readme.md

R Consortium ISC Grants

The R Consortium Infrastructure Steering Committee (ISC) Grant Program will accept proposals again between March 1 and April 1, 2024 (and then again in the fall).

This initiative is a cornerstone of our commitment to bolstering and enhancing the R Ecosystem. We fund projects contributing to the R community’s technical and social infrastructures.

Learn more in their blog post announcing this round of grants.

The R Consortium ISC has been awarding grants since 2016. This week's data is an exploration of past grant recipients.

Are there any keywords that stand out in the titles or summaries of awarded grants? Have the funded amounts changed over time?

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-02-20')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 8)

isc_grants <- tuesdata$isc_grants

# Option 2: Read directly from GitHub

isc_grants <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-02-20/isc_grants.csv')

How to Participate

• Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
• Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
• Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.

Data Dictionary

isc_grants.csv

variable	class	description
year	integer	The year in which the grant was awarded.
group	integer	Whether the grant was awarded in the spring cycle (1) or the fall cycle (2).
title	character	The title of the project.
funded	integer	The dollar amount funded for the project.
proposed_by	character	The name of the person who requested the grant.
summary	character	A description of the project.
website	character	The website associated with the project, if available.

Cleaning Script

library(tidyverse)
library(janitor)
library(rlang)
library(rvest)
library(here)

working_dir <- here::here("data", "2024", "2024-02-20")

all_grants_url <- "https://www.r-consortium.org/all-projects/awarded-projects"
urls <-
  all_grants_url |> 
  rvest::read_html() |> 
  rvest::html_node(".main-content") |> 
  rvest::html_nodes("a") |> 
  rvest::html_attr("href") |> 
  stringr::str_subset(stringr::fixed("/projects/awarded-projects/")) |> 
  stringr::str_remove("#.+$") |> 
  unique() |> 
  rvest::url_absolute(all_grants_url)

get_grant_data <- function(url) {
  year <- as.integer(stringr::str_extract(url, "\\d{4}"))
  group <- as.integer(stringr::str_extract(url, "\\d$"))
  project_description_html <- 
    rvest::read_html(url) |> 
    rvest::html_nodes(".project-description") 
  titles <- 
    project_description_html |> 
    rvest::html_nodes("h3") |> 
    rvest::html_text2()
  other_data <- 
    project_description_html |> 
    purrr::map(parse_project_description) |> 
    purrr::list_rbind()
  
  tibble::tibble(
    year = year,
    group = group,
    title = titles,
    other_data
  )
}

parse_project_description <- function(project_description_html) {
  pieces <- project_description_html |> 
    rvest::html_nodes("p") |> 
    rvest::html_text2() |> 
    stringr::str_split(":\n")
  
  pieces <- purrr::reduce(
    pieces,
    \(x, y) {
      if (!rlang::is_named(x) && length(x) == 2) {
        x <- rlang::set_names(x[[2]], x[[1]])
      }
      if (length(y) == 2) {
        y <- rlang::set_names(y[[2]], y[[1]])
      } else {
        x[[length(x)]] <- paste(x[[length(x)]], y, sep = "\n")
        y <- NULL
      }
      return(c(x, y))
    }
  )
  
  pieces |> 
    tibble::enframe() |> 
    tidyr::pivot_wider(names_from = name, values_from = value)
}

isc_grants <- 
  urls |> 
  purrr::map(get_grant_data) |> 
  purrr::list_rbind() |> 
  janitor::clean_names() |> 
  dplyr::mutate(
    funded = stringr::str_remove(funded, "\\$") |> 
      stringr::str_remove(",") |> 
      as.integer(),
    website = dplyr::na_if(website, "")
  )

readr::write_csv(
  isc_grants,
  fs::path(working_dir, "isc_grants.csv")
)