Apply Functions Across Multiple Columns

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.

Note

across does not support purr-style lambdas, and does not support dplyr-style predicate functions e.g. across(where(is.numeric), sum), simply use across(is.numeric, sum). In contrast to dplyr, you can also compute on grouping columns.

Examples

# Basic (Weighted) Summaries
fsummarise(wlddev, across(PCGDP:GINI, fmean, w = POP))
#>      PCGDP   LIFEEX     GINI
#> 1 7956.238 65.88068 39.52428

wlddev |> fgroup_by(region, income) |>
    fsummarise(across(PCGDP:GINI, fmean, w = POP))
#>                        region              income      PCGDP   LIFEEX     GINI
#> 1         East Asia & Pacific         High income 29172.7552 76.83283 32.79182
#> 2         East Asia & Pacific Lower middle income  1756.6480 64.25623 36.07647
#> 3         East Asia & Pacific Upper middle income  2357.6168 68.40768 39.94810
#> 4       Europe & Central Asia         High income 29335.5511 75.66616 32.25404
#> 5       Europe & Central Asia          Low income   803.2234 62.45228 32.22326
#> 6       Europe & Central Asia Lower middle income  2256.9684 68.48909 28.97857
#> 7       Europe & Central Asia Upper middle income  7772.5035 68.01573 38.70512
#> 8   Latin America & Caribbean         High income 10217.0626 73.04484 49.41109
#> 9   Latin America & Caribbean          Low income  1317.9024 55.45075 41.10000
#> 10  Latin America & Caribbean Lower middle income  1913.8993 63.86360 50.65115
#> 11  Latin America & Caribbean Upper middle income  7564.8294 69.46947 52.90072
#> 12 Middle East & North Africa         High income 25889.0715 72.38335 36.93006
#> 13 Middle East & North Africa          Low income  1049.8255 63.62748 35.89218
#> 14 Middle East & North Africa Lower middle income  2015.0739 65.55189 33.21199
#>  [ reached 'max' / getOption("max.print") -- omitted 9 rows ]

# Note that for these we don't actually need across...
fselect(wlddev, PCGDP:GINI) |> fmean(w = wlddev$POP, drop = FALSE)
#>      PCGDP   LIFEEX     GINI
#> 1 7956.238 65.88068 39.52428
wlddev |> fgroup_by(region, income) |>
    fselect(PCGDP:GINI, POP) |> fmean(POP, keep.w = FALSE)
#>                        region              income      PCGDP   LIFEEX     GINI
#> 1         East Asia & Pacific         High income 29172.7552 76.83283 32.79182
#> 2         East Asia & Pacific Lower middle income  1756.6480 64.25623 36.07647
#> 3         East Asia & Pacific Upper middle income  2357.6168 68.40768 39.94810
#> 4       Europe & Central Asia         High income 29335.5511 75.66616 32.25404
#> 5       Europe & Central Asia          Low income   803.2234 62.45228 32.22326
#> 6       Europe & Central Asia Lower middle income  2256.9684 68.48909 28.97857
#> 7       Europe & Central Asia Upper middle income  7772.5035 68.01573 38.70512
#> 8   Latin America & Caribbean         High income 10217.0626 73.04484 49.41109
#> 9   Latin America & Caribbean          Low income  1317.9024 55.45075 41.10000
#> 10  Latin America & Caribbean Lower middle income  1913.8993 63.86360 50.65115
#> 11  Latin America & Caribbean Upper middle income  7564.8294 69.46947 52.90072
#> 12 Middle East & North Africa         High income 25889.0715 72.38335 36.93006
#> 13 Middle East & North Africa          Low income  1049.8255 63.62748 35.89218
#> 14 Middle East & North Africa Lower middle income  2015.0739 65.55189 33.21199
#>  [ reached 'max' / getOption("max.print") -- omitted 9 rows ]
collap(wlddev, PCGDP + LIFEEX + GINI ~ region + income, w = ~ POP, keep.w = FALSE)
#>                        region              income      PCGDP   LIFEEX     GINI
#> 1         East Asia & Pacific         High income 29172.7552 76.83283 32.79182
#> 2         East Asia & Pacific Lower middle income  1756.6480 64.25623 36.07647
#> 3         East Asia & Pacific Upper middle income  2357.6168 68.40768 39.94810
#> 4       Europe & Central Asia         High income 29335.5511 75.66616 32.25404
#> 5       Europe & Central Asia          Low income   803.2234 62.45228 32.22326
#> 6       Europe & Central Asia Lower middle income  2256.9684 68.48909 28.97857
#> 7       Europe & Central Asia Upper middle income  7772.5035 68.01573 38.70512
#> 8   Latin America & Caribbean         High income 10217.0626 73.04484 49.41109
#> 9   Latin America & Caribbean          Low income  1317.9024 55.45075 41.10000
#> 10  Latin America & Caribbean Lower middle income  1913.8993 63.86360 50.65115
#> 11  Latin America & Caribbean Upper middle income  7564.8294 69.46947 52.90072
#> 12 Middle East & North Africa         High income 25889.0715 72.38335 36.93006
#> 13 Middle East & North Africa          Low income  1049.8255 63.62748 35.89218
#> 14 Middle East & North Africa Lower middle income  2015.0739 65.55189 33.21199
#>  [ reached 'max' / getOption("max.print") -- omitted 9 rows ]

