add_missing_column() adds one or more columns to an existing data frame only if they
do not already exist.
This is a simple wrapper around add_column().
add_missing_column(
.data,
...,
.before = NULL,
.after = NULL,
.name_repair = c("check_unique", "unique", "universal", "minimal")
)Data frame to append to.
<dynamic-dots>
Name-value pairs, passed on to tibble(). All values must have
the same size of .data or size 1.
One-based column index or column name where to add the new columns, default: after last column.
Treatment of problematic column names:
"minimal": No name repair or checks, beyond basic existence,
"unique": Make sure names are unique and not empty,
"check_unique": (default value), no name repair, but check they are
unique,
"universal": Make the names unique and syntactic
a function: apply custom name repair (e.g., .name_repair = make.names
for names in the style of base R).
A purrr-style anonymous function, see rlang::as_function()
This argument is passed on as repair to vctrs::vec_as_names().
See there for more details on these terms and the strategies used
to enforce them.
# add new columns x and y
add_missing_column(mtcars, x = 1, y = NA)
#> mpg cyl disp hp drat wt qsec vs am gear carb x y
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1 NA
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1 NA
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1 NA
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1 NA
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1 NA
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1 NA
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1 NA
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1 NA
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1 NA
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1 NA
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1 NA
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1 NA
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1 NA
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1 NA
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 1 NA
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1 NA
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 1 NA
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 1 NA
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1 NA
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1 NA
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1 NA
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1 NA
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1 NA
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1 NA
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1 NA
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 1 NA
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1 NA
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1 NA
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1 NA
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1 NA
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1 NA
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 1 NA
# add new columns from a named vector
new_cols <- c(x = 1, y = NA)
add_missing_column(mtcars, !!!new_cols)
#> mpg cyl disp hp drat wt qsec vs am gear carb x y
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1 NA
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1 NA
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 1 NA
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1 NA
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 1 NA
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1 NA
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1 NA
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1 NA
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1 NA
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1 NA
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1 NA
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 1 NA
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 1 NA
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 1 NA
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 1 NA
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 1 NA
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 1 NA
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 1 NA
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1 NA
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1 NA
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1 NA
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 1 NA
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 1 NA
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 1 NA
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 1 NA
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 1 NA
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1 NA
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1 NA
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1 NA
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1 NA
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1 NA
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 1 NA