The goal of covid19mobility is to make mobility data from different sources available using the Covid19R Project Data Format Standard. Currently, this package imports data from:
You can install the current version of covid19mobility from github with:
remotes::install_github("covid19r/covid19mobility")The covid19mobility library follows the Covid19R Project Data Format
Standard,
with some data sets holding extra data columns. To see what data is
available, use get_info_covid19mobility()
library(covid19mobility)
get_info_covid19mobility() %>%
dplyr::select(data_set_name, function_to_get_data, data_details) %>%
knitr::kable()| data_set_name | function_to_get_data | data_details |
|---|---|---|
| covid19mobility_apple_country | refresh_covid19mobility_apple_country | Data reflects relative volume of directions requests compared to a baseline volume on January 13th, 2020 for multiple transportation modes aggregated at the country level. |
| covid19mobility_apple_subregion | refresh_covid19mobility_apple_subregion | Data reflects relative volume of directions requests compared to a baseline volume on January 13th, 2020 for multiple transportation modes aggregated at the subregion (state) level. |
| covid19mobility_apple_city | refresh_covid19mobility_apple_city | Data reflects relative volume of directions requests compared to a baseline volume on January 13th, 2020 for multiple transportation modes aggregated at the city level. |
| covid19mobility_google_country | refresh_covid19mobility_google_country | Changes for each day are compared to a baseline value for that day of the week as compared to the 5-week period Jan 3-Feb 6, 2020 for visits to places falling in to certain categories. |
| covid19mobility_google_subregions | refresh_covid19mobility_google_subregions | Changes for each day are compared to a baseline value for that day of the week as compared to the 5-week period Jan 3-Feb 6, 2020 for visits to places falling in to certain categories. Data is aggregated at the state or subdivision level. |
| covid19mobility_google_us_counties | refresh_covid19mobility_google_us_counties | Changes for each day are compared to a baseline value for that day of the week as compared to the 5-week period Jan 3-Feb 6, 2020 for visits to places falling in to certain categories. Data is aggregated at the county level for the USA only. |
The refresh methods bring in the different data sets. Currently
available are: * refresh_covid19mobility_apple_country() - Apple
Mobility Data at the country
level.
* refresh_covid19mobility_subregion() - Apple Mobility
Data at the state/subregion
level.
* refresh_covid19mobility_apple_city() - Apple Mobility
Data at the city level.
Contains some lat/longs for some cities.
* refresh_covid19mobility_google_country() - Google Mobility
Data at the country level.
* refresh_covid19mobility_google_subregions() - Google Mobility
Data at the state/subregion
level.
* refresh_covid19mobility_google_us_counties() - Google Mobility
Data at the US county level
with FIPS codes.
For example
refresh_covid19mobility_google_us_counties() %>%
head()
#> | | | 0% | |====== | 8% | |============ | 17% | |============= | 19% | |=================== | 27% | |======================== | 34% | |============================== | 42% | |==================================== | 51% | |======================================= | 55% | |============================================= | 64% | |================================================ | 68% | |====================================================== | 77% | |============================================================ | 85% | |=============================================================== | 89% | |===================================================================== | 98% | |======================================================================| 100%
#> | | | 0% | |= | 2% | |=== | 4% | |==== | 6% | |===== | 7% | |====== | 9% | |======== | 11% | |======== | 12% | |========== | 14% | |========== | 15% | |============ | 17% | |============= | 19% | |============== | 20% | |=============== | 21% | |================ | 22% | |================= | 24% | |================== | 26% | |=================== | 27% | |==================== | 29% | |===================== | 30% | |====================== | 32% | |======================== | 34% | |======================== | 35% | |========================== | 37% | |========================== | 38% | |============================ | 39% | |============================= | 41% | |============================== | 42% | |=============================== | 44% | |================================ | 45% | |================================= | 47% | |================================== | 49% | |=================================== | 50% | |==================================== | 52% | |===================================== | 53% | |====================================== | 54% | |======================================= | 56% | |======================================== | 57% | |========================================= | 59% | |========================================== | 60% | |=========================================== | 62% | |============================================= | 64% | |============================================= | 65% | |=============================================== | 67% | |=============================================== | 68% | |================================================= | 70% | |================================================== | 71% | |=================================================== | 72% | |==================================================== | 74% | |===================================================== | 75% | |====================================================== | 77% | |======================================================= | 79% | |======================================================== | 80% | |========================================================= | 82% | |========================================================== | 83% | |=========================================================== | 85% | |============================================================= | 87% | |============================================================= | 88% | |=============================================================== | 89% | |=============================================================== | 90% | |================================================================= | 92% | |================================================================== | 94% | |=================================================================== | 95% | |==================================================================== | 97% | |===================================================================== | 98% | |======================================================================| 100%
#> # A tibble: 6 x 7
#> date location location_type location_code location_code_t… data_type
#> <date> <chr> <chr> <chr> <chr> <chr>
#> 1 2020-02-15 Autauga… county 01001 fips_code retail_a…
#> 2 2020-02-15 Autauga… county 01001 fips_code grocery_…
#> 3 2020-02-15 Autauga… county 01001 fips_code parks_pe…
#> 4 2020-02-15 Autauga… county 01001 fips_code transit_…
#> 5 2020-02-15 Autauga… county 01001 fips_code workplac…
#> 6 2020-02-15 Autauga… county 01001 fips_code resident…
#> # … with 1 more variable: value <int>Please see the relevant information about each data set and their licenses if you plan on using these data in any published works.