R/pkg.R
table.express-package.RdA specialization of dplyr verbs, as well as a set of custom ones, that
build expressions that can be used within a data.table's
frame.
Note that since version 0.3.0, it is not possible to load table.express and dtplyr at
the same time, since they define the same data.table methods for many dplyr generics.
Bearing in mind that data.tables are also data.frames, we have to consider that other
packages may uses dplyr internally without importing data.table. Since dplyr's methods are
generic, calls to these methods in such packages would fail. The functions in this package try to
detect when this happens and delegate to the data.frame methods with a warning, which can be
safely ignored if you know that the error originates from a package that is not meant to work
with data.table. To avoid the warning, use options(table.express.warn.cedta = FALSE).
This software package was developed independently of any organization or institution that is or has been associated with the author.
require("data.table")
#> Loading required package: data.table
data("mtcars")
DT <- as.data.table(mtcars)
# ====================================================================================
# Simple dplyr-like transformations
DT %>%
group_by(cyl) %>%
filter(vs == 0, am == 1) %>%
transmute(mean_mpg = mean(mpg)) %>%
arrange(-cyl)
#> cyl mean_mpg
#> 1: 8 15.40000
#> 2: 6 20.56667
#> 3: 4 26.00000
# Equivalent to previous
DT %>%
start_expr %>%
transmute(mean_mpg = mean(mpg)) %>%
where(vs == 0, am == 1) %>%
group_by(cyl) %>%
order_by(-cyl) %>%
end_expr
#> cyl mean_mpg
#> 1: 8 15.40000
#> 2: 6 20.56667
#> 3: 4 26.00000
# Modification by reference
DT %>%
where(gear %% 2 != 0, carb %% 2 == 0) %>%
mutate(wt_squared = wt ^ 2)
#> mpg cyl disp hp drat wt qsec vs am gear carb wt_squared
#> 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 NA
#> 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 NA
#> 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 NA
#> 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 NA
#> 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 11.833600
#> 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 NA
#> 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 12.744900
#> 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 NA
#> 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 NA
#> 10: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 NA
#> 11: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 NA
#> 12: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 NA
#> 13: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 NA
#> 14: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 NA
#> 15: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 27.562500
#> 16: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 29.419776
#> 17: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 28.569025
#> 18: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 NA
#> 19: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 NA
#> 20: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 NA
#> 21: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 NA
#> 22: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 12.390400
#> 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 11.799225
#> 24: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14.745600
#> 25: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14.784025
#> 26: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 NA
#> 27: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 4.579600
#> 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2.289169
#> 29: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 10.048900
#> 30: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 7.672900
