The Coronavirus Dashboard
This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic with information focused on the evolution of this pandemic in the European Continent with some studies about the exponential evolution of Infected cases. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available here
Data
The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository The raw tempoerature data is pulled from the imants repository with obtained the data from the Dark Sky Api
Packages
Deployment and reproducibly
The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:
For any question or feedback, you can either open an issue.
Contribution
The base code for this dashboard was provided by Rami Krispin, principally the first page, where the code is written by him. You can visit the diverse Coronavirus packages that he is developing.
Thanks Rami for the work developed!
---
title: "Coronavirus"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(readr)
library(dplyr)
library(tidyr)
library(lubridate)
library(sp)
library(rworldmap)
library(plotly)
library(flexdashboard)
library(rnaturalearth)
#------------------ Data ------------------
## get confirmed cases
confirmed <- read_csv(file = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
## get death cases
death <- read_csv(file = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")
## get recovered cases
recovered <- read_csv(file = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv")
## temperatures
temperature_max <- read_csv('https://raw.githubusercontent.com/imantsm/COVID-19/master/csv/tMax.csv')
temperature_min <- read_csv('https://raw.githubusercontent.com/imantsm/COVID-19/master/csv/tMin.csv')
## portugal data
portugal_data <- read_csv('https://raw.githubusercontent.com/dssg-pt/covid19pt-data/master/data.csv')
excel <- read_csv2('https://raw.githubusercontent.com/joaosilvagmr/coronavirus/master/data/DistritosConcelhosFreguesias.csv')
concelhos <- read_delim('https://raw.githubusercontent.com/joaosilvagmr/coronavirus/master/data/concelhos_new.txt', delim = ",")
#------------------ Data Treatment ------------------
# The single argument to this function, points, is a data.frame in which:
# - column 1 contains the longitude in degrees
# - column 2 contains the latitude in degrees
coords2continent = function(points)
{
countriesSP <- getMap(resolution='low')
# converting points to a SpatialPoints object
# setting CRS directly to that from rworldmap
pointsSP = SpatialPoints(points, proj4string=CRS(proj4string(countriesSP)))
# use 'over' to get indices of the Polygons object containing each point
indices = over(pointsSP, countriesSP)
indices$REGION # returns the continent (7 continent model)
}
confirmed <- confirmed %>%
## convert the dataset from wide to long
pivot_longer(., cols = 5:ncol(confirmed), names_to = "Date", values_to = "Confirmed") %>%
## correct portugal values
mutate(
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/2/20"),2,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/3/20"),4,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/4/20"),6,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/5/20"),9,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/6/20"),13,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/7/20"),21,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/8/20"),30,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/9/20"),39,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/10/20"),41,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/11/20"),59,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/12/20"),78,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/13/20"),112,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/14/20"),169,Confirmed),
Confirmed = if_else((`Country/Region`=="Portugal") & (Date == "3/18/20"),642,Confirmed),
Confirmed = if_else((`Country/Region`=="Italy") & (Date == "3/12/20"),15113,Confirmed),
Confirmed = if_else((`Country/Region`=="Spain") & (Date == "3/12/20"),3146,Confirmed),
