collapse (version 2.0.2)

across: Apply Functions Across Multiple Columns

Description

across() can be used inside fmutate and fsummarise to apply one or more functions to a selection of columns. It is overall very similar to dplyr::across, but does not support some rlang features, has some additional features (arguments), and is optimized to work with collapse's, .FAST_FUN, yielding much faster computations.

Usage

across(.cols = NULL, .fns, ..., .names = NULL,
       .apply = "auto", .transpose = "auto")
# acr(...) can be used to abbreviate across(...)

Arguments

.cols

select columns using column names and expressions (e.g. a:b or c(a, b, c:f)), column indices, logical vectors, or functions yielding a logical value e.g. is.numeric. NULL applies functions to all columns except for grouping columns.

.fns

A function, character vector of functions or list of functions. Vectors / lists can be named to yield alternative names in the result (see .names). This argument is evaluated inside substitute(), and the content (not the names of vectors/lists) is checked against .FAST_FUN and .OPERATOR_FUN. Matching functions receive vectorized execution, other functions are applied to the data in a standard way.

...

further arguments to .fns. Arguments are evaluated in the data environment and split by groups as well (for non-vectorized functions, if of the same length as the data).

.names

controls the naming of computed columns. NULL generates names of the form coli_funj if multiple functions are used. .names = TRUE enables this for a single function, .names = FALSE disables it for multiple functions (sensible for functions such as .OPERATOR_FUN that rename columns (if .apply = FALSE)). Setting .names = "flip" generates names of the form funj_coli. It is also possible to supply a function with two arguments for column and function names e.g. function(c, f) paste0(f, "_", c). Finally, you can supply a custom vector of names which must match length(.cols) * length(.fns).

.apply

controls whether functions are applied column-by-column (TRUE) or to multiple columns at once (FALSE). The default, "auto", does the latter for vectorized functions, which have an efficient data frame method. It can also be sensible to use .apply = FALSE for non-vectorized functions, especially multivariate functions like lm or pwcor, or functions renaming the data. See Examples.

.transpose

with multiple .fns, .transpose controls whether the result is ordered first by column, then by function (TRUE), or vice-versa (FALSE). "auto" does the former if all functions yield results of the same dimensions (dimensions may differ if .apply = FALSE). See Examples.

See Also

fsummarise, fmutate, Fast Data Manipulation, Collapse Overview

Examples

# Basic (Weighted) Summaries
fsummarise(wlddev, across(PCGDP:GINI, fmean, w = POP))
 
library(magrittr) # Note: Used because |> is not available on older R versions
wlddev %>% fgroup_by(region, income) %>%
    fsummarise(across(PCGDP:GINI, fmean, w = POP))

# Note that for these we don't actually need across...
fselect(wlddev, PCGDP:GINI) %>% fmean(w = wlddev$POP, drop = FALSE)
wlddev %>% fgroup_by(region, income) %>%
    fselect(PCGDP:GINI, POP) %>% fmean(POP, keep.w = FALSE)
collap(wlddev, PCGDP + LIFEEX + GINI ~ region + income, w = ~ POP, keep.w = FALSE)

# But if we want to use some base R function that reguires argument splitting...
wlddev %>% na_omit(cols = "POP") %>% fgroup_by(region, income) %>%
    fsummarise(across(PCGDP:GINI, weighted.mean, w = POP, na.rm = TRUE))

# Or if we want to apply different functions...
wlddev %>% fgroup_by(region, income) %>%
    fsummarise(across(PCGDP:GINI, list(mu = fmean, sd = fsd), w = POP),
               POP_sum = fsum(POP), OECD = fmean(OECD))
# Note that the above 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

# Same, naming in a different way
wlddev %>% fgroup_by(region, income) %>%
    fsummarise(across(PCGDP:GINI, list(mu = fmean, sd = fsd), w = POP, .names = "flip"),
               sum_POP = fsum(POP), OECD = fmean(OECD))

# Or we want to do more advanced things..
# Such as nesting data frames..
qTBL(wlddev) %>% fgroup_by(region, income) %>%
    fsummarise(across(c(PCGDP, LIFEEX, ODA),
               function(x) list(Nest = list(x)),
               .apply = FALSE))
# Or linear models..
qTBL(wlddev) %>% fgroup_by(region, income) %>%
    fsummarise(across(c(PCGDP, LIFEEX, ODA),
               function(x) list(Mods = list(lm(PCGDP ~., x))),
               .apply = FALSE))
# Or cumputing grouped correlation matrices
qTBL(wlddev) %>% fgroup_by(region, income) %>%
    fsummarise(across(c(PCGDP, LIFEEX, ODA),
      function(x) qDF(pwcor(x), "Variable"), .apply = FALSE))

# Here calculating 1- and 10-year lags and growth rates of these variables
qTBL(wlddev) %>% fgroup_by(country) %>%
    fmutate(across(c(PCGDP, LIFEEX, ODA), list(L, G),
                   n = c(1, 10), t = year, .names = FALSE))

# Same but variables in different order
qTBL(wlddev) %>% fgroup_by(country) %>%
    fmutate(across(c(PCGDP, LIFEEX, ODA), list(L, G), n = c(1, 10),
                   t = year, .names = FALSE, .transpose = FALSE))