<!-- README.md is generated from README.Rmd. Please edit that file -->
# DFplyr
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The goal of DFplyr is to enable `dplyr` and `ggplot2` support for
`S4Vectors::DataFrame` by providing the appropriate extension methods.
As row names are an important feature of many Bioconductor structures,
these are preserved where possible.
## Installation
You can install the development version from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("jonocarroll/DFplyr")
```
You can install from [Bioconductor](https://bioconductor.org) with:
``` r
if (!require("BiocManager", quietly =TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("DFplyr")
```
## Examples
First create an S4Vectors `DataFrame`, including S4 columns if desired
``` r
suppressMessages(library(S4Vectors))
m <- mtcars[, c("cyl", "hp", "am", "gear", "disp")]
d <- as(m, "DataFrame")
d$grX <- GenomicRanges::GRanges("chrX", IRanges::IRanges(1:32, width = 10))
d$grY <- GenomicRanges::GRanges("chrY", IRanges::IRanges(1:32, width = 10))
d$nl <- IRanges::NumericList(lapply(d$gear, function(n) round(rnorm(n), 2)))
d
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl
#> <GRanges> <NumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,...
```
`DataFrame`s can then be used in `dplyr` calls the same as `data.frame`
or `tibble` objects. Support for working with S4 columns is enabled
provided they have appropriate functions. Adding multiple columns will
result in the new columns being created in alphabetical order
``` r
suppressMessages(library(DFplyr))
mutate(d, newvar = cyl + hp)
#> DataFrame with 32 rows and 9 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl newvar
#> <GRanges> <CompressedNumericList> <numeric>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,... 116
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,... 116
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,... 97
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25 116
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97 183
#> ... ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,... 117
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,... 272
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,... 181
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,... 343
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,... 113
mutate(d, nl2 = nl * 2)
#> DataFrame with 32 rows and 9 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl nl2
#> <GRanges> <CompressedNumericList> <CompressedNumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,... -1.0, 1.9, 0.8,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,... 0.56,-1.80, 0.48,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,... -0.06, 3.52, 0.50,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25 0.50,-3.52,-2.50
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97 3.20,1.32,1.94
#> ... ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,... 0.94,-0.12, 2.38,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,... -2.40,-1.32,-1.38,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,... -0.98,-3.62, 0.34,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,... 0.88,-1.98, 2.34,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,... 0.52,0.90,3.58,...
mutate(d, length_nl = lengths(nl))
#> DataFrame with 32 rows and 9 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl length_nl
#> <GRanges> <CompressedNumericList> <integer>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,... 4
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,... 4
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,... 4
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25 3
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97 3
#> ... ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,... 5
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,... 5
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,... 5
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,... 5
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,... 4
mutate(d,
chr = GenomeInfoDb::seqnames(grX),
strand_X = BiocGenerics::strand(grX),
end_X = BiocGenerics::end(grX)
)
#> DataFrame with 32 rows and 11 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl chr end_X strand_X
#> <GRanges> <CompressedNumericList> <Rle> <integer> <Rle>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,... chrX 10 *
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,... chrX 11 *
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,... chrX 12 *
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25 chrX 13 *
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97 chrX 14 *
#> ... ... ... ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,... chrX 37 *
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,... chrX 38 *
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,... chrX 39 *
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,... chrX 40 *
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,... chrX 41 *
```
the object returned remains a standard `DataFrame`, and further calls
can be piped with `%>%`
``` r
mutate(d, newvar = cyl + hp) %>%
pull(newvar)
#> [1] 116 116 97 116 183 111 253 66 99 129 129 188 188 188 213 223 238 70 56
#> [20] 69 101 158 158 253 183 70 95 117 272 181 343 113
```
Some of the variants of the `dplyr` verbs also work
``` r
mutate_if(d, is.numeric, ~ .^2)
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 36 12100 1 16 25600 chrX:1-10
#> Mazda RX4 Wag 36 12100 1 16 25600 chrX:2-11
#> Datsun 710 16 8649 1 16 11664 chrX:3-12
#> Hornet 4 Drive 36 12100 0 9 66564 chrX:4-13
#> Hornet Sportabout 64 30625 0 9 129600 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 16 12769 1 25 9044.01 chrX:28-37
#> Ford Pantera L 64 69696 1 25 123201.00 chrX:29-38
#> Ferrari Dino 36 30625 1 25 21025.00 chrX:30-39
#> Maserati Bora 64 112225 1 25 90601.00 chrX:31-40
#> Volvo 142E 16 11881 1 16 14641.00 chrX:32-41
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,...
