Summary Codebook

Source: vignettes/codebook.Rmd

Overview of Included Tables

This vignette provides an overview of the contents of each table of data in the package. For further details on the variables, consult each table’s help page. Note that many of these data tables are provisional, or are estimates subject to significant interpretive limits. Read the supplied documentation carefully before working with any of the tables.

Mobility Data from Apple 

data(apple_mobility)
skimr::skim(apple_mobility)
Data summary
Name apple_mobility
Number of rows 1970220
Number of columns 8
_______________________
Column type frequency:
character 6
Date 1
numeric 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
geo_type 0 1.00 4 14 0 4 0
region 0 1.00 4 48 0 2325 0
transportation_type 0 1.00 7 7 0 3 0
alternative_name 1531740 0.22 2 85 0 519 0
sub_region 572040 0.71 4 33 0 162 0
country 64260 0.97 5 20 0 47 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date 0 1 2020-01-13 2021-03-07 2020-08-09 420

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
score 23552 0.99 120.1 61.71 0.44 84.31 112.79 145.79 2148.12 ▇▁▁▁▁

Mobilty Data from Google 

data(google_mobility)
skimr::skim(google_mobility)
Data summary
Name google_mobility
Number of rows 27094812
Number of columns 11
_______________________
Column type frequency:
character 9
Date 1
numeric 1
________________________
Group variables None

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: 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: 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 ▇▁▁▁▁

Other Tables

Country Codes

data(country_codes)
country_codes %>%
  dplyr::ungroup() %>%
  skimr::skim()
Data summary
Name Piped data
Number of rows 258
Number of columns 4
_______________________
Column type frequency:
character 4
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
iso2 1 1.00 2 2 0 248 0
iso3 0 1.00 3 3 0 249 0
cname 0 1.00 4 44 0 249 0
continent 10 0.96 4 13 0 6 0