R/get_latest_combined_data.R
get_latest_combined_data.Rd
Extract the Lastest Data for Each Variable of Interest
get_latest_combined_data(data)
data | Dataframe containing variables to be mapped. Must contain a |
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A dataframe containing the latest data on each variable of interest - along with a flag indicating which year the data is from.
#> # A tibble: 244 x 30 #> country country_code g_whoregion tb_year tb_cases tb_inc tb_inc_lo tb_inc_hi #> <chr> <fct> <fct> <dbl> <int> <dbl> <dbl> <dbl> #> 1 Afghan… AFG Eastern Me… 2018 70000 189 122 270 #> 2 Albania ALB Europe 2018 510 18 15 20 #> 3 Algeria DZA Africa 2018 29000 69 53 88 #> 4 Americ… ASM Western Pa… 2018 0 0 0 0 #> 5 Andorra AND Europe 2018 2 3 2.6 3.5 #> 6 Angola AGO Africa 2018 109000 355 230 507 #> 7 Anguil… AIA Americas 2018 3 22 14 31 #> 8 Antigu… ATG Americas 2018 6 6 5.1 6.9 #> 9 Argent… ARG Americas 2018 12000 27 23 31 #> 10 Armenia ARM Europe 2018 920 31 24 39 #> # … with 234 more rows, and 22 more variables: prop_tb_ep <dbl>, #> # prop_hiv <dbl>, prop_hiv_lo <dbl>, prop_hiv_hi <dbl>, z_tb_year <dbl>, #> # z_tb_id <int>, z_tb_geo_coverage <fct>, z_tb_study_pop <fct>, #> # z_tb_multi_year_study <fct>, tb_z_prop <dbl>, tb_z_prop_lo <dbl>, #> # tb_z_prop_hi <dbl>, tb_z_prop_se <dbl>, z_tb_animal_year <dbl>, #> # z_tb_dom_animal <fct>, z_tb_wild_animal <fct>, demo_year <dbl>, #> # population <dbl>, prop_rural <dbl>, animal_year <dbl>, cattle <int>, #> # cattle_per_head <dbl>