Confirmed = if_else((`Country/Region`=="France") & (is.na(`Province/State`)) & (Date == "3/12/20"),2876,Confirmed),
Confirmed = if_else((`Country/Region`=="France") & (is.na(`Province/State`)) & (Date == "3/15/20"),5423,Confirmed),
Confirmed = if_else((`Country/Region`=="United Kingdom") & (is.na(`Province/State`)) & (Date == "3/12/20"),590,Confirmed),
Confirmed = if_else((`Country/Region`=="Germany") & (Date == "3/12/20"),2745,Confirmed),
Confirmed = replace_na(Confirmed, 0)
) %>%
## Convert the date column to a date format
mutate(Date = mdy(Date)) %>%
rename(
State = `Province/State`,
Country = `Country/Region`
) %>%
mutate(State = if_else(is.na(State),Country,State)) %>%
group_by(Country, State) %>%
arrange(Date) %>%
mutate(
Confirmed_day = Confirmed - lag(Confirmed, default = 0),
Continent = coords2continent(data.frame(lon=c(Long), lat=c(Lat)))) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
Continent = if_else(Country=="Australia","Australia",Continent),
Continent = if_else(Country=="Canada","North America",Continent),
Continent = if_else(Country=="China","Asia",Continent),
Continent = if_else(Country=="Denmark","Europe",Continent),
Continent = if_else(Country=="France","Europe",Continent),
Continent = if_else(Country=="Netherlands","Europe",Continent),
Continent = if_else(Country=="United Kingdom","Europe",Continent),
Continent = if_else(Country=="US","North America",Continent),
Continent = if_else(Country=="Monaco","Europe",Continent),
Continent = if_else(Country=="Philippines","Asia",Continent),
Continent = if_else(Country=="New Zealand","Europe",Continent),
Continent = if_else(Country=="Saint Vincent and the Grenadine","North America",Continent)
) %>%
select(Continent, State, Country, Lat, Long, Date, Confirmed, Confirmed_day) %>%
arrange(State, Continent, Country, Date) %>%
data.frame()
death <- death %>%
## convert the dataset from wide to long
pivot_longer(., cols = 5:ncol(death), names_to = "Date", values_to = "Death") %>%
## Convert the date column to a date format
mutate(Date = mdy(Date),
Death = replace_na(Death, 0)) %>%
rename(
State = `Province/State`,
Country = `Country/Region`,
) %>%
mutate(State = if_else(is.na(State),Country,State)) %>%
group_by(Country, State) %>%
arrange(Date) %>%
mutate(Death_day = Death - lag(Death, default = 0),
Continent = coords2continent(data.frame(lon=c(Long), lat=c(Lat)))) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
Continent = if_else(Country=="Australia","Australia",Continent),
Continent = if_else(Country=="Canada","North America",Continent),
Continent = if_else(Country=="China","Asia",Continent),
Continent = if_else(Country=="Denmark","Europe",Continent),
Continent = if_else(Country=="France","Europe",Continent),
Continent = if_else(Country=="Netherlands","Europe",Continent),
Continent = if_else(Country=="United Kingdom","Europe",Continent),
Continent = if_else(Country=="US","North America",Continent),
Continent = if_else(Country=="Monaco","Europe",Continent),
Continent = if_else(Country=="Philippines","Asia",Continent),
Continent = if_else(Country=="New Zealand","Europe",Continent),
Continent = if_else(Country=="Saint Vincent and the Grenadine","North America",Continent),
) %>%
select(Continent, State, Country, Lat, Long, Date, Death, Death_day) %>%
arrange(State, Continent, Country, Date) %>%
ungroup() %>%
data.frame()
recovered <- recovered %>%
## convert the dataset from wide to long
pivot_longer(., cols = 5:ncol(recovered), names_to = "Date", values_to = "Recovered") %>%
## Convert the date column to a date format
mutate(
Date = mdy(Date),
Recovered = replace_na(Recovered, 0)) %>%
rename(
State = `Province/State`,
Country = `Country/Region`
) %>%
mutate(State = if_else(is.na(State),Country,State)) %>%
group_by(Country, State) %>%
arrange(Date) %>%
mutate(Recovered_day = Recovered - lag(Recovered, default = 0),
Continent = coords2continent(data.frame(lon=c(Long), lat=c(Lat)))) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
Continent = if_else(Country=="Australia","Australia",Continent),
Continent = if_else(Country=="Canada","North America",Continent),
