---
title: "Coronavirus (II)"
author: "Rubén F. Bustillo"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
theme: cerulean
---
```{r setup, include=FALSE}
#------------------ paquetes ------------------
devtools::install_github("RamiKrispin/coronavirus")
coronavirus<-coronavirus::coronavirus
library(flexdashboard)
library(coronavirus)
library(tidyverse)
library(echarts4r)
library(DT)
library(plotly)
data(coronavirus)
# COLORES:
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "lightsteelblue"
active_color <- "orange"
recovered_color <- "limegreen"
death_color <- "red"
# DATASETS:
# CORONAVIRUS DATASET
coronavirus_b <- coronavirus %>%
mutate(country = recode_factor(Country.Region,
"Czechia"= "Czech Rep.",
"United Arab Emirates" = "UAE",
"Bosnia and Herzegovina"= "Bosnia and Herz.",
"Korea, South" = "Korea",
"Dominican Republic" = "Dominican Rep.",
"Faroe Islands" = "Faeroe Is.",
"North Macedonia" = "Macedonia",
"occupied Palestinian territory" = "Palestine",
"Congo (Kinshasa)" = "Dem. Rep. Congo",
"Cote d'Ivoire"= "Côte d’Ivoire",
"Republic of Moldova" = "Moldova"))
# CASES by country
df <- coronavirus %>%
group_by(Country.Region, type) %>%
summarise(total = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
arrange(-confirmed) %>%
ungroup() %>%
mutate(country = recode_factor(Country.Region,
"Czechia"= "Czech Rep.",
"United Arab Emirates" = "UAE",
"Bosnia and Herzegovina"= "Bosnia and Herz.",
"Korea, South" = "Korea",
"Dominican Republic" = "Dominican Rep.",
"Faroe Islands" = "Faeroe Is.",
"North Macedonia" = "Macedonia",
"occupied Palestinian territory" = "Palestine",
"Congo (Kinshasa)" = "Dem. Rep. Congo",
"Cote d'Ivoire"= "Côte d’Ivoire",
"Republic of Moldova" = "Moldova"))
# CUMULATIVE CASES:
df_daily <- coronavirus %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
df1 <- coronavirus %>%
filter(date == max(date))
```
TOTAL
=======================================================================
```{r, include=FALSE}
df_total <- coronavirus_b %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_table <- df %>%
select(country, confirmed, recovered, death)
df_table %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_total) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### lastupdate {.value-box}
```{r}
valueBox(value = head(df1$date, n=1),
caption = "Last update")
```
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","),
" (", round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)", sep = ""),
caption = "Death Cases",
color = death_color)
```
TOTAL (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
China
=======================================================================
```{r, include=FALSE}
df_china <- coronavirus_b %>%
filter(country == "China") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_china <- coronavirus_b %>%
filter (country == "China")
df_china_valuebox<-df %>%
filter (country == "China")
df_daily_china <- coronavirus_china %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_china %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_china) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_china) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_china) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_china_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_china_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_china_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_china_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
China (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_china) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
Spain
=======================================================================
```{r, include=FALSE}
df_spain <- coronavirus_b %>%
filter(country == "Spain") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_spain <- coronavirus_b %>%
filter (country == "Spain")
df_spain_valuebox<-df %>%
filter (country == "Spain")
df_daily_spain <- coronavirus_spain %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_spain %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_spain) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_spain) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_spain) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_spain_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_spain_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_spain_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_spain_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
Spain (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_spain) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
Italy
=======================================================================
```{r, include=FALSE}
df_italy <- coronavirus_b %>%
filter(country == "Italy") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_italy <- coronavirus_b %>%
filter (country == "Italy")
df_italy_valuebox<-df %>%
filter (country == "Italy")
df_daily_italy <- coronavirus_italy %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_italy %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_italy) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_italy) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_italy) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_italy_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_italy_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_italy_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_italy_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
Italy (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_italy) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
France
=======================================================================
```{r, include=FALSE}
df_france <- coronavirus_b %>%
filter(country == "France") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_france <- coronavirus_b %>%
filter (country == "France")
df_france_valuebox<-df %>%
filter (country == "France")
df_daily_france <- coronavirus_france %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_france %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_france) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_france) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_france) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_france_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_france_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_france_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_france_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
