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R interface to 'Gos'

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**Here be dragons** 🐉

# g(r)osling

The goal of **g(r)osling** is to help you build interactive genomics
visualizations with
[Gosling](https://github.com/gosling-lang/gosling.js). This package uses
[reticulate](https://rstudio.github.io/reticulate/) to provide an
interface to the [Gos](https://github.com/gosling-lang/gos) Python
package.

## Example

``` r
library(gosling)

data <- gos$bigwig(
  url = "https://s3.amazonaws.com/gosling-lang.org/data/ExcitatoryNeurons-insertions_bin100_RIPnorm.bw",
  column = "position",
  value = "peak"
)

track <- gos$Track(data, height = 100)$mark_point()$encode(
  x = gos$X("position:G"),
  y = gos$Y("peak:Q")
)

track$view()
```

![Gosling
visualization](https://user-images.githubusercontent.com/24403730/180341226-a9ac36b4-47cb-40d8-bfd2-a2e6f18c153f.png)

## Installation

You can install the development version of gosling like so:

``` r
devtools::install_github("gosling-lang/grosling") 
```

Because of Python, there may be some additional installation steps.

1.) Python must be installed on your system. We recommend `conda`.

2.) Create a conda environment called `"r-reticulate"`. It is
[recommended](https://rstudio.github.io/reticulate/articles/python_packages.html)
to use this environment name for all packages that use reticulate.

3.) Install Gos into your `"r-reticulate"` environment using
`gosling::install_gosling()`.

You may wish to add a line like this to the `.First()` function in your
`.Rprofile`:

``` r
reticulate::use_condaenv("r-reticulate")
```

The `use_condaenv()` function is called to provide a hint to reticulate
on which Python environment to use.

## Local Data

Gos [transparently
serves](https://gosling-lang.github.io/gos/user_guide/local_data.html)
local and in-memory datasets for the Gosling client via a background
data server. This feature is enabled automatically whenever a local file
path is detected (rather than a URL).

Due to [a temporary limitation with
`reticulate`](https://github.com/rstudio/reticulate/issues/515#issuecomment-1196609327),
some additional setup is required to enable this feature in
**grosling**. Hopefully this will be resolved in `reticulate` soon, but
the current workaround is shown below.

``` r
library(gosling)

### Setup reticulate to continually yield R <> Python. You only need to run this *once*.
local({
  reticulate::py_run_string("from time import sleep")
  py_yield_and_register_next_yield <- function() {
    reticulate::py_eval("sleep(0.001)")
    later::later(py_yield_and_register_next_yield, .001)
    invisible()
  }
  later::later(py_yield_and_register_next_yield)
})
###

file <- "./ExcitatoryNeurons-insertions_bin100_RIPnorm.bw" # local file
data <- gos$bigwig(file, column = "position", value = "peak")

track <- gos$Track(data, height = 100, width = 750)$mark_bar()$encode(
  x = gos$X("position:G"),
  y = gos$Y("peak:Q"),
  color = gos$Color("peak:Q")
)

track$view()
```

![Gosling visualization using local
file](https://user-images.githubusercontent.com/24403730/181269342-9e8d1246-d227-4850-b1ae-66f747654369.png)

grosling

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