R/refresh_covid19mobility_google.R

In Covid19R/covid19mobility: Fetches Data on Covid-19 Mobility Trends from Different Sources

#' Get Google Mobility Data at the Country Level
#'
#' @description  From Google:
#' "Each Community Mobility Report dataset is presented by location
#' and highlights the percent change in visits to places like grocery
#' stores and parks within a geographic area.
#'
#' Location accuracy and the understanding of categorized places
#' varies from region to region, so we don’t recommend using this
#' data to compare changes between countries, or between regions
#' with different characteristics (e.g. rural versus urban areas).
#'
#' Changes for each day are compared to a baseline value for that
#' day of the week: The baseline is the median value, for the corresponding day
#' of the week, during the 5-week period Jan 3–Feb 6, 2020.
#' The datasets show trends over several months with the most
#' recent data representing approximately 2-3 days ago—this is how
#' long it takes to produce the datasets."
#'
#' Data represents changes from baseline visits for the following types
#' of locations visited:
#'
#' * retail and recreation
#' * grocery and pharmacy
#' * parks
#' * transit stations
#' * workplaces
#' * residential
#'
#' @return A tibble meeting the Covid19R Project data standard. Columns include:
#'
#' * date - The date in YYYY-MM-DD form
#' * location - The name of the location as provided by the data source.
#' * location_type - The type of location using the covid19R controlled vocabulary.
#' * location_code - A standardized location code using a national or international standard. In this case, FIPS state or county codes. See https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code and https://en.wikipedia.org/wiki/FIPS_county_code for more
#' * location_code_type The type of standardized location code being used according to the covid19R controlled vocabulary. Here we use `iso_3166_2`
#' * data_type - the type of data in that given row. See description.
#' * value - number of cases of each data type
#'
#' @export
#' @references Google Covid-19 Mobility Reports \url{https://www.google.com/covid19/mobility/}
#' @references The Covid19R Project \url{https://covid19r.github.io/documentation/}
#'
#' @examples
#' \donttest{
#' covid19mobility_google_country <- refresh_covid19mobility_google_country()
#'
#' head(covid19mobility_google_country)
#' }
#'
refresh_covid19mobility_google_country <- function() {

  # read in
  gmob <- read_google_mobility() %>%
    # filter to nations only
    dplyr::filter(is.na(sub_region_1))

  gmob %>%
    dplyr::select(-sub_region_1, sub_region_2) %>%
    dplyr::rename(location = country_region) %>%
    dplyr::mutate(
      location_code_type = "iso_3166_2",
      location_type = "country"
    ) %>%
    covid_19_r_format() %>%
    dplyr::mutate(location_code = ifelse(location == "Namibia", "NA", location_code)) # agh!
}


#' Get Google Mobility Data at the State of Subdivision Level
#'
#' @description From Google:
#' "Each Community Mobility Report dataset is presented by location
#' and highlights the percent change in visits to places like grocery
#' stores and parks within a geographic area.
#'
#' Location accuracy and the understanding of categorized places
#' varies from region to region, so we don’t recommend using this
#' data to compare changes between countries, or between regions
#' with different characteristics (e.g. rural versus urban areas).
#'
#' Changes for each day are compared to a baseline value for that
#' day of the week: The baseline is the median value, for the corresponding day
#' of the week, during the 5-week period Jan 3–Feb 6, 2020.
#' The datasets show trends over several months with the most
#' recent data representing approximately 2-3 days ago—this is how
#' long it takes to produce the datasets."
#'
#' Data represents changes from baseline visits for the following types
#' of locations visited:
#'
#' * retail and recreation
#' * grocery and pharmacy
#' * parks
#' * transit stations
#' * workplaces
#' * residential
#'
#' @return A tibble meeting the Covid19R Project data standard. Columns include:
#'
#' * date - The date in YYYY-MM-DD form
#' * location - The name of the location as provided by the data source.
#' * location_type - The type of location using the covid19R controlled vocabulary.
#' * location_code - A standardized location code using a national or international standard. In this case, FIPS state or county codes. See https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code and https://en.wikipedia.org/wiki/FIPS_county_code for more
#' * location_code_type The type of standardized location code being used according to the covid19R controlled vocabulary. Here we use `iso_3166_2`
#' * data_type - the type of data in that given row. See description.
#' * value - number of cases of each data type
#'
#' @export
#' @references Google Covid-19 Mobility Reports \url{https://www.google.com/covid19/mobility/}
#' @references The Covid19R Project \url{https://covid19r.github.io/documentation/}
#'
#' @examples
#' \donttest{
#' covid19mobility_google_subregions <- refresh_covid19mobility_google_subregions()
#'
#' head(covid19mobility_google_subregions)
#' }
#'
refresh_covid19mobility_google_subregions <- function() {

