Overview
Base R functions that provide a grammar of data manipulation similar to the functions found in the tidyverse. Think of it as the namespace of dplyr, but the internal code of plyr.
Installation
Browse the code at https://bitbucket.org/bklamer/bkdat.
devtools::install_bitbucket("bklamer/bkdat")Examples
arrange() - ordering rows of a data frame
library(bkdat)
#>
#> Attaching package: 'bkdat'
#> The following object is masked from 'package:stats':
#>
#> filter
arrange_(data = mtcars, x = "cyl")
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> 3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> 4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 5 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 6 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> 8 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> 9 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> 10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 11 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> 12 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> 13 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> 14 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> 15 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> 16 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> 17 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> 18 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> 19 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> 20 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> 21 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> 22 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> 23 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> 24 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> 25 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> 26 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> 27 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> 28 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> 29 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 30 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> 31 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> 32 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8filter() - subsetting rows of a data frame
filter(.data = mtcars, cyl == 4)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> 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
#> 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
#> 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
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2select() - subsetting columns of a data frame
select(data = mtcars, x = c("mpg", "cyl"))
#> mpg cyl
#> Mazda RX4 21.0 6
#> Mazda RX4 Wag 21.0 6
#> Datsun 710 22.8 4
#> Hornet 4 Drive 21.4 6
#> Hornet Sportabout 18.7 8
#> Valiant 18.1 6
#> Duster 360 14.3 8
#> Merc 240D 24.4 4
#> Merc 230 22.8 4
#> Merc 280 19.2 6
#> Merc 280C 17.8 6
#> Merc 450SE 16.4 8
#> Merc 450SL 17.3 8
#> Merc 450SLC 15.2 8
#> Cadillac Fleetwood 10.4 8
#> Lincoln Continental 10.4 8
#> Chrysler Imperial 14.7 8
#> Fiat 128 32.4 4
#> Honda Civic 30.4 4
#> Toyota Corolla 33.9 4
#> Toyota Corona 21.5 4
#> Dodge Challenger 15.5 8
#> AMC Javelin 15.2 8
#> Camaro Z28 13.3 8
#> Pontiac Firebird 19.2 8
#> Fiat X1-9 27.3 4
#> Porsche 914-2 26.0 4
#> Lotus Europa 30.4 4
#> Ford Pantera L 15.8 8
#> Ferrari Dino 19.7 6
#> Maserati Bora 15.0 8
#> Volvo 142E 21.4 4mutate() - adding new variables to a data frame
mutate(.data = mtcars, wt_pounds = wt*1000)
#> mpg cyl disp hp drat wt qsec vs am gear carb wt_pounds
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 2620
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 2875
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 2320
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 3215
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 3440
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 3460
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 3570
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 3190
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 3150
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 3440
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 3440
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 4070
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 3730
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 3780
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 5250
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 5424
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 5345
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2200
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 1615
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 1835
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 2465
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 3520
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 3435
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 3840
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 3845
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 1935
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 2140
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 1513
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 3170
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 2770
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 3570
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 2780Functions
The following functions are implemented:
| Tidyverse function | bkdat function |
|---|---|
| tibble::add_column() | add_column() |
| tibble::add_row() | add_row() |
| dplyr::arrange() | arrange() |
| NA | as_df() |
| dplyr::bind_cols() | bind_cols() |
| dplyr::bind_rows() | bind_fill_rows() |
| dplyr::bind_rows() | bind_rows() |
| dplyr::desc() | desc() |
| dplyr::do() | do() |
| dplyr::slice() & dplyr::filter() | filter() |
| tidyr::gather() | gather() |
| dplyr::group_by() | group_by() |
| rlang::is_atomic() | is_atomic() |
| NA | is_date() |
| rlang::is_integer() | is_integer() |
| rlang::is_string() | is_string() |
| dplyr::full_join() | join_full() |
| dplyr::inner_join() | join_inner() |
| dplyr::left_join() | join_left() |
| dplyr::right_join() | join_right() |
| purrr::map() | map() |
| purrr::map_chr() | map_chr() |
| purrr::map_dbl() | map_dbl() |
| purrr::map_df() | map_df() |
| purrr::map_int() | map_int() |
| dplyr::mutate() | mutate() |
| dplyr::nest_by() | nest_by() |
| dplyr::pull() | pull() |
| dplyr::rename() | rename() |
| tibble::remove_rownames() | reset_row_names() |
| tibble::rownames_to_column() | ronames_to_column() |
| dplyr::select() | select() |
| tidyr::separate() | separate() |
| tidyr::spread() | spread() |
| dplyr::summarise() | summarise() |
| tidyr::unite() | unite() |
Changes were made to unify the naming scheme and remove base R naming conflicts:
- The dplyr join functions are renamed to
join_*()instead of*_join(). -
bkdat::filter()provides the same functionality as the combination ofdplyr::filter()anddplyr::slice().