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Make a feature the first column in data frame
Move a column convenientlyDrop factor levels in a subsetted data frameHow to sort a dataframe by multiple column(s)?How to join (merge) data frames (inner, outer, left, right)Convert a list of data frames into one data frameR - list to data frameDrop data frame columns by nameHow to make a great R reproducible exampleChanging column names of a data frameExtracting specific columns from a data frameHow can we make xkcd style graphs?
0
I have a data frame, say mtcars:
> glimpse(mtcars)
Observations: 32
Variables: 11
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Suppose I want to reorder the features so that hp is the first column to appear in the data frame as opposed to mpg.
I know that I could use dplyr like so:
> glimpse(mtcars %>% select_at(vars(hp, mpg:disp, drat:carb)))
Observations: 32
Variables: 11
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Is there a shorter, more elegant way of doing this?
(tidyverse or base r solutions particularly welcome)
r dplyr
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
add a comment |
0
I have a data frame, say mtcars:
> glimpse(mtcars)
Observations: 32
Variables: 11
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Suppose I want to reorder the features so that hp is the first column to appear in the data frame as opposed to mpg.
I know that I could use dplyr like so:
> glimpse(mtcars %>% select_at(vars(hp, mpg:disp, drat:carb)))
Observations: 32
Variables: 11
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Is there a shorter, more elegant way of doing this?
(tidyverse or base r solutions particularly welcome)
r dplyr
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
1
mtcars %>% select(hp, everything())
– camille
Mar 8 at 20:49
1
You generally only needselect_atfor helper functions, likestarts_with. For simple column selection and bare column names, just useselect
– camille
Mar 8 at 20:50
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Related Move a column conveniently
– markus
Mar 8 at 20:59
add a comment |
0
0
0
I have a data frame, say mtcars:
> glimpse(mtcars)
Observations: 32
Variables: 11
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Suppose I want to reorder the features so that hp is the first column to appear in the data frame as opposed to mpg.
I know that I could use dplyr like so:
> glimpse(mtcars %>% select_at(vars(hp, mpg:disp, drat:carb)))
Observations: 32
Variables: 11
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Is there a shorter, more elegant way of doing this?
(tidyverse or base r solutions particularly welcome)
r dplyr
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
I have a data frame, say mtcars:
> glimpse(mtcars)
Observations: 32
Variables: 11
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Suppose I want to reorder the features so that hp is the first column to appear in the data frame as opposed to mpg.
I know that I could use dplyr like so:
> glimpse(mtcars %>% select_at(vars(hp, mpg:disp, drat:carb)))
Observations: 32
Variables: 11
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150, 150, 245, 175, 66, 91, 113, 264, 175, 335, 109
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4, 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, …
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472.0, 460.0, 440.0, 78.7, 75.7, 71.1, 120.1, 318.0,…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.07, 3.07, 2.93, 3.00, 3.23, 4.08, 4.93, 4.22, 3.70, 2.76, 3.15, 3.73, 3.08, …
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440, 4.070, 3.730, 3.780, 5.250, 5.424, 5.345, 2.200, 1.615, 1.835, 2.465, 3.5…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90, 17.40, 17.60, 18.00, 17.98, 17.82, 17.42, 19.47, 18.52, 19.90, 20.01, 16.…
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 4
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1, 2, 2, 4, 6, 8, 2
Is there a shorter, more elegant way of doing this?
(tidyverse or base r solutions particularly welcome)
r dplyr
r dplyr
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
asked Mar 8 at 20:46
Doug FirDoug Fir
5,6062887159
5,6062887159
1
mtcars %>% select(hp, everything())
– camille
Mar 8 at 20:49
1
You generally only needselect_atfor helper functions, likestarts_with. For simple column selection and bare column names, just useselect
– camille
Mar 8 at 20:50
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Related Move a column conveniently
– markus
Mar 8 at 20:59
add a comment |
1
mtcars %>% select(hp, everything())
– camille
Mar 8 at 20:49
1
You generally only needselect_atfor helper functions, likestarts_with. For simple column selection and bare column names, just useselect
– camille
Mar 8 at 20:50
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Related Move a column conveniently
– markus
Mar 8 at 20:59
1
1
mtcars %>% select(hp, everything())– camille
Mar 8 at 20:49
mtcars %>% select(hp, everything())– camille
Mar 8 at 20:49
1
1
You generally only need
select_at for helper functions, like starts_with. For simple column selection and bare column names, just use select– camille
Mar 8 at 20:50
You generally only need
select_at for helper functions, like starts_with. For simple column selection and bare column names, just use select– camille
Mar 8 at 20:50
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Related Move a column conveniently
– markus
Mar 8 at 20:59
Related Move a column conveniently
– markus
Mar 8 at 20:59
add a comment |
2 Answers
2
active
oldest
votes
0
dplyr imports the tidyselect helper function everything, which, as its name implies, selects everything. This can be used in combination with other column selection, so that in this case, you select hp, then everything—since a column can only occur once, this takes on the meaning hp and then everything else.
