ftransform.Rdftransform is a much faster version of transform for data frames. It returns the data frame with new columns computed and/or existing columns modified or deleted. settransform does all of that by reference. fcompute computes and returns new columns. These functions evaluate all arguments simultaneously, allow list-input (nested pipelines) and disregard grouped data.
Catering to the tidyverse user, v1.7.0 introduced fmutate, providing familiar functionality i.e. arguments are evaluated sequentially, computation on grouped data is done by groups, and functions can be applied to multiple columns using across. See also the Details.
# dplyr-style mutate (sequential evaluation + across() feature)
fmutate(.data, ..., .keep = "all", .cols = NULL)
mtt(.data, ..., .keep = "all", .cols = NULL) # Shorthand for fmutate
# Modify and return data frame
ftransform(.data, ...)
ftransformv(.data, vars, FUN, ..., apply = TRUE)
tfm(.data, ...) # Shorthand for ftransform
tfmv(.data, vars, FUN, ..., apply = TRUE)
# Modify data frame by reference
settransform(.data, ...)
settransformv(.data, ...) # Same arguments as ftransformv
settfm(.data, ...) # Shorthand for settransform
settfmv(.data, ...)
# Replace/add modified columns in/to a data frame
ftransform(.data) <- value
tfm(.data) <- value
# Compute columns, returned as a new data frame
fcompute(.data, ..., keep = NULL)
fcomputev(.data, vars, FUN, ..., apply = TRUE, keep = NULL)a data frame or named list of columns.
further arguments of the form column = value. The value can be a combination of other columns, a scalar value, or NULL, which deletes column. Alternatively it is also possible to place a single list here, which will be treated like a list of column = value arguments. For ftransformv and fcomputev, ... can be used to pass further arguments to FUN. The ellipsis (...) is always evaluated within the data frame (.data) environment. See Examples.
fmutate additionally supports across statements, and evaluates tagged vector expressions sequentially. With grouped execution, dots can also contain arbitrary expressions that result in a list of data-length columns. See Examples.
variables to be transformed by applying FUN to them: select using names, indices, a logical vector or a selector function (e.g. is.numeric). Since v1.7 vars is evaluated within the .data environment, permitting expressions on columns e.g. c(col1, col3:coln).
a single function yielding a result of length NROW(.data) or 1. See also apply.
logical. TRUE (default) will apply FUN to each column selected in vars; FALSE will apply FUN to the subsetted data frame i.e. FUN(get_vars(.data, vars), ...). The latter is useful for collapse functions with data frame or grouped / panel data frame methods, yielding performance gains and enabling grouped transformations. See Examples.
a named list of replacements, it will be treated like an evaluated list of column = value arguments.
select columns to preserve using column names, indices or a function (e.g. is.numeric). By default computed columns are added after the preserved ones, unless they are assigned the same name in which case the preserved columns will be replaced in order.
either one of "all", "used", "unused" or "none" (see mutate), or columns names/indices/function as keep. Note that this does not work well with across() or other expressions supported since v1.9.0. The only sensible option you have there is to supply a character vector of all columns in the final dataset that you want to keep.
for expressions involving .data, .cols can be used to subset columns, e.g. mtcars |> gby(cyl) |> mtt(broom::augment(lm(mpg ~., .data)), .cols = 1:7). Can pass column names, indices, a logical vector or a selector function (e.g. is.numericr).
The ... arguments to ftransform are tagged
vector expressions, which are evaluated in the data frame
.data. The tags are matched against names(.data), and for
those that match, the values replace the corresponding variable in
.data, whereas the others are appended to .data. It is also possible to delete columns by assigning NULL to them, i.e. ftransform(data, colk = NULL) removes colk from the data. Note that names(.data) and the names of the ... arguments are checked for uniqueness beforehand, yielding an error if this is not the case.
Since collapse v1.3.0, is is also possible to pass a single named list to ..., i.e. ftransform(data, newdata). This list will be treated like a list of tagged vector expressions. Note the different behavior: ftransform(data, list(newcol = col1)) is the same as ftransform(data, newcol = col1), whereas ftransform(data, newcol = as.list(col1)) creates a list column. Something like ftransform(data, as.list(col1)) gives an error because the list is not named. See Examples.
The function ftransformv added in v1.3.2 provides a fast replacement for the functions dplyr::mutate_at and dplyr::mutate_if (without the grouping feature) facilitating mutations of groups of columns (dplyr::mutate_all is already accounted for by dapply). See Examples.
The function settransform does all of that by reference, but uses base-R's copy-on modify semantics, which is equivalent to replacing the data with <- (thus it is still memory efficient but the data will have a different memory address afterwards).
The function fcompute(v) works just like ftransform(v), but returns only the changed / computed columns without modifying or appending the data in .data. See Examples.
