matt-dray

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 access demo_df by name, or access it with pac

matt-dray

/*! jQuery v3.2.1 | (c) JS Foundation and other contributors | jquery.org/license */
/*! Adapted from https://jeroen.github.io/clippy/bundle.js by Jeroen Ooms */
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matt-dray

arsenal::tableby()
DT
flextable
formattable
gt
huxtable
knitr::kable()
kableExtra
mmtable2
modelsummary

matt-dray

x <- palmerpenguins::penguins |>
  tidyr::drop_na() |>
  dplyr::mutate(
    sp = species,
    bill = bill_length_mm,
    sex = as.character(sex),
    .keep = "none"
  ) |>
  head() |>
  as.data.frame()

matt-dray

---
title: "Autogenerating Rmd sections from a nested list"
output: 
  html_document:
    code_folding: hide
---

```{r}
remove_blanks_recursively <- function(x) {

matt-dray

#' Check Class of Argument Inputs
#' @param ... Objects to be checked for class.
#' @param .expected_class Character. The name of the class against which objects
#' @param .call Environment. The environment in which this function is called.
#'     will be checked.
#' @noRd
check_class <- function(
    ...,
    .expected_class = c("numeric", "character"),
    .call = rlang::caller_env()

matt-dray

# As above, but checks for exact name and affiliation matches between
# sifters and abstracts.
#
# It's possible certain abstracts might not get assigned, especially given combos
# of sifters writing abstracts, abstracts from sifters' affiliations, assignment
# capping and sifter capping. Should probably report any abstracts that have 
# <assignment_cap assignments. As a precaution, have built in a max_iterations arg
# in case of infinite looping, but I don't think that will come into play.

.resample <- function(x, ...) x[sample.int(length(x), ...)]  # see ?sample

matt-dray

# Make a README badge for your R Shiny repo
# Matt Dray, March 2021

# {badgr} blog post: https://www.rostrum.blog/2020/05/08/readme-badge/
# Shiny badge blog post: https://www.rostrum.blog/2021/03/23/shiny-badge/

# {badgr} is available from GitHub via {remotes}
install.packages("remotes")  # if not already installed
remotes::install_github("matt-dray/badgr")  # install {badgr}

matt-dray

pair_inputs <- function(...) {
  
  args <- rlang::dots_list(..., .named = TRUE)
  args_lengths <- lapply(args, length)
  
  pairs <- combn(args_lengths, 2, simplify = FALSE)
  
  failed <- lapply(pairs, function(x) max(unlist(x)) %% min(unlist(x)) != 0)
  
  if (any(unlist(failed))) {

matt-dray

test_fn <- function(x = 1L, y = "x", z = 1.1) {
  .check_type(x, y, z, type = "numeric")
  cat("success")
}

.check_type <- function(..., type = "numeric") {
  
  args <- list(...)
  arg_names <- as.character(substitute(...()))
  names(args) <- arg_names