The edfinr package provides a simple, consistent
interface for accessing comprehensive education finance data for U.S.
school districts. This vignette will help you get started with the
package’s core functionality.
The primary function in edfinr is
get_finance_data(), which provides access to comprehensive
school finance data from school years 2011-12 through 2021-22. The
function combines data from multiple sources:
The simplest way to use get_finance_data() is to specify
a year and state. For example, to get finance data for Kentucky school
districts from the 2015-16 school year:
# download "skinny" dataset for a single year and a single staet
ky_sy16 <- get_finance_data(yr = 2016, geo = "KY")
# view the structure of the returned data
glimpse(ky_sy16)## Rows: 173
## Columns: 41
## $ ncesid <chr> "2100030", "2100070", "2100081", "2100090", "2100120",...
## $ year <chr> "2016", "2016", "2016", "2016", "2016", "2016", "2016"...
## $ state <chr> "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", "KY", ...
## $ dist_name <chr> "Adair County", "Allen County", "Muhlenberg County", "...
## $ enroll <dbl> 2689, 3071, 5023, 363, 3811, 3265, 273, 1371, 718, 277...
## $ rev_total_pp <dbl> 10622.908, 9635.949, 11004.977, 20374.656, 9874.574, 1...
## $ rev_local_pp <dbl> 1922.276, 1924.129, 3204.260, 14935.863, 3189.452, 270...
## $ rev_state_pp <dbl> 7046.858, 6523.608, 6721.481, 5081.578, 5700.866, 6064...
## $ rev_fed_pp <dbl> 1653.7746, 1188.2123, 1079.2355, 357.2145, 984.2561, 1...
## $ rev_total <dbl> 28565000, 29592000, 55278000, 7396000, 37632000, 34758...
## $ rev_local <dbl> 5169000.0, 5909000.0, 16095000.0, 5421718.4, 12155000....
## $ rev_state <dbl> 18949000, 20034000, 33762000, 1844613, 21726000, 19799...
## $ rev_fed <dbl> 4447000.0, 3649000.0, 5421000.0, 129668.9, 3751000.0, ...
## $ rev_total_unadj <dbl> 30303000, 31613000, 58033000, 7621000, 38072000, 36802...
## $ rev_local_unadj <dbl> 5169000, 6166000, 16125000, 5561000, 12156000, 8970000...
## $ rev_state_unadj <dbl> 20687000, 21798000, 36487000, 1927000, 22165000, 21685...
## $ rev_fed_unadj <dbl> 4447000, 3649000, 5421000, 133000, 3751000, 6147000, 3...
## $ exp_cur_pp <dbl> 9461.510, 8820.580, 9074.457, 18002.755, 8372.343, 999...
## $ rev_exp_pp_diff <dbl> 1161.3983, 815.3696, 1930.5196, 2371.9008, 1502.2304, ...
## $ exp_cur_st_loc <dbl> 22888000, 25129000, 39656000, 6443000, 29720000, 28532...
## $ exp_cur_fed <dbl> 2554000, 1959000, 5925000, 92000, 2187000, 4090000, 21...
## $ exp_cur_resa <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ exp_cur_total <dbl> 25442000, 27088000, 45581000, 6535000, 31907000, 32622...
## $ cpi_sy12 <dbl> 1.047057, 1.047057, 1.047057, 1.047057, 1.047057, 1.04...
## $ mhi <dbl> 33873, 41166, 40445, 162188, 53513, 39526, 34871, 4392...
## $ mpv <dbl> 86100, 98200, 81400, 604100, 136300, 96500, 107300, 10...
## $ adult_pop <dbl> 12753, 13827, 21649, 1372, 14970, 14736, 902, 5766, 17...
## $ ba_plus_pop <dbl> 2103, 1885, 2627, 1133, 2835, 3419, 158, 880, 478, 139...
## $ ba_plus_pct <dbl> 0.16490238, 0.13632748, 0.12134510, 0.82580175, 0.1893...
## $ total_pop <dbl> 19280, 20631, 31028, 2531, 22158, 21143, 1423, 8054, 3...
## $ student_pop <dbl> 2928, 3633, 4768, 433, 3937, 3253, 249, 1340, 434, 239...
## $ stpov_pop <dbl> 967, 905, 1130, 23, 536, 894, 81, 287, 154, 530, 1288,...
## $ stpov_pct <dbl> 0.33025956, 0.24910542, 0.23699664, 0.05311778, 0.1361...
## $ cong_dist <int> 2101, 2101, 2101, 2103, 2106, 2104, 2104, 2101, 2105, ...
## $ state_leaid <chr> "KY-001001000", "KY-002005000", "KY-089445000", "KY-05...
## $ county <chr> "Adair County", "Allen County", "Muhlenberg County", "...
## $ cbsa <chr> "N", "14540", "N", "31140", "23180", "26580", "17140",...
## $ urbanicity <fct> Town, Rural, Rural, Suburb, Town, City, Rural, Rural, ...
## $ schlev <chr> "03", "03", "03", "01", "03", "03", "03", "03", "03", ...
## $ lea_type <fct> Regular public school district that is not a component...
## $ lea_type_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...By default, get_finance_data() returns a "skinny"
dataset with 41 essential variables covering: - District identifiers and
characteristics. - Total revenues by source (local, state, federal). -
Current expenditures. - Key demographic and economic indicators.