#> 31: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 12.744900
#> 32: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 NA
#> mpg cyl disp hp drat wt qsec vs am gear carb wt_squared
print(DT)
#> mpg cyl disp hp drat wt qsec vs am gear carb wt_squared
#> 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 NA
#> 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 NA
#> 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 NA
#> 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 NA
#> 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 11.833600
#> 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 NA
#> 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 12.744900
#> 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 NA
#> 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 NA
#> 10: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 NA
#> 11: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 NA
#> 12: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 NA
#> 13: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 NA
#> 14: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 NA
#> 15: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 27.562500
#> 16: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 29.419776
#> 17: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 28.569025
#> 18: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 NA
#> 19: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 NA
#> 20: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 NA
#> 21: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 NA
#> 22: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 12.390400
#> 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 11.799225
#> 24: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 14.745600
#> 25: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14.784025
#> 26: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 NA
#> 27: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 4.579600
#> 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2.289169
#> 29: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 10.048900
#> 30: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 7.672900
#> 31: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 12.744900
#> 32: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 NA
#> mpg cyl disp hp drat wt qsec vs am gear carb wt_squared
# Deletion by reference
DT %>%
mutate(wt_squared = NULL) %>%
print
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> mpg cyl disp hp drat wt qsec vs am gear carb
# Support for tidyslect helpers
DT %>%
select(ends_with("t"))
#> drat wt
#> 1: 3.90 2.620
#> 2: 3.90 2.875
#> 3: 3.85 2.320
#> 4: 3.08 3.215
#> 5: 3.15 3.440
#> 6: 2.76 3.460
#> 7: 3.21 3.570
#> 8: 3.69 3.190
#> 9: 3.92 3.150
#> 10: 3.92 3.440
#> 11: 3.92 3.440
#> 12: 3.07 4.070
#> 13: 3.07 3.730
#> 14: 3.07 3.780
#> 15: 2.93 5.250
#> 16: 3.00 5.424
#> 17: 3.23 5.345
#> 18: 4.08 2.200
#> 19: 4.93 1.615
#> 20: 4.22 1.835
#> 21: 3.70 2.465
#> 22: 2.76 3.520
#> 23: 3.15 3.435
#> 24: 3.73 3.840
#> 25: 3.08 3.845
#> 26: 4.08 1.935
#> 27: 4.43 2.140
#> 28: 3.77 1.513
#> 29: 4.22 3.170
#> 30: 3.62 2.770
#> 31: 3.54 3.570
#> 32: 4.11 2.780
#> drat wt
# ====================================================================================
# Helpers to transform a subset of data
# Like DT[, (whole) := lapply(.SD, as.integer), .SDcols = whole]
whole <- names(DT)[sapply(DT, function(x) { all(x %% 1 == 0) })]
DT %>%