library(dplyr)
mtcars %>%
select(hp, everything()) %>%
head()
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
A couple base R ways could involve cbinding columns based on position. In this case, hp is the 4th column, so I bind together the 4th column of mtcars with everything but the 4th column of mtcars (same output as above):
cbind(mtcars[4], mtcars[-4])
Or based on name, where I'm subsetting first for column names equal to "hp", then not equal to "hp":
cbind(mtcars[names(mtcars) == "hp"], mtcars[names(mtcars) != "hp"])
I'm sure there are other base R ways as well--definitely could rig something up with subset.
answered Mar 8 at 21:05
camillecamille
7,87531833
add a comment |
1
library(tidyverse)
mtcars %>%
select(hp, everything())
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160.0 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160.0 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108.0 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258.0 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360.0 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225.0 2.76 3.460 20.22 1 0 3 1
#> Duster 360 245 14.3 8 360.0 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 62 24.4 4 146.7 3.69 3.190 20.00 1 0 4 2
#> Merc 230 95 22.8 4 140.8 3.92 3.150 22.90 1 0 4 2
#> Merc 280 123 19.2 6 167.6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 123 17.8 6 167.6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 180 16.4 8 275.8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 180 17.3 8 275.8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 180 15.2 8 275.8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 205 10.4 8 472.0 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 215 10.4 8 460.0 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 230 14.7 8 440.0 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 66 32.4 4 78.7 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 52 30.4 4 75.7 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 65 33.9 4 71.1 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 97 21.5 4 120.1 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 150 15.5 8 318.0 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 150 15.2 8 304.0 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 245 13.3 8 350.0 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 175 19.2 8 400.0 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 66 27.3 4 79.0 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 91 26.0 4 120.3 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 113 30.4 4 95.1 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 264 15.8 8 351.0 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 175 19.7 6 145.0 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 335 15.0 8 301.0 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 109 21.4 4 121.0 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-08 by the reprex package (v0.2.1)
answered Mar 8 at 20:56
dylanjmdylanjm
825216
add a comment |
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2 Answers
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2 Answers
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0
dplyr imports the tidyselect helper function everything, which, as its name implies, selects everything. This can be used in combination with other column selection, so that in this case, you select hp, then everything—since a column can only occur once, this takes on the meaning hp and then everything else.
library(dplyr)
mtcars %>%
select(hp, everything()) %>%
head()
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
A couple base R ways could involve cbinding columns based on position. In this case, hp is the 4th column, so I bind together the 4th column of mtcars with everything but the 4th column of mtcars (same output as above):
cbind(mtcars[4], mtcars[-4])
Or based on name, where I'm subsetting first for column names equal to "hp", then not equal to "hp":
cbind(mtcars[names(mtcars) == "hp"], mtcars[names(mtcars) != "hp"])
I'm sure there are other base R ways as well--definitely could rig something up with subset.
answered Mar 8 at 21:05
camillecamille
7,87531833
add a comment |
0
dplyr imports the tidyselect helper function everything, which, as its name implies, selects everything. This can be used in combination with other column selection, so that in this case, you select hp, then everything—since a column can only occur once, this takes on the meaning hp and then everything else.
library(dplyr)
mtcars %>%
select(hp, everything()) %>%
head()
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
A couple base R ways could involve cbinding columns based on position. In this case, hp is the 4th column, so I bind together the 4th column of mtcars with everything but the 4th column of mtcars (same output as above):
cbind(mtcars[4], mtcars[-4])
Or based on name, where I'm subsetting first for column names equal to "hp", then not equal to "hp":
cbind(mtcars[names(mtcars) == "hp"], mtcars[names(mtcars) != "hp"])
I'm sure there are other base R ways as well--definitely could rig something up with subset.
