matt-dray

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.

matt-dray

Title

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

Angle

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 acc

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 */
!function(a,b){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){"use strict";var c=[],d=a.document,e=Object.getPrototypeOf,f=c.slice,g=c.concat,h=c.push,i=c.indexOf,j={},k=j.toString,l=j.hasOwnProperty,m=l.toString,n=m.call(Object),o={};function p(a,b){b=b||d;var c=b.createElement("script");c.text=a,b.head.appendChild(c).parentNode.removeChild(c)}var q="3.2.1",r=function(a,b){return new r.fn.init(a,b)},s=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,t=/^-ms-/,u=/-([a-z])/g,v=function(a,b){return b.toUpperCase()};r.fn=r.prototype={jquery:q,constructor:r,length:0,toArray:function(){return f.call(this)},get:function(a){return null==a?f.call(this):a<0?t

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

# Add abstract info and assessment columns to assignments
.prepare_sifter_outputs <- function(
  assignments,
  abstracts_df,
  strip_abstract_info = TRUE,
  add_assessment_columns = TRUE
) {
  assignments <- lapply(
    assignments,
    \(x) abstracts_df[abstracts_df$abstract_id %in% x, ]

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))) {