---
title: "Coronavirus dashboard"
author: "Antoine Soetewey (adapted by Vincent Berthet)"
date: "26 mars 2020"
output:
flexdashboard::flex_dashboard:
orientation: rows
# social: ["facebook", "twitter", "linkedin"]
source_code: embed
#vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_datasets()
# View(coronavirus)
# max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "France") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily_France <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "France") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_France = cumsum(confirmed),
death_cum_France = cumsum(death)
)
df_daily_Italy <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "Italy") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_Italy = cumsum(confirmed),
death_cum_Italy = cumsum(death)
)
df_daily_Spain <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "Spain") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_Spain = cumsum(confirmed),
death_cum_Spain = cumsum(death)
)
df_daily_Korea <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "Korea, South") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_Korea = cumsum(confirmed),
death_cum_Korea = cumsum(death)
)
df_daily_Germany <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "Germany") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_Germany = cumsum(confirmed),
death_cum_Germany = cumsum(death)
)
df_daily_US <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "US") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_US = cumsum(confirmed),
death_cum_US = cumsum(death)
)
df_daily_China <- coronavirus %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::filter(Country.Region == "China") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
dplyr::ungroup() %>%
dplyr::mutate(
confirmed_cum_China = cumsum(confirmed),
death_cum_China = cumsum(death)
)
df_daily <- cbind(df_daily_France, df_daily_Italy, df_daily_Spain, df_daily_Korea, df_daily_Germany, df_daily_US, df_daily_China)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```
Page 1
=======================================================================
Row
-------------------
### **Cumulative confirmed cases by country**
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_France,
type = "scatter",
mode = "lines+markers",
name = "France",
line = list(color = "green"),
marker = list(color = "green")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy",
line = list(color = "blue"),
marker = list(color = "blue")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain",
line = list(color = "red"),
marker = list(color = "red")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_Germany,
type = "scatter",
mode = "lines+markers",
name = "Germany",
line = list(color = "brown"),
marker = list(color = "brown")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_US,
type = "scatter",
mode = "lines+markers",
name = "USA",
line = list(color = "goldenrod"),
marker = list(color = "goldenrod")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_Korea,
type = "scatter",
mode = "lines+markers",
name = "South Korea",
line = list(color = "yellow"),
marker = list(color = "yellow")
) %>%
plotly::add_trace(
x = ~date,
y = ~confirmed_cum_China,
type = "scatter",
mode = "lines+markers",
name = "China",
line = list(color = "darkcyan"),
marker = list(color = "darkcyan")
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Cumulative death cases by country**
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_France,
type = "scatter",
mode = "lines+markers",
name = "France",
line = list(color = "green"),
marker = list(color = "green")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy",
line = list(color = "blue"),
marker = list(color = "blue")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain",
line = list(color = "red"),
marker = list(color = "red")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_Germany,
type = "scatter",
mode = "lines+markers",
name = "Germany",
line = list(color = "brown"),
marker = list(color = "brown")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_US,
type = "scatter",
mode = "lines+markers",
name = "USA",
line = list(color = "goldenrod"),
marker = list(color = "goldenrod")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_Korea,
type = "scatter",
mode = "lines+markers",
name = "South Korea",
line = list(color = "yellow"),
marker = list(color = "yellow")
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum_China,
type = "scatter",
mode = "lines+markers",
name = "China",
line = list(color = "darkcyan"),
marker = list(color = "darkcyan")
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Page 2
=======================================================================
Row
-------------------
### **Daily new death cases**
```{r}
daily_confirmed <- coronavirus %>%
#dplyr::filter(type == "confirmed") %>%
dplyr::filter(type == "death") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~France,
type = "scatter",
mode = "lines+markers",
name = "France",
line = list(color = "green"),
marker = list(color = "green")
) %>%
plotly::add_trace(
x = ~date,
y = ~Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy",
line = list(color = "blue"),
marker = list(color = "blue")
) %>%
plotly::add_trace(
x = ~date,
y = ~Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain",
line = list(color = "red"),
marker = list(color = "red")
) %>%
plotly::add_trace(
x = ~date,
y = ~Germany,
type = "scatter",
mode = "lines+markers",
name = "Germany",
line = list(color = "brown"),
marker = list(color = "brown")
) %>%
plotly::add_trace(
x = ~date,
y = ~US,
type = "scatter",
mode = "lines+markers",
name = "USA",
line = list(color = "goldenrod"),
marker = list(color = "goldenrod")
) %>%
plotly::add_trace(
x = ~date,
y = ~`Korea, South`,
type = "scatter",
mode = "lines+markers",
name = "South Korea",
line = list(color = "yellow"),
marker = list(color = "yellow")
) %>%
plotly::add_trace(
x = ~date,
y = ~`China`,
type = "scatter",
mode = "lines+markers",
name = "China",
line = list(color = "darkcyan"),
marker = list(color = "darkcyan")
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Cases distribution by type**
```{r daily_summary}
df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "France" |
Country.Region == "Italy" |
Country.Region == "Spain" |
Country.Region == "Germany" |
Country.Region == "US" |
Country.Region == "Korea, South" |
Country.Region == "China") %>%
dplyr::group_by(Country.Region, type) %>%
#dplyr::summarise(total = sum(cases)) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```