collapse (version 1.7.6)

fsummarise: Fast Summarise

Description

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

Usage

fsummarise(.data, ..., keep.group_vars = TRUE)
smr(.data, ..., keep.group_vars = TRUE)        # Shortcut

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, or across statements. For fast performance use the Fast Statistical Functions.

keep.group_vars

logical. FALSE removes grouping variables after computation.

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.

See Also

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

Examples

# NOT RUN {
library(magrittr) # Note: Used because |> is not available on older R versions
## 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))

# 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))

#----------------------------------------------------------------------------
# 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))
# }
# NOT RUN {
 <!-- % No code relying on suggested package or base Pipe -->
# 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))

# }
# NOT RUN {

# }