Continent = if_else(Country=="China","Asia",Continent),
Continent = if_else(Country=="Denmark","Europe",Continent),
Continent = if_else(Country=="France","Europe",Continent),
Continent = if_else(Country=="Netherlands","Europe",Continent),
Continent = if_else(Country=="United Kingdom","Europe",Continent),
Continent = if_else(Country=="US","North America",Continent),
Continent = if_else(Country=="Monaco","Europe",Continent),
Continent = if_else(Country=="Philippines","Asia",Continent),
Continent = if_else(Country=="New Zealand","Europe",Continent),
Continent = if_else(Country=="Saint Vincent and the Grenadine","North America",Continent)
) %>%
select(Continent, State, Country, Lat, Long, Date, Recovered, Recovered_day) %>%
arrange(State, Country, Date) %>%
ungroup() %>%
data.frame()
active <- confirmed %>%
select(-Confirmed_day) %>%
left_join(death[, c("Country","Date","State","Death")],
by=c("Country"="Country","Date"="Date","State"="State")) %>%
left_join(recovered[, c("Country","Date","State","Recovered")],
by=c("Country"="Country","Date"="Date","State"="State")) %>%
na.omit() %>%
mutate(
Recovered = replace_na(Recovered, 0),
Active = Confirmed - (Death + Recovered)
) %>%
group_by(Country, State) %>%
arrange(Date) %>%
mutate(Active_day = Active - lag(Active, default = 0)) %>%
select(Continent, State, Country, Lat, Long, Date, Active, Active_day) %>%
arrange(State, Country, Date) %>%
ungroup() %>%
data.frame()
temperature_max <- temperature_max %>%
## convert the dataset from wide to long
pivot_longer(., cols = 5:ncol(temperature_max), names_to = "Date", values_to = "TempMax") %>%
## Convert the date column to a date format
mutate(Date = mdy(Date),
Continent = coords2continent(data.frame(lon=c(Long), lat=c(Lat)))) %>%
rename(
State = `Province/State`,
Country = `Country/Region`
) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
Continent = if_else(Country=="Australia","Australia",Continent),
Continent = if_else(Country=="Canada","North America",Continent),
Continent = if_else(Country=="China","Asia",Continent),
Continent = if_else(Country=="Denmark","Europe",Continent),
Continent = if_else(Country=="France","Europe",Continent),
Continent = if_else(Country=="Netherlands","Europe",Continent),
Continent = if_else(Country=="United Kingdom","Europe",Continent),
Continent = if_else(Country=="US","North America",Continent),
Continent = if_else(Country=="Monaco","Europe",Continent),
Continent = if_else(Country=="Philippines","Asia",Continent),
Continent = if_else(Country=="New Zealand","Europe",Continent),
Continent = if_else(Country=="Saint Vincent and the Grenadine","North America",Continent)
) %>%
select(Continent, State, Country, Lat, Long, Date, TempMax) %>%
arrange(Continent, State, Country, Date) %>%
data.frame()
temperature_min <- temperature_min %>%
## convert the dataset from wide to long
pivot_longer(., cols = 5:ncol(temperature_min), names_to = "Date", values_to = "TempMin") %>%
## Convert the date column to a date format
mutate(Date = mdy(Date),
Continent = coords2continent(data.frame(lon=c(Long), lat=c(Lat)))) %>%
rename(
State = `Province/State`,
Country = `Country/Region`
) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
Continent = if_else(Country=="Australia","Australia",Continent),
Continent = if_else(Country=="Canada","North America",Continent),
Continent = if_else(Country=="China","Asia",Continent),
Continent = if_else(Country=="Denmark","Europe",Continent),
Continent = if_else(Country=="France","Europe",Continent),
Continent = if_else(Country=="Netherlands","Europe",Continent),
Continent = if_else(Country=="United Kingdom","Europe",Continent),
Continent = if_else(Country=="US","North America",Continent),
Continent = if_else(Country=="Monaco","Europe",Continent),
Continent = if_else(Country=="Philippines","Asia",Continent),
Continent = if_else(Country=="New Zealand","Europe",Continent),
Continent = if_else(Country=="Saint Vincent and the Grenadine","North America",Continent)
) %>%
select(Continent, State, Country, Lat, Long, Date, TempMin) %>%
