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 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~
#>   
#> 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 , days_gt_10 ,
#> #   days_gt_100 
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~
#>   
#> 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 , days_gt_10 ,
#> #   days_gt_100 
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 [1]
#>   city  date       state place_type cases deaths is_last estimated_popul~
#>   
#> 1 Abae~ 2020-03-31 PA    city           1      0 FALSE             157698
#> 2 Abae~ 2020-04-01 PA    city           1      0 FALSE             157698
#> 3 Abae~ 2020-04-02 PA    city           1      0 FALSE             157698
#> 4 Abae~ 2020-04-03 PA    city           1      0 FALSE             157698
#> 5 Abae~ 2020-04-04 PA    city           1      0 FALSE             157698
#> 6 Abae~ 2020-04-05 PA    city           1      0 FALSE             157698
#> # ... with 9 more variables: city_ibge_code ,
#> #   confirmed_per_100k_inhabitants , death_rate , new_cases ,
#> #   new_deaths , percent_case_increase ,
#> #   percent_death_increase , days_gt_10 , days_gt_100

There are also geospatial datasets avaiable:

dplyr::glimpse(spatial_br_states)
#> Observations: 27
#> Variables: 16
#> $ id                      "AC", "AL", "AM", "AP", "BA", "CE", "DF", "E...
#> $ name                    "Acre", "Alagoas", "Amazonas", "Amapá", "Bah...
#> $ uf                      "AC", "AL", "AM", "AP", "BA", "CE", "DF", "E...
#> $ codigo                  12, 27, 13, 16, 29, 23, 53, 32, 52, 21, 31, ...
#> $ regiao                  "Norte", "Nordeste", "Norte", "Norte", "Nord...
#> $ geometry                [,  $ date                    2020-04-09, 2020-04-09, 2020-04-09, 2020-04...
#> $ cases                   62, 37, 899, 128, 559, 1425, 527, 273, 179, ...
#> $ deaths                  2, 3, 40, 2, 19, 55, 13, 6, 7, 12, 15, 2, 2,...
#> $ new_cases               8, 0, 95, 21, 62, 134, 18, 46, 21, 43, 41, 4...
#> $ new_deaths              0, 1, 10, 0, 4, 12, 1, 0, 0, 1, 1, 0, 1, 1, ...
#> $ death_rate              0.03225806, 0.08108108, 0.04449388, 0.015625...
#> $ percent_case_increase   14.8148148, 0.0000000, 11.8159204, 19.626168...
#> $ percent_death_increase  0.000000, 50.000000, 33.333333, 0.000000, 26...
#> $ log_cases               1.792392, 1.568202, 2.953760, 2.107210, 2.74...
#> $ log_deaths              0.3010300, 0.4771213, 1.6020600, 0.3010300, ...
ggplot2::ggplot(spatial_br_states, ggplot2::aes(color=cases, size=cases)) + ggplot2::geom_sf()

dplyr::glimpse(spatial_br_cities)
#> Observations: 908
#> Variables: 7
#> $ date        2020-04-08, 2020-04-04, 2020-04-08, 2020-04-08, 2020-04...
#> $ city        "Abaetetuba", "Abaiara", "Açailândia", "Acrelândia", "Aç...
#> $ cases       2, 1, 1, 9, 8, 0, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 3, 1,...
#> $ deaths      0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,...
#> $ geometry    POINT (-48.8788 -1.72183), POINT (-39.0416 -7.34588), ...
#> $ log_cases   0.3010300, 0.0000000, 0.0000000, 0.9542425, 0.9030900, -...
#> $ log_deaths  -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 0, -Inf, 0, -Inf, -I...
ggplot2::ggplot(spatial_br_cities, ggplot2::aes(color=cases, size=cases)) + ggplot2::geom_sf()

Data Sources