tests/testthat/test-load_jhu_data-fill_na.R

In reichlab/covidData: Versioned Data for COVID-19

library(dplyr)
testthat::context("load_jhu_data-fill_na")

# # read csv file
# jhu_data <- readr::read_csv('test data/2020-08-12_time_series_covid19_confirmed_US.csv')

# test_that("agreement with daily cumulative counts in csv file
#           for all locations other than colorado", {
#   states_to_keep <- c(
#     'Alabama', 'Alaska', 'American Samoa', 'Arizona', 'Arkansas', 'California',
#     'Colorado', 'Connecticut', 'Delaware', 'District of Columbia',
#     'Florida', 'Georgia', 'Guam', 'Hawaii', 'Idaho', 'Illinois',
#     'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine',
#     'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',
#     'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',
#     'New Hampshire', 'New Jersey', 'New Mexico', 'New York',
#     'North Carolina', 'North Dakota', 'Northern Mariana Islands',
#     'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Puerto Rico',
#     'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee',
#     'Texas', 'Utah', 'Vermont', 'Virgin Islands', 'Virginia',
#     'Washington', 'West Virginia', 'Wisconsin', 'Wyoming'
#   )

#   state_results <- jhu_data %>%
#     tidyr::pivot_longer(
#       matches('^\\d{1,2}\\/\\d{1,2}\\/\\d{2,4}$'),
#       names_to = 'date',
#       values_to = 'cum'
#     ) %>%
#     dplyr::mutate(
#       date = as.character(lubridate::mdy(date))
#     ) %>%
#     dplyr::filter(Province_State %in% states_to_keep) %>%
#     dplyr::mutate(location_name = Province_State) %>%
#     dplyr::group_by(location_name, date) %>%
#     dplyr::summarize(cum = sum(cum)) %>%
#     dplyr::group_by(location_name) %>%
#     dplyr::mutate(inc = diff(c(0, cum))) %>%
#     dplyr::ungroup() %>%
#     dplyr::left_join(covidData::fips_codes, by = 'location_name') %>%
#     dplyr::select(location, date, cum, inc) %>%
#     dplyr::mutate(date = as.Date(date))

#   case1 <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   )

#   case1 <- case1 %>%
#     dplyr::select(-inc) %>%
#     dplyr::filter(location != '08')
#   # take out DC county
#   state_results_case1 <- state_results %>%
#     dplyr::select(-inc) %>%
#     dplyr::filter(location != '08' & location != '11001')


#   expect_equal(case1, state_results_case1)
# })


# test_that("agreement in daily incident and cumulative counts between 
# a call to function with adjustment_cases = 'none' and adjustment_cases = 'CO-2020-04-24'
#           for all locations other than colorado", {
#   no_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     adjustment_cases = 'none',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(location != '08')


#   with_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(location != '08')

#   expect_equal(no_adjustments, with_adjustments)
# })

# test_that("daily incident count for CO on 2020-04-24 to be NA
#   and cumulative counts on or after 2020-04-24 to be NA", {
#   after_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(location == '08')

#   no_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     adjustment_cases = 'none',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(location == '08')



#   # make sure Colorado inc only changes on '2020-04-24'
#   expect_true(is.na(after_adjustments$inc[after_adjustments$date == '2020-04-24']))
#   expect_equal(
#     after_adjustments$inc[after_adjustments$date != '2020-04-24'],
#     no_adjustments$inc[no_adjustments$date != '2020-04-24']
#   )


#   # makes sure Colorado cum only changes for all dates >= '2020-04-24'
#   expect_true(all(is.na(after_adjustments$cum[after_adjustments$date >= "2020-04-24"])))
#   expect_equal(
#     after_adjustments$inc[after_adjustments$date < "2020-04-24"],
#     no_adjustments$inc[no_adjustments$date < "2020-04-24"]
#   )
# })

# test_that("agreement with daily cumulative counts in csv file 
#           for all locations other than counties in colorado", {
#   county_results <- jhu_data %>%
#     tidyr::pivot_longer(
#       matches('^\\d{1,2}\\/\\d{1,2}\\/\\d{2,4}$'),
#       names_to = 'date',
#       values_to = 'cum'
#     ) %>%
#     dplyr::mutate(
#       date = as.character(lubridate::mdy(date))
#     ) %>%
#     dplyr::filter(FIPS > 100) %>%
#     dplyr::mutate(
#       location = sprintf('%05d', FIPS)
#     ) %>%
#     dplyr::filter(location < '80001') %>%
#     dplyr::group_by(location) %>%
#     dplyr::mutate(inc = diff(c(0, cum))) %>%
#     dplyr::select(location, date, cum, inc) %>%
#     dplyr::ungroup() %>%
#     dplyr::mutate(date = as.Date(date))

#   case1 <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     spatial_resolution = 'county',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   )

#   case1 <- case1 %>%
#     dplyr::select(-inc) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) != '08')
#   county_results_case1 <- county_results %>%
#     dplyr::select(-inc) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) != '08')

#   expect_equal(case1, county_results_case1)
# })


# test_that("agreement in daily incident and cumulative counts between 
# a call to function with adjustment_cases = 'none' and adjustment_cases = 'CO-2020-04-24'
#           for all locations other than counties in colorado", {
#   no_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     spatial_resolution = 'county',
#     adjustment_cases = 'none',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) != '08')


#   with_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     spatial_resolution = 'county',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) != '08')

#   expect_equal(no_adjustments, with_adjustments)
# })

# test_that("daily incident count for counties in CO on 2020-04-24 to be NA
#   and cumulative counts on or after 2020-04-24 to be NA.", {
#   after_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     spatial_resolution = 'county',
#     adjustment_cases = 'CO-2020-04-24',
#     adjustment_method = 'fill_na',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) == '08')

#   no_adjustments <- covidData::load_jhu_data(
#     issue_date = '2020-08-12',
#     temporal_resolution = 'daily',
#     spatial_resolution = 'county',
#     adjustment_cases = 'none',
#     measure = 'cases',
#     replace_negatives = FALSE
#   ) %>%
#     dplyr::filter(stringr::str_sub(location, start = 1, end = 2) == '08')

#   # make sure all county-level inc in Colorado only changes on '2020-04-24'
#   expect_true(all(is.na(after_adjustments$inc[after_adjustments$date == '2020-04-24'])))
#   expect_equal(
#     after_adjustments$inc[after_adjustments$date != '2020-04-24'],
#     no_adjustments$inc[no_adjustments$date != '2020-04-24']
#   )

#   # makes sure all county-level cum in Colorado only changes for all dates >= '2020-04-24
#   expect_true(all(is.na(after_adjustments$cum[after_adjustments$date >= '2020-04-24'])))
#   expect_equal(
#     after_adjustments$inc[after_adjustments$date < '2020-04-24'],
#     no_adjustments$inc[no_adjustments$date < '2020-04-24']
#   )
# })