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set.seed(123)
n <- 5
scheme <- paste("Scheme", 1:n)
mitigator <- paste("Mitigator", LETTERS[1:n])
combos <- tidyr::crossing(scheme, mitigator)
nrows <- nrow(combos)
dat <- combos |>
  dplyr::mutate(
    mitigator_group = dplyr::if_else(
      stringr::str_detect(mitigator, " [ABC]$"),

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# Calculate how many plots will be produced
count_plots <- function(df, var) {
  length(unique(df[[var]]))
}

# Plot faceted chart, save with height dependent on plot count
write_responsive_facets <- function(
  df, 
  var = "species",
  plot_count = count_plots(df, var), 

matt-dray

# {nara} by Mike Cheng: https://coolbutuseless.github.io/package/nara/index.html
# Kenney by Kenney: https://www.kenney.nl

# Read png tiles

tile_paths <- list.files(
  "~/Desktop/kenney/kenney_tiny-dungeon/Tiles",
  pattern = ".png$",
  full.names = TRUE,
  recursive = TRUE

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# 1. Generate temporary Excel files ----

# Make a temporary spreadsheet with named sheets
make_temp_xlsx <- function(sheet_names) {
  wb <- openxlsx2::wb_workbook()
  for (sheet in sheet_names) wb <- wb |> openxlsx2::wb_add_worksheet(sheet)
  temp <- openxlsx::temp_xlsx()
  openxlsx2::wb_save(wb, temp)
}

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#' Convenience Function: Read a (Zipped) JSON File from an Azure Container
#'
#' @param container A blob_container/storage_container object. Probably
#'     generated by [connect_to_container].
#' @param file Character. Path to file within the container named by
#'     `container`.
#'
#' @return A list.
#' @export
#'

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generate_num_format <- function(number = 8634567.890) {
  
  num_val <- number %/% 1
  num_dp <- number %% 1
  
  num_split <- strsplit(as.character(number), "\\.")[[1]]
  n_dp <-  nchar(num_split[2])
  
  num_commas <- scales::number(as.numeric(num_val), big.mark = ",")
  fmt_out <- gsub("[[:digit:]]", "#", num_commas)

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1. On the repo homepage, click the text 'Releases' in the right-hand section.
2. Click the 'Draft a new release' button (upper-right).
3. Click the 'Choose a tag' button (upper-left). This is going to 'tag' this point in the Git history with the version number.
4. Type in the box the version you're releasing, e.g. 'v0.1.1'.
5. Click '➕ Create new tag: v0.1.1 on publish' that appears under the box.
6. Click 'Generate release notes', which will autoname the release with the tag autofill the notes with a bullet per PR since the last release
7. Click 'Publish release' button (lower-right) to publish the release and add the tag to the version history.
8. Return to the repo's homepage, where you'll see that the version number has incremented under the 'Releases' section.

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Title

• GitHub etiquette
• GitHub in a small team
• GitHub: what works for us
• A loose GitHub playbook
• GitHub is a team sport

Angle

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1. In your package project, run usethis::use_data_raw("demo-data") to set up a data-raw/ folder with a demo-data.R file inside.
2. Write a script in demo-data.R to produce the data object (e.g. demo_df).
3. Insert and run the line usethis::use_data(demo_df) in demo-data.R, which will save the data object to a data/demo_df.rda file.
4. Run usethis::use_r("demo-data") to create a corresponding R/demo-data.R file where you can document the data.
5. In R/demo-data.R, quote the name of the data object (i.e. "demo_df") and put {roxygen2} code above it (probably at least @title, @description and maybe @format, which might contain a \describe{} block to explain the content of your object, itself containing an \item{} to describe each column if it's a data.frame).
6. Run devtools::document() to generate the man/ pages for the data.
7. Run devtools::load_all() to reload your package and make demo_df available in your session.
8. Once pushed, users can attach the package and acc

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