tests/testthat/test-estimates.R

In tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames

test_that("2025 place population estimates parse from the city totals file", {
  skip_on_cran()

  captured_url <- NULL

  local_mocked_bindings(
    read_estimates_csv = function(https_url, ftp_url, required_col) {
      captured_url <<- https_url
      expect_match(https_url, "2020-2025/cities/totals/sub-est2025[.]csv")
      expect_match(ftp_url, "2020-2025/cities/totals/sub-est2025[.]csv")
      expect_equal(required_col, "SUMLEV")

      data.frame(
        SUMLEV = c("040", "162", "162"),
        STATE = c("48", "48", "06"),
        COUNTY = c("000", "000", "000"),
        PLACE = c("00000", "05000", "44000"),
        COUSUB = "00000",
        CONCIT = "00000",
        PRIMGEO_FLAG = 0,
        FUNCSTAT = "A",
        NAME = c("Texas", "Austin city", "Los Angeles city"),
        STNAME = c("Texas", "Texas", "California"),
        ESTIMATESBASE2020 = c(29145505, 961855, 3898747),
        POPESTIMATE2020 = c(29234361, 965827, 3895848),
        POPESTIMATE2021 = c(29561286, 974447, 3847114),
        POPESTIMATE2022 = c(30029848, 974013, 3820914),
        POPESTIMATE2023 = c(30503301, 979882, 3821576),
        POPESTIMATE2024 = c(30976754, 993588, 3822808),
        POPESTIMATE2025 = c(31450000, 1001000, 3825000),
        stringsAsFactors = FALSE
      )
    }
  )

  estimates <- suppressMessages(
    get_estimates(
      geography = "place",
      product = "population",
      vintage = 2025,
      year = 2025,
      state = "TX",
      output = "wide"
    )
  )

  expect_match(captured_url, "sub-est2025[.]csv")
  expect_equal(nrow(estimates), 1)
  expect_equal(estimates$GEOID, "4805000")
  expect_equal(estimates$NAME, "Austin city, Texas")
  expect_equal(estimates$POPESTIMATE, 1001000)
})