mutate_if(d, ~ inherits(., "GRanges"), BiocGenerics::start)
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <integer>
#> Mazda RX4 6 110 1 4 160 1
#> Mazda RX4 Wag 6 110 1 4 160 2
#> Datsun 710 4 93 1 4 108 3
#> Hornet 4 Drive 6 110 0 3 258 4
#> Hornet Sportabout 8 175 0 3 360 5
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 28
#> Ford Pantera L 8 264 1 5 351.0 29
#> Ferrari Dino 6 175 1 5 145.0 30
#> Maserati Bora 8 335 1 5 301.0 31
#> Volvo 142E 4 109 1 4 121.0 32
#> grY nl
#> <integer> <CompressedNumericList>
#> Mazda RX4 1 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag 2 0.28,-0.90, 0.24,...
#> Datsun 710 3 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive 4 0.25,-1.76,-1.25
#> Hornet Sportabout 5 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa 28 0.47,-0.06, 1.19,...
#> Ford Pantera L 29 -1.20,-0.66,-0.69,...
#> Ferrari Dino 30 -0.49,-1.81, 0.17,...
#> Maserati Bora 31 0.44,-0.99, 1.17,...
#> Volvo 142E 32 0.26,0.45,1.79,...
```
Use of `tidyselect` helpers is limited to within `dplyr::vars()` calls
and using the `_at` variants
``` r
mutate_at(d, vars(starts_with("c")), ~ .^2)
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 36 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 36 110 1 4 160 chrX:2-11
#> Datsun 710 16 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 36 110 0 3 258 chrX:4-13
#> Hornet Sportabout 64 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 16 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 64 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 36 175 1 5 145.0 chrX:30-39
#> Maserati Bora 64 335 1 5 301.0 chrX:31-40
#> Volvo 142E 16 109 1 4 121.0 chrX:32-41
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,...
select_at(d, vars(starts_with("gr")))
#> DataFrame with 32 rows and 2 columns
#> grX grY
#> <GRanges> <GRanges>
#> Mazda RX4 chrX:1-10 chrY:1-10
#> Mazda RX4 Wag chrX:2-11 chrY:2-11
#> Datsun 710 chrX:3-12 chrY:3-12
#> Hornet 4 Drive chrX:4-13 chrY:4-13
#> Hornet Sportabout chrX:5-14 chrY:5-14
#> ... ... ...
#> Lotus Europa chrX:28-37 chrY:28-37
#> Ford Pantera L chrX:29-38 chrY:29-38
#> Ferrari Dino chrX:30-39 chrY:30-39
#> Maserati Bora chrX:31-40 chrY:31-40
#> Volvo 142E chrX:32-41 chrY:32-41
```
Importantly, grouped operations are supported. `DataFrame` does not
natively support groups (the same way that `data.frame` does not) so
these are implemented specifically for `DFplyr`
``` r
group_by(d, cyl, am)
#> DataFrame with 32 rows and 8 columns
#> Groups: cyl, am
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,...
```
Other verbs are similarly implemented, and preserve row names where
possible
``` r
select(d, am, cyl)
#> DataFrame with 32 rows and 2 columns
#> am cyl
#> <numeric> <numeric>
#> Mazda RX4 1 6
#> Mazda RX4 Wag 1 6
#> Datsun 710 1 4
#> Hornet 4 Drive 0 6
#> Hornet Sportabout 0 8
#> ... ... ...