# 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))
#>                        region              income      PCGDP   LIFEEX     GINI
#> 1         East Asia & Pacific         High income 29172.7552 76.83283 32.79182
#> 2         East Asia & Pacific Lower middle income  1756.6480 64.25623 36.07647
#> 3         East Asia & Pacific Upper middle income  2357.6168 68.40768 39.94810
#> 4       Europe & Central Asia         High income 29335.5511 75.66616 32.25404
#> 5       Europe & Central Asia          Low income   803.2234 62.45228 32.22326
#> 6       Europe & Central Asia Lower middle income  2256.9684 68.48909 28.97857
#> 7       Europe & Central Asia Upper middle income  7772.5035 68.01573 38.70512
#> 8   Latin America & Caribbean         High income 10217.0626 73.04484 49.41109
#> 9   Latin America & Caribbean          Low income  1317.9024 55.45075 41.10000
#> 10  Latin America & Caribbean Lower middle income  1913.8993 63.86360 50.65115
#> 11  Latin America & Caribbean Upper middle income  7564.8294 69.46947 52.90072
#> 12 Middle East & North Africa         High income 25889.0715 72.38335 36.93006
#> 13 Middle East & North Africa          Low income  1049.8255 63.62748 35.89218
#> 14 Middle East & North Africa Lower middle income  2015.0739 65.55189 33.21199
#>  [ reached 'max' / getOption("max.print") -- omitted 9 rows ]

# 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))
#>                  region              income   PCGDP_mu   PCGDP_sd LIFEEX_mu
#> 1   East Asia & Pacific         High income 29172.7552 14714.1754  76.83283
#> 2   East Asia & Pacific Lower middle income  1756.6480  1064.2676  64.25623
#> 3   East Asia & Pacific Upper middle income  2357.6168  2457.9024  68.40768
#> 4 Europe & Central Asia         High income 29335.5511 13038.1111  75.66616
#> 5 Europe & Central Asia          Low income   803.2234   307.7395  62.45228
#> 6 Europe & Central Asia Lower middle income  2256.9684   970.2648  68.48909
#> 7 Europe & Central Asia Upper middle income  7772.5035  3184.4987  68.01573
#>   LIFEEX_sd  GINI_mu  GINI_sd     POP_sum      OECD
#> 1  5.964994 32.79182 1.230489 11407808149 0.3076923
#> 2  7.536813 36.07647 4.358228 22174820629 0.0000000
#> 3  7.689033 39.94810 3.120103 69639871478 0.0000000
#> 4  4.175866 32.25404 3.023778 27285316560 0.7027027
#> 5  6.050875 32.22326 1.547793   311485944 0.0000000
#> 6  2.452041 28.97857 4.573107  4511786205 0.0000000
#> 7  4.796135 38.70512 4.233085 16972478305 0.0625000
#>  [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
# 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))
#>                  region              income   mu_PCGDP   sd_PCGDP mu_LIFEEX
#> 1   East Asia & Pacific         High income 29172.7552 14714.1754  76.83283
#> 2   East Asia & Pacific Lower middle income  1756.6480  1064.2676  64.25623
#> 3   East Asia & Pacific Upper middle income  2357.6168  2457.9024  68.40768
#> 4 Europe & Central Asia         High income 29335.5511 13038.1111  75.66616
#> 5 Europe & Central Asia          Low income   803.2234   307.7395  62.45228
#> 6 Europe & Central Asia Lower middle income  2256.9684   970.2648  68.48909
#> 7 Europe & Central Asia Upper middle income  7772.5035  3184.4987  68.01573
#>   sd_LIFEEX  mu_GINI  sd_GINI     sum_POP      OECD
#> 1  5.964994 32.79182 1.230489 11407808149 0.3076923
#> 2  7.536813 36.07647 4.358228 22174820629 0.0000000
#> 3  7.689033 39.94810 3.120103 69639871478 0.0000000
#> 4  4.175866 32.25404 3.023778 27285316560 0.7027027
#> 5  6.050875 32.22326 1.547793   311485944 0.0000000
#> 6  2.452041 28.97857 4.573107  4511786205 0.0000000
#> 7  4.796135 38.70512 4.233085 16972478305 0.0625000
#>  [ reached 'max' / getOption("max.print") -- omitted 16 rows ]

