---
title: "Dashboard COVID-19 no Brasil em 2023"
date: "Atualização: `r format(Sys.time(), '%d')`/`r format(Sys.time(), '%m')`/`r format(Sys.time(), '%Y')` às `r format(Sys.time(), '%H:%M')`h"
output:
flexdashboard::flex_dashboard:
theme:
bg: "#EAECEE"
fg: "black"
primary: "#21618C"
base_font:
google: Space Grotesk
code_font:
google: Fira Code
orientation: columns
vertical_layout: scroll
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
df <- covid19br::downloadCovid19(level = "brazil")
```
Column 1
---------------------------------------------------------------
### Casos por semana epidemiológica
```{r fig.height=4, fig.width=8}
df |>
mutate(ano = as.factor(lubridate::year(date))) |>
filter(date >= "2022-01-01") |>
ggplot(aes(y = newCases, x = epi_week, fill = ano)) +
geom_col() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_fill_manual(values = c("#2E8BC0", "#05445E")) +
labs(y = "Casos novos", x = "Semana epidemiológica") +
hrbrthemes::theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
### Incidência em março/2023 por unidade da federação
```{r include=FALSE}
uf <- covid19br::downloadCovid19(level = "state")
uf <- uf |> filter(date >= "2023-03-01")
uf <- uf |>
group_by(state) |>
summarise(incidencia = round((sum(newCases) / max(pop)) * 100000, digits = 2),
mortalidade = round((sum(newDeaths) / max(pop)) * 100000, digits = 2),
letalidade = round((sum(newDeaths) / max(newCases)) * 100, digits = 2))
geo <- geobr::read_state()
```
```{r fig.height=5, fig.width=5}
geo |>
left_join(uf, by = c("abbrev_state" = "state")) %>%
ggplot(aes(fill = incidencia)) +
geom_sf(colour = "black") +
theme_void() +
labs(fill = "Casos a cada 100.000 habitantes") +
theme(legend.position = "bottom",
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"),
legend.background = element_rect(fill = "transparent", color = NA)) +
scale_fill_distiller(direction = 1, palette = "Blues")
```
### Média móvel dos casos para 14 dias
```{r fig.height=4, fig.width=8}
df |>
filter(date >= "2022-01-01") |>
mutate(mediamovel = zoo::rollmean(newCases, k = 14, fill = NA, align = "right"),
ano = as.factor(lubridate::year(date))) |>
ggplot(aes(y = mediamovel, x = date, color = ano)) +
geom_line(size = 1.5) +
hrbrthemes::theme_ipsum() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_color_manual(values = c("#2E8BC0", "#05445E")) +
labs(x = "Data da notificação", y = "Média móvel") +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
### Evolução mensal dos casos
```{r fig.height=4, fig.width=8}
df |>
filter(date >= "2022-01-01") |>
mutate(mes = lubridate::month(date, label = TRUE, abbr = TRUE),
ano = as.factor(lubridate::year(date))) %>%
ggplot(aes(y = newCases, x = mes, fill = ano)) +
geom_col() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_fill_manual(values = c("#2E8BC0", "#05445E")) +
labs(y = "Casos novos", x = "Mês de notificação") +
hrbrthemes::theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
Column 2
---------------------------------------------------------------
### Óbitos por semana epidemiológica
```{r fig.height=4, fig.width=8}
df |>
mutate(ano = as.factor(lubridate::year(date))) |>
filter(date >= "2022-01-01") |>
ggplot(aes(y = newDeaths, x = epi_week, fill = ano)) +
geom_col() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_fill_manual(values = c("#2E8BC0", "#05445E")) +
labs(y = "Novos óbitos", x = "Semana epidemiológica") +
hrbrthemes::theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
### Mortalidade em março/2023 por unidade da federação
```{r fig.height=5, fig.width=5}
geo |>
left_join(uf, by = c("abbrev_state" = "state")) %>%
ggplot(aes(fill = mortalidade)) +
geom_sf(colour = "black") +
theme_void() +
labs(fill = "Óbitos a cada 100.000 habitantes") +
theme(legend.position = "bottom",
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"),
legend.background = element_rect(fill = "transparent", color = NA)) +
scale_fill_distiller(direction = 1, palette = "Blues")
```
### Média móvel dos óbitos
```{r fig.height=4, fig.width=8}
df |>
filter(date >= "2022-01-01") |>
mutate(mediamovel = zoo::rollmean(newDeaths, k = 14, fill = NA, align = "right"),
ano = as.factor(lubridate::year(date))) |>
ggplot(aes(y = mediamovel, x = date, color = ano)) +
geom_line(size = 1.5) +
hrbrthemes::theme_ipsum() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_color_manual(values = c("#2E8BC0", "#05445E")) +
labs(x = "Data da notificação", y = "Média móvel") +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
### Evolução mensal dos óbitos
```{r fig.height=4, fig.width=8}
df |>
filter(date >= "2022-01-01") |>
mutate(mes = lubridate::month(date, label = TRUE, abbr = TRUE),
ano = as.factor(lubridate::year(date))) %>%
ggplot(aes(y = newDeaths, x = mes, fill = ano)) +
geom_col() +
scale_y_continuous(labels = scales::comma_format(big.mark = ".")) +
scale_fill_manual(values = c("#2E8BC0", "#05445E")) +
labs(y = "Novos óbitos", x = "Mês de notificação") +
hrbrthemes::theme_ipsum() +
theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(linetype = "dashed", color = "grey50"),
plot.title = element_text(size = 15),
axis.title.x = element_text(size = 12, color = "#F39C12"),
axis.title.y = element_text(size = 12, color = "#F39C12"),
axis.text.x = element_text(size = 11),
axis.text.y = element_text(size = 11),
plot.background = element_rect(fill = "#EAECEE", color = "#EAECEE"))
```
