This package provides tools to access and process data from 360Giving, a data standard for publishing information about charitable grant giving in the UK. The ability to search for and query different data is similar to the GrantNav tool created by 360Giving. There are several advantages to using the software package instead of GrantNav:
It lets you retrieve 360Giving data, search and summarise that data, create charts and tables entirely within R.
It gives you access to the raw data provided by funders. The 360Giving standard allows for optional information that is not provided by most funders or included in GrantNav. This information can be useful to fundraising managers in some situations, as well as to researchers wanting to understand the size, scope and approaches to charitable trust giving in the UK.
threesixtygiving returns data in types and
structures designed for R.
The example below shows the average size of grants given by various funders:
library(threesixtygiving) library(dplyr) ## A package used to manipulate and process data # Download all the grant data available. # It comes out in a list of tibbles but that's not a problem: grants <- tsg_all_grants(timeout = 10, retries = 0) # Get just the core data required by the 360Giving standard. # Convert that list of tibbles into a tidy format: all_data <- tsg_core_data(grants) avg_size <- all_data %>% #This %>% thing is a called a pipe. Pipes are love. Pipes are life. filter(currency == "GBP") %>% # Filter out non-£ data group_by(funding_org_name) %>% # Group all the funders together summarise(avg = mean(amount_awarded)) # Get the average of given value for each group, kinda like =AVERAGEIFS in Excel. avg_size # # A tibble: 109 x 2 # funding_org_name avg # <chr> <dbl> # 1 A B Charitable Trust 13714. # 2 Barrow Cadbury Trust 35889. # 3 BBC Children in Need 55968. # 4 Birmingham City Council 32097. # 5 Calouste Gulbenkian Foundation, UK Branch 34352. # 6 Camden Giving 18254. # 7 Cheshire Community Foundation 6027. # 8 City Bridge Trust 81381. # 9 Cloudesley 14441. # 10 Co-operative Group 3238. # # ... with 99 more rows
tsg_all_grants() function retrieves all the grant
data. And if data isn’t available, or there is a downloading error, it
will print a message warning you. No more silently failing downloads.
There is even a
tsg_missing() function, which will take the
tsg_all_grants() and tell you what’s missing.
Say you want to include all the grants that are in currencies other
than pounds, but convert their value to pounds? In the example below I
show a way to do that where I just looked up the currency conversion
rates on my phone and changed the currency based on that, but you could
also write some code that takes the currency code, the award date, and
passes them to the https://fixer.io currency conversion service using the
package, which provided you have the right account type, will convert
the currency value to pound using the conversion rate on the day the
grant was awarded.
Below are all the R packages that I’m using create the graphs and such in this article.
library(threesixtygiving) library(ggrepel) library(readr) library(purrr) library(dplyr) library(stringr) library(ggplot2)
First, I’m going to retrieve all the grant data. The
timeout = 8 means I’m having the function give up trying to
retrieve data after 8 seconds for each file, and
retries = 1 means it is only going to retry failed
retrievals once, then it will give up and move on to retrieve the next
grants <- tsg_all_grants(timeout = 8, retries = 1)
Because of my minimal timeout and retries, as well as possible other problems with how organisations configure their servers, there are some datasets missing:
tsg_missing(grants) # # A tibble: 7 x 14 # title description identifier license license_name issued modified download_url access_url data_type # <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> # 1 Gran~ "" a001p0000~ https:~ Creative Co~ 2019-~ 2019-11~ https://jof~ https://j~ xlsx # 2 Gran~ "" a00240000~ https:~ Creative Co~ 2017-~ 2018-08~ https://pea~ https://p~ xlsx # 3 Gran~ "" a00240000~ https:~ Creative Co~ 2017-~ 2018-08~ https://pea~ https://p~ xlsx # 4 Gran~ "" a001p0000~ https:~ Creative Co~ 2018-~ 2019-03~ https://pea~ https://p~ xlsx # 5 Gran~ "" a001p0000~ https:~ Creative Co~ 2018-~ 2019-08~ https://tud~ https://t~ xlsx # 6 True~ "" a00240000~ https:~ Creative Co~ 2016-~ 2018-08~ http://ods.~ http://ww~ unknown # 7 Wood~ "" a001p0000~ https:~ Creative Co~ 2017-~ 2018-08~ http://ods.~ http://ww~ unknown # # ... with 4 more variables: publisher_name <chr>, publisher_website <chr>, publisher_logo <chr>, # # publisher_prefix <chr>
However, we know they are missing, and we can retry downloading just
them if needed using the
tsg_specific_df() function. But
I’ll ignore them for now.
tsg_all_grants returns a list of tibbles, due to
differences in the data provided by each funder.
tsg_core_data returns a tibble with the 10 columns in the
Open Standard, plus the publisher prefix used to identify each
tsg_process_data() converts a list of tibbles into
a single tibble, preserving all columns (with an option to only preserve
columns where a minimum proportion have data). In the case below I have
converted non-GBP currencies to GBP on the rates on 2020-01-17.
df <- tsg_core_data(grants) df2 <- df %>% mutate(amount_awarded = case_when( currency == "USD" ~ amount_awarded/1.3044, ## rate on 2020-01-17 currency == "CAD" ~ amount_awarded/1.7017, currency == "CHF" ~ amount_awarded/1.2630, currency == "EUR" ~ amount_awarded/1.1750, TRUE ~ amount_awarded)) %>% group_by(funding_org_name) %>% summarise(n = n(), amount_awarded = sum(amount_awarded)) %>% mutate(avg = amount_awarded/n)
proc_df <- tsg_process_data(grants) proc_amount_df <- proc_df %>% select(identifier, title, description, funding_org_name, amount_awarded, amount_applied_for) %>% mutate_at(.vars = vars(amount_awarded, amount_applied_for), as.numeric) %>% mutate(difference = amount_awarded-amount_applied_for, percentage_of = amount_awarded/amount_applied_for) %>% filter(!is.na(amount_applied_for), amount_applied_for > 2500, amount_awarded > 2500, percent_rank(difference) >= 0.02 & percent_rank(difference) <= 0.98) %>% ## remove outliers mutate(difference = round(difference)) count0 <- proc_amount_df %>% filter(difference==0) %>% tally() p4 <- ggplot(proc_amount_df %>% filter(difference != 0), aes(x = difference)) + geom_freqpoly(bins = 100) + geom_label_repel( aes(x = 0, y = 4000, label = paste0(scales::comma(n), " applications awarded exact amount applied for")), data = count0) + scale_color_viridis_c() + scale_y_continuous(labels = scales::comma) + scale_x_continuous(labels = scales::dollar_format(prefix = "£"), breaks = c(-20000, -10000, -5000, -2500, 0, 2500, 5000, 7500)) + labs(x = "Difference between Award and Application Amount", y = "Number of Grants", caption = "(c) Evan Odell | Disability Rights UK | 2020 | CC-BY-SA Data from 360Giving") p4
p5 <- ggplot(proc_amount_df %>% filter(difference != 0, percentage_of != Inf), aes(x = percentage_of)) + geom_freqpoly(bins = 200) + geom_label_repel( aes(x = 1, y = 3000, hjust = 1, label = paste0(scales::comma(n), " applications awarded exact amount applied for")), data = count0) + scale_color_viridis_c() + scale_y_continuous(labels = scales::comma) + scale_x_continuous(labels = scales::percent, breaks = c(0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2)) + labs(x = "Award size as percentage of amount applied for", y = "Number of Grants", caption = "(c) Evan Odell | Disability Rights UK | 2020 | CC-BY-SA Data from 360Giving") p5