goslingr

The goal of goslingr is to ...

Installation

You can install the development version of goslingr like so:

# install.packages('remotes')
remotes::install_github('ccb-hms/goslingr')

# install gosling python package (one time)
library(goslingr)
install_gosling()

Define a Gosling Specification

gosling visualizations are defined using a Gosling specification, which is an R list or JSON object. You can define your spec directly as an R list:

library(goslingr)
library(GenomicRanges)

# toy GRanges object
gr <- GRanges(
  seqnames = paste0('chr', c(1:22, 'X', 'Y')),
  peak = rnorm(24, mean = 500, sd = 100),
  ranges = IRanges(
    start = rep(0, 24),
    end = rep(10, 24)
  )
)

# data part of spec
gr_data_spec <- list(
  type = "json",
  chromosomeField = "seqnames",
  genomicFields = c("start", "end"),
  values = as.data.frame(gr)
)

# example bar chart spec
spec <- list(
  title = "Basic Marks: bar",
  subtitle = "Tutorial Examples",
  tracks = list(
    list(
      layout = "linear",
      width = 800,
      height = 180,
      data = gr_data_spec,
      mark = "bar",
      x = list(field = "start", type = "genomic", axis = "bottom"),
      xe = list(field = "end", type = "genomic"),
      y = list(field = "peak", type = "quantitative", axis = "right"),
      size = list(value = 5)
    )
  )
)

goslingr(spec)

You can alternatively define the specification using the python gos api and then extract the JSON specification:

Note that you need to substitute with R equivalents ($ instead of ., c() instead of [], TRUE instead of True, etc).

library(goslingr)

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")
)

view <- track$view()

# extract gosling specification
spec <- view$to_json()

# generate the plot
goslingr(spec)

Finally, you can provide the specification as a JSON string (useful for getting started from gosling.js examples):

spec <- '{
  "title": "Basic Marks: bar",
  "subtitle": "Tutorial Examples",
  "tracks": [
    {
      "layout": "linear",
      "width": 800,
      "height": 180,
      "data": {
        "url": "https://resgen.io/api/v1/tileset_info/?d=UvVPeLHuRDiYA3qwFlm7xQ",
        "type": "multivec",
        "row": "sample",
        "column": "position",
        "value": "peak",
        "categories": ["sample 1"],
        "binSize": 5
      },
      "mark": "bar",
      "x": {"field": "start", "type": "genomic", "axis": "bottom"},
      "xe": {"field": "end", "type": "genomic"},
      "y": {"field": "peak", "type": "quantitative", "axis": "right"},
      "size": {"value": 5}
    }
  ]
}'

# generate the plot
goslingr(spec)

For full details on how to define a gosling specification check out the docs.

Local Data

See local data example. goslingr will server data using gos if a local file path is detected. This will work no matter how you define your specification.