Mutate multiple columns
Usage
mutateAll(object, fun, ...)
mutateAll(object, fun, ...)
mutateIf(object, predicate, fun, ...)
transmuteAt(object, vars, fun, ...)
transmuteIf(object, predicate, fun, ...)
# S4 method for DFrame,`function`
mutateAll(object, fun, ...)
# S4 method for DFrame,character,`function`
mutateAt(object, vars, fun, ...)
# S4 method for DFrame,`function`,`function`
mutateIf(object, predicate, fun, ...)
# S4 method for DFrame,character,`function`
transmuteAt(object, vars, fun, ...)
# S4 method for DFrame,`function`,`function`
transmuteIf(object, predicate, fun, ...)Arguments
- object
Object.
- fun
function. Mutation function.- ...
Passthrough arguments to function declared in
funargument.- vars
character. Column names.- predicate
function. A predicate function to be applied to the columns. For example,is.atomic().
See also
These functions are inspired by dplyr. However, they are designed to only work on Bioconductor S4 class objects, and use base R code internally.
Examples
data(mtcars, package = "datasets")
## DFrame ====
x <- as(mtcars, "DFrame")
mutateAll(x, fun = log, base = 2L)
#> DataFrame with 32 rows and 11 columns
#> mpg cyl disp hp drat wt
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> Mazda RX4 4.39232 2.58496 7.32193 6.78136 1.96347 1.38957
#> Mazda RX4 Wag 4.39232 2.58496 7.32193 6.78136 1.96347 1.52356
#> Datsun 710 4.51096 2.00000 6.75489 6.53916 1.94486 1.21412
#> Hornet 4 Drive 4.41954 2.58496 8.01123 6.78136 1.62293 1.68482
#> Hornet Sportabout 4.22497 3.00000 8.49185 7.45121 1.65535 1.78241
#> ... ... ... ... ... ... ...
#> Lotus Europa 4.92600 2.00000 6.57137 6.82018 1.91456 0.597412
#> Ford Pantera L 3.98185 3.00000 8.45533 8.04439 2.07724 1.664483
#> Ferrari Dino 4.30012 2.58496 7.17991 7.45121 1.85599 1.469886
#> Maserati Bora 3.90689 3.00000 8.23362 8.38802 1.82375 1.835924
#> Volvo 142E 4.41954 2.00000 6.91886 6.76818 2.03914 1.475085
#> qsec vs am gear carb
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> Mazda RX4 4.04089 -Inf 0 2.00000 2
#> Mazda RX4 Wag 4.08916 -Inf 0 2.00000 2
#> Datsun 710 4.21801 0 0 2.00000 0
#> Hornet 4 Drive 4.28096 0 -Inf 1.58496 0
#> Hornet Sportabout 4.08916 -Inf -Inf 1.58496 1
#> ... ... ... ... ... ...
#> Lotus Europa 4.07895 0 0 2.32193 1.00000
#> Ford Pantera L 3.85798 -Inf 0 2.32193 2.00000
#> Ferrari Dino 3.95420 -Inf 0 2.32193 2.58496
#> Maserati Bora 3.86790 -Inf 0 2.32193 3.00000
#> Volvo 142E 4.21723 0 0 2.00000 1.00000
mutateAt(x, vars = c("mpg", "cyl"), fun = log, base = 2L)
#> DataFrame with 32 rows and 11 columns
#> mpg cyl disp hp drat wt
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> Mazda RX4 4.39232 2.58496 160 110 3.90 2.620
#> Mazda RX4 Wag 4.39232 2.58496 160 110 3.90 2.875
#> Datsun 710 4.51096 2.00000 108 93 3.85 2.320
#> Hornet 4 Drive 4.41954 2.58496 258 110 3.08 3.215
#> Hornet Sportabout 4.22497 3.00000 360 175 3.15 3.440
#> ... ... ... ... ... ... ...
#> Lotus Europa 4.92600 2.00000 95.1 113 3.77 1.513
#> Ford Pantera L 3.98185 3.00000 351.0 264 4.22 3.170
#> Ferrari Dino 4.30012 2.58496 145.0 175 3.62 2.770
#> Maserati Bora 3.90689 3.00000 301.0 335 3.54 3.570
#> Volvo 142E 4.41954 2.00000 121.0 109 4.11 2.780
#> qsec vs am gear carb
#> <numeric> <numeric> <numeric> <numeric> <numeric>
#> Mazda RX4 16.46 0 1 4 4
#> Mazda RX4 Wag 17.02 0 1 4 4
#> Datsun 710 18.61 1 1 4 1
#> Hornet 4 Drive 19.44 1 0 3 1
#> Hornet Sportabout 17.02 0 0 3 2
#> ... ... ... ... ... ...