For more detailed analysis, you can request the "full" dataset with 89 variables that includes: - All skinny dataset variables. - Detailed expenditure data. - Data on spending of temporary pandemic-related federal funding.
# download the full dataset with detailed expenditure data for a single year/state
ky_full_sy16 <- get_finance_data(yr = "2016", geo = "KY", dataset_type = "full")
# view additional variables in "full" dataset
names(ky_full_sy16)[42:89]## [1] "exp_emp_salary" "exp_emp_bene"
## [3] "exp_textbooks" "exp_utilities"
## [5] "exp_tech_supp" "exp_tech_equip"
## [7] "exp_pay_private_sch" "exp_pay_charter_sch"
## [9] "exp_pay_other_lea" "exp_other_sys_pay"
## [11] "exp_instr_total" "exp_instr_sal"
## [13] "exp_instr_bene" "exp_supp_stu_total"
## [15] "exp_supp_stu_sal" "exp_supp_stu_bene"
## [17] "exp_supp_instr_total" "exp_supp_instr_sal"
## [19] "exp_supp_instr_bene" "exp_supp_gen_admin_total"
## [21] "exp_supp_gen_admin_sal" "exp_supp_gen_admin_bene"
## [23] "exp_supp_sch_admin_total" "exp_supp_sch_admin_sal"
## [25] "exp_supp_sch_admin_bene" "exp_supp_ops_total"
## [27] "exp_supp_ops_sal" "exp_supp_ops_bene"
## [29] "exp_supp_trans_total" "exp_supp_trans_sal"
## [31] "exp_supp_trans_bene" "exp_central_serv_total"
## [33] "exp_central_serv_sal" "exp_central_serv_bene"
## [35] "exp_noninstr_food_total" "exp_noninstr_food_sal"
## [37] "exp_noninstr_food_bene" "exp_noninstr_ent_ops_total"
## [39] "exp_noninstr_ent_ops_bene" "exp_noninstr_other"
## [41] "exp_covid_total" "exp_covid_instr"
## [43] "exp_covid_supp" "exp_covid_cap_out"
## [45] "exp_covid_tech_supp" "exp_covid_tech_equip"
## [47] "exp_covid_supp_plant" "exp_covid_food"The get_finance_data() function makes it easy to access
data across multiple years and states:
# get data for multiple states across multiple years
sec_data <- get_finance_data(
yr = "2018:2022", # years 2018 through 2022
geo = "AL,AR,FL,GA,KY,LA,MS,MO,OK,SC,TN,TX" # comma-separated state codes
)
# get the most recent year of data for all states
us_sy22 <- get_finance_data(yr = 2022, geo = "all")Once you’ve retrieved the data, you can use standard data manipulation tools to analyze it. Here are some common analysis patterns:
# download 2022 data for connecticut
ct_sy22 <- get_finance_data(yr = "2022", geo = "CT")
# plot local revenue vs. total revenue w/ urbanicity + enrollment
ggplot(ct_sy22) +
geom_point(aes(
x = rev_local_pp,
y = rev_total_pp,
color = urbanicity,
size = enroll),
alpha = .6) +
scale_size_area(
max_size = 10,
labels = scales::label_comma()
) +
scale_x_continuous(labels = scales::label_dollar()) +
scale_y_continuous(labels = scales::label_dollar()) +
labs(
title = "Connecticut Districts' Local vs. Total Revenue Per-Pupil, SY2021-22",
x = "Local Revenue Per-Pupil",
y = "Total Revenue Per-Pupil",
size = "Enrollment",
color = "Urbanicity") +
theme_bw() # compare revenue sources across districts
revenue_analysis <- ct_sy22 |>
mutate(
pct_local = rev_local / rev_total,
pct_state = rev_state / rev_total,
pct_federal = rev_fed / rev_total
) |>
select(dist_name, urbanicity, enroll, pct_local, pct_state, pct_federal) |>
group_by(urbanicity) |>
summarize(
avg_pct_local = mean(pct_local, na.rm = TRUE),
avg_pct_state = mean(pct_state, na.rm = TRUE),
avg_pct_federal = mean(pct_federal, na.rm = TRUE),
n_districts = n(),
enrollment = sum(enroll, na.rm = TRUE)
)
print(revenue_analysis)## # A tibble: 4 ×ばつ 6
## urbanicity avg_pct_local avg_pct_state avg_pct_federal n_districts enrollment
## <fct> <dbl> <dbl> <dbl> <int> <dbl>
## 1 City 0.250 0.608 0.143 29 126751
## 2 Suburb 0.649 0.289 0.0623 85 285922
## 3 Town 0.584 0.351 0.0649 8 14823
## 4 Rural 0.649 0.303 0.0490 65 52477For more information about the data and methods used in this package:
list_variables() to see all available variables and
their descriptions.get_states() to see valid state codes.