Defined methods for dplyr generic single table functions. Most of them
preserve 'keyed_df' class and 'keys' attribute (excluding summarise with
scoped variants, distinct and do which remove them). Also these methods
modify rows in keys according to the rows modification in reference
data frame (if any).
# S3 method for keyed_df
select(.tbl, ...)
# S3 method for keyed_df
rename(.tbl, ...)
# S3 method for keyed_df
mutate(.tbl, ...)
# S3 method for keyed_df
transmute(.tbl, ...)
# S3 method for keyed_df
summarise(.tbl, ...)
# S3 method for keyed_df
group_by(.tbl, ...)
# S3 method for keyed_df
ungroup(.tbl, ...)
# S3 method for keyed_df
rowwise(.tbl)
# S3 method for keyed_df
distinct(.tbl, ..., .keep_all = FALSE)
# S3 method for keyed_df
do(.tbl, ...)
# S3 method for keyed_df
arrange(.tbl, ..., .by_group = FALSE)
# S3 method for keyed_df
filter(.data, ...)
# S3 method for keyed_df
slice(.tbl, ...)A keyed object.
Appropriate arguments for functions.
Parameter for dplyr::distinct.
Parameter for dplyr::arrange.
dplyr::transmute() is supported implicitly with dplyr::mutate()
support.
dplyr::rowwise() is not supposed to be generic in dplyr. Use
rowwise.keyed_df directly.
All scoped variants of present functions are also supported.
mtcars %>% key_by(vs, am) %>% dplyr::mutate(gear = 1)
#> # A keyed object. Keys: 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 1 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 1 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 1 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 1 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 1 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 1 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 1 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 1 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 1 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 1 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 1 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 1 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 1 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 1 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 1 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 1 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 1 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 1 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 1 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 1 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 1 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 1 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 1 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 1 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 1 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 1 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 1 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 1 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 1 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 1 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 1 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 1 2