DFplyr

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 with:

# install.packages("devtools")
devtools::install_github("jonocarroll/DFplyr")

You can install from Bioconductor with:

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

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,...

DataFrames 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

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 %>%

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

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

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

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

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

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.

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

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.