mutate_sd(as.integer, .SDcols = whole)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9: 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10: 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11: 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12: 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13: 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14: 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15: 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16: 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17: 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21: 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22: 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23: 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24: 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25: 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27: 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29: 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30: 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31: 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> mpg cyl disp hp drat wt qsec vs am gear carb
sapply(DT, class)
#> mpg cyl disp hp drat wt qsec vs
#> "numeric" "integer" "numeric" "integer" "numeric" "numeric" "numeric" "integer"
#> am gear carb
#> "integer" "integer" "integer"
# Like DT[, lapply(.SD, fun), .SDcols = ...]
DT %>%
transmute_sd((.COL - mean(.COL)) / sd(.COL),
.SDcols = setdiff(names(DT), whole))
#> mpg disp drat wt qsec
#> 1: 0.15088482 -0.57061982 0.56751369 -0.610399567 -0.77716515
#> 2: 0.15088482 -0.57061982 0.56751369 -0.349785269 -0.46378082
#> 3: 0.44954345 -0.99018209 0.47399959 -0.917004624 0.42600682
#> 4: 0.21725341 0.22009369 -0.96611753 -0.002299538 0.89048716
#> 5: -0.23073453 1.04308123 -0.83519779 0.227654255 -0.46378082
#> 6: -0.33028740 -0.04616698 -1.56460776 0.248094592 1.32698675
#> 7: -0.96078893 1.04308123 -0.72298087 0.360516446 -1.12412636
#> 8: 0.71501778 -0.67793094 0.17475447 -0.027849959 1.20387148
#> 9: 0.44954345 -0.72553512 0.60491932 -0.068730634 2.82675459
#> 10: -0.14777380 -0.50929918 0.60491932 0.227654255 0.25252621
#> 11: -0.38006384 -0.50929918 0.60491932 0.227654255 0.58829513
#> 12: -0.61235388 0.36371309 -0.98482035 0.871524874 -0.25112717
#> 13: -0.46302456 0.36371309 -0.98482035 0.524039143 -0.13920420
#> 14: -0.81145962 0.36371309 -0.98482035 0.575139986 0.08464175
#> 15: -1.60788262 1.94675381 -1.24665983 2.077504765 0.07344945
#> 16: -1.60788262 1.84993175 -1.11574009 2.255335698 -0.01608893
#> 17: -0.89442035 1.68856165 -0.68557523 2.174596366 -0.23993487
#> 18: 2.04238943 -1.22658929 0.90416444 -1.039646647 0.90727560
#> 19: 1.71054652 -1.25079481 2.49390411 -1.637526508 0.37564148
#> 20: 2.29127162 -1.28790993 1.16600392 -1.412682800 1.14790999
#> 21: 0.23384555 -0.89255318 0.19345729 -0.768812180 1.20946763
#> 22: -0.76168319 0.70420401 -1.56460776 0.309415603 -0.54772305
#> 23: -0.81145962 0.59124494 -0.83519779 0.222544170 -0.30708866
#> 24: -1.12671039 0.96239618 0.24956575 0.636460997 -1.36476075
#> 25: -0.14777380 1.36582144 -0.96611753 0.641571082 -0.44699237
#> 26: 1.19619000 -1.22416874 0.90416444 -1.310481114 0.58829513
#> 27: 0.98049211 -0.89093948 1.55876313 -1.100967659 -0.64285758
#> 28: 1.71054652 -1.09426581 0.32437703 -1.741772228 -0.53093460
#> 29: -0.71190675 0.97046468 1.16600392 -0.048290296 -1.87401028
#> 30: -0.06481307 -0.69164740 0.04383473 -0.457097039 -1.31439542
#> 31: -0.84464392 0.56703942 -0.10578782 0.360516446 -1.81804880
#> 32: 0.21725341 -0.88529152 0.96027290 -0.446876870 0.42041067
#> mpg disp drat wt qsec
# Filter several with the same condition
DT %>%
filter_sd(.COL == 1, .SDcols = c("vs", "am"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 2: 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 3: 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 4: 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 5: 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 6: 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 7: 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
