Tools for Analyzing Nonprofit Trends and Impacts Survey Data • ntistools

ntistools provides reusable data-cleaning functions for wrangling Nonprofit Trends and Impacts Survey (NTIS) data. It replaces repetitive dplyr::case_when() and dplyr::across() patterns common in NTIS analysis scripts with purpose-built, pipe-friendly functions.

Developed and maintained by the Urban Institute.

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("UrbanInstitute/ntistools")

Or using remotes:

# install.packages("remotes")
remotes::install_github("UrbanInstitute/ntistools")

Functions

ntistools exports 10 functions organized into four categories:

Recode and collapse

Function	Description
`recode_binary()`	Collapse multi-level values into 0/1 (e.g., 0 = no, 1&2 = yes, 98 = NA)
`recode_sentinel()`	Replace sentinel values (e.g., 98 = “don’t know”) with `NA`
`collapse_likert()`	Collapse a 5-point Likert scale into 3 categories

Label

Function	Description
`label_binary()`	Convert 0/1 columns to descriptive strings (e.g., `"Yes"` / `"No"`)
`label_likert()`	Map Likert-scale values to descriptive strings (e.g., `"Decrease"` / `"No change"` / `"Increase"`)

Combine and count

Function	Description
`combine_binary()`	OR-combine several 0/1 columns into one (1 if any = 1, NA if all NA, else 0)
`count_binary()`	Create `_any`, `_count`, and `_all` summary columns from binary indicators

Impute, propagate, and filter

Function	Description
`impute_from_flag()`	Replace NA with a value when a companion flag column indicates a valid skip
`propagate_parent()`	Push a parent question’s value (e.g., 0 or 97) to all child columns
`apply_filter()`	Set variables to NA for respondents who don’t meet a condition

Usage

library(ntistools)
library(dplyr)

cleaned <- raw_survey |>
  recode_binary(c("ProgDem_Children", "ProgDem_Elders")) |>
  recode_sentinel(c("DonImportance", "PrgSrvc_Suspend")) |>
  impute_from_flag("PplSrv_NumWait") |>
  combine_binary(GeoAreas_Locally,
                 GeoAreas_Local, GeoAreas_MultipleLocal,
                 GeoAreas_RegionalWithin) |>
  collapse_likert("FinanceChng_Benefits") |>
  label_binary(labels = list(
    GeoAreas_Locally = c("Serving locally", "Not serving locally")
  )) |>
  label_likert("FinanceChng_Benefits",
               labels = c("Decrease", "No change", "Increase")) |>
  propagate_parent(c("Regulations_Federal", "Regulations_State"),
                   parent_var = "Regulations") |>
  apply_filter(c("StaffVacancies", "BenefitsImpact"),
               condition = HaveStaff == 1)

See vignette("before-and-after") for side-by-side comparisons of verbose case_when() code and the equivalent ntistools calls.

NTIS sentinel value conventions

NTIS uses numeric sentinel values to represent special responses:

98 = “Don’t know”
97 = “Not applicable”
0 = “None” (context-dependent)

Many ntistools functions have defaults that reflect these conventions (e.g., recode_sentinel() defaults to replacing 98 with NA, collapse_likert() treats 97 as NA).

Contributing

This package is maintained by the Urban Institute. If you find a bug or want to suggest a feature, please open an issue.

License

MIT