Download Google Community Mobility Reports Data

Downloads Google Community Mobility Report data (https://www.google.com/covid19/mobility/). As stated on this webpage, the "reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential". Google prepares these reports to help interested parties to assess responses to social distancing guidance related to Covid-19. As Google is no longer updating this data since October 15, 2022 static historic is downloaded and calling the function with cache = FALSE yields a warning.

download_google_cmr_data(type = "country", silent = FALSE, cached = FALSE)

Arguments

type

The type of data that you want to retrieve. Can be any subset of

  • "country": Movement trends by country.

  • "country_region": Movement trends by country regions as classified by Google (only available for some countries).

  • "country_sub_region": Movement trends by country sub-regions as classified by Google (only available for some countries).

  • "us_county": Movement trends at the U.S. county level.
silent

Whether you want the function to send some status messages to the console. Might be informative as downloading will take some time and thus defaults to TRUE.
cached

Whether you want to download the cached version of the data from the tidycovid19 Github repository instead of retrieving the data from the authorative source. Downloading the cached version is faster and the cache is updated daily. Defaults to FALSE.

Value

If only one type was selected, a data frame containing the data. Otherwise, a list containing the desired data frames ordered as in type.

Examples

df <- download_google_cmr_data(silent = TRUE, cached = TRUE)
df %>%
  dplyr::group_by(date) %>%
  dplyr::summarize(
    retail_recreation = mean(retail_recreation, na.rm = TRUE)
  ) %>%
  ggplot2::ggplot(ggplot2::aes(x = date, y = retail_recreation)) +
  ggplot2::geom_line()


df <- download_google_cmr_data(type = "country_region", silent = TRUE, cached = TRUE)
df %>%
  dplyr::filter(iso3c == "USA") %>%
  dplyr::select(-iso3c) %>%
  dplyr::group_by(region) %>%
  dplyr::summarise(`Retail and Recreation Effect` =
     max(retail_recreation, na.rm = TRUE) -
     min(retail_recreation, na.rm = TRUE)) %>%
  dplyr::rename(`U.S. State` = region) %>%
  dplyr::arrange(-`Retail and Recreation Effect`)

#> # A tibble: 51 × 2
#>    `U.S. State`  `Retail and Recreation Effect`
#>    <chr>                                  <dbl>
#>  1 South Dakota                             140
#>  2 Maine                                    136
#>  3 Alaska                                   131
#>  4 Montana                                  125
#>  5 New Hampshire                            125
#>  6 Wyoming                                  120
#>  7 Vermont                                  117
#>  8 West Virginia                            117
#>  9 Idaho                                    115
#> 10 North Dakota                             115
#> # ℹ 41 more rows