# Using secondary indices, i.e. DT[.(4, 5), on = .(cyl, gear)]
DT %>%
filter_on(cyl = 4, gear = 5) # note we don't use ==
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1: 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
#> 2: 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
scale_undim <- function(...) {
as.numeric(scale(...)) # remove dimensions
}
# Chaining
DT %>%
start_expr %>%
mutate_sd(as.integer, .SDcols = whole) %>%
chain %>%
filter_sd(.COL == 1, .SDcols = c("vs", "am"), .collapse = `|`) %>%
transmute_sd(scale_undim, .SDcols = !is.integer(.COL)) %>%
end_expr
#> mpg disp drat wt qsec
#> 1: -0.39184157 0.07107789 0.03955921 -0.09987243 -0.87064903
#> 2: -0.39184157 0.07107789 0.03955921 0.29103270 -0.60164850
#> 3: -0.05835938 -0.61147991 -0.07032748 -0.55976081 0.16212085
#> 4: -0.31773442 1.35743682 -1.76258247 0.81223954 0.56081807
#> 5: -0.92911843 0.92427514 -2.46585727 1.18781505 0.93549737
#> 6: 0.23806923 -0.10349939 -0.42196488 0.77391551 0.82981859
#> 7: -0.05835938 -0.18094345 0.08351388 0.71259705 2.22285704
#> 8: -0.72532376 0.17083634 0.08351388 1.15715583 0.01320985
#> 9: -0.98469880 0.17083634 0.08351388 1.15715583 0.30142470
#> 10: 1.72021229 -0.99607497 0.43515128 -0.74371617 0.57522881
#> 11: 1.34967653 -1.03545331 2.30322498 -1.64049852 0.11888863
#> 12: 1.99811411 -1.09583342 0.74283401 -1.30324704 0.78178279
#> 13: -0.29920763 -0.45265396 -0.39998754 -0.33748143 0.83462217
#> 14: 0.77534609 -0.99213714 0.43515128 -1.14995091 0.30142470
#> 15: 0.53449784 -0.45002873 1.20435810 -0.83569385 -0.75536309
#> 16: 1.34967653 -0.78080674 -0.24614618 -1.79686057 -0.65929147
#> 17: -1.35523456 2.57816520 0.74283401 0.74325628 -1.81215088
#> 18: -0.63268982 -0.12581378 -0.57580624 0.13007177 -1.33179279
#> 19: -1.50344887 1.92185962 -0.75162494 1.35644079 -1.76411507
#> 20: -0.31773442 -0.44084046 0.50108330 0.14540138 0.15731727
# The previous is quivalent to
DT[, (whole) := lapply(.SD, as.integer), .SDcols = whole
][vs == 1 | am == 1,
lapply(.SD, scale_undim),
.SDcols = names(DT)[sapply(DT, Negate(is.integer))]]
#> mpg disp drat wt qsec
#> 1: -0.39184157 0.07107789 0.03955921 -0.09987243 -0.87064903
#> 2: -0.39184157 0.07107789 0.03955921 0.29103270 -0.60164850
#> 3: -0.05835938 -0.61147991 -0.07032748 -0.55976081 0.16212085
#> 4: -0.31773442 1.35743682 -1.76258247 0.81223954 0.56081807
#> 5: -0.92911843 0.92427514 -2.46585727 1.18781505 0.93549737
#> 6: 0.23806923 -0.10349939 -0.42196488 0.77391551 0.82981859
#> 7: -0.05835938 -0.18094345 0.08351388 0.71259705 2.22285704
#> 8: -0.72532376 0.17083634 0.08351388 1.15715583 0.01320985
#> 9: -0.98469880 0.17083634 0.08351388 1.15715583 0.30142470
#> 10: 1.72021229 -0.99607497 0.43515128 -0.74371617 0.57522881
#> 11: 1.34967653 -1.03545331 2.30322498 -1.64049852 0.11888863
#> 12: 1.99811411 -1.09583342 0.74283401 -1.30324704 0.78178279
#> 13: -0.29920763 -0.45265396 -0.39998754 -0.33748143 0.83462217
#> 14: 0.77534609 -0.99213714 0.43515128 -1.14995091 0.30142470
#> 15: 0.53449784 -0.45002873 1.20435810 -0.83569385 -0.75536309
#> 16: 1.34967653 -0.78080674 -0.24614618 -1.79686057 -0.65929147
#> 17: -1.35523456 2.57816520 0.74283401 0.74325628 -1.81215088
#> 18: -0.63268982 -0.12581378 -0.57580624 0.13007177 -1.33179279
#> 19: -1.50344887 1.92185962 -0.75162494 1.35644079 -1.76411507
#> 20: -0.31773442 -0.44084046 0.50108330 0.14540138 0.15731727
# Alternative to keep all columns (*copying* non-scaled ones)
scale_non_integers <- function(x) {
if (is.integer(x)) x else scale_undim(x)
}
DT %>%
filter_sd(.COL == 1, .SDcols = c("vs", "am"), .collapse = `|`) %>%
transmute_sd(everything(), scale_non_integers)
#> mpg cyl disp hp drat wt qsec vs am