The function fmutate added in v1.7.0, provides functionality familiar from dplyr 1.0.0 and higher. It evaluates tagged vector expressions sequentially and does operations by groups on a grouped frame (thus it is slower than ftransform if you have many tagged expressions or a grouped data frame). Note however that collapse does not depend on rlang, so things like lambda expressions are not available. Note also that fmutate operates differently on grouped data whether you use .FAST_FUN or base R functions / functions from other packages. With .FAST_FUN (including .OPERATOR_FUN, excluding fhdbetween / fhdwithin / HDW / HDB), fmutate performs an efficient vectorized execution, i.e. the grouping object from the grouped data frame is passed to the g argument of these functions, and for .FAST_STAT_FUN also TRA = "replace_fill" is set (if not overwritten by the user), yielding internal grouped computation by these functions without the need for splitting the data by groups. For base R and other functions, fmutate performs classical split-apply combine computing i.e. the relevant columns of the data are selected and split into groups, the expression is evaluated for each group, and the result is recombined and suitably expanded to match the original data frame. Note that it is not possible to mix vectorized and standard execution in the same expression!! Vectorized execution is performed if any .FAST_FUN or .OPERATOR_FUN is part of the expression, thus a code like mtcars |> gby(cyl) |> fmutate(new = fmin(mpg) / min(mpg)) will be expanded to something like mtcars |> gby(cyl) |> ftransform(new = fmin(mpg, g = GRP(.), TRA = "replace_fill") / min(mpg)) and then executed, i.e. fmin(mpg) will be executed in a vectorized way, and min(mpg) will not be executed by groups at all.
ftransform ignores grouped data. This is on purpose as it allows non-grouped transformation inside a pipeline on grouped data, and affords greater flexibility and performance in programming with the .FAST_FUN. In particular, you can run a nested pipeline inside ftransform, and decide which expressions should be grouped, and you can use the ad-hoc grouping functionality of the .FAST_FUN, allowing operations where different groupings are applied simultaneously in an expression. See Examples or the answer provided here.
fmutate on the other hand supports grouped operations just like dplyr::mutate, but works in two different ways depending on whether you use .FAST_FUN in an expression or other functions. See the Examples.
The modified data frame .data, or, for fcompute, a new data frame with the columns computed on .data. All attributes of .data are preserved.
## fmutate() examples ---------------------------------------------------------------
# Please note that expressions are vectorized whenever they contain 'ANY' fast function
mtcars |>
fgroup_by(cyl, vs, am) |>
fmutate(mean_mpg = fmean(mpg), # Vectorized
mean_mpg_base = mean(mpg), # Non-vectorized
mpg_cumpr = fcumsum(mpg) / fsum(mpg), # Vectorized
mpg_cumpr_base = cumsum(mpg) / sum(mpg), # Non-vectorized
mpg_cumpr_mixed = fcumsum(mpg) / sum(mpg)) # Vectorized: division by overall sum
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> mean_mpg mean_mpg_base mpg_cumpr mpg_cumpr_base
#> Mazda RX4 20.56667 20.56667 0.3403566 0.3403566
#> Mazda RX4 Wag 20.56667 20.56667 0.6807131 0.6807131
#> Datsun 710 28.37143 28.37143 0.1148036 0.1148036
#> Hornet 4 Drive 19.12500 19.12500 0.2797386 0.2797386
#> Hornet Sportabout 15.05000 15.05000 0.1035437 0.1035437
#> Valiant 19.12500 19.12500 0.5163399 0.5163399
#> Duster 360 15.05000 15.05000 0.1827243 0.1827243
#> Merc 240D 22.90000 22.90000 0.3551674 0.3551674
#> Merc 230 22.90000 22.90000 0.6870451 0.6870451
#> Merc 280 19.12500 19.12500 0.7673203 0.7673203
#> Merc 280C 19.12500 19.12500 1.0000000 1.0000000
#> Merc 450SE 15.05000 15.05000 0.2735327 0.2735327
#> Merc 450SL 15.05000 15.05000 0.3693245 0.3693245
#> Merc 450SLC 15.05000 15.05000 0.4534884 0.4534884
#> Cadillac Fleetwood 15.05000 15.05000 0.5110742 0.5110742
#> Lincoln Continental 15.05000 15.05000 0.5686600 0.5686600
#> Chrysler Imperial 15.05000 15.05000 0.6500554 0.6500554
#> Fiat 128 28.37143 28.37143 0.2779456 0.2779456
#> Honda Civic 28.37143 28.37143 0.4310171 0.4310171
#> Toyota Corolla 28.37143 28.37143 0.6017120 0.6017120
#> Toyota Corona 22.90000 22.90000 1.0000000 1.0000000
#> Dodge Challenger 15.05000 15.05000 0.7358804 0.7358804
#> AMC Javelin 15.05000 15.05000 0.8200443 0.8200443
#> Camaro Z28 15.05000 15.05000 0.8936877 0.8936877
#> Pontiac Firebird 15.05000 15.05000 1.0000000 1.0000000
#> Fiat X1-9 28.37143 28.37143 0.7391742 0.7391742
#> Porsche 914-2 26.00000 26.00000 1.0000000 1.0000000
#> Lotus Europa 28.37143 28.37143 0.8922457 0.8922457
#> Ford Pantera L 15.40000 15.40000 0.5129870 0.5129870
#> Ferrari Dino 20.56667 20.56667 1.0000000 1.0000000
#> Maserati Bora 15.40000 15.40000 1.0000000 1.0000000
#> Volvo 142E 28.37143 28.37143 1.0000000 1.0000000
#> mpg_cumpr_mixed
#> Mazda RX4 0.03266449
#> Mazda RX4 Wag 0.06532898
#> Datsun 710 0.03546430
#> Hornet 4 Drive 0.03328667
#> Hornet Sportabout 0.02908695
#> Valiant 0.06144035
#> Duster 360 0.05132991
#> Merc 240D 0.03795303
#> Merc 230 0.07341733
#> Merc 280 0.09130502
#> Merc 280C 0.11899207
#> Merc 450SE 0.07683932
#> Merc 450SL 0.10374864
#> Merc 450SLC 0.12739151
#> Cadillac Fleetwood 0.14356821
#> Lincoln Continental 0.15974491
#> Chrysler Imperial 0.18261005
#> Fiat 128 0.08586094
#> Honda Civic 0.13314668
#> Toyota Corolla 0.18587650
#> Toyota Corona 0.10685954
#> Dodge Challenger 0.20671955
#> AMC Javelin 0.23036242
#> Camaro Z28 0.25104993
#> Pontiac Firebird 0.28091461
#> Fiat X1-9 0.22834033
#> Porsche 914-2 0.04044175
#> Lotus Europa 0.27562607
#> Ford Pantera L 0.02457614
#> Ferrari Dino 0.09597138
#> Maserati Bora 0.04790792
#> Volvo 142E 0.30891274
#>
#> Grouped by: cyl, vs, am [7 | 5 (3.8) 1-12]