#> Lotus Europa 1 4
#> Ford Pantera L 1 8
#> Ferrari Dino 1 6
#> Maserati Bora 1 8
#> Volvo 142E 1 4
arrange(d, desc(hp))
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Maserati Bora 8 335 1 5 301 chrX:31-40
#> Ford Pantera L 8 264 1 5 351 chrX:29-38
#> Duster 360 8 245 0 3 360 chrX:7-16
#> Camaro Z28 8 245 0 3 350 chrX:24-33
#> Chrysler Imperial 8 230 0 3 440 chrX:17-26
#> ... ... ... ... ... ... ...
#> Fiat 128 4 66 1 4 78.7 chrX:18-27
#> Fiat X1-9 4 66 1 4 79.0 chrX:26-35
#> Toyota Corolla 4 65 1 4 71.1 chrX:20-29
#> Merc 240D 4 62 0 4 146.7 chrX:8-17
#> Honda Civic 4 52 1 4 75.7 chrX:19-28
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Duster 360 chrY:7-16 -0.02,-1.50, 1.26
#> Camaro Z28 chrY:24-33 0.19,-0.23, 0.89
#> Chrysler Imperial chrY:17-26 0.62,2.37,0.55
#> ... ... ...
#> Fiat 128 chrY:18-27 0.25, 0.40,-0.65,...
#> Fiat X1-9 chrY:26-35 2.39,-1.31,-1.35,...
#> Toyota Corolla chrY:20-29 -0.08,-0.50, 0.73,...
#> Merc 240D chrY:8-17 0.68,0.46,1.23,...
#> Honda Civic chrY:19-28 0.83,0.03,0.06,...
filter(d, am == 0)
#> DataFrame with 19 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Hornet 4 Drive 6 110 0 3 258.0 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360.0 chrX:5-14
#> Valiant 6 105 0 3 225.0 chrX:6-15
#> Duster 360 8 245 0 3 360.0 chrX:7-16
#> Merc 240D 4 62 0 4 146.7 chrX:8-17
#> ... ... ... ... ... ... ...
#> Toyota Corona 4 97 0 3 120.1 chrX:21-30
#> Dodge Challenger 8 150 0 3 318.0 chrX:22-31
#> AMC Javelin 8 150 0 3 304.0 chrX:23-32
#> Camaro Z28 8 245 0 3 350.0 chrX:24-33
#> Pontiac Firebird 8 175 0 3 400.0 chrX:25-34
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> Valiant chrY:6-15 -0.71, 1.49,-0.07
#> Duster 360 chrY:7-16 -0.02,-1.50, 1.26
#> Merc 240D chrY:8-17 0.68,0.46,1.23,...
#> ... ... ...
#> Toyota Corona chrY:21-30 -1.06, 0.76,-1.05
#> Dodge Challenger chrY:22-31 0.04,1.61,0.54
#> AMC Javelin chrY:23-32 -0.36, 0.59,-0.63
#> Camaro Z28 chrY:24-33 0.19,-0.23, 0.89
#> Pontiac Firebird chrY:25-34 -0.82, 1.73,-0.89
slice(d, 3:6)
#> DataFrame with 4 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> Valiant 6 105 0 3 225 chrX:6-15
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> Valiant chrY:6-15 -0.71, 1.49,-0.07
group_by(d, gear) %>%
slice(1:2)
#> DataFrame with 6 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Hornet Sportabout 8 175 0 3 360.0 chrX:5-14
#> Merc 450SL 8 180 0 3 275.8 chrX:13-22
#> Mazda RX4 6 110 1 4 160.0 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160.0 chrX:2-11
#> Porsche 914-2 4 91 1 5 120.3 chrX:27-36
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> Merc 450SL chrY:13-22 1.51,0.35,0.68
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Porsche 914-2 chrY:27-36 -0.47,-0.06,-0.42,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
```
`rename` works in the {dplyr} sense of taking `new = old` replacements
with NSE syntax
``` r
select(d, am, cyl) %>%
rename(foo = am)
#> DataFrame with 32 rows and 2 columns
#> foo cyl
#> <numeric> <numeric>
#> Mazda RX4 1 6
#> Mazda RX4 Wag 1 6
#> Datsun 710 1 4
#> Hornet 4 Drive 0 6
#> Hornet Sportabout 0 8
#> ... ... ...