arrange(Continent, State, Country, Date) %>%
data.frame()
portugal_data <- portugal_data %>%
mutate(
data = lubridate::dmy(data),
confinamento_casa = confirmados - internados - internados_uci,
ativos = confirmados - obitos - recuperados
)
#------------------ 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"
#------------------ Work Data ------------------
dataset_evo <- active %>%
left_join(confirmed %>%
select(-c(Continent, Lat, Long)), by=c("Date"="Date", "Country"="Country", "State"="State")) %>%
left_join(death %>%
select(-c(Continent, Lat, Long)), by=c("Date"="Date", "Country"="Country", "State"="State")) %>%
left_join(recovered %>%
select(-c(Continent, Lat, Long)), by=c("Date"="Date", "Country"="Country", "State"="State")) %>%
na.omit() %>%
select(-c(Confirmed_day, Active_day, Death_day)) %>%
group_by(Continent, Country, Date) %>%
summarise(Confirmed = sum(Confirmed), Active = sum(Active), Recovered = sum(Recovered), Death = sum(Death)) %>%
ungroup()
dataset_evo_daily <- dataset_evo %>%
group_by(Country) %>%
arrange(Date)
status <- dataset_evo %>%
filter(Date == max(Date)) %>%
arrange(desc(Confirmed)) %>%
mutate(Country = trimws(Country)) %>%
mutate(Country = factor(Country, levels = Country))
excel_2 <- excel %>%
group_by(`Designação CC`, `Designação DT`) %>%
count() %>%
ungroup() %>%
rename(Concelho = `Designação CC`,
Distrito = `Designação DT`) %>%
select(Concelho, Distrito)
```
Summary
=======================================================================
Row
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(value = paste(format(sum(status$Confirmed), big.mark = ","), "", sep = " "),
caption = "Total Confirmed Cases",
icon = "fas fa-user-md",
color = confirmed_color)
```
### active {.value-box}
```{r}
valueBox(value = paste(format(sum(status$Active, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(status$Active, na.rm = TRUE) / sum(status$Confirmed), 1),
"%)", sep = ""),
caption = "Active Cases", icon = "fas fa-ambulance",
color = active_color)
```
### recovered {.value-box}
```{r}
valueBox(value = paste(format(sum(status$Recovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(status$Recovered, na.rm = TRUE) / sum(status$Confirmed), 1),
"%)", sep = ""),
caption = "Recovered Cases", icon = "fas fa-heartbeat",
color = recovered_color)
```
### death {.value-box}
```{r}
valueBox(value = paste(format(sum(status$Death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(status$Death, na.rm = TRUE) / sum(status$Confirmed), 1),
"%)", sep = ""),
caption = "Death Cases",
icon = "fas fa-heart-broken",
color = death_color)
```
Row
-----------------------------------------------------------------------
### Cases Distribution by Type (Top 25 Countries)
```{r daily_summary}
plotly::plot_ly(data = status[1:30,],
x = ~ Country,
y = ~ Active,
type = "bar",
name = "Active",
marker = list(color = active_color)) %>%
plotly::add_trace(y = ~ Recovered,
name = "Recovered",
marker = list(color = recovered_color)) %>%
plotly::add_trace(y = ~ Death,
name = "Death",
marker = list(color = death_color)) %>%
plotly::layout(barmode = 'stack',
yaxis = list(title = "Total Cases (log scaled)",
type = "log"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
b = 10,
t = 10,
pad = 2
))
```
Row {data-width=400}
-----------------------------------------------------------------------
### Daily Cumulative Cases by Type
```{r}
dataset_evo_daily %>%
group_by(Date) %>%
summarise(active = sum(Active), recovered = sum(Recovered), death = sum(Death)) %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~ Date,
y = ~ active,
type = "scatter",
mode = "lines+markers",
name = "Active",
line = list(color = active_color),
marker = list(color = active_color)) %>%
plotly::add_trace(x = ~ Date,
y = ~ recovered,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)) %>%
plotly::add_trace(x = ~ Date,
y = ~ death,
type = "scatter",
mode = 'lines+markers',
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)) %>%
plotly::layout(title = "",