answered Mar 8 at 21:05
camillecamille
7,87531833
add a comment |
0
0
0
dplyr imports the tidyselect helper function everything, which, as its name implies, selects everything. This can be used in combination with other column selection, so that in this case, you select hp, then everything—since a column can only occur once, this takes on the meaning hp and then everything else.
library(dplyr)
mtcars %>%
select(hp, everything()) %>%
head()
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
A couple base R ways could involve cbinding columns based on position. In this case, hp is the 4th column, so I bind together the 4th column of mtcars with everything but the 4th column of mtcars (same output as above):
cbind(mtcars[4], mtcars[-4])
Or based on name, where I'm subsetting first for column names equal to "hp", then not equal to "hp":
cbind(mtcars[names(mtcars) == "hp"], mtcars[names(mtcars) != "hp"])
I'm sure there are other base R ways as well--definitely could rig something up with subset.
answered Mar 8 at 21:05
camillecamille
7,87531833
dplyr imports the tidyselect helper function everything, which, as its name implies, selects everything. This can be used in combination with other column selection, so that in this case, you select hp, then everything—since a column can only occur once, this takes on the meaning hp and then everything else.
library(dplyr)
mtcars %>%
select(hp, everything()) %>%
head()
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225 2.76 3.460 20.22 1 0 3 1
A couple base R ways could involve cbinding columns based on position. In this case, hp is the 4th column, so I bind together the 4th column of mtcars with everything but the 4th column of mtcars (same output as above):
cbind(mtcars[4], mtcars[-4])
Or based on name, where I'm subsetting first for column names equal to "hp", then not equal to "hp":
cbind(mtcars[names(mtcars) == "hp"], mtcars[names(mtcars) != "hp"])
I'm sure there are other base R ways as well--definitely could rig something up with subset.
answered Mar 8 at 21:05
camillecamille
7,87531833
answered Mar 8 at 21:05
camillecamille
7,87531833
answered Mar 8 at 21:05
camillecamille
7,87531833
answered Mar 8 at 21:05
camillecamille
7,87531833
7,87531833
add a comment |
add a comment |
1
library(tidyverse)
mtcars %>%
select(hp, everything())
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160.0 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160.0 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108.0 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258.0 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360.0 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225.0 2.76 3.460 20.22 1 0 3 1
#> Duster 360 245 14.3 8 360.0 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 62 24.4 4 146.7 3.69 3.190 20.00 1 0 4 2
#> Merc 230 95 22.8 4 140.8 3.92 3.150 22.90 1 0 4 2
#> Merc 280 123 19.2 6 167.6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 123 17.8 6 167.6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 180 16.4 8 275.8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 180 17.3 8 275.8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 180 15.2 8 275.8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 205 10.4 8 472.0 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 215 10.4 8 460.0 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 230 14.7 8 440.0 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 66 32.4 4 78.7 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 52 30.4 4 75.7 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 65 33.9 4 71.1 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 97 21.5 4 120.1 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 150 15.5 8 318.0 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 150 15.2 8 304.0 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 245 13.3 8 350.0 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 175 19.2 8 400.0 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 66 27.3 4 79.0 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 91 26.0 4 120.3 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 113 30.4 4 95.1 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 264 15.8 8 351.0 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 175 19.7 6 145.0 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 335 15.0 8 301.0 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 109 21.4 4 121.0 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-08 by the reprex package (v0.2.1)
answered Mar 8 at 20:56
dylanjmdylanjm
825216
add a comment |
1
library(tidyverse)
mtcars %>%
select(hp, everything())
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160.0 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160.0 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108.0 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258.0 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360.0 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225.0 2.76 3.460 20.22 1 0 3 1
#> Duster 360 245 14.3 8 360.0 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 62 24.4 4 146.7 3.69 3.190 20.00 1 0 4 2
#> Merc 230 95 22.8 4 140.8 3.92 3.150 22.90 1 0 4 2
#> Merc 280 123 19.2 6 167.6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 123 17.8 6 167.6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 180 16.4 8 275.8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 180 17.3 8 275.8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 180 15.2 8 275.8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 205 10.4 8 472.0 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 215 10.4 8 460.0 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 230 14.7 8 440.0 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 66 32.4 4 78.7 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 52 30.4 4 75.7 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 65 33.9 4 71.1 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 97 21.5 4 120.1 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 150 15.5 8 318.0 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 150 15.2 8 304.0 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 245 13.3 8 350.0 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 175 19.2 8 400.0 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 66 27.3 4 79.0 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 91 26.0 4 120.3 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 113 30.4 4 95.1 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 264 15.8 8 351.0 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 175 19.7 6 145.0 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 335 15.0 8 301.0 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 109 21.4 4 121.0 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-08 by the reprex package (v0.2.1)
