Tools for working with row names or row ids
Usage
rownames_as_column(x, var = "rowname")
column_as_rownames(x, var = "rowname")
rowid_as_column(x, var = "rowid")Arguments
- x
A data frame.
- var
Name of column to use for row names/ids. For
column_as_rownames(), this argument can be the variable name or the column number. Forrownames_as_column()androwid_as_column(), the column name must not already exist in the data.
Details
These are similar to tibble's functions column_to_rownames(),
rownames_to_column() and rowid_to_column(). Note that the behavior of
rowid_as_column() is different for grouped dataframe: instead of making
the rowid unique across the full dataframe, it creates rowid per group.
Therefore, there can be several rows with the same rowid if they belong to
different groups.
If you are familiar with dplyr, this is similar to doing the following:
data |>
group_by(grp) |>
mutate(id = row_number()) |>
ungroup()Examples
# Convert between row names and column --------------------------------
test <- rownames_as_column(mtcars, var = "car")
test
#> car mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
head(column_as_rownames(test, var = "car"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
test_data <- head(iris)
rowid_as_column(test_data)
#> rowid Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 1 5.1 3.5 1.4 0.2 setosa
#> 2 2 4.9 3.0 1.4 0.2 setosa
#> 3 3 4.7 3.2 1.3 0.2 setosa
#> 4 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 5.0 3.6 1.4 0.2 setosa
#> 6 6 5.4 3.9 1.7 0.4 setosa
rowid_as_column(test_data, var = "my_id")
#> my_id Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 1 5.1 3.5 1.4 0.2 setosa
#> 2 2 4.9 3.0 1.4 0.2 setosa
#> 3 3 4.7 3.2 1.3 0.2 setosa
#> 4 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 5.0 3.6 1.4 0.2 setosa
#> 6 6 5.4 3.9 1.7 0.4 setosa