# Using across: here fmean() gets vectorized across both groups and columns (requiring a single
# call to fmean.data.frame which goes to C), whereas weighted.mean needs to be called many times.
mtcars |> fgroup_by(cyl, vs, am) |>
fmutate(across(disp:qsec, list(mu = fmean, mu2 = weighted.mean), w = wt, .names = "flip"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> mu_disp mu2_disp mu_hp mu2_hp mu_drat mu2_drat
#> Mazda RX4 154.9728 154.9728 131.78463 131.78463 3.806158 3.806158
#> Mazda RX4 Wag 154.9728 154.9728 131.78463 131.78463 3.806158 3.806158
#> Datsun 710 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Hornet 4 Drive 203.6930 203.6930 115.32202 115.32202 3.424670 3.424670
#> Hornet Sportabout 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Valiant 203.6930 203.6930 115.32202 115.32202 3.424670 3.424670
#> Duster 360 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Merc 240D 137.1425 137.1425 83.60420 83.60420 3.775082 3.775082
#> Merc 230 137.1425 137.1425 83.60420 83.60420 3.775082 3.775082
#> Merc 280 203.6930 203.6930 115.32202 115.32202 3.424670 3.424670
#> Merc 280C 203.6930 203.6930 115.32202 115.32202 3.424670 3.424670
#> Merc 450SE 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Merc 450SL 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Merc 450SLC 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Cadillac Fleetwood 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Lincoln Continental 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Chrysler Imperial 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Fiat 128 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Honda Civic 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Toyota Corolla 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Toyota Corona 137.1425 137.1425 83.60420 83.60420 3.775082 3.775082
#> Dodge Challenger 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> AMC Javelin 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Camaro Z28 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Pontiac Firebird 367.2725 367.2725 196.74988 196.74988 3.116132 3.116132
#> Fiat X1-9 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Porsche 914-2 120.3000 120.3000 91.00000 91.00000 4.430000 4.430000
#> Lotus Europa 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> Ford Pantera L 324.5163 324.5163 301.60682 301.60682 3.859822 3.859822
#> Ferrari Dino 154.9728 154.9728 131.78463 131.78463 3.806158 3.806158
#> Maserati Bora 324.5163 324.5163 301.60682 301.60682 3.859822 3.859822
#> Volvo 142E 92.2352 92.2352 82.11819 82.11819 4.130037 4.130037
#> mu_wt mu2_wt mu_qsec mu2_qsec
#> Mazda RX4 2.758975 2.758975 16.33306 16.33306
#> Mazda RX4 Wag 2.758975 2.758975 16.33306 16.33306
#> Datsun 710 2.110131 2.110131 18.75509 18.75509
#> Hornet 4 Drive 3.391739 3.391739 19.21275 19.21275
#> Hornet Sportabout 4.235929 4.235929 17.20449 17.20449
#> Valiant 3.391739 3.391739 19.21275 19.21275
#> Duster 360 4.235929 4.235929 17.20449 17.20449
#> Merc 240D 2.972723 2.972723 21.04028 21.04028
#> Merc 230 2.972723 2.972723 21.04028 21.04028
#> Merc 280 3.391739 3.391739 19.21275 19.21275
#> Merc 280C 3.391739 3.391739 19.21275 19.21275
#> Merc 450SE 4.235929 4.235929 17.20449 17.20449
#> Merc 450SL 4.235929 4.235929 17.20449 17.20449
#> Merc 450SLC 4.235929 4.235929 17.20449 17.20449
#> Cadillac Fleetwood 4.235929 4.235929 17.20449 17.20449
#> Lincoln Continental 4.235929 4.235929 17.20449 17.20449
#> Chrysler Imperial 4.235929 4.235929 17.20449 17.20449
#> Fiat 128 2.110131 2.110131 18.75509 18.75509
#> Honda Civic 2.110131 2.110131 18.75509 18.75509
#> Toyota Corolla 2.110131 2.110131 18.75509 18.75509
#> Toyota Corona 2.972723 2.972723 21.04028 21.04028
#> Dodge Challenger 4.235929 4.235929 17.20449 17.20449
#> AMC Javelin 4.235929 4.235929 17.20449 17.20449
#> Camaro Z28 4.235929 4.235929 17.20449 17.20449
#> Pontiac Firebird 4.235929 4.235929 17.20449 17.20449
#> Fiat X1-9 2.110131 2.110131 18.75509 18.75509
#> Porsche 914-2 2.140000 2.140000 16.70000 16.70000
#> Lotus Europa 2.110131 2.110131 18.75509 18.75509
#> Ford Pantera L 3.381869 3.381869 14.55297 14.55297
#> Ferrari Dino 2.758975 2.758975 16.33306 16.33306
#> Maserati Bora 3.381869 3.381869 14.55297 14.55297
#> Volvo 142E 2.110131 2.110131 18.75509 18.75509
#>
#> Grouped by: cyl, vs, am [7 | 5 (3.8) 1-12]
