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https://github.com/jonocarroll/DFplyr

A `DataFrame` (`S4Vectors`) backend for `dplyr`
https://github.com/jonocarroll/DFplyr

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A `DataFrame` (`S4Vectors`) backend for `dplyr`

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# 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](https://github.com/) with:



``` r

# install.packages("devtools")

devtools::install_github("jonocarroll/DFplyr")

```

## Examples



Most `dplyr` functions are implemented. If you find any which are not, please [file an issue](https://github.com/jonocarroll/DFplyr/issues/new).



```{r}

suppressPackageStartupMessages(

  suppressWarnings({

    library(S4Vectors)

    library(dplyr)

    library(DFplyr)

  }))

```



First create an S4Vectors DataFrame, including S4 columns if desired



```{r}

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

```



This will appear in RStudio's environment pane as a `Formal class DataFrame (dplyr-compatible)` when 

using `DFplyr`. No interference with the actual object is required, but this helps 

identify that `dplyr`-compatibility is available.



`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}

mutate(d, newvar = cyl + hp)



mutate(d, nl2 = nl * 2)



mutate(d, length_nl = lengths(nl))



mutate(d, 

       chr = GenomeInfoDb::seqnames(grX), 

       strand_X = BiocGenerics::strand(grX), 

       end_X = BiocGenerics::end(grX))

```



the object returned remains a standard `DataFrame`, and further calls can be 

piped with `%>%`



```{r}

mutate(d, newvar = cyl + hp) %>%

  pull(newvar)

```



Some of the variants of the `dplyr` verbs also work



```{r}

mutate_if(d, is.numeric, ~.^2)



mutate_if(d, ~inherits(., "GRanges"), BiocGenerics::start)

```



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)



select_at(d, vars(starts_with("gr")))

```



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)



# group_by(d, cyl) %>% 

#   top_n(1, disp)

```



Other verbs are similiarly implemented, and preserve row names where possible



```{r}

select(d, am, cyl)



select(d, am, cyl) %>%

  DFplyr::rename(foo = am)



arrange(d, desc(hp))



filter(d, am == 0) 



slice(d, 3:6)



# dd <- rbind(data.frame(d[1, ], row.names = "MyCar"), d)

# dd

```



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)



group_by(d, cyl, am) %>%

  tally(gear)



count(d, gear, am, cyl)

```



`ggplot2` support is also enabled



```{r}

library(ggplot2)

ggplot(d, aes(disp, cyl)) + geom_point()

```



## Implementation



Most of the `dplyr` verbs for `DataFrame`s are implmented by first converting to `tibble`, 

performing the verb operation, then converting back to `DataFrame`. Care has been taken to 

retain groups and row names through these operations, but this may introduce some 

complications. If you spot any, please [file an issue](https://github.com/jonocarroll/DFplyr/issues/new).