  # read in
  gmob <- read_google_mobility() %>%
    # filter to subregions
    dplyr::filter(
      !is.na(sub_region_1),
      is.na(sub_region_2)
    )

  # put puerto rico in properly
  pr <- gmob %>%
    # filter to subregions
    dplyr::filter(country_region == "Puerto Rico") %>%
    dplyr::select(-location_code) %>%
    dplyr::mutate(
      sub_region_2 = sub_region_1,
      sub_region_1 = "Puerto Rico",
      country_region = "United States"
    ) %>%
    dplyr::filter(is.na(sub_region_2))

  gmob <- dplyr::bind_rows(
    gmob %>% dplyr::filter(country_region != "Puerto Rico"),
    pr
  )


  # make a clean subregion_1
  gmob <- dplyr::left_join(
    gmob,
    goog_subdivision_lut(gmob)
  )
  # subregion_codes
  subdivision_codes <- load_subdivisions() %>%
    dplyr::mutate(
      subdivision_name = gsub("^Al ", "", subdivision_name),
      subdivision_name = gsub("s lan$", "", subdivision_name),
      subdivision_name = tolower(subdivision_name)
    ) %>%
    dplyr::group_by(code, country_code, subdivision_name) %>%
    dplyr::slice(1L) %>%
    dplyr::ungroup()

  gmob_joined <- dplyr::left_join(gmob,
    subdivision_codes,
    by = c(
      "location_code" = "country_code",
      "subregion_1_clean" = "subdivision_name"
    )
  )


  # return
  gmob_joined <- gmob_joined %>%
    dplyr::rename(
      country_code = location_code,
      location = sub_region_1,
      location_code = code
    ) %>%
    dplyr::mutate(
      location_type = "state",
      location_code_type = "iso_3166_2"
    )

  gmob_joined %>% covid_19_r_format()
}


#' Get Google Mobility Data for US States
#'
#' @description From Google:
#' "Each Community Mobility Report dataset is presented by location
#' and highlights the percent change in visits to places like grocery
#' stores and parks within a geographic area.
#'
#' Location accuracy and the understanding of categorized places
#' varies from region to region, so we don’t recommend using this
#' data to compare changes between countries, or between regions
#' with different characteristics (e.g. rural versus urban areas).
#'
#' Changes for each day are compared to a baseline value for that
#' day of the week: The baseline is the median value, for the corresponding day
#' of the week, during the 5-week period Jan 3–Feb 6, 2020.
#' The datasets show trends over several months with the most
#' recent data representing approximately 2-3 days ago—this is how
#' long it takes to produce the datasets."
#'
#' Data represents changes from baseline visits for the following types
#' of locations visited:
#'
#' * retail and recreation
#' * grocery and pharmacy
#' * parks
#' * transit stations
#' * workplaces
#' * residential
#'
#' @return A tibble meeting the Covid19R Project data standard. Columns include:
#'
#' * date - The date in YYYY-MM-DD form
#' * location - The name of the location as provided by the data source.
#' * location_type - The type of location using the covid19R controlled vocabulary.
#' * location_code - A standardized location code using a national or international standard. In this case, FIPS state or county codes. See https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code and https://en.wikipedia.org/wiki/FIPS_county_code for more
#' * location_code_type The type of standardized location code being used according to the covid19R controlled vocabulary. Here we use `iso_3166_2`
#' * data_type - the type of data in that given row. See description.
#' * value - number of cases of each data type
#'
#' @export
#' @references Google Covid-19 Mobility Reports \url{https://www.google.com/covid19/mobility/}
#' @references The Covid19R Project \url{https://covid19r.github.io/documentation/}
#'
#' @examples
#' \donttest{
#' covid19mobility_google_us_counties <- refresh_covid19mobility_google_us_counties()
#'
#' head(covid19mobility_google_us_counties)
#' }
#'
refresh_covid19mobility_google_us_counties <- function() {

