Largest Active and Reporting Civil Rights, Social Action & Advocacy Public Charities by Assets

9.5.2018
Deondre' Jones

More from this project:

Largest Active and Reporting Civil Rights, Social Action & Advocacy Public Charities by Assets

  1. library(tidyverse)
  2. library(knitr)
  3. library(stringr)
  4. library(scales)
  5. library(httr)
  6. source('https://raw.githubusercontent.com/UrbanInstitute/urban_R_theme/master/urban_theme_windows.R')
  7.  
  8. #Create NTEE grouping categories
  9. arts <- c("A")
  10. highered <- c("B4", "B5")
  11. othered <- c("B")
  12. envanimals <- c("C", "D")
  13. hospitals <- c('E20','E21','E22','E23','E24','F31','E30','E31','E32')
  14. otherhlth <- c("E", "F", "G", "H")
  15. humanserv <- c("I", "J", "K", "L", "M", "N", "O", "P")
  16. intl <- c("Q")
  17. pubben <- c("R", "S", "T", "U", "V", "W", "Y", "Z")
  18. relig <- c("X")
  19.  
  20. #Import the Reduced NCCS Data Archive
  21. nteedocalleins <- read.csv("Data/nteedocalleins.csv")
  22.  
  23. #convert variable names to upper case
  24. names(nteedocalleins) <- toupper(names(nteedocalleins))
  25.  
  26. #This function will apply the most common NTEE Grouping categories to your data.
  27. NTEEclassify <- function(dataset) {
  28.   #merge in Master NTEE look up file
  29.   dataset <- dataset %>%
  30.     left_join(nteedocalleins, by = "EIN")
  31.   #create NTEEGRP classifications
  32.   dataset$NTEEGRP <- "  "
  33.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% arts ] <- "Arts"
  34.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% othered ] <- "Education: Other"
  35.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,2) %in% highered ] <- "Education: Higher"
  36.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% envanimals] <- "Environment and Animals"
  37.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% otherhlth] <- "Health Care: Other"
  38.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,3) %in% hospitals] <- "Health Care: Hospitals and primary care facilities"
  39.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% humanserv] <- "Human Services"
  40.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% intl] <- "International"
  41.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% pubben] <- "Other Public and social benefit"
  42.   dataset$NTEEGRP[str_sub(dataset$NTEEFINAL,1,1) %in% relig] <- "Religion related"
  43.   dataset$NTEEGRP[is.na(dataset$NTEEFINAL)] <- "Other Public and social benefit"
  44.   return(dataset)
  45. }
  46.  
  47.  
  48. #Import reduced NCCS Core File Function
  49. prepcorepcfile <- function(corefilepath) {
  50.   output <- read_csv(corefilepath,
  51.                      col_types = cols_only(EIN = col_character(),
  52.                                            FISYR = col_integer(),
  53.                                            NAME = col_character(),
  54.                                            STATE = col_character(),
  55.                                            ADDRESS = col_character(),
  56.                                            CITY = col_character(),
  57.                                            ZIP = col_character(),
  58.                                            MSA_NECH = col_character(),
  59.                                            FIPS = col_character(),
  60.                                            PMSA = col_character(),
  61.                                            STYEAR = col_double(),
  62.                                            TAXPER = col_integer(),
  63.                                            OUTNCCS = col_character(),
  64.                                            OutNCCS = col_character(),
  65.                                            SUBSECCD = col_character(),
  66.                                            RULEDATE = col_character(),
  67.                                            FNDNCD = col_character(),
  68.                                            FRCD = col_character(),
  69.                                            TOTREV = col_double(),
  70.                                            EXPS = col_double(),
  71.                                            ASS_EOY = col_double(),
  72.                                            GRREC = col_double()
  73.  
  74.                      ))
  75.   names(output) <- toupper(names(output))
  76.   return(output)
  77. }
  78.  
  79. #Import NCCS Core File for given year
  80. corefile <- prepcorepcfile(as.character(paste("Data/core", "2015", "pc.csv", sep="")))
  81.  
  82. #Add NTEE Classifications to the Core File
  83. corefile <- NTEEclassify(corefile)
  84.  
  85. #Filter out of scope organizations 
  86. corefile <- corefile %>%
  87.   filter((OUTNCCS != "OUT")) %>%
  88.   filter((FNDNCD != "02" & FNDNCD!= "03" & FNDNCD != "04")) %>%
  89.   filter(str_detect(NTEEFINAL, "R"))
  90.  
  91. #Sort the corefile in descending order by assets
  92. LargestAssets <- corefile[with(corefile,order(-ASS_EOY)),]
  93.  
  94. #Limit the list to 10
  95. LargestAssets <- LargestAssets[1:10,]
  96.  
  97. #Select the appropriate columns, drop the rest
  98. LargestAssets <- LargestAssets %>% 
  99.   select(EIN, NTEEFINAL, NAME, ASS_EOY)
  100.  
  101. #Rename columns appropriately
  102. colnames(LargestAssets) <- c("EIN", "NTEE Code", "Name", "Total Assets")
  1. #display table
  2. kable(LargestAssets, format.args = list(decimal.mark = '.', big.mark = ","))
EIN NTEE Code Name Total Assets
136213516 R60 AMERICAN CIVIL LIBERTIES UNION FOUNDATION INC 341,682,167
135563393 R20 AMERICAN JEWISH COMMITTEE 152,719,181
132887439 R1120 ANTI DEFAMATION LEAGUE FOUNDATION 131,541,010
521710886 R0160 THE NRA FOUNDATION INC 123,162,862
541806317 R27 PATIENT ADVOCATE FOUNDATION INC 84,425,651
521744337 R60 INSTITUTE FOR JUSTICE 73,113,753
910873623 R22 ARCTIC SLOPE NATIVE ASSOCIATION LTD 64,787,117
860212873 R22 NATIONAL COUNCIL OF LA RAZA 62,087,640
461344768 R20 NATIONAL CENTER FOR CIVIL AND HUMAN RIGHTS FOUNDATION INC 55,661,395
131655255 R22 N A A C P LEGAL DEFENSE AND EDUCATIONAL FUND INC 54,303,424

Source: NCCS 501(c)(3) Public Charities Core File 2015