#> 1: -0.39184157 6 0.07107789 110 0.03955921 -0.09987243 -0.87064903 0 1
#> 2: -0.39184157 6 0.07107789 110 0.03955921 0.29103270 -0.60164850 0 1
#> 3: -0.05835938 4 -0.61147991 93 -0.07032748 -0.55976081 0.16212085 1 1
#> 4: -0.31773442 6 1.35743682 110 -1.76258247 0.81223954 0.56081807 1 0
#> 5: -0.92911843 6 0.92427514 105 -2.46585727 1.18781505 0.93549737 1 0
#> 6: 0.23806923 4 -0.10349939 62 -0.42196488 0.77391551 0.82981859 1 0
#> 7: -0.05835938 4 -0.18094345 95 0.08351388 0.71259705 2.22285704 1 0
#> 8: -0.72532376 6 0.17083634 123 0.08351388 1.15715583 0.01320985 1 0
#> 9: -0.98469880 6 0.17083634 123 0.08351388 1.15715583 0.30142470 1 0
#> 10: 1.72021229 4 -0.99607497 66 0.43515128 -0.74371617 0.57522881 1 1
#> 11: 1.34967653 4 -1.03545331 52 2.30322498 -1.64049852 0.11888863 1 1
#> 12: 1.99811411 4 -1.09583342 65 0.74283401 -1.30324704 0.78178279 1 1
#> 13: -0.29920763 4 -0.45265396 97 -0.39998754 -0.33748143 0.83462217 1 0
#> 14: 0.77534609 4 -0.99213714 66 0.43515128 -1.14995091 0.30142470 1 1
#> 15: 0.53449784 4 -0.45002873 91 1.20435810 -0.83569385 -0.75536309 0 1
#> 16: 1.34967653 4 -0.78080674 113 -0.24614618 -1.79686057 -0.65929147 1 1
#> 17: -1.35523456 8 2.57816520 264 0.74283401 0.74325628 -1.81215088 0 1
#> 18: -0.63268982 6 -0.12581378 175 -0.57580624 0.13007177 -1.33179279 0 1
#> 19: -1.50344887 8 1.92185962 335 -0.75162494 1.35644079 -1.76411507 0 1
#> 20: -0.31773442 4 -0.44084046 109 0.50108330 0.14540138 0.15731727 1 1
#> gear carb
#> 1: 4 4
#> 2: 4 4
#> 3: 4 1
#> 4: 3 1
#> 5: 3 1
#> 6: 4 2
#> 7: 4 2
#> 8: 4 4
#> 9: 4 4
#> 10: 4 1
#> 11: 4 2
#> 12: 4 1
#> 13: 3 1
#> 14: 4 1
#> 15: 5 2
#> 16: 5 2
#> 17: 5 4
#> 18: 5 6
#> 19: 5 8
#> 20: 4 2
# Without copying non-scaled
DT %>%
where(vs == 1 | am == 1) %>%
mutate_sd(scale, .SDcols = names(DT)[sapply(DT, Negate(is.integer))])
#> mpg cyl disp hp drat wt qsec vs am
#> 1: -0.39184157 6 0.07107789 110 0.03955921 -0.09987243 -0.87064903 0 1
#> 2: -0.39184157 6 0.07107789 110 0.03955921 0.29103270 -0.60164850 0 1
#> 3: -0.05835938 4 -0.61147991 93 -0.07032748 -0.55976081 0.16212085 1 1
#> 4: -0.31773442 6 1.35743682 110 -1.76258247 0.81223954 0.56081807 1 0
#> 5: 18.70000000 8 360.00000000 175 3.15000000 3.44000000 17.02000000 0 0
#> 6: -0.92911843 6 0.92427514 105 -2.46585727 1.18781505 0.93549737 1 0
#> 7: 14.30000000 8 360.00000000 245 3.21000000 3.57000000 15.84000000 0 0
#> 8: 0.23806923 4 -0.10349939 62 -0.42196488 0.77391551 0.82981859 1 0
#> 9: -0.05835938 4 -0.18094345 95 0.08351388 0.71259705 2.22285704 1 0
#> 10: -0.72532376 6 0.17083634 123 0.08351388 1.15715583 0.01320985 1 0
#> 11: -0.98469880 6 0.17083634 123 0.08351388 1.15715583 0.30142470 1 0
#> 12: 16.40000000 8 275.80000000 180 3.07000000 4.07000000 17.40000000 0 0
#> 13: 17.30000000 8 275.80000000 180 3.07000000 3.73000000 17.60000000 0 0
#> 14: 15.20000000 8 275.80000000 180 3.07000000 3.78000000 18.00000000 0 0
#> 15: 10.40000000 8 472.00000000 205 2.93000000 5.25000000 17.98000000 0 0
#> 16: 10.40000000 8 460.00000000 215 3.00000000 5.42400000 17.82000000 0 0
#> 17: 14.70000000 8 440.00000000 230 3.23000000 5.34500000 17.42000000 0 0
#> 18: 1.72021229 4 -0.99607497 66 0.43515128 -0.74371617 0.57522881 1 1
#> 19: 1.34967653 4 -1.03545331 52 2.30322498 -1.64049852 0.11888863 1 1
#> 20: 1.99811411 4 -1.09583342 65 0.74283401 -1.30324704 0.78178279 1 1
#> 21: -0.29920763 4 -0.45265396 97 -0.39998754 -0.33748143 0.83462217 1 0
#> 22: 15.50000000 8 318.00000000 150 2.76000000 3.52000000 16.87000000 0 0
#> 23: 15.20000000 8 304.00000000 150 3.15000000 3.43500000 17.30000000 0 0
#> 24: 13.30000000 8 350.00000000 245 3.73000000 3.84000000 15.41000000 0 0
#> 25: 19.20000000 8 400.00000000 175 3.08000000 3.84500000 17.05000000 0 0