# Can do more complex things...
mtcars |> fgroup_by(cyl) |>
fmutate(res = resid(lm(mpg ~ carb + hp, weights = wt)))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> res
#> Mazda RX4 1.12500751
#> Mazda RX4 Wag 1.12500751
#> Datsun 710 -2.71674844
#> Hornet 4 Drive 1.87040966
#> Hornet Sportabout 2.22962066
#> Valiant -1.49568375
#> Duster 360 -1.11912742
#> Merc 240D -3.49712332
#> Merc 230 -1.26788866
#> Merc 280 -0.50314962
#> Merc 280C -1.90314962
#> Merc 450SE 1.76941662
#> Merc 450SL 2.66941662
#> Merc 450SLC 0.56941662
#> Cadillac Fleetwood -3.26690076
#> Lincoln Continental -3.70495742
#> Chrysler Imperial -0.06204242
#> Fiat 128 3.75024138
#> Honda Civic 1.34250254
#> Toyota Corolla 5.13420397
#> Toyota Corona -3.55259879
#> Dodge Challenger 0.12476232
#> AMC Javelin -0.17523768
#> Camaro Z28 -2.11912742
#> Pontiac Firebird 2.72962066
#> Fiat X1-9 -1.34975862
#> Porsche 914-2 1.46796168
#> Lotus Europa 8.42078479
#> Ford Pantera L -0.45143508
#> Ferrari Dino 0.45395375
#> Maserati Bora 3.87365975
#> Volvo 142E -1.04336487
#>
#> Grouped by: cyl [3 | 11 (3.5) 7-14]
# Since v1.9.0: supports arbitrary expressions returning suitable lists
if (FALSE)
mtcars |> fgroup_by(cyl) |>
fmutate(broom::augment(lm(mpg ~ carb + hp, weights = wt)))
# Same thing using across() (supported before 1.9.0)
modelfun <- function(data) broom::augment(lm(mpg ~ carb + hp, data, weights = wt))
mtcars |> fgroup_by(cyl) |>
fmutate(across(c(mpg, carb, hp, wt), modelfun, .apply = FALSE))
#> Error in loadNamespace(x): there is no package called ‘broom’
# \dontrun{}
## ftransform() / fcompute() examples: ----------------------------------------------
## ftransform modifies and returns a data.frame
head(ftransform(airquality, Ozone = -Ozone))
#> Ozone Solar.R Wind Temp Month Day
#> 1 -41 190 7.4 67 5 1
#> 2 -36 118 8.0 72 5 2
#> 3 -12 149 12.6 74 5 3
#> 4 -18 313 11.5 62 5 4
#> 5 NA NA 14.3 56 5 5
#> 6 -28 NA 14.9 66 5 6
head(ftransform(airquality, new = -Ozone, Temp = (Temp-32)/1.8))
#> Ozone Solar.R Wind Temp Month Day new
#> 1 41 190 7.4 19.44444 5 1 -41
#> 2 36 118 8.0 22.22222 5 2 -36
#> 3 12 149 12.6 23.33333 5 3 -12
#> 4 18 313 11.5 16.66667 5 4 -18
#> 5 NA NA 14.3 13.33333 5 5 NA
#> 6 28 NA 14.9 18.88889 5 6 -28
head(ftransform(airquality, new = -Ozone, new2 = 1, Temp = NULL)) # Deleting Temp
#> Ozone Solar.R Wind Month Day new new2
#> 1 41 190 7.4 5 1 -41 1
#> 2 36 118 8.0 5 2 -36 1
#> 3 12 149 12.6 5 3 -12 1
#> 4 18 313 11.5 5 4 -18 1
#> 5 NA NA 14.3 5 5 NA 1
#> 6 28 NA 14.9 5 6 -28 1
head(ftransform(airquality, Ozone = NULL, Temp = NULL)) # Deleting columns
#> Solar.R Wind Month Day
#> 1 190 7.4 5 1
#> 2 118 8.0 5 2
#> 3 149 12.6 5 3
#> 4 313 11.5 5 4
#> 5 NA 14.3 5 5
#> 6 NA 14.9 5 6
# With collapse's grouped and weighted functions, complex operations are done on the fly
head(ftransform(airquality, # Grouped operations by month:
Ozone_Month_median = fmedian(Ozone, Month, TRA = "fill"),
Ozone_Month_sd = fsd(Ozone, Month, TRA = "replace"),
Ozone_Month_centered = fwithin(Ozone, Month)))
#> Ozone Solar.R Wind Temp Month Day Ozone_Month_median Ozone_Month_sd
#> 1 41 190 7.4 67 5 1 18 22.22445
