list and detail available sars2pack datasets
available_datasets()
dataset_details()A data.frame in which each row represents an available dataset.
name: the short name of the dataset
accessor: accessor function
a list, with each dataset as an item
dataset_details: returns a pre-computed set of column names and types,
dimensions of the datasets, and for datasets with a date (time course),
the min and max dates included in the dataset. Each dataset is an item
in the list. See examples for details and for viewing suggestions.
dataset_details:
res = available_datasets()
res
#> # A tibble: 50 × 8
#> name accessor data_type geographical geospatial region resolution url
#> <chr> <chr> <list> <lgl> <lgl> <list> <list> <chr>
#> 1 United St… cdc_soc… <chr [1]> TRUE FALSE <chr> <chr [1]> http…
#> 2 Extensive… us_hosp… <chr [1]> TRUE TRUE <chr> <chr [1]> http…
#> 3 The Econo… economi… <chr [3]> TRUE FALSE <chr> <chr [2]> http…
#> 4 The : Exc… financi… <chr [3]> TRUE FALSE <chr> <chr [2]> http…
#> 5 US county… us_coun… <chr [1]> TRUE FALSE <chr> <chr [3]> http…
#> 6 CoronaNet… coronan… <chr [1]> TRUE FALSE <chr> <chr [2]> http…
#> 7 Country m… country… <chr [1]> TRUE FALSE <chr> <chr [1]> http…
#> 8 Our World… owid_da… <chr [4]> TRUE FALSE <chr> <chr [1]> http…
#> 9 GISAID me… cov_glu… <chr [1]> TRUE FALSE <chr> <chr [1]> http…
#> 10 Newick tr… cov_glu… <chr [1]> FALSE FALSE <chr> <chr [1]> http…
#> # … with 40 more rows
# and how to use the accessor programmatically
get(res[1,]$accessor)()
#> # A tibble: 3,142 × 126
#> grasp_id state_fips state st_abbr county fips location area_sqmi e_totpop
#> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 1 00001 Alabama AL Autauga 01001 Autauga… 594. 55200
#> 2 2 00001 Alabama AL Baldwin 01003 Baldwin… 1590. 208107
#> 3 3 00001 Alabama AL Barbour 01005 Barbour… 885. 25782
#> 4 4 00001 Alabama AL Bibb 01007 Bibb Co… 622. 22527
#> 5 5 00001 Alabama AL Blount 01009 Blount … 645. 57645
#> 6 6 00001 Alabama AL Bullock 01011 Bullock… 623. 10352
#> 7 7 00001 Alabama AL Butler 01013 Butler … 777. 20025
#> 8 8 00001 Alabama AL Calhoun 01015 Calhoun… 606. 115098
#> 9 9 00001 Alabama AL Chambe… 01017 Chamber… 597. 33826
#> 10 10 00001 Alabama AL Cherok… 01019 Cheroke… 554. 25853
#> # … with 3,132 more rows, and 117 more variables: m_totpop <dbl>, e_hu <dbl>,
#> # m_hu <dbl>, e_hh <dbl>, m_hh <dbl>, e_pov <dbl>, m_pov <dbl>,
#> # e_unemp <dbl>, m_unemp <dbl>, e_pci <dbl>, m_pci <dbl>, e_nohsdp <dbl>,
#> # m_nohsdp <dbl>, e_age65 <dbl>, m_age65 <dbl>, e_age17 <dbl>, m_age17 <dbl>,
#> # e_disabl <dbl>, m_disabl <dbl>, e_sngpnt <dbl>, m_sngpnt <dbl>,
#> # e_minrty <dbl>, m_minrty <dbl>, e_limeng <dbl>, m_limeng <dbl>,
#> # e_munit <dbl>, m_munit <dbl>, e_mobile <dbl>, m_mobile <dbl>, …
dd = dataset_details()
str(dd,list.len=3)
#> List of 2
#> $ datasets :List of 50
#> ..$ cdc_social_vulnerability_index :List of 2
#> .. ..$ columns :List of 126
#> .. .. ..$ grasp_id : chr "numeric"
#> .. .. ..$ state_fips : chr "character"
#> .. .. ..$ state : chr "character"
#> .. .. .. [list output truncated]
#> .. ..$ dimensions:List of 2
#> .. .. ..$ nrow: int 3142
#> .. .. ..$ ncol: int 126
#> ..$ us_hospital_details :List of 2
#> .. ..$ columns :List of 33
#> .. .. ..$ x : chr "numeric"
#> .. .. ..$ y : chr "numeric"
#> .. .. ..$ objectid : chr "numeric"
#> .. .. .. [list output truncated]
#> .. ..$ dimensions:List of 2
#> .. .. ..$ nrow: int 7596
#> .. .. ..$ ncol: int 33
#> ..$ economist_excess_deaths :List of 2
#> .. ..$ columns :List of 14
#> .. .. ..$ country : chr "character"
#> .. .. ..$ region : chr "character"
#> .. .. ..$ region_code : chr "character"
#> .. .. .. [list output truncated]
#> .. ..$ dimensions:List of 2
#> .. .. ..$ nrow: int 16890
#> .. .. ..$ ncol: int 14
#> .. [list output truncated]
#> $ eval_date: chr "2021-09-02"
names(dd$datasets)
#> [1] "cdc_social_vulnerability_index"
#> [2] "us_hospital_details"
#> [3] "economist_excess_deaths"
#> [4] "financial_times_excess_deaths"
#> [5] "us_county_health_rankings"
#> [6] "coronanet_government_response_data"
#> [7] "country_metadata"
#> [8] "owid_data"
#> [9] "cov_glue_lineage_data"
#> [10] "cov_glue_newick_data"
#> [11] "cdc_excess_deaths"
#> [12] "param_estimates_published"
#> [13] "cdc_aggregated_projections"
#> [14] "google_mobility_data"
#> [15] "google_search_trends_data"
#> [16] "who_cases"
#> [17] "ecdc_data"
#> [18] "beoutbreakprepared_data"
#> [19] "descartes_mobility_data"
#> [20] "apple_mobility_data"
#> [21] "usa_facts_data"
#> [22] "jhu_data"
#> [23] "jhu_us_data"
#> [24] "nytimes_county_data"
#> [25] "nytimes_state_data"
#> [26] "kff_icu_beds"
#> [27] "world_population_data"
#> [28] "us_county_geo_details"
#> [29] "covidtracker_data"
#> [30] "us_healthcare_capacity"
#> [31] "government_policy_timeline"
#> [32] "us_state_distancing_policy"
#> [33] "oecd_unemployment_data"
#> [34] "acaps_secondary_impact_data"
#> [35] "cci_us_vaccine_data"
#> [36] "coronadatascraper_data"
#> [37] "test_and_trace_data"
#> [38] "cdc_us_linelist_data"
#> [39] "econ_tracker_unemp_national_data"
#> [40] "econ_tracker_unemp_state_data"
#> [41] "econ_tracker_unemp_city_data"
#> [42] "econ_tracker_unemp_county_data"
#> [43] "econ_tracker_consumer_spending_city_data"
#> [44] "econ_tracker_consumer_spending_county_data"
#> [45] "econ_tracker_consumer_spending_state_data"
#> [46] "econ_tracker_consumer_spending_national_data"
#> [47] "econ_tracker_employment_national_data"
#> [48] "econ_tracker_employment_city_data"
#> [49] "econ_tracker_employment_state_data"
#> [50] "econ_tracker_employment_county_data"
# evaluated
dd$eval_date
#> [1] "2021-09-02"