R/test_utils.R

In CT-Data-Haven/cwi: Prep for DataHaven's Community Wellbeing Index

demo_xt <- function(f) {
  system.file("extdata", sprintf("test_xtab%s.xlsx", f), package = "cwi")
}

all_xt <- function(.fun, args = NULL) {
  c(2015, 2018, 2020, 2021) |>
    rlang::set_names() |>
    purrr::map(demo_xt) |>
    purrr::imap(~R.utils::doCall(.fcn = .fun, path = .x, year = .y, args = args))
}

multi_test <- function(src = "acs",
                       table = "B01003",
                       year = 2021,
                       towns = "all",
                       regions = NULL,
                       counties = "all",
                       state = "09",
                       neighborhoods = NULL,
                       tracts = NULL,
                       blockgroups = NULL,
                       pumas = NULL,
                       msa = FALSE,
                       us = FALSE,
                       new_england = TRUE,
                       nhood_name = "name",
                       nhood_geoid = NULL,
                       dataset = "acs5",
                       verbose = TRUE,
                       key = NULL) {
  multi_geo_prep(src, table, year, towns, regions, counties, state, neighborhoods, tracts, blockgroups, pumas, msa, us, new_england, {{ nhood_name }}, {{ nhood_geoid }}, dataset, verbose, key)
}

bls_test <- function(series = "CUUR0000SA0",
                     year = year,
                     base_year = 2021,
                     verbose = TRUE,
                     key = NULL) {
  yrs <- cpi_yrs(year, base_year)
  query <- cpi_prep(series, yrs, verbose, key)
  return(list(yrs = yrs, query = query))
}


dummy_df <- function(seed = 123, n = 20) {
  set.seed(seed)
  df <- data.frame(
    name = sample(letters, n, replace = FALSE),
    region = sample(LETTERS[1:5], n, replace = TRUE),
    value = stats::rnorm(n)
  )
  df
}