#> 2 36 118 8.0 72 5 2 18 22.22445
#> 3 12 149 12.6 74 5 3 18 22.22445
#> 4 18 313 11.5 62 5 4 18 22.22445
#> 5 NA NA 14.3 56 5 5 18 NA
#> 6 28 NA 14.9 66 5 6 18 22.22445
#> Ozone_Month_centered
#> 1 17.384615
#> 2 12.384615
#> 3 -11.615385
#> 4 -5.615385
#> 5 NA
#> 6 4.384615
# Grouping by month and above/below average temperature in each month
head(ftransform(airquality, Ozone_Month_high_median =
fmedian(Ozone, list(Month, Temp > fbetween(Temp, Month)), TRA = "fill")))
#> Ozone Solar.R Wind Temp Month Day Ozone_Month_high_median
#> 1 41 190 7.4 67 5 1 28
#> 2 36 118 8.0 72 5 2 28
#> 3 12 149 12.6 74 5 3 28
#> 4 18 313 11.5 62 5 4 14
#> 5 NA NA 14.3 56 5 5 14
#> 6 28 NA 14.9 66 5 6 28
## ftransformv can be used to modify multiple columns using a function
head(ftransformv(airquality, 1:3, log))
#> Ozone Solar.R Wind Temp Month Day
#> 1 3.713572 5.247024 2.001480 67 5 1
#> 2 3.583519 4.770685 2.079442 72 5 2
#> 3 2.484907 5.003946 2.533697 74 5 3
#> 4 2.890372 5.746203 2.442347 62 5 4
#> 5 NA NA 2.660260 56 5 5
#> 6 3.332205 NA 2.701361 66 5 6
head(`[<-`(airquality, 1:3, value = lapply(airquality[1:3], log))) # Same thing in base R
#> Ozone Solar.R Wind Temp Month Day
#> 1 3.713572 5.247024 2.001480 67 5 1
#> 2 3.583519 4.770685 2.079442 72 5 2
#> 3 2.484907 5.003946 2.533697 74 5 3
#> 4 2.890372 5.746203 2.442347 62 5 4
#> 5 NA NA 2.660260 56 5 5
#> 6 3.332205 NA 2.701361 66 5 6
head(ftransformv(airquality, 1:3, log, apply = FALSE))
#> Ozone Solar.R Wind Temp Month Day
#> 1 3.713572 5.247024 2.001480 67 5 1
#> 2 3.583519 4.770685 2.079442 72 5 2
#> 3 2.484907 5.003946 2.533697 74 5 3
#> 4 2.890372 5.746203 2.442347 62 5 4
#> 5 NA NA 2.660260 56 5 5
#> 6 3.332205 NA 2.701361 66 5 6
head(`[<-`(airquality, 1:3, value = log(airquality[1:3]))) # Same thing in base R
#> Ozone Solar.R Wind Temp Month Day
#> 1 3.713572 5.247024 2.001480 67 5 1
#> 2 3.583519 4.770685 2.079442 72 5 2
#> 3 2.484907 5.003946 2.533697 74 5 3
#> 4 2.890372 5.746203 2.442347 62 5 4
#> 5 NA NA 2.660260 56 5 5
#> 6 3.332205 NA 2.701361 66 5 6
# Using apply = FALSE yields meaningful performance gains with collapse functions
# This calls fwithin.default, and repeates the grouping by month 3 times:
head(ftransformv(airquality, 1:3, fwithin, Month))
#> Ozone Solar.R Wind Temp Month Day
#> 1 17.384615 8.703704 -4.2225806 67 5 1
#> 2 12.384615 -63.296296 -3.6225806 72 5 2
#> 3 -11.615385 -32.296296 0.9774194 74 5 3
#> 4 -5.615385 131.703704 -0.1225806 62 5 4
#> 5 NA NA 2.6774194 56 5 5
#> 6 4.384615 NA 3.2774194 66 5 6
# This calls fwithin.data.frame, and only groups one time -> 5x faster!
head(ftransformv(airquality, 1:3, fwithin, Month, apply = FALSE))
#> Ozone Solar.R Wind Temp Month Day
#> 1 17.384615 8.703704 -4.2225806 67 5 1
#> 2 12.384615 -63.296296 -3.6225806 72 5 2
#> 3 -11.615385 -32.296296 0.9774194 74 5 3
#> 4 -5.615385 131.703704 -0.1225806 62 5 4
#> 5 NA NA 2.6774194 56 5 5
#> 6 4.384615 NA 3.2774194 66 5 6
# This also works for grouped and panel data frames (calling fwithin.grouped_df)
airquality |> fgroup_by(Month) |>
ftransformv(1:3, fwithin, apply = FALSE) |> head()