yaxis = list(title = "Cumulative Number of Cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare")
```
### Recovery and Death Rates by Country
```{r}
summary <- dataset_evo %>%
filter(Date == max(Date)) %>%
mutate(recover_rate = Recovered/Confirmed,
death_rate = Death/Confirmed) %>%
select(-c(Continent, Active))
summary %>%
DT::datatable(rownames = FALSE,
colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
options = list(pageLength = nrow(summary), dom = 'tip')) %>%
DT::formatPercentage("recover_rate", 2) %>%
DT::formatPercentage("death_rate", 2)
```
World
=======================================================================
Row
-----------------------------------------------------------------------
### Evolution of Infected Cases by Continent
```{r}
confirmed %>%
filter(!is.na(Continent)) %>%
group_by(Date, Continent) %>%
summarise(confirmed_total = sum(Confirmed), confirmed_day = sum(Confirmed_day)) %>%
ungroup() %>%
reshape2::dcast(Date ~ Continent, value.var = c("confirmed_total")) %>%
plot_ly() %>%
add_trace(x=~Date, y=~Asia, name = "Asia", type="scatter", mode="lines", line = list(color = 'rgb(222, 41, 16)', width = 4)) %>%
add_trace(x=~Date, y=~Europe, name = "Europe", type="scatter", mode="lines", line = list(color = 'rgb(0, 51, 153)', width = 4)) %>%
add_trace(x=~Date, y=~Australia, name = "Australia", type="scatter", mode="lines", line = list(color = 'rgb(255,205,0)', width = 4)) %>%
add_trace(x=~Date, y=~`North America`, name = "North America", type="scatter", mode="lines", line = list(color = 'rgb(60,59,110)', width = 4)) %>%
add_trace(x=~Date, y=~`South America`, name = "South America", type="scatter", mode="lines", line = list(color = 'rgb(0,156,59)', width = 4)) %>%
add_trace(x=~Date, y=~Africa, name = "Africa", type="scatter", mode="lines", line = list(color = 'rgb(0,0,0)', width = 4)) %>%
layout(xaxis = list(title = "Date"),
yaxis = list(title = "Number of Infected Cases"),
title = "Evolution of Number of People Infected with Covid-19")
```
### Evolution of Active Cases by Continent
```{r}
active %>%
filter(!is.na(Continent)) %>%
group_by(Date, Continent) %>%
summarise(active_total = sum(Active), active_day = sum(Active_day)) %>%
ungroup() %>%
reshape2::dcast(Date ~ Continent, value.var = c("active_total")) %>%
plot_ly() %>%
add_trace(x=~Date, y=~Asia, name = "Asia", type="scatter", mode="lines", line = list(color = 'rgb(222, 41, 16)', width = 4)) %>%
add_trace(x=~Date, y=~Europe, name = "Europe", type="scatter", mode="lines", line = list(color = 'rgb(0, 51, 153)', width = 4)) %>%
add_trace(x=~Date, y=~Australia, name = "Australia", type="scatter", mode="lines", line = list(color = 'rgb(255,205,0)', width = 4)) %>%
add_trace(x=~Date, y=~`North America`, name = "North America", type="scatter", mode="lines", line = list(color = 'rgb(60,59,110)', width = 4)) %>%
add_trace(x=~Date, y=~`South America`, name = "South America", type="scatter", mode="lines", line = list(color = 'rgb(0,156,59)', width = 4)) %>%
add_trace(x=~Date, y=~Africa, name = "Africa", type="scatter", mode="lines", line = list(color = 'rgb(0,0,0)', width = 4)) %>%
layout(xaxis = list(title = "Date"),
yaxis = list(title = "Number of Active Cases"),
title = "Evolution of Number of Active Cases of People with Covid-19")
```
Row
-----------------------------------------------------------------------
### Evolution of Recovereds by Continent
```{r}
recovered %>%
filter(!is.na(Continent)) %>%
group_by(Date, Continent) %>%
summarise(recovered_total = sum(Recovered), recovered_day = sum(Recovered_day)) %>%
ungroup() %>%
reshape2::dcast(Date ~ Continent, value.var = c("recovered_total")) %>%
plot_ly() %>%
add_trace(x=~Date, y=~Asia, name = "Asia", type="scatter", mode="lines", line = list(color = 'rgb(222, 41, 16)', width = 4)) %>%
add_trace(x=~Date, y=~Europe, name = "Europe", type="scatter", mode="lines", line = list(color = 'rgb(0, 51, 153)', width = 4)) %>%
add_trace(x=~Date, y=~Australia, name = "Australia", type="scatter", mode="lines", line = list(color = 'rgb(255,205,0)', width = 4)) %>%