#> Lotus Europa 1 4
#> Ford Pantera L 1 8
#> Ferrari Dino 1 6
#> Maserati Bora 1 8
#> Volvo 142E 1 4
```
Row names are not preserved when there may be duplicates or they don’t
make sense, otherwise the first label (according to the current
de-duplication method, in the case of `distinct`, this is via
`BiocGenerics::duplicated`). This may have complications for S4 columns.
``` r
distinct(d)
#> DataFrame with 32 rows and 8 columns
#> cyl hp am gear disp grX
#> <numeric> <numeric> <numeric> <numeric> <numeric> <GRanges>
#> Mazda RX4 6 110 1 4 160 chrX:1-10
#> Mazda RX4 Wag 6 110 1 4 160 chrX:2-11
#> Datsun 710 4 93 1 4 108 chrX:3-12
#> Hornet 4 Drive 6 110 0 3 258 chrX:4-13
#> Hornet Sportabout 8 175 0 3 360 chrX:5-14
#> ... ... ... ... ... ... ...
#> Lotus Europa 4 113 1 5 95.1 chrX:28-37
#> Ford Pantera L 8 264 1 5 351.0 chrX:29-38
#> Ferrari Dino 6 175 1 5 145.0 chrX:30-39
#> Maserati Bora 8 335 1 5 301.0 chrX:31-40
#> Volvo 142E 4 109 1 4 121.0 chrX:32-41
#> grY nl
#> <GRanges> <CompressedNumericList>
#> Mazda RX4 chrY:1-10 -0.50, 0.95, 0.40,...
#> Mazda RX4 Wag chrY:2-11 0.28,-0.90, 0.24,...
#> Datsun 710 chrY:3-12 -0.03, 1.76, 0.25,...
#> Hornet 4 Drive chrY:4-13 0.25,-1.76,-1.25
#> Hornet Sportabout chrY:5-14 1.60,0.66,0.97
#> ... ... ...
#> Lotus Europa chrY:28-37 0.47,-0.06, 1.19,...
#> Ford Pantera L chrY:29-38 -1.20,-0.66,-0.69,...
#> Ferrari Dino chrY:30-39 -0.49,-1.81, 0.17,...
#> Maserati Bora chrY:31-40 0.44,-0.99, 1.17,...
#> Volvo 142E chrY:32-41 0.26,0.45,1.79,...
group_by(d, cyl, am) %>%
tally(gear)
#> DataFrame with 6 rows and 3 columns
#> cyl am n
#> <numeric> <numeric> <numeric>
#> 1 4 0 11
#> 2 4 1 34
#> 3 6 0 14
#> 4 6 1 13
#> 5 8 0 36
#> 6 8 1 10
count(d, gear, am, cyl)
#> DataFrame with 10 rows and 4 columns
#> gear am cyl n
#> <factor> <Rle> <Rle> <integer>
#> 1 3 0 4 1
#> 2 3 0 6 2
#> 3 3 0 8 12
#> 4 4 0 4 2
#> 5 4 0 6 2
#> 6 4 1 4 6
#> 7 4 1 6 2
#> 8 5 1 4 2
#> 9 5 1 6 1
#> 10 5 1 8 2
```
## Joins
Joins attempt to preserve rownames and grouping wherever possible
``` r
Da <- as(starwars[, c("name", "eye_color", "height", "mass")], "DataFrame") |>
head(10) |>
group_by(eye_color)
Da
#> DataFrame with 10 rows and 4 columns
#> Groups: eye_color
#> name eye_color height mass
#> <character> <character> <integer> <numeric>
#> 1 Luke Skywalker blue 172 77
#> 2 C-3PO yellow 167 75
#> 3 R2-D2 red 96 32
#> 4 Darth Vader yellow 202 136
#> 5 Leia Organa brown 150 49
#> 6 Owen Lars blue 178 120
#> 7 Beru Whitesun Lars blue 165 75
#> 8 R5-D4 red 97 32
#> 9 Biggs Darklighter brown 183 84
#> 10 Obi-Wan Kenobi blue-gray 182 77
Db <- as(starwars[, c("name", "eye_color", "homeworld")], "DataFrame")
Db
#> DataFrame with 87 rows and 3 columns
#> name eye_color homeworld
#> <character> <character> <character>
#> 1 Luke Skywalker blue Tatooine
#> 2 C-3PO yellow Tatooine
#> 3 R2-D2 red Naboo
#> 4 Darth Vader yellow Tatooine
#> 5 Leia Organa brown Alderaan
#> ... ... ... ...