#> Ozone Solar.R Wind Temp Month Day
#> 1 17.384615 8.703704 -4.2225806 67 5 1
#> 2 12.384615 -63.296296 -3.6225806 72 5 2
#> 3 -11.615385 -32.296296 0.9774194 74 5 3
#> 4 -5.615385 131.703704 -0.1225806 62 5 4
#> 5 NA NA 2.6774194 56 5 5
#> 6 4.384615 NA 3.2774194 66 5 6
# But this gives the WRONG result (calling fwithin.default). Need option apply = FALSE!!
airquality |> fgroup_by(Month) |>
ftransformv(1:3, fwithin) |> head()
#> Ozone Solar.R Wind Temp Month Day
#> 1 -1.12931 4.068493 -2.557516 67 5 1
#> 2 -6.12931 -67.931507 -1.957516 72 5 2
#> 3 -30.12931 -36.931507 2.642484 74 5 3
#> 4 -24.12931 127.068493 1.542484 62 5 4
#> 5 NA NA 4.342484 56 5 5
#> 6 -14.12931 NA 4.942484 66 5 6
# For grouped modification of single columns in a grouped dataset, we can use GRP():
library(magrittr)
airquality |> fgroup_by(Month) %>%
ftransform(W_Ozone = fwithin(Ozone, GRP(.)), # Grouped centering
sd_Ozone_m = fsd(Ozone, GRP(.), TRA = "replace"), # In-Month standard deviation
sd_Ozone = fsd(Ozone, TRA = "replace"), # Overall standard deviation
sd_Ozone2 = fsd(Ozone, TRA = "fill"), # Same, overwriting NA's
sd_Ozone3 = fsd(Ozone)) |> head() # Same thing (calling alloc())
#> Ozone Solar.R Wind Temp Month Day W_Ozone sd_Ozone_m sd_Ozone sd_Ozone2
#> 1 41 190 7.4 67 5 1 17.384615 22.22445 32.98788 32.98788
#> 2 36 118 8.0 72 5 2 12.384615 22.22445 32.98788 32.98788
#> 3 12 149 12.6 74 5 3 -11.615385 22.22445 32.98788 32.98788
#> 4 18 313 11.5 62 5 4 -5.615385 22.22445 32.98788 32.98788
#> 5 NA NA 14.3 56 5 5 NA NA NA 32.98788
#> 6 28 NA 14.9 66 5 6 4.384615 22.22445 32.98788 32.98788
#> sd_Ozone3
#> 1 32.98788
#> 2 32.98788
#> 3 32.98788
#> 4 32.98788
#> 5 32.98788
#> 6 32.98788
## For more complex mutations we can use ftransform with compound pipes
airquality |> fgroup_by(Month) %>%
ftransform(get_vars(., 1:3) |> fwithin() |> flag(0:2)) |> head()
#> Ozone Solar.R Wind Temp Month Day L1.Ozone L2.Ozone
#> 1 17.384615 8.703704 -4.2225806 67 5 1 NA NA
#> 2 12.384615 -63.296296 -3.6225806 72 5 2 17.384615 NA
#> 3 -11.615385 -32.296296 0.9774194 74 5 3 12.384615 17.384615
#> 4 -5.615385 131.703704 -0.1225806 62 5 4 -11.615385 12.384615
#> 5 NA NA 2.6774194 56 5 5 -5.615385 -11.615385
#> 6 4.384615 NA 3.2774194 66 5 6 NA -5.615385
#> L1.Solar.R L2.Solar.R L1.Wind L2.Wind
#> 1 NA NA NA NA
#> 2 8.703704 NA -4.2225806 NA
#> 3 -63.296296 8.703704 -3.6225806 -4.2225806
#> 4 -32.296296 -63.296296 0.9774194 -3.6225806
#> 5 131.703704 -32.296296 -0.1225806 0.9774194
#> 6 NA 131.703704 2.6774194 -0.1225806
airquality %>% ftransform(STD(., cols = 1:3) |> replace_na(0)) |> head()
#> Ozone Solar.R Wind Temp Month Day STD.Ozone STD.Solar.R STD.Wind
#> 1 41 190 7.4 67 5 1 -0.03423409 0.04517615 -0.7259482
#> 2 36 118 8.0 72 5 2 -0.18580489 -0.75430487 -0.5556388
#> 3 12 149 12.6 74 5 3 -0.91334473 -0.41008388 0.7500660
#> 4 18 313 11.5 62 5 4 -0.73145977 1.41095624 0.4378323
#> 5 NA NA 14.3 56 5 5 0.00000000 0.00000000 1.2326091
#> 6 28 NA 14.9 66 5 6 -0.42831817 0.00000000 1.4029185
# The list argument feature also allows flexible operations creating multiple new columns
airquality |> # The variance of Wind and Ozone, by month, weighted by temperature:
ftransform(fvar(list(Wind_var = Wind, Ozone_var = Ozone), Month, Temp, "replace")) |> head()
#> Ozone Solar.R Wind Temp Month Day Wind_var Ozone_var
#> 1 41 190 7.4 67 5 1 12.08975 533.2819
#> 2 36 118 8.0 72 5 2 12.08975 533.2819
#> 3 12 149 12.6 74 5 3 12.08975 533.2819
