SpatialDataChronicles - RConsortiumISC Grants TidyTuesday

Introduction

The R Consortium Infrastructure Steering Committee (ISC) Grant Program is a key initiative aimed at strengthening the R ecosystem. This program supports projects that contribute to both the technical and social infrastructure of the R community. With a new round of grant proposals being accepted between March 1 and April 1, 2024, this analysis explores the distribution and impact of past grants awarded since 2016.

Libraries used

tidyverse: A collection of R packages for data manipulation and visualization.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Data Acquisition

The dataset for this analysis is sourced from the TidyTuesday project, which provides weekly data sets for the R community to explore.

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

Rows: 85 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): title, proposed_by, summary, website
dbl (3): year, group, funded

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Data Preparation

We’ll start by summarizing the number of grants awarded per cycle.

summary_of_grants_awarded_per_cycle <- isc_grants %>% 
                                     dplyr::group_by(group,year) %>%
                                     dplyr::summarise(`grant count` = n())

`summarise()` has grouped output by 'group'. You can override using the
`.groups` argument.

Visualization

Let’s visualize the total number of grants awarded per cycle.

summary_of_grants_awarded_per_cycle %>%
  ggplot(aes(x = year, y = factor(group),
             fill = `grant count`)) +
  geom_tile(color = 'white') +
  labs(x = "Year",
       y = "Award Cycle",
       fill = "Grant Count",
       title = "Total grant awarded per cycle") +
  scale_fill_viridis_c(direction = -1) + 
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, 
                                   hjust = 1)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        plot.title = element_text(hjust = 0.5)) # hjust = 0.5 centers the title

Data Preparation

Next, we’ll prepare the data to visualize the total dollar amount awarded per cycle.

total_amount_of_grants_awarded_per_cycle <- isc_grants %>% 
                                       dplyr::group_by(group,
                                                       year) %>%
                                       dplyr::summarise(`dollar amount` = sum(funded))

`summarise()` has grouped output by 'group'. You can override using the
`.groups` argument.

Visualization

And here’s the visualization for the total dollar amount awarded per cycle.

total_amount_of_grants_awarded_per_cycle %>%
  ggplot(aes(x = year, y = factor(group), fill = `dollar amount`)) +
  geom_tile(color = 'white') +
  labs(x = "Year",
       y = "Award Cycle",
       fill = "Grant dollar amount",
       title = "Total dollar amount awarded") +
  scale_fill_distiller(palette = "Spectral") +  # Change palette name ahttp://127.0.0.1:11411/chunk_output/s/9C4F5FC2/c49ht5ihk2fr1/000010.pngs needed
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1),
        plot.title = element_text(hjust = 0.5))

Conclusion

This analysis provides a visual overview of the R Consortium ISC Grants awarded over the years. By exploring the number of grants and the total funding amounts, we can gain insights into the trends and focus areas of the R Consortium’s support for the R community. As the program continues to accept new proposals, it will be interesting to see how these trends evolve and how they impact the growth and development of the R ecosystem.

Code:

The code for this project can be found in the project GitHub repository