![[Experimental]](figures/lifecycle-experimental.svg)
This function sets new row names for a data frame based on a tidy evaluation expression.
Arguments
- .data
A data frame.
- expr
A tidy evaluation expression specifying the columns to use for the new row names.
- .remove
A logical indicating whether to remove the columns used in
expr. Default is FALSE. Note that columns are detected withall.vars().- .uniqueSep
A character string to separate duplicate row names when ensuring uniqueness with
make.unique(). Default is ".".
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
# if you just wish to make a column (`wt`) into a rowname:
mtcars %>%
head() %>%
mutateToRownames(wt)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 2.62 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2.875 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 2.32 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 3.215 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 3.44 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> 3.46 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# simple example of using tidy evaluation
mtcars %>%
head() %>%
mutateToRownames(wt + 3*vs)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 2.62 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> 2.875 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> 5.32 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> 6.215 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> 3.44 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> 6.46 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
# use .remove = TRUE to get rid of the column if unneeded
mtcars %>%
mutateToRownames(qsec + (gear / carb) * wt, .remove = TRUE)
#> mpg cyl disp hp drat vs am
#> 19.08 21.0 6 160.0 110 3.90 0 1
#> 19.895 21.0 6 160.0 110 3.90 0 1
#> 27.89 22.8 4 108.0 93 3.85 1 1
#> 29.085 21.4 6 258.0 110 3.08 1 0
#> 22.18 18.7 8 360.0 175 3.15 0 0
#> 30.6 18.1 6 225.0 105 2.76 1 0
#> 18.5175 14.3 8 360.0 245 3.21 0 0
#> 26.38 24.4 4 146.7 62 3.69 1 0
#> 29.2 22.8 4 140.8 95 3.92 1 0
#> 21.74 19.2 6 167.6 123 3.92 1 0
#> 22.34 17.8 6 167.6 123 3.92 1 0
#> 21.47 16.4 8 275.8 180 3.07 0 0
#> 21.33 17.3 8 275.8 180 3.07 0 0
#> 21.78 15.2 8 275.8 180 3.07 0 0
#> 21.9175 10.4 8 472.0 205 2.93 0 0
#> 21.888 10.4 8 460.0 215 3.00 0 0
#> 21.42875 14.7 8 440.0 230 3.23 0 0
#> 28.27 32.4 4 78.7 66 4.08 1 1
#> 21.75 30.4 4 75.7 52 4.93 1 1
#> 27.24 33.9 4 71.1 65 4.22 1 1
#> 27.405 21.5 4 120.1 97 3.70 1 0
#> 22.15 15.5 8 318.0 150 2.76 0 0
#> 22.4525 15.2 8 304.0 150 3.15 0 0
#> 18.29 13.3 8 350.0 245 3.73 0 0
#> 22.8175 19.2 8 400.0 175 3.08 0 0
#> 26.64 27.3 4 79.0 66 4.08 1 1
#> 22.05 26.0 4 120.3 91 4.43 0 1
#> 20.6825 30.4 4 95.1 113 3.77 1 1
#> 18.4625 15.8 8 351.0 264 4.22 0 1
#> 17.8083333333333 19.7 6 145.0 175 3.62 0 1
#> 16.83125 15.0 8 301.0 335 3.54 0 1
#> 24.16 21.4 4 121.0 109 4.11 1 1