

# This can also be achieved using across():
corfun <- function(x) mctl(cor(x), names = TRUE)
mtcars |> fgroup_by(cyl, vs, am) |>
   fsummarise(across(c(mpg, wt, carb), corfun, .apply = FALSE))

#----------------------------------------------------------------------------
# Examples that also work for pre 1.7 versions

# Simple use
fsummarise(mtcars, mean_mpg = fmean(mpg),
                   sd_mpg = fsd(mpg))

# Using base functions (not a big difference without groups)
fsummarise(mtcars, mean_mpg = mean(mpg),
                   sd_mpg = sd(mpg))

# Grouped use
mtcars |> fgroup_by(cyl) |>
  fsummarise(mean_mpg = fmean(mpg),
             sd_mpg = fsd(mpg))

# This is still efficient but quite a bit slower on large data (many groups)
mtcars |> fgroup_by(cyl) |>
  fsummarise(mean_mpg = mean(mpg),
             sd_mpg = sd(mpg))

# Weighted aggregation
mtcars |> fgroup_by(cyl) |>
  fsummarise(w_mean_mpg = fmean(mpg, wt),
             w_sd_mpg = fsd(mpg, wt))

 
## Can also group with dplyr::group_by, but at a conversion cost, see ?GRP
library(dplyr)
mtcars |> group_by(cyl) |>
  fsummarise(mean_mpg = fmean(mpg),
             sd_mpg = fsd(mpg))

# Again less efficient...
mtcars |> group_by(cyl) |>
  fsummarise(mean_mpg = mean(mpg),
             sd_mpg = sd(mpg))

SebKrantz/collapse documentation built on Dec. 16, 2024, 7:26 p.m.