Grant Opportunities

library(tidyverse) # ggplot, lubridate, dplyr, stringr, readr...
library(praise)

The Data

All grants past and present from Grants.gov.

grant_opportunity_details <- read_csv("grant_opportunity_details.csv")
grants <- read_csv("grants.csv") |>
  separate(agency_code, c("agency", "sub_agency"), remove = FALSE) |>
  mutate(year = ymd(paste0(year(posted_date), "01", "01")))

Exploring the data

grants |>
  ggplot(aes(x = posted_date, y = estimated_funding)) +
  geom_point(aes(color = agency), size = .5, alpha = 0.5) +
  scale_y_log10() + 
  geom_line(data = summarize(group_by(grants, year), mean_fund = mean(estimated_funding, na.rm = TRUE)),
            aes(x = year, y = mean_fund)) +
  labs(title = "estimated funding",
       y = "", x = "date",
       caption = "From entries at grants.gov, we plot each grant's\ndate and estimating funding.\nBlack line is the average funding per year (with NA removed).")

grants |>
  select(agency) |>
  table() |>
  length()

[1] 47

grants |>
  ggplot(aes(x = agency, y = estimated_funding)) + 
  geom_boxplot(color = "red")  +
  geom_jitter(size = 0.001, height = 0, width = 0.2) + 
  scale_y_log10() + 
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1, size = 7)) + 
  ggtitle("a terrible plot of funding")

grants |>
  group_by(agency) |>
  summarize(fund_na = sum(!is.na(estimated_funding)), fund_count = n(),
            prop_na = mean(!is.na(estimated_funding))) |>
  arrange(desc(fund_count))

# A tibble: 48 × 4
   agency fund_na fund_count prop_na
   <chr>    <int>      <int>   <dbl>
 1 HHS      11978      21349  0.561 
 2 DOI      18520      20134  0.920 
 3 DOS       3468       6192  0.560 
 4 DOD       2381       3788  0.629 
 5 USDOJ     2063       3091  0.667 
 6 USAID     1621       3062  0.529 
 7 USDA      2386       2577  0.926 
 8 DOE       1371       1548  0.886 
 9 ED        1309       1513  0.865 
10 NASA       101       1507  0.0670
# ℹ 38 more rows

grants |>
  group_by(agency) |>
  summarize(count_low_fund = sum(estimated_funding == 0, na.rm = TRUE),
            prop_low_fund = mean(estimated_funding == 0, na.rm = TRUE),
            count_fund = n(),
            count_fund_isna = sum(is.na(estimated_funding))) |>
  arrange(desc(prop_low_fund))

# A tibble: 48 × 5
   agency count_low_fund prop_low_fund count_fund count_fund_isna
   <chr>           <int>         <dbl>      <int>           <int>
 1 GDIT                2        1               3               1
 2 DOE               586        0.427        1548             177
 3 PAMS               41        0.125         361              34
 4 NSF               111        0.107        1100              63
 5 HHS               877        0.0732      21349            9371
 6 DOI               971        0.0524      20134            1614
 7 USAID              67        0.0413       3062            1441
 8 NRC                 1        0.0345         54              25
 9 DOC                14        0.0163       1238             380
10 HUD                 7        0.0156        541              93
# ℹ 38 more rows

grants |>
  filter(agency == "GDIT")

# A tibble: 3 × 20
  opportunity_id opportunity_number         opportunity_title agency_code agency
           <dbl> <chr>                      <chr>             <chr>       <chr> 
1         127193 10112011-TESTOPPORTUNITY-2 10112011-TESTOPP… GDIT-08     GDIT  
2          51678 MARWAN-020610              marwan-020610     GDIT-02072… GDIT  
3          51031 201010090445               Marwan2009-01-09  GDIT-KV032… GDIT  
# ℹ 15 more variables: sub_agency <chr>, agency_name <chr>,
#   estimated_funding <dbl>, expected_number_of_awards <chr>,
#   grantor_contact <chr>, agency_contact_phone <chr>,
#   agency_contact_email <chr>, estimated_post_date <date>,
#   estimated_application_due_date <date>, posted_date <date>,
#   close_date <date>, last_updated_date_time <dttm>, version <chr>,
#   opportunity_status <chr>, year <date>