# 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))
#> # A tibble: 23 × 3
#>    region                    income              Nest                
#>    <fct>                     <fct>               <list>              
#>  1 East Asia & Pacific       High income         <tibble [793 × 3]>  
#>  2 East Asia & Pacific       Lower middle income <tibble [793 × 3]>  
#>  3 East Asia & Pacific       Upper middle income <tibble [610 × 3]>  
#>  4 Europe & Central Asia     High income         <tibble [2,257 × 3]>
#>  5 Europe & Central Asia     Low income          <tibble [61 × 3]>   
#>  6 Europe & Central Asia     Lower middle income <tibble [244 × 3]>  
#>  7 Europe & Central Asia     Upper middle income <tibble [976 × 3]>  
#>  8 Latin America & Caribbean High income         <tibble [1,037 × 3]>
#>  9 Latin America & Caribbean Low income          <tibble [61 × 3]>   
#> 10 Latin America & Caribbean Lower middle income <tibble [244 × 3]>  
#> # ℹ 13 more rows
# 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))
#> # A tibble: 23 × 3
#>    region                    income              Mods  
#>    <fct>                     <fct>               <list>
#>  1 East Asia & Pacific       High income         <lm>  
#>  2 East Asia & Pacific       Lower middle income <lm>  
#>  3 East Asia & Pacific       Upper middle income <lm>  
#>  4 Europe & Central Asia     High income         <lm>  
#>  5 Europe & Central Asia     Low income          <lm>  
#>  6 Europe & Central Asia     Lower middle income <lm>  
#>  7 Europe & Central Asia     Upper middle income <lm>  
#>  8 Latin America & Caribbean High income         <lm>  
#>  9 Latin America & Caribbean Low income          <lm>  
#> 10 Latin America & Caribbean Lower middle income <lm>  
#> # ℹ 13 more rows
# 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))
#> # A tibble: 69 × 6
#>    region                income              Variable  PCGDP  LIFEEX     ODA
#>    <fct>                 <fct>               <chr>     <dbl>   <dbl>   <dbl>
#>  1 East Asia & Pacific   High income         PCGDP     1      0.662  -0.388 
#>  2 East Asia & Pacific   High income         LIFEEX    0.662  1      -0.444 
#>  3 East Asia & Pacific   High income         ODA      -0.388 -0.444   1     
#>  4 East Asia & Pacific   Lower middle income PCGDP     1      0.395  -0.146 
#>  5 East Asia & Pacific   Lower middle income LIFEEX    0.395  1       0.206 
#>  6 East Asia & Pacific   Lower middle income ODA      -0.146  0.206   1     
#>  7 East Asia & Pacific   Upper middle income PCGDP     1      0.700  -0.378 
#>  8 East Asia & Pacific   Upper middle income LIFEEX    0.700  1       0.0796
#>  9 East Asia & Pacific   Upper middle income ODA      -0.378  0.0796  1     
#> 10 Europe & Central Asia High income         PCGDP     1      0.586  -0.329 
#> # ℹ 59 more rows

