coronavirusbrazil
The coronavirusbrazil package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Brazil. The datasets were obtained from RamiKrispin/coronavirus, Ministerio da Saúde, brasil.io and Secretaria de Saúde - RJ.
This repository was inspired by the RamiKrispin/coronavirus package repository.
Installation
You can install the released version of coronavirusbrazil from CRAN with:
# install.packages("devtools")
devtools::install_github("mralbu/coronavirusbrazil")Usage
The package contains the following datasets:
library(coronavirusbrazil)
library(ggplot2)
data("coronavirus_br")
head(coronavirus_br)
#> # A tibble: 6 x 10
#> date cases deaths new_cases new_deaths death_rate percent_case_in~
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-02-25 0 0 NA NA NaN NA
#> 2 2020-02-26 1 0 1 0 0 Inf
#> 3 2020-02-27 1 0 0 0 0 0
#> 4 2020-02-28 1 0 0 0 0 0
#> 5 2020-02-29 2 0 1 0 0 100
#> 6 2020-03-01 2 0 0 0 0 0
#> # ... with 3 more variables: percent_death_increase <dbl>, days_gt_10 <dbl>,
#> # days_gt_100 <dbl>plot_coronavirus(coronavirus_br, xaxis = "date", yaxis = "cases", log_scale = F, linear_smooth = F)
data("coronavirus_br_states")
head(coronavirus_br_states)
#> # A tibble: 6 x 11
#> # Groups: state [1]
#> state date cases deaths new_cases new_deaths death_rate percent_case_in~
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 RO 2020-02-25 0 0 0 0 NaN NaN
#> 2 RO 2020-02-26 0 0 0 0 NaN NaN
#> 3 RO 2020-02-27 0 0 0 0 NaN NaN
#> 4 RO 2020-02-28 0 0 0 0 NaN NaN
#> 5 RO 2020-02-29 0 0 0 0 NaN NaN
#> 6 RO 2020-03-01 0 0 0 0 NaN NaN
#> # ... with 3 more variables: percent_death_increase <dbl>, days_gt_10 <dbl>,
#> # days_gt_100 <dbl>plot_coronavirus(coronavirus_br_states, yaxis = "percent_case_increase", color = "state", filter_variable = "state", facet = "state", filter_values = c("RJ", "SP", "DF", "CE", "RS", "MG"), log_scale = TRUE, linear_smooth = TRUE)
data("coronavirus_br_cities")
head(coronavirus_br_cities)
#> # A tibble: 6 x 17
#> # Groups: city [2]
#> city date state place_type cases deaths is_last estimated_popul~
#> <chr> <date> <chr> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 Abae~ 2020-04-12 MG city 1 0 TRUE 23237
#> 2 Abae~ 2020-03-31 PA city 1 0 FALSE 157698
#> 3 Abae~ 2020-04-01 PA city 1 0 FALSE 157698
#> 4 Abae~ 2020-04-02 PA city 1 0 FALSE 157698
#> 5 Abae~ 2020-04-03 PA city 1 0 FALSE 157698
#> 6 Abae~ 2020-04-04 PA city 1 0 FALSE 157698
#> # ... with 9 more variables: city_ibge_code <dbl>,
#> # confirmed_per_100k_inhabitants <dbl>, death_rate <dbl>, new_cases <dbl>,
#> # new_deaths <dbl>, percent_case_increase <dbl>,
#> # percent_death_increase <dbl>, days_gt_10 <dbl>, days_gt_100 <dbl>There are also geospatial datasets avaiable:
dplyr::glimpse(spatial_br_states)
#> Observations: 27
#> Variables: 16
#> $ id <chr> "AC", "AL", "AM", "AP", "BA", "CE", "DF", "E...
#> $ name <chr> "Acre", "Alagoas", "Amazonas", "Amapá", "Bah...
#> $ uf <chr> "AC", "AL", "AM", "AP", "BA", "CE", "DF", "E...
#> $ codigo <int> 12, 27, 13, 16, 29, 23, 53, 32, 52, 21, 31, ...
#> $ regiao <chr> "Norte", "Nordeste", "Norte", "Norte", "Nord...
#> $ geometry <list> [<-70.470805, -9.213489>, <-36.622412, -9.5...
#> $ date <date> 2020-04-12, 2020-04-12, 2020-04-12, 2020-04...
#> $ cases <dbl> 77, 48, 1206, 230, 673, 1676, 614, 383, 229,...
#> $ deaths <dbl> 2, 3, 62, 5, 21, 74, 14, 9, 14, 24, 20, 2, 3...
#> $ new_cases <dbl> 5, 0, 156, 37, 38, 94, 35, 34, 20, 54, 56, 1...
#> $ new_deaths <dbl> 0, 0, 9, 2, 0, 7, 0, 0, 4, 3, 3, 0, 0, 3, 2,...
#> $ death_rate <dbl> 0.02597403, 0.06250000, 0.05140962, 0.021739...
#> $ percent_case_increase <dbl> 6.944444, 0.000000, 14.857143, 19.170984, 5....
#> $ percent_death_increase <dbl> 0.000000, 0.000000, 16.981132, 66.666667, 0....
#> $ log_cases <dbl> 1.886491, 1.681241, 3.081347, 2.361728, 2.82...
#> $ log_deaths <dbl> 0.3010300, 0.4771213, 1.7923917, 0.6989700, ...
ggplot2::ggplot(spatial_br_states, ggplot2::aes(color=cases, size=cases)) + ggplot2::geom_sf()
dplyr::glimpse(spatial_br_cities)
#> Observations: 1,082
#> Variables: 7
#> $ date <date> 2020-04-12, 2020-04-10, 2020-04-12, 2020-04-11, 2020-04...
#> $ city <chr> "Abaeté", "Abaetetuba", "Abreu e Lima", "Açailândia", "A...
#> $ cases <dbl> 1, 2, 10, 1, 9, 8, 0, 1, 1, 1, 1, 2, 1, 2, 5, 1, 1, 1, 2...
#> $ deaths <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,...
#> $ geometry <POINT> POINT (-45.4444 -19.1551), POINT (-48.8788 -1.72183), ...
#> $ log_cases <dbl> 0.0000000, 0.3010300, 1.0000000, 0.0000000, 0.9542425, 0...
#> $ log_deaths <dbl> -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 0, -Inf, 0, -I...
ggplot2::ggplot(spatial_br_cities, ggplot2::aes(color=cases, size=cases)) + ggplot2::geom_sf()
Data Sources
- Countries: RamiKrispin/coronavirus
- States: Ministerio da Saúde
- Cities: brasil.io
- Rio de Janeiro: Secretaria de Saúde - RJ