France (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_france) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
South Korea
=======================================================================
```{r, include=FALSE}
df_southkorea <- coronavirus_b %>%
filter(country == "Korea") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_southkorea <- coronavirus_b %>%
filter (country == "Korea")
df_southkorea_valuebox<-df %>%
filter (country == "Korea")
df_daily_southkorea <- coronavirus_southkorea %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_southkorea %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_southkorea) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_southkorea) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_southkorea) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_southkorea_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_southkorea_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_southkorea_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_southkorea_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
South Korea (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_southkorea) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
Iran
=======================================================================
```{r, include=FALSE}
df_Iran <- coronavirus_b %>%
filter(country == "Iran") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_Iran <- coronavirus_b %>%
filter (country == "Iran")
df_Iran_valuebox<-df %>%
filter (country == "Iran")
df_daily_Iran <- coronavirus_Iran %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_Iran %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_Iran) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_Iran) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_Iran) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_Iran_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_Iran_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_Iran_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_Iran_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
Iran (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_Iran) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
United Kingdom
=======================================================================
```{r, include=FALSE}
df_uk <- coronavirus_b %>%
filter(country == "United Kingdom") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_uk <- coronavirus_b %>%
filter (country == "United Kingdom")
df_uk_valuebox<-df %>%
filter (country == "United Kingdom")
df_daily_uk <- coronavirus_uk %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_uk %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_uk) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_uk) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_uk) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_uk_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_uk_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_uk_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_uk_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
United Kingdom (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_uk) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```
United States
=======================================================================
```{r, include=FALSE}
df_us <- coronavirus_b %>%
filter(country == "US") %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = F)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
arrange(date) %>%
ungroup()
coronavirus_us <- coronavirus_b %>%
filter (country == "US")
df_us_valuebox<-df %>%
filter (country == "US")
df_daily_us <- coronavirus_us %>%
group_by(date, type) %>%
summarise(total = sum(cases, na.rm = TRUE)) %>%
pivot_wider(names_from = type,
values_from = total) %>%
mutate_if(is.numeric, ~replace(., is.na(.),0)) %>%
arrange(date) %>%
ungroup() %>%
mutate(active = confirmed - death - recovered) %>%
mutate(confirmed_cumulative = cumsum(confirmed),
death_cumulative = cumsum(death),
recovered_cumulative = cumsum(recovered),
active_cumulative = cumsum(active))
```
Column
-------------------------------------
###
```{r, figh.height = 10}
df_us %>%
datatable(rownames = FALSE)
```
### Confirmed Cases (daily)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_us) %>%
plotly::add_trace(x = ~ date,
y = ~ confirmed,
type = "bar",
line = list(color = confirmed_color),
marker = list(color = confirmed_color)) %>%
plotly::layout(yaxis = list(title = "Confirmed Cases"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### Recovered Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_us) %>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::layout(yaxis = list(title = "Recovered Cases (cumulative)"),
xaxis = list(title = ""))
```
### Death Cases (cumulative)
```{r, fig.width=10, fig.height=10}
plotly::plot_ly(data = df_daily_us) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::layout(yaxis = list(title = "Death Cases (cumulative)"),
xaxis = list(title = ""))
```
Column
-------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(df_us_valuebox$confirmed), big.mark = ","), "", sep = " "),
caption = "Confirmed Cases",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(df_us_valuebox$unrecovered), big.mark = ","), "", sep = " "),
caption = "Active Cases",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(df_us_valuebox$recovered), big.mark = ","), "", sep = " "),
caption = "Recovered Cases",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(df_us_valuebox$death), big.mark = ","), "", sep = " "),
caption = "Death Cases",
color = death_color)
```
United States (b)
=======================================================================
### Active, recovered and death cases over time (cumulative cases)
```{r}
plotly::plot_ly(data = df_daily_us) %>%
plotly::add_trace(x = ~ date,
y = ~ death_cumulative,
type = "bar",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color))%>%
plotly::add_trace(x = ~ date,
y = ~ recovered_cumulative,
type = "bar",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ date,
y = ~ active_cumulative,
type = "bar",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases"),
xaxis = list(title = ""),
hovermode = "compare")
```