Largest Active and Reporting Human Services Public Charities by Assets

9.5.2018
Deondre' Jones

More from this project:

Largest Active and Reporting Human Services 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((NTEEGRP == "Human Services"))
  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
530196605 P21 AMERICAN NATIONAL RED CROSS SHARED SERVICES CENTER 3,486,142,571
266163668 P84 RURAL INDIA SUPPORTING TR INDU RAWAT TTEE 1,550,202,560
221576300 O41 BOY SCOUTS OF AMERICA 1,329,793,787
470376606 P73 FATHER FLANAGANS BOYS HOME BOYS TOWN 1,320,060,670
631173425 L40 COLLEGIATE HOUSING FOUNDATION 1,226,812,140
363262111 P20 WHEATON FRANCISCAN SERVICES INC 1,192,021,587
231900132 P75 ACTS RETIREMENT LIFE COMMUNITIES INC 1,177,166,504
911884698 L21 NDC HOUSING AND ECONOMIC DEVELOPMENT CORPORATION GROUP RETURN 1,145,379,211
455023260 N32 PARK NICOLLET GROUP RETURN 1,111,149,840
230846955 P75 MASONIC VILLAGES OF THE GRAND LODGE OF PENNSYLVANIA 1,078,171,949

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