#> Lotus Europa 16.9 1 1 5 2
#> Ford Pantera L 14.5 0 1 5 4
#> Ferrari Dino 15.5 0 1 5 6
#> Maserati Bora 14.6 0 1 5 8
#> Volvo 142E 18.6 1 1 4 2
mutateIf(x, predicate = is.double, fun = as.integer)
#> DataFrame with 32 rows and 11 columns
#> mpg cyl disp hp drat wt
#> <integer> <integer> <integer> <integer> <integer> <integer>
#> Mazda RX4 21 6 160 110 3 2
#> Mazda RX4 Wag 21 6 160 110 3 2
#> Datsun 710 22 4 108 93 3 2
#> Hornet 4 Drive 21 6 258 110 3 3
#> Hornet Sportabout 18 8 360 175 3 3
#> ... ... ... ... ... ... ...
#> Lotus Europa 30 4 95 113 3 1
#> Ford Pantera L 15 8 351 264 4 3
#> Ferrari Dino 19 6 145 175 3 2
#> Maserati Bora 15 8 301 335 3 3
#> Volvo 142E 21 4 121 109 4 2
#> qsec vs am gear carb
#> <integer> <integer> <integer> <integer> <integer>
#> Mazda RX4 16 0 1 4 4
#> Mazda RX4 Wag 17 0 1 4 4
#> Datsun 710 18 1 1 4 1
#> Hornet 4 Drive 19 1 0 3 1
#> Hornet Sportabout 17 0 0 3 2
#> ... ... ... ... ... ...
#> Lotus Europa 16 1 1 5 2
#> Ford Pantera L 14 0 1 5 4
#> Ferrari Dino 15 0 1 5 6
#> Maserati Bora 14 0 1 5 8
#> Volvo 142E 18 1 1 4 2
transmuteAt(x, vars = c("mpg", "cyl"), fun = log, base = 2L)
#> DataFrame with 32 rows and 2 columns
#> mpg cyl
#> <numeric> <numeric>
#> Mazda RX4 4.39232 2.58496
#> Mazda RX4 Wag 4.39232 2.58496
#> Datsun 710 4.51096 2.00000
#> Hornet 4 Drive 4.41954 2.58496
#> Hornet Sportabout 4.22497 3.00000
#> ... ... ...
#> Lotus Europa 4.92600 2.00000
#> Ford Pantera L 3.98185 3.00000
#> Ferrari Dino 4.30012 2.58496
#> Maserati Bora 3.90689 3.00000
#> Volvo 142E 4.41954 2.00000
transmuteIf(x, predicate = is.double, fun = as.integer)
#> DataFrame with 32 rows and 11 columns
#> mpg cyl disp hp drat wt
#> <integer> <integer> <integer> <integer> <integer> <integer>
#> Mazda RX4 21 6 160 110 3 2
#> Mazda RX4 Wag 21 6 160 110 3 2
#> Datsun 710 22 4 108 93 3 2
#> Hornet 4 Drive 21 6 258 110 3 3
#> Hornet Sportabout 18 8 360 175 3 3
#> ... ... ... ... ... ... ...
#> Lotus Europa 30 4 95 113 3 1
#> Ford Pantera L 15 8 351 264 4 3
#> Ferrari Dino 19 6 145 175 3 2
#> Maserati Bora 15 8 301 335 3 3
#> Volvo 142E 21 4 121 109 4 2
#> qsec vs am gear carb
#> <integer> <integer> <integer> <integer> <integer>
#> Mazda RX4 16 0 1 4 4
#> Mazda RX4 Wag 17 0 1 4 4
#> Datsun 710 18 1 1 4 1
#> Hornet 4 Drive 19 1 0 3 1
#> Hornet Sportabout 17 0 0 3 2
#> ... ... ... ... ... ...
#> Lotus Europa 16 1 1 5 2
#> Ford Pantera L 14 0 1 5 4
#> Ferrari Dino 15 0 1 5 6
#> Maserati Bora 14 0 1 5 8
#> Volvo 142E 18 1 1 4 2