answered Mar 8 at 20:56
dylanjmdylanjm
825216
add a comment |
1
1
1
library(tidyverse)
mtcars %>%
select(hp, everything())
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160.0 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160.0 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108.0 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258.0 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360.0 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225.0 2.76 3.460 20.22 1 0 3 1
#> Duster 360 245 14.3 8 360.0 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 62 24.4 4 146.7 3.69 3.190 20.00 1 0 4 2
#> Merc 230 95 22.8 4 140.8 3.92 3.150 22.90 1 0 4 2
#> Merc 280 123 19.2 6 167.6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 123 17.8 6 167.6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 180 16.4 8 275.8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 180 17.3 8 275.8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 180 15.2 8 275.8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 205 10.4 8 472.0 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 215 10.4 8 460.0 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 230 14.7 8 440.0 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 66 32.4 4 78.7 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 52 30.4 4 75.7 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 65 33.9 4 71.1 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 97 21.5 4 120.1 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 150 15.5 8 318.0 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 150 15.2 8 304.0 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 245 13.3 8 350.0 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 175 19.2 8 400.0 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 66 27.3 4 79.0 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 91 26.0 4 120.3 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 113 30.4 4 95.1 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 264 15.8 8 351.0 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 175 19.7 6 145.0 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 335 15.0 8 301.0 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 109 21.4 4 121.0 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-08 by the reprex package (v0.2.1)
answered Mar 8 at 20:56
dylanjmdylanjm
825216
library(tidyverse)
mtcars %>%
select(hp, everything())
#> hp mpg cyl disp drat wt qsec vs am gear carb
#> Mazda RX4 110 21.0 6 160.0 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 110 21.0 6 160.0 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 93 22.8 4 108.0 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 110 21.4 6 258.0 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 175 18.7 8 360.0 3.15 3.440 17.02 0 0 3 2
#> Valiant 105 18.1 6 225.0 2.76 3.460 20.22 1 0 3 1
#> Duster 360 245 14.3 8 360.0 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 62 24.4 4 146.7 3.69 3.190 20.00 1 0 4 2
#> Merc 230 95 22.8 4 140.8 3.92 3.150 22.90 1 0 4 2
#> Merc 280 123 19.2 6 167.6 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 123 17.8 6 167.6 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 180 16.4 8 275.8 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 180 17.3 8 275.8 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 180 15.2 8 275.8 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 205 10.4 8 472.0 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 215 10.4 8 460.0 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 230 14.7 8 440.0 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 66 32.4 4 78.7 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 52 30.4 4 75.7 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 65 33.9 4 71.1 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 97 21.5 4 120.1 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 150 15.5 8 318.0 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 150 15.2 8 304.0 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 245 13.3 8 350.0 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 175 19.2 8 400.0 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 66 27.3 4 79.0 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 91 26.0 4 120.3 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 113 30.4 4 95.1 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 264 15.8 8 351.0 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 175 19.7 6 145.0 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 335 15.0 8 301.0 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 109 21.4 4 121.0 4.11 2.780 18.60 1 1 4 2
Created on 2019-03-08 by the reprex package (v0.2.1)
answered Mar 8 at 20:56
dylanjmdylanjm
825216
answered Mar 8 at 20:56
dylanjmdylanjm
825216
answered Mar 8 at 20:56
dylanjmdylanjm
825216
answered Mar 8 at 20:56
dylanjmdylanjm
825216
825216
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1
mtcars %>% select(hp, everything())– camille
Mar 8 at 20:49
1
You generally only need
select_atfor helper functions, likestarts_with. For simple column selection and bare column names, just useselect– camille
Mar 8 at 20:50
Tyank you @camille. I never knew about everything(). I'll accept as answer if you want some SO karma brownie points?
– Doug Fir
Mar 8 at 20:51
Related Move a column conveniently
– markus
Mar 8 at 20:59