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Select comparison set for appraisal

Source: R/select-sample.R
select_sample.Rd

select_sample is used to select all other nonprofits that match a specified criteria, as well as calculate their respective distances from a reference organization using calc_dist. Generally, the reference organization is the nonprofit for which the user is obtaining an appraisal. This is Step 2 in the Appraisal Process Vignette.

See Appraisal Process Vignette for detailed explanation on how distance between nonprofits is calculated.

Usage

select_sample(
  org = get_org_values(state = "AL", location.type = "rural", total.expense = 1e+05, ntee
    = "P20"),
  search.criteria = list(type.org = "RG", broad.category = NA, major.group = LETTERS,
    division = NA, subdivision = NA, univ = FALSE, hosp = FALSE, location.type =
    c("rural", "town"), state = state.abb52, total.expense = c(1e+06, 1e+07))
)

Arguments

org

List output from get_org_values()

search.criteria

A list with the following elements:

  • type.org: vector of the types of organization you want to include. Options are RG, AA, MT, PA, RP, MS, MM, and/or NS.

  • broad.category: vector of broad categories you wish to include in returned data set Options are ART, EDU, ENV, HEL, HMS, IFA, PSB, REL, MMB, UNU, UNI, and/or HOS

  • major.group: vector of major groups you wish to include in returned data set. Options are A-Z.

  • division: vector of divisions you wish to include. Divisions exist entirely inside major groups. We suggest you do not use this parameter if you have more than one item in major.group. Options are 0, 2, 3, ..., 9 (1 is not an option.

  • subdivision: vector of subdivision you wish to include. Subdivisions exist entirely inside divisions. We suggest you do not use this parameter if you have more than one item in division. Options are 0 - 9.

  • univ: TRUE of FALSE. Are universities to be included?

  • hosp: TRUE of FALSE, Are hospitals to be included?

  • location.type: vector of "metro", "suburban", "town", and/or "rural" for which city types to include

  • state: vector of 2 letter state abbreviations to be included

  • total.expense: vector of c(min,max) of range of total expenses to be included

Value

A data frame with all nonprofits that match the search criteria. Each nonprofit has the following variables:

  • EIN: IRS Employer Identification Number

  • form.year: IRS filing year from which this nonprofits information was obtained

  • name: Name of the nonprofit

  • total.employee: Total number of employees at the nonprofit

  • gross.receipts: Gross receipts reported for the year

  • total.assests: Total assets reported for the year

  • total.expense: Total expenses reported for the year

  • ceo.compensation: Total CEO compensation reported for the year

  • gender: Imputed gender of the CEO

  • zip5: 5 digit zip code of where the nonprofit is located

  • state: Two letter abbreviation of the state where the nonprofit is located

  • location.type: Either "metro" or "rural" for type of location the nonprofit is in

  • ntee: Original NTEE code

  • new.code: New NTEE code

  • org.type, broad.category, major.group, division, subdivision, univ, hosp`: Parts of the dissagregated NTEE code. See ... for details.

  • expense.dist: Total Expense distance between the nonprofit and the reference organization

  • mission.dist: Mission distance between the nonprofit and the reference organization

  • geo.dist: Geographic distance between the nonprofit and the reference organization

  • total.dist: Total distance between the nonprofit and the reference organization

  • rank: Ranking of all nonprofits that match the reference set from closest to farthest from the reference organization.

Examples


input.org <- 
  get_org_values(state = "FL",#
                 location.type = "rural",
                 total.expense = 1.2e6,
                 ntee = "B20")

search.criteria <-
  list(
    type.org = "RG",
    broad.category = "EDU",
    major.group = "B",
    division = 2:9,
    subdivision = NA,
    univ = FALSE,
    hosp = FALSE,
    location.type = NA,
    state = c("FL", "GA", "SC", "MS", "AL", "PR"),
    total.expense = c(1.2e5, 1.2e7)
  )
      
samp <- select_sample(input.org, search.criteria)