#> 26: 0.77534609 4 -0.99213714 66 0.43515128 -1.14995091 0.30142470 1 1
#> 27: 0.53449784 4 -0.45002873 91 1.20435810 -0.83569385 -0.75536309 0 1
#> 28: 1.34967653 4 -0.78080674 113 -0.24614618 -1.79686057 -0.65929147 1 1
#> 29: -1.35523456 8 2.57816520 264 0.74283401 0.74325628 -1.81215088 0 1
#> 30: -0.63268982 6 -0.12581378 175 -0.57580624 0.13007177 -1.33179279 0 1
#> 31: -1.50344887 8 1.92185962 335 -0.75162494 1.35644079 -1.76411507 0 1
#> 32: -0.31773442 4 -0.44084046 109 0.50108330 0.14540138 0.15731727 1 1
#> mpg cyl disp hp drat wt qsec vs am
#> gear carb
#> 1: 4 4
#> 2: 4 4
#> 3: 4 1
#> 4: 3 1
#> 5: 3 2
#> 6: 3 1
#> 7: 3 4
#> 8: 4 2
#> 9: 4 2
#> 10: 4 4
#> 11: 4 4
#> 12: 3 3
#> 13: 3 3
#> 14: 3 3
#> 15: 3 4
#> 16: 3 4
#> 17: 3 4
#> 18: 4 1
#> 19: 4 2
#> 20: 4 1
#> 21: 3 1
#> 22: 3 2
#> 23: 3 2
#> 24: 3 4
#> 25: 3 2
#> 26: 4 1
#> 27: 5 2
#> 28: 5 2
#> 29: 5 4
#> 30: 5 6
#> 31: 5 8
#> 32: 4 2
#> gear carb
print(DT)
#> mpg cyl disp hp drat wt qsec vs am
#> 1: -0.39184157 6 0.07107789 110 0.03955921 -0.09987243 -0.87064903 0 1
#> 2: -0.39184157 6 0.07107789 110 0.03955921 0.29103270 -0.60164850 0 1
#> 3: -0.05835938 4 -0.61147991 93 -0.07032748 -0.55976081 0.16212085 1 1
#> 4: -0.31773442 6 1.35743682 110 -1.76258247 0.81223954 0.56081807 1 0
#> 5: 18.70000000 8 360.00000000 175 3.15000000 3.44000000 17.02000000 0 0
#> 6: -0.92911843 6 0.92427514 105 -2.46585727 1.18781505 0.93549737 1 0
#> 7: 14.30000000 8 360.00000000 245 3.21000000 3.57000000 15.84000000 0 0
#> 8: 0.23806923 4 -0.10349939 62 -0.42196488 0.77391551 0.82981859 1 0
#> 9: -0.05835938 4 -0.18094345 95 0.08351388 0.71259705 2.22285704 1 0
#> 10: -0.72532376 6 0.17083634 123 0.08351388 1.15715583 0.01320985 1 0
#> 11: -0.98469880 6 0.17083634 123 0.08351388 1.15715583 0.30142470 1 0
#> 12: 16.40000000 8 275.80000000 180 3.07000000 4.07000000 17.40000000 0 0
#> 13: 17.30000000 8 275.80000000 180 3.07000000 3.73000000 17.60000000 0 0
#> 14: 15.20000000 8 275.80000000 180 3.07000000 3.78000000 18.00000000 0 0
#> 15: 10.40000000 8 472.00000000 205 2.93000000 5.25000000 17.98000000 0 0
#> 16: 10.40000000 8 460.00000000 215 3.00000000 5.42400000 17.82000000 0 0
#> 17: 14.70000000 8 440.00000000 230 3.23000000 5.34500000 17.42000000 0 0
#> 18: 1.72021229 4 -0.99607497 66 0.43515128 -0.74371617 0.57522881 1 1
#> 19: 1.34967653 4 -1.03545331 52 2.30322498 -1.64049852 0.11888863 1 1
#> 20: 1.99811411 4 -1.09583342 65 0.74283401 -1.30324704 0.78178279 1 1
#> 21: -0.29920763 4 -0.45265396 97 -0.39998754 -0.33748143 0.83462217 1 0
#> 22: 15.50000000 8 318.00000000 150 2.76000000 3.52000000 16.87000000 0 0
#> 23: 15.20000000 8 304.00000000 150 3.15000000 3.43500000 17.30000000 0 0
#> 24: 13.30000000 8 350.00000000 245 3.73000000 3.84000000 15.41000000 0 0
#> 25: 19.20000000 8 400.00000000 175 3.08000000 3.84500000 17.05000000 0 0
#> 26: 0.77534609 4 -0.99213714 66 0.43515128 -1.14995091 0.30142470 1 1
#> 27: 0.53449784 4 -0.45002873 91 1.20435810 -0.83569385 -0.75536309 0 1
#> 28: 1.34967653 4 -0.78080674 113 -0.24614618 -1.79686057 -0.65929147 1 1
#> 29: -1.35523456 8 2.57816520 264 0.74283401 0.74325628 -1.81215088 0 1
#> 30: -0.63268982 6 -0.12581378 175 -0.57580624 0.13007177 -1.33179279 0 1
#> 31: -1.50344887 8 1.92185962 335 -0.75162494 1.35644079 -1.76411507 0 1
#> 32: -0.31773442 4 -0.44084046 109 0.50108330 0.14540138 0.15731727 1 1
#> mpg cyl disp hp drat wt qsec vs am
#> gear carb
#> 1: 4 4
#> 2: 4 4
#> 3: 4 1
#> 4: 3 1
#> 5: 3 2
#> 6: 3 1
#> 7: 3 4
#> 8: 4 2
#> 9: 4 2
#> 10: 4 4
#> 11: 4 4
#> 12: 3 3
#> 13: 3 3
#> 14: 3 3
#> 15: 3 4
#> 16: 3 4
#> 17: 3 4
#> 18: 4 1
#> 19: 4 2
#> 20: 4 1
#> 21: 3 1
#> 22: 3 2
#> 23: 3 2
#> 24: 3 4
#> 25: 3 2
#> 26: 4 1
#> 27: 5 2
#> 28: 5 2
#> 29: 5 4
#> 30: 5 6
#> 31: 5 8
#> 32: 4 2
#> gear carb