  # read in
  gmob <- read_google_mobility() %>%
    # filter to nations only
    dplyr::filter(
      !is.na(sub_region_1),
      !is.na(sub_region_2)
    ) %>%
    dplyr::select(-location_code)

  # get puerto rico
  pr <- read_google_mobility() %>%
    # filter to subregions
    dplyr::filter(country_region == "Puerto Rico") %>%
    dplyr::select(-location_code) %>%
    dplyr::mutate(
      sub_region_2 = sub_region_1,
      sub_region_1 = "Puerto Rico",
      country_region = "United States"
    ) %>%
    dplyr::filter(!is.na(sub_region_2))

  # combine and make a clean column
  gmob <- dplyr::bind_rows(gmob, pr)

  # make a counties lookup table
  suppressMessages(
    counties_lut <- goog_counties_lut(gmob, load_fips())
  )

  # combine state/county
  suppressMessages(
    gmob_joined <- dplyr::left_join(
      gmob,
      counties_lut
    )
  )

  # reshape and return
  gmob_joined %>%
    dplyr::rename(
      state = sub_region_1,
      location = sub_region_2
    ) %>%
    dplyr::mutate(
      location_type = "county",
      location_code_type = "fips_code"
    ) %>%
    covid_19_r_format()
}

read_google_mobility <- function() {
  url <- "https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv"

  gmob <- readr::read_csv(url,
    na = c("", "N/A"), # col_types = "ccccDiiiiii")
    col_types = readr::cols(
      country_region_code = readr::col_character(),
      country_region = readr::col_character(),
      sub_region_1 = readr::col_character(),
      sub_region_2 = readr::col_character(),
      iso_3166_2_code = readr::col_character(),
      census_fips_code = readr::col_character(),
      date = readr::col_date(),
      retail_and_recreation_percent_change_from_baseline = readr::col_integer(),
      grocery_and_pharmacy_percent_change_from_baseline = readr::col_integer(),
      parks_percent_change_from_baseline = readr::col_integer(),
      transit_stations_percent_change_from_baseline = readr::col_integer(),
      workplaces_percent_change_from_baseline = readr::col_integer(),
      residential_percent_change_from_baseline = readr::col_integer()
    )
  )

  gmob_longer <- gmob %>%
    dplyr::rename(location_code = country_region_code) %>%
    tidyr::pivot_longer(
      cols = retail_and_recreation_percent_change_from_baseline:residential_percent_change_from_baseline,
      names_to = "data_type",
      values_to = "value"
    )

  gmob_longer
}

covid_19_r_format <- . %>%
  janitor::clean_names() %>%
  dplyr::select(
    any_of(
      c(
        "date",
        "location",
        "location_type",
        "location_code",
        "location_code_type",
        "state",
        "data_type",
        "value"
      )
    )
  ) %>%
  dplyr::mutate(
    data_type = gsub("_from_baseline", "", data_type),
    data_type = gsub("_percent_change", "_perc_ch", data_type)
  )