While a tibble can have row names (e.g., when converting from a regular data frame), they are removed when subsetting with the [ operator. A warning will be raised when attempting to assign non-NULL row names to a tibble. Generally, it is best to avoid row names, because they are basically a character column with different semantics to every other column. These functions allow to you detect if a data frame has row names (has_rownames()), remove them (remove_rownames()), or convert them back-and-forth between an explicit column (rownames_to_column() and column_to_rownames()).

has_rownames(df)

remove_rownames(df)

rownames_to_column(df, var = "rowname")

column_to_rownames(df, var = "rowname")

Arguments

df

A data frame

var

Name of column to use for rownames.

Details

In the printed output, the presence of row names is indicated by a star just above the row numbers.

Examples

has_rownames(mtcars)
#> [1] TRUE
has_rownames(iris)
#> [1] FALSE
has_rownames(remove_rownames(mtcars))
#> [1] FALSE
head(rownames_to_column(mtcars))
#> rowname mpg cyl disp hp drat wt qsec vs am gear carb #> 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 #> 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 #> 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
mtcars_tbl <- as_tibble(rownames_to_column(mtcars)) mtcars_tbl
#> # A tibble: 32 × 12 #> rowname mpg cyl disp hp drat wt qsec vs am #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 #> 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 #> 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 #> 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 #> 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 #> 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 #> 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 #> 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 #> 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 #> 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 #> # ... with 22 more rows, and 2 more variables: gear <dbl>, carb <dbl>
column_to_rownames(as.data.frame(mtcars_tbl))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2