#> 4 18 313 11.5 62 5 4 12.08975 533.2819
#> 5 NA NA 14.3 56 5 5 12.08975 NA
#> 6 28 NA 14.9 66 5 6 12.08975 533.2819
# Same as above using a grouped data frame (a bit more complex)
airquality |> fgroup_by(Month) %>%
ftransform(fselect(., Wind, Ozone) |> fvar(Temp, "replace") |> add_stub("_var", FALSE)) |>
fungroup() |> head()
#> Ozone Solar.R Wind Temp Month Day Wind_var Ozone_var
#> 1 41 190 7.4 67 5 1 12.08975 533.2819
#> 2 36 118 8.0 72 5 2 12.08975 533.2819
#> 3 12 149 12.6 74 5 3 12.08975 533.2819
#> 4 18 313 11.5 62 5 4 12.08975 533.2819
#> 5 NA NA 14.3 56 5 5 12.08975 NA
#> 6 28 NA 14.9 66 5 6 12.08975 533.2819
# This performs 2 different multi-column grouped operations (need c() to make it one list)
ftransform(airquality, c(fmedian(list(Wind_Day_median = Wind,
Ozone_Day_median = Ozone), Day, TRA = "replace"),
fsd(list(Wind_Month_sd = Wind,
Ozone_Month_sd = Ozone), Month, TRA = "replace"))) |> head()
#> Ozone Solar.R Wind Temp Month Day Wind_Day_median Ozone_Day_median
#> 1 41 190 7.4 67 5 1 6.9 68.5
#> 2 36 118 8.0 72 5 2 9.2 42.5
#> 3 12 149 12.6 74 5 3 9.2 24.0
#> 4 18 313 11.5 62 5 4 9.2 78.0
#> 5 NA NA 14.3 56 5 5 7.4 NA
#> 6 28 NA 14.9 66 5 6 14.3 36.0
#> Wind_Month_sd Ozone_Month_sd
#> 1 3.53145 22.22445
#> 2 3.53145 22.22445
#> 3 3.53145 22.22445
#> 4 3.53145 22.22445
#> 5 3.53145 NA
#> 6 3.53145 22.22445
## settransform(v) works like ftransform(v) but modifies a data frame in the global environment..
settransform(airquality, Ratio = Ozone / Temp, Ozone = NULL, Temp = NULL)
head(airquality)
#> Solar.R Wind Month Day Ratio
#> 1 190 7.4 5 1 0.6119403
#> 2 118 8.0 5 2 0.5000000
#> 3 149 12.6 5 3 0.1621622
#> 4 313 11.5 5 4 0.2903226
#> 5 NA 14.3 5 5 NA
#> 6 NA 14.9 5 6 0.4242424
rm(airquality)
# Grouped and weighted centering
settransformv(airquality, 1:3, fwithin, Month, Temp, apply = FALSE)
head(airquality)
#> Ozone Solar.R Wind Temp Month Day
#> 1 16.22536 3.571669 -4.08917323 67 5 1
#> 2 11.22536 -68.428331 -3.48917323 72 5 2
#> 3 -12.77464 -37.428331 1.11082677 74 5 3
#> 4 -6.77464 126.571669 0.01082677 62 5 4
#> 5 NA NA 2.81082677 56 5 5
#> 6 3.22536 NA 3.41082677 66 5 6
rm(airquality)
# Suitably lagged first-differences
settransform(airquality, get_vars(airquality, 1:3) |> fdiff() |> flag(0:2))
head(airquality)
#> Ozone Solar.R Wind Temp Month Day L1.Ozone L2.Ozone L1.Solar.R L2.Solar.R
#> 1 NA NA NA 67 5 1 NA NA NA NA
#> 2 -5 -72 0.6 72 5 2 NA NA NA NA
#> 3 -24 31 4.6 74 5 3 -5 NA -72 NA
#> 4 6 164 -1.1 62 5 4 -24 -5 31 -72
#> 5 NA NA 2.8 56 5 5 6 -24 164 31
#> 6 NA NA 0.6 66 5 6 NA 6 NA 164
#> L1.Wind L2.Wind
#> 1 NA NA
#> 2 NA NA
#> 3 0.6 NA
#> 4 4.6 0.6
#> 5 -1.1 4.6
#> 6 2.8 -1.1
rm(airquality)
# Same as above using magrittr::`%<>%`
airquality %<>% ftransform(get_vars(., 1:3) |> fdiff() |> flag(0:2))
head(airquality)
#> Ozone Solar.R Wind Temp Month Day L1.Ozone L2.Ozone L1.Solar.R L2.Solar.R
#> 1 NA NA NA 67 5 1 NA NA NA NA
#> 2 -5 -72 0.6 72 5 2 NA NA NA NA
#> 3 -24 31 4.6 74 5 3 -5 NA -72 NA
#> 4 6 164 -1.1 62 5 4 -24 -5 31 -72
#> 5 NA NA 2.8 56 5 5 6 -24 164 31
#> 6 NA NA 0.6 66 5 6 NA 6 NA 164
#> L1.Wind L2.Wind
#> 1 NA NA
#> 2 NA NA
#> 3 0.6 NA
#> 4 4.6 0.6
#> 5 -1.1 4.6
#> 6 2.8 -1.1
rm(airquality)
# It is also possible to achieve the same thing via a replacement method (if needed)
ftransform(airquality) <- get_vars(airquality, 1:3) |> fdiff() |> flag(0:2)
head(airquality)
#> Ozone Solar.R Wind Temp Month Day L1.Ozone L2.Ozone L1.Solar.R L2.Solar.R
