Convenience function to paste together multiple columns into one.

unite(data, col, ..., sep = "_", remove = TRUE)

Arguments

data

A data frame.

col

The name of the new column, as a string or symbol.

This argument is passed by expression and supports quasiquotation (you can unquote strings and symbols). The name is captured from the expression with rlang::quo_name() (note that this kind of interface where symbols do not represent actual objects is now discouraged in the tidyverse; we support it here for backward compatibility).

...

A selection of columns. If empty, all variables are selected. You can supply bare variable names, select all variables between x and z with x:z, exclude y with -y. For more options, see the dplyr::select() documentation. See also the section on selection rules below.

sep

Separator to use between values.

remove

If TRUE, remove input columns from output data frame.

Rules for selection

Arguments for selecting columns are passed to tidyselect::vars_select() and are treated specially. Unlike other verbs, selecting functions make a strict distinction between data expressions and context expressions.

  • A data expression is either a bare name like x or an expression like x:y or c(x, y). In a data expression, you can only refer to columns from the data frame.

  • Everything else is a context expression in which you can only refer to objects that you have defined with <-.

For instance, col1:col3 is a data expression that refers to data columns, while seq(start, end) is a context expression that refers to objects from the contexts.

If you really need to refer to contextual objects from a data expression, you can unquote them with the tidy eval operator !!. This operator evaluates its argument in the context and inlines the result in the surrounding function call. For instance, c(x, !! x) selects the x column within the data frame and the column referred to by the object x defined in the context (which can contain either a column name as string or a column position).

See also

separate(), the complement.

Examples

library(dplyr) unite_(mtcars, "vs_am", c("vs","am"))
#> 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
# Separate is the complement of unite mtcars %>% unite(vs_am, vs, am) %>% separate(vs_am, c("vs", "am"))
#> 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