add_trace(x=~Date, y=~`North America`, name = "North America", type="scatter", mode="lines", line = list(color = 'rgb(60,59,110)', width = 4)) %>%
add_trace(x=~Date, y=~`South America`, name = "South America", type="scatter", mode="lines", line = list(color = 'rgb(0,156,59)', width = 4)) %>%
add_trace(x=~Date, y=~Africa, name = "Africa", type="scatter", mode="lines", line = list(color = 'rgb(0,0,0)', width = 4)) %>%
layout(xaxis = list(title = "Date"),
yaxis = list(title = "Number of Recovered Cases"),
title = "Evolution of Number of Recovered Cases of People with Covid-19")
```
### Evolution of Deaths by Continent
```{r}
death %>%
filter(!is.na(Continent)) %>%
group_by(Date, Continent) %>%
summarise(death_total = sum(Death), death_day = sum(Death_day)) %>%
ungroup() %>%
reshape2::dcast(Date ~ Continent, value.var = c("death_total")) %>%
plot_ly() %>%
add_trace(x=~Date, y=~Asia, name = "Asia", type="scatter", mode="lines", line = list(color = 'rgb(222, 41, 16)', width = 4)) %>%
add_trace(x=~Date, y=~Europe, name = "Europe", type="scatter", mode="lines", line = list(color = 'rgb(0, 51, 153)', width = 4)) %>%
add_trace(x=~Date, y=~Australia, name = "Australia", type="scatter", mode="lines", line = list(color = 'rgb(255,205,0)', width = 4)) %>%
add_trace(x=~Date, y=~`North America`, name = "North America", type="scatter", mode="lines", line = list(color = 'rgb(60,59,110)', width = 4)) %>%
add_trace(x=~Date, y=~`South America`, name = "South America", type="scatter", mode="lines", line = list(color = 'rgb(0,156,59)', width = 4)) %>%
add_trace(x=~Date, y=~Africa, name = "Africa", type="scatter", mode="lines", line = list(color = 'rgb(0,0,0)', width = 4)) %>%
layout(xaxis = list(title = "Date"),
yaxis = list(title = "Number of Deaths"),
title = "Evolution of Number of Deaths by Covid-19")
```
Europe
=======================================================================
Row
-----------------------------------------------------------------------
### Covid-19: Distribution in Europe
```{r}
dataset_evo %>%
filter(Continent=="Europe") %>%
group_by(Date) %>%
filter(Date > as.Date("2020-02-20")) %>%
summarise(Active = sum(Active), Recovered = sum(Recovered), Death = sum(Death)) %>%
plot_ly(
x = ~ Date,
y = ~ Active,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace(
y = ~ Death,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(
y = ~ Recovered,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Europe - Distribution of Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"))
```
Row
-----------------------------------------------------------------------
### Grotwh
```{r}
countries <- c("China","Italy","Spain","Portugal","US", "United Kingdom", "Germany", "France")
top15 <- confirmed %>%
filter(Date == max(Date),
Country %in% countries) %>%
group_by(Country) %>%
summarise(Confirmed = sum(Confirmed)) %>%
ungroup() %>%
arrange(desc(Confirmed)) %>%
filter(row_number()<=15)
confirmed %>%
filter(!Confirmed==0,
Country %in% top15$Country) %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed), Confirmed_day = sum(Confirmed_day)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after = row_number(),
day_after_100 = if_else(Confirmed>100,1,0)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
filter(day_after_100 > 0) %>%
mutate(day_after_100 = cumsum(day_after_100),
Confirmed = if_else((Country=="United Kingdom") & (Date == as.Date("2020-03-15")),1391,Confirmed)) %>%
select(Country, day_after_100, Confirmed) %>%
reshape2::dcast(day_after_100 ~ Country, value.var = c("Confirmed")) %>%
plot_ly() %>%
add_trace(x=~day_after_100, y=~Portugal, name = "Portugal", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 255, 0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Spain, name = "Spain", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,215,0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~France, name = "France", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 85, 164)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Germany, name = "Germany", type = 'scatter', mode = 'lines', line = list