#> 1 NA NA NA 67 5 1 NA NA NA NA
#> 2 -5 -72 0.6 72 5 2 NA NA NA NA
#> 3 -24 31 4.6 74 5 3 -5 NA -72 NA
#> 4 6 164 -1.1 62 5 4 -24 -5 31 -72
#> 5 NA NA 2.8 56 5 5 6 -24 164 31
#> 6 NA NA 0.6 66 5 6 NA 6 NA 164
#> L1.Wind L2.Wind
#> 1 NA NA
#> 2 NA NA
#> 3 0.6 NA
#> 4 4.6 0.6
#> 5 -1.1 4.6
#> 6 2.8 -1.1
rm(airquality)
## fcompute only returns the modified / computed columns
head(fcompute(airquality, Ozone = -Ozone))
#> Ozone
#> 1 -41
#> 2 -36
#> 3 -12
#> 4 -18
#> 5 NA
#> 6 -28
head(fcompute(airquality, new = -Ozone, Temp = (Temp-32)/1.8))
#> new Temp
#> 1 -41 19.44444
#> 2 -36 22.22222
#> 3 -12 23.33333
#> 4 -18 16.66667
#> 5 NA 13.33333
#> 6 -28 18.88889
head(fcompute(airquality, new = -Ozone, new2 = 1))
#> new new2
#> 1 -41 1
#> 2 -36 1
#> 3 -12 1
#> 4 -18 1
#> 5 NA 1
#> 6 -28 1
# Can preserve existing columns, computed ones are added to the right if names are different
head(fcompute(airquality, new = -Ozone, new2 = 1, keep = 1:3))
#> Ozone Solar.R Wind new new2
#> 1 41 190 7.4 -41 1
#> 2 36 118 8.0 -36 1
#> 3 12 149 12.6 -12 1
#> 4 18 313 11.5 -18 1
#> 5 NA NA 14.3 NA 1
#> 6 28 NA 14.9 -28 1
# If given same name as preserved columns, preserved columns are replaced in order...
head(fcompute(airquality, Ozone = -Ozone, new = 1, keep = 1:3))
#> Ozone Solar.R Wind new
#> 1 -41 190 7.4 1
#> 2 -36 118 8.0 1
#> 3 -12 149 12.6 1
#> 4 -18 313 11.5 1
#> 5 NA NA 14.3 1
#> 6 -28 NA 14.9 1
# Same holds for fcomputev
head(fcomputev(iris, is.numeric, log)) # Same as:
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 1.629241 1.252763 0.3364722 -1.6094379
#> 2 1.589235 1.098612 0.3364722 -1.6094379
#> 3 1.547563 1.163151 0.2623643 -1.6094379
#> 4 1.526056 1.131402 0.4054651 -1.6094379
#> 5 1.609438 1.280934 0.3364722 -1.6094379
#> 6 1.686399 1.360977 0.5306283 -0.9162907
iris |> get_vars(is.numeric) |> dapply(log) |> head()
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 1.629241 1.252763 0.3364722 -1.6094379
#> 2 1.589235 1.098612 0.3364722 -1.6094379
#> 3 1.547563 1.163151 0.2623643 -1.6094379
#> 4 1.526056 1.131402 0.4054651 -1.6094379
#> 5 1.609438 1.280934 0.3364722 -1.6094379
#> 6 1.686399 1.360977 0.5306283 -0.9162907
head(fcomputev(iris, is.numeric, log, keep = "Species")) # Adds in front
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 1 setosa 1.629241 1.252763 0.3364722 -1.6094379
#> 2 setosa 1.589235 1.098612 0.3364722 -1.6094379
#> 3 setosa 1.547563 1.163151 0.2623643 -1.6094379
#> 4 setosa 1.526056 1.131402 0.4054651 -1.6094379
#> 5 setosa 1.609438 1.280934 0.3364722 -1.6094379
#> 6 setosa 1.686399 1.360977 0.5306283 -0.9162907
head(fcomputev(iris, is.numeric, log, keep = names(iris))) # Preserve order
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 1.629241 1.252763 0.3364722 -1.6094379 setosa
#> 2 1.589235 1.098612 0.3364722 -1.6094379 setosa
#> 3 1.547563 1.163151 0.2623643 -1.6094379 setosa
#> 4 1.526056 1.131402 0.4054651 -1.6094379 setosa
#> 5 1.609438 1.280934 0.3364722 -1.6094379 setosa
#> 6 1.686399 1.360977 0.5306283 -0.9162907 setosa
# Keep a subset of the data, add standardized columns
head(fcomputev(iris, 3:4, STD, apply = FALSE, keep = names(iris)[3:5]))
#> Petal.Length Petal.Width Species STD.Petal.Length STD.Petal.Width
#> 1 1.4 0.2 setosa -1.335752 -1.311052
#> 2 1.4 0.2 setosa -1.335752 -1.311052
#> 3 1.3 0.2 setosa -1.392399 -1.311052
#> 4 1.5 0.2 setosa -1.279104 -1.311052
#> 5 1.4 0.2 setosa -1.335752 -1.311052
#> 6 1.7 0.4 setosa -1.165809 -1.048667