#> 83 Finn dark NA
#> 84 Rey hazel NA
#> 85 Poe Dameron brown NA
#> 86 BB8 black NA
#> 87 Captain Phasma unknown NA
left_join(Da, Db)
#> Joining with `by = c("name", "eye_color")`
#> DataFrame with 10 rows and 5 columns
#> Groups: eye_color
#> name eye_color height mass homeworld
#> <character> <character> <integer> <numeric> <character>
#> 1 Luke Skywalker blue 172 77 Tatooine
#> 2 C-3PO yellow 167 75 Tatooine
#> 3 R2-D2 red 96 32 Naboo
#> 4 Darth Vader yellow 202 136 Tatooine
#> 5 Leia Organa brown 150 49 Alderaan
#> 6 Owen Lars blue 178 120 Tatooine
#> 7 Beru Whitesun Lars blue 165 75 Tatooine
#> 8 R5-D4 red 97 32 Tatooine
#> 9 Biggs Darklighter brown 183 84 Tatooine
#> 10 Obi-Wan Kenobi blue-gray 182 77 Stewjon
right_join(Da, Db)
#> Joining with `by = c("name", "eye_color")`
#> DataFrame with 87 rows and 5 columns
#> Groups: eye_color
#> name eye_color height mass homeworld
#> <character> <character> <integer> <numeric> <character>
#> 1 Luke Skywalker blue 172 77 Tatooine
#> 2 C-3PO yellow 167 75 Tatooine
#> 3 R2-D2 red 96 32 Naboo
#> 4 Darth Vader yellow 202 136 Tatooine
#> 5 Leia Organa brown 150 49 Alderaan
#> ... ... ... ... ... ...
#> 83 BB8 black NA NA NA
#> 84 Captain Phasma unknown NA NA NA
#> 85 San Hill gold NA NA Muunilinst
#> 86 Shaak Ti black NA NA Shili
#> 87 Grievous green, yellow NA NA Kalee
inner_join(Da, Db[1:3, ])
#> Joining with `by = c("name", "eye_color")`
#> DataFrame with 3 rows and 5 columns
#> Groups: eye_color
#> name eye_color height mass homeworld
#> <character> <character> <integer> <numeric> <character>
#> 1 Luke Skywalker blue 172 77 Tatooine
#> 2 C-3PO yellow 167 75 Tatooine
#> 3 R2-D2 red 96 32 Naboo
full_join(Da, Db[1:3, ])
#> Joining with `by = c("name", "eye_color")`
#> DataFrame with 10 rows and 5 columns
#> Groups: eye_color
#> name eye_color height mass homeworld
#> <character> <character> <integer> <numeric> <character>
#> 1 Luke Skywalker blue 172 77 Tatooine
#> 2 C-3PO yellow 167 75 Tatooine
#> 3 R2-D2 red 96 32 Naboo
#> 4 Leia Organa brown 150 49 NA
#> 5 Owen Lars blue 178 120 NA
#> 6 Beru Whitesun Lars blue 165 75 NA
#> 7 Darth Vader yellow 202 136 NA
#> 8 Biggs Darklighter brown 183 84 NA
#> 9 Obi-Wan Kenobi blue-gray 182 77 NA
#> 10 R5-D4 red 97 32 NA
```
## Coverage
Most `dplyr` functions are implemented.
If you find any which are not, please [file an
issue](https://github.com/jonocarroll/DFplyr/issues/new).
