Data from Google's Community Mobility Reports on relative changes in movement trends by location type.
google_mobility
A data frame with 27,094,812 rows and 11 variables:
country_region_codecharacter Country Code
country_regioncharacter Country or Region name
sub_region_1character Subregion (e.g. US state) name
sub_region_2character Subregion (e.g. US county) name
metro_areaMetropolitan area name
iso3166_2character ISO 3166-2 Country/Region code
census_fips_codecharacter US Census FIPS code
place_idcharacter Place ID (hashed)
datedouble Date in yyyy-mm-dd format
typecharacter Type of location. Values are retail, grocery (and pharmacy), parts, transit (hubs/stations), workplaces, and residential
pct_diffinteger Percent change from baseline activity
Google LLC "Google COVID-19 Community Mobility Reports." https://www.google.com/covid19/mobility/ Accessed: 2021-03-09
Table: Data summary
| Name | google_mobility |
| Number of rows | 27094812 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| Date | 1 |
| character | 9 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
| date | 0 | 1 | 2020-02-15 | 2021-03-05 | 2020-09-01 | 385 |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
| country_region_code | 17244 | 1.00 | 2 | 2 | 0 | 134 | 0 |
| country_region | 0 | 1.00 | 4 | 22 | 0 | 135 | 0 |
| sub_region_1 | 459858 | 0.98 | 3 | 74 | 0 | 1860 | 0 |
| sub_region_2 | 4500534 | 0.83 | 2 | 56 | 0 | 9915 | 0 |
| metro_area | 26945472 | 0.01 | 21 | 34 | 0 | 65 | 0 |
| iso3166_2 | 22258770 | 0.18 | 4 | 6 | 0 | 2224 | 0 |
| census_fips_code | 21336414 | 0.21 | 5 | 5 | 0 | 2838 | 0 |
| place_id | 48912 | 1.00 | 27 | 27 | 0 | 13249 | 0 |
| type | 0 | 1.00 | 5 | 11 | 0 | 6 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
| pct_diff | 10131130 | 0.63 | -13.02 | 31.05 | -100 | -31 | -10 | 5 | 1206 | ▇▁▁▁▁ |
Location accuracy and the understanding of categorized places varies from region to region, so Google does not recommend using this data to compare changes between countries, or between regions with different characteristics (e.g. rural versus urban areas). Regions or categories are omitted if Google does not have have sufficient statistically significant levels of data for it. 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. What data is included in the calculation depends on user settings, connectivity, and whether it meets our privacy threshold. If the privacy threshold isn’t met (when somewhere isn’t busy enough to ensure anonymity) we don’t show a change for the day. As a result, you may encounter empty fields for certain places and dates. We calculate these insights based on data from users who have opted-in to Location History for their Google Account, so the data represents a sample of our users. As with all samples, this may or may not represent the exact behavior of a wider population. Google updated the way we calculate changes for Groceries & pharmacy, Retail & recreation, Transit stations, and Parks categories. For regions published before May 2020, the data may contain a consistent shift either up or down that starts between April 11–18, 2020. On October 5, 2020, Google added an improvement to the dataset to ensure consistent data reporting in the Groceries & pharmacy, Retail & recreation, Transit, Parks, and Workplaces categories. The update applies to all regions, starting on August 17, 2020. For more detailed information on considerations to bear in mind before using this data, see this overview from Google.
Kieran Healy