# 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))
#> # A tibble: 13,176 × 25
#>    country  iso3c date        year decade region income OECD  PCGDP LIFEEX  GINI
#>    <chr>    <fct> <date>     <int>  <int> <fct>  <fct>  <lgl> <dbl>  <dbl> <dbl>
#>  1 Afghani… AFG   1961-01-01  1960   1960 South… Low i… FALSE    NA   32.4    NA
#>  2 Afghani… AFG   1962-01-01  1961   1960 South… Low i… FALSE    NA   33.0    NA
#>  3 Afghani… AFG   1963-01-01  1962   1960 South… Low i… FALSE    NA   33.5    NA
#>  4 Afghani… AFG   1964-01-01  1963   1960 South… Low i… FALSE    NA   34.0    NA
#>  5 Afghani… AFG   1965-01-01  1964   1960 South… Low i… FALSE    NA   34.5    NA
#>  6 Afghani… AFG   1966-01-01  1965   1960 South… Low i… FALSE    NA   34.9    NA
#>  7 Afghani… AFG   1967-01-01  1966   1960 South… Low i… FALSE    NA   35.4    NA
#>  8 Afghani… AFG   1968-01-01  1967   1960 South… Low i… FALSE    NA   35.9    NA
#>  9 Afghani… AFG   1969-01-01  1968   1960 South… Low i… FALSE    NA   36.4    NA
#> 10 Afghani… AFG   1970-01-01  1969   1960 South… Low i… FALSE    NA   36.9    NA
#> # ℹ 13,166 more rows
#> # ℹ 14 more variables: ODA <dbl>, POP <dbl>, L1.PCGDP <dbl>, G1.PCGDP <dbl>,
#> #   L10.PCGDP <dbl>, L10G1.PCGDP <dbl>, L1.LIFEEX <dbl>, G1.LIFEEX <dbl>,
#> #   L10.LIFEEX <dbl>, L10G1.LIFEEX <dbl>, L1.ODA <dbl>, G1.ODA <dbl>,
#> #   L10.ODA <dbl>, L10G1.ODA <dbl>
#> 
#> Grouped by:  country  [216 | 61 (0)] 

# 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))
#> # A tibble: 13,176 × 25
#>    country  iso3c date        year decade region income OECD  PCGDP LIFEEX  GINI
#>    <chr>    <fct> <date>     <int>  <int> <fct>  <fct>  <lgl> <dbl>  <dbl> <dbl>
#>  1 Afghani… AFG   1961-01-01  1960   1960 South… Low i… FALSE    NA   32.4    NA
#>  2 Afghani… AFG   1962-01-01  1961   1960 South… Low i… FALSE    NA   33.0    NA
#>  3 Afghani… AFG   1963-01-01  1962   1960 South… Low i… FALSE    NA   33.5    NA
#>  4 Afghani… AFG   1964-01-01  1963   1960 South… Low i… FALSE    NA   34.0    NA
#>  5 Afghani… AFG   1965-01-01  1964   1960 South… Low i… FALSE    NA   34.5    NA
#>  6 Afghani… AFG   1966-01-01  1965   1960 South… Low i… FALSE    NA   34.9    NA
#>  7 Afghani… AFG   1967-01-01  1966   1960 South… Low i… FALSE    NA   35.4    NA
#>  8 Afghani… AFG   1968-01-01  1967   1960 South… Low i… FALSE    NA   35.9    NA
#>  9 Afghani… AFG   1969-01-01  1968   1960 South… Low i… FALSE    NA   36.4    NA
#> 10 Afghani… AFG   1970-01-01  1969   1960 South… Low i… FALSE    NA   36.9    NA
#> # ℹ 13,166 more rows
#> # ℹ 14 more variables: ODA <dbl>, POP <dbl>, L1.PCGDP <dbl>, L10.PCGDP <dbl>,
#> #   L1.LIFEEX <dbl>, L10.LIFEEX <dbl>, L1.ODA <dbl>, L10.ODA <dbl>,
#> #   G1.PCGDP <dbl>, L10G1.PCGDP <dbl>, G1.LIFEEX <dbl>, L10G1.LIFEEX <dbl>,
#> #   G1.ODA <dbl>, L10G1.ODA <dbl>
#> 
#> Grouped by:  country  [216 | 61 (0)]

Apply Functions Across Multiple Columns

Usage

Arguments

Note

See also

Examples