Plot data in loop

Johannes Krietsch

This article shows how to make and save plots in a loop and parallel loop using the package foreach and doFuture. This is useful for example when checking the data by tag ID using atl_check_tag().

Load packages

# packages
library(tools4watlas)
library(ggplot2)
library(foreach)
library(doFuture)
library(ragg)

Plot and save data in loop

Example of a simple loop by tag ID and using atl_check_tag().

# load example data
data <- data_example

# file path
path <- "./outputs/maps_by_tag/tag_"

# unique ID (here by tag)
id <- unique(data$tag)

# loop to make plots for all
foreach(i = id) %do% {

  # subset data
  data_subset <- data[tag == i]

  # plot data
  p <- atl_check_tag(
    data_subset,
    option = "datetime",
    highlight_first = TRUE, highlight_last = TRUE
  )

  # save
  agg_png(
    filename = paste0(path, i, ".png"),
    width = 3840, height = 2160, units = "px", res = 300
  )
  print(p)
  dev.off()

}

Plot and save data in parallel loop

We can use the same structure, simply replacing %do% with %dofuture%, which is the key advantage of using foreach. The only additional step is setting up parallel processing with registerDoFuture() and plan(multisession).

# load example data
data <- data_example

# file path
path <- "./outputs/maps_by_tag/tag_"

# unique ID (here by tag)
id <- unique(data$tag)

# register cores and backend for parallel processing
registerDoFuture()
plan(multisession)

# loop to make plots for all
foreach(i = id) %dofuture% {

  # subset data
  data_subset <- data[tag == i]

  # plot data
  p <- atl_check_tag(
    data_subset,
    option = "datetime",
    highlight_first = TRUE, highlight_last = TRUE
  )

  # save
  agg_png(
    filename = paste0(path, i, ".png"),
    width = 3840, height = 2160, units = "px", res = 300
  )
  print(p)
  dev.off()

}

# close parallel workers
plan(sequential)