rnsf

Unofficial package to interface with NSF API

It also has abstracts, dates, and more information for all grants up to 2026-09-23.

Installation

New (Oct 2026) You can just install this package the way you would any R package on github:

remotes::install_github("bomeara/rnsf")

(You’ll need to first install remotes (by doing install.packages("remotes")) if you don’t already have it).

Thanks to the piggyback package, the huge cached files are now stored as releases rather than within the package, making install easy.

Usage

There are three main ways to get data:

  • Use rnsf::nsf_return() with various arguments to pull in data directly from NSF’s API for awards. Good if you want to find info by keyword, state, or other fields.
    • For example, if we’re curious about grants relevant to Yellowstone National Park, we could call rnsf:nsf_return(keyword="Yellowstone"). It will call the API multiple times until it has downloaded info on all grants with that keyword and returned a data.frame object.
  • Use rnsf::load_nsf_grants() to load cached data (available here) for years you specify. By default, it loads cached data from the most recent year. Good if you want to do an analysis across thousands of grants.
    • If we wanted to look at grants from 2020 to the present, we can call rnsf::load_nsf_grants(year_start=2020) and it will download the cached grants.
    • If we wanted to add any grants since I last updated the cache, we can call rnsf::load_nsf_grants(year_start=2020, update_current_year=TRUE). This will re-download all grants from the current year.
  • Use data(grfp) after loading the package to load information on all the Graduate Research Fellowship Program awards and honorable mentions (these are not available from NSF’s API, so I had to download each year’s pair of spreadsheets).

Below are some examples of the package’s utility; see https://github.com/bomeara/rnsf/blob/master/README.Rmd for the details of the code to make the plots.

Bergograms

Scientist Jeremy Berg often graphs federal funding over time (see https://jeremymberg.github.io/jeremyberg.github.io/). The very useful website Grant Witness has adopted these graphs, including invaluable updates of national funding and breakdowns by NSF division (see, for example, here). With the rnsf package we can look at finer detail. For example, the US Census splits the 50 US states into four different regions: Northeast, Midwest, South, and West. We can look at funding over time by region instead of nationally:

Note some important differences between the plots above and those from Grant Witness: they typically plot by financial year, not calendar year; they also filter out transfer grants (a grant moves between PIs or institutions) and the code above does not do that.

Topic frequency over time

The ozone hole was discovered in 1985: pollution led to a depletion of the ozone layer, which shields people (and other organisms) from a great deal of UV radiation. The world came together and passed the Montreal Protocol to limit the pollution, chlorofluorocarbons, that was causing the damage. The hole is healing, but still requires research and monitoring. We can see how NSF grants mentioning “ozone hole” changed over time (though not all grants mentioning “ozone hole” study this issue; some could be using it as a comparison to some other issue, for example). We can include a regression before and after 1985 and show the 95% CI for the proportion in each year (truncated by the y-axis limits).

Keywords

Let’s look just at awards made in any program with “bio” or “systematics” or “evolution” in the name (biology grants) and see how words have changed between those that mention collections-related work (voucher, collection, field work, fieldwork, specimen) or those that mention AI (AI, artificial intelligence, LLM, large language model) in the abstract. First as a proportion of all the grants of either kind:

And then the actual amount of funding awarded:

Table of award info

We can also look at a table with the number, not total money, of grants by state or territory by academic semester, for example (only including this year up to the last cache of the data, and just showing the top few states by grants).

Area 2024 Spring 2024 Fall 2025 Spring 2025 Fall 2026 Spring 2026 Fall
California 508 752 361 586 269 451
New York 371 475 256 371 160 287
Texas 324 415 224 355 140 273
Massachusetts 327 410 189 305 127 229
Pennsylvania 243 282 165 233 116 190
Illinois 218 277 122 224 79 132

Comparisons by state

We can see how number of awards and total value of awards by state or territory versus the average so far this year compares to average funding at this point of the year for 2017-2024 (so it encompasses two different administrations). Note that the color scale is symmetric – a reduction is orange, a gain is purple, and no change is white.

Note that VT had an increase of 288% but was truncated at 179 so that no change would remain at the center of the plot colors.

Rolling window

How is NSF awarding grants over time? This uses a two week rolling interval, showing the average grants awarded per day in that interval. This can pick up things like temporary stoppages of awards.

And rolling window not on a log scale:

Wordclouds

This pulls in all grants mentioning the clade of ants (ants <- rnsf::nsf_return(keyword="Formicidae") and then uses the rnsf::nsf_wordcloud() function to list the words by frequency.

#> [1] "Finished first batch"

GRFP data

The NSF Graduate Research Fellowship Program (GRFP) is one of NSF’s oldest and most impactful programs: it provides funding for three years of study for people in grad school, giving them flexibility (no need for a research or teaching assistantship); the stipends are also often higher than those for most grad students. Every year, names, affiliations, and research areas of awardees (those receiving the money) and those with honorable mentions are released here but you can only get one year at a time. I have manually downloaded them all and incorporated them into the package. To use:

You can then plot information or do other analyses. For example, the number of awards in Mathematical Sciences over time:

And the frequency of different subfields of math, showing just the first twenty from the past ten years of awards:

Algebra Or Number Theory 786
Analysis 758
Mathematical Sciences 539
Topology 466
Applications Of Mathematics (Including Biometrics And Bi 465
Algebra, Number Theory, And Combinatorics 359
Applied Mathematics 239
Probability And Statistics 227
Logic Or Foundations Of Mathematics 172
Geometry 139
Statistics 131
Mathematical Biology 84
Operations Research 76
Biostatistics 70
Computational Mathematics 51
Geometric Analysis 37
Probability 31
Computational And Data-Enabled Science 25
Artificial Intelligence 16
Computational Statistics 16

Updating the package

First, update the package version in the DESCRIPTION.

Then the directory containing the package source:

library(rnsf)
grants_this_year <- nsf_update_cached_this_year() # perhaps worth doing a new nsf_get_all() after the beginning of the year
devtools::build_readme()
pkgdown::build_site()
system("git add */*")
system("git commit -m'automatic update of page and data' -a --no-verify") # ignore the readme warning
system("git push")

If new GRFP results are out, download them, retitle them as Awardee or HonorableMention with year and .tsv (i.e., “Awardee2030.tsv”) and put them in the inst/extdata directory in the package source. Reinstall the package. Then,

library(rnsf)
grfp <- compile_grfp()
usethis::use_data(grfp, overwrite=TRUE)

Then do the usual git things.