This function maps lineage name to snps and to taxon names. Everything is GISAID-specific.
cov_glue_snp_lineage()a data.frame
cov_glue_lineage_data(), cov_glue_newick_data()
Other lineage:
cov_glue_lineage_data(),
cov_glue_newick_data()
Other data-import:
acaps_government_measures_data(),
acaps_secondary_impact_data(),
apple_mobility_data(),
beoutbreakprepared_data(),
cci_us_vaccine_data(),
cdc_aggregated_projections(),
cdc_excess_deaths(),
cdc_social_vulnerability_index(),
coronadatascraper_data(),
coronanet_government_response_data(),
cov_glue_lineage_data(),
cov_glue_newick_data(),
covidtracker_data(),
descartes_mobility_data(),
ecdc_data(),
econ_tracker_consumer_spending,
econ_tracker_employment,
econ_tracker_unemp_data,
economist_excess_deaths(),
financial_times_excess_deaths(),
google_mobility_data(),
government_policy_timeline(),
jhu_data(),
jhu_us_data(),
kff_icu_beds(),
nytimes_county_data(),
oecd_unemployment_data(),
owid_data(),
param_estimates_published(),
test_and_trace_data(),
us_county_geo_details(),
us_county_health_rankings(),
us_healthcare_capacity(),
us_hospital_details(),
us_state_distancing_policy(),
usa_facts_data(),
who_cases()
res = cov_glue_snp_lineage()
res
#> # A tibble: 14,793 × 3
#> lineage snp taxon
#> <chr> <chr> <chr>
#> 1 A 29301AT Beijing/233/2020
#> 2 A 1691AG India/1-31/2020
#> 3 A 16877CT India/1-31/2020
#> 4 A 24351CT India/1-31/2020
#> 5 A 24990CT Australia/QLD02/2020
#> 6 A 25587CT Australia/QLD02/2020
#> 7 A 2875GA Beijing/Wuhan_IME-BJ04/2020
#> 8 A 11937GA Beijing/Wuhan_IME-BJ01/2020
#> 9 A 2445CT Australia/NSW04/2020
#> 10 A 20367TC Australia/NSW04/2020
#> # … with 14,783 more rows
colnames(res)
#> [1] "lineage" "snp" "taxon"
dplyr::glimpse(res)
#> Rows: 14,793
#> Columns: 3
#> $ lineage <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A…
#> $ snp <chr> "29301AT", "1691AG", "16877CT", "24351CT", "24990CT", "25587CT…
#> $ taxon <chr> "Beijing/233/2020", "India/1-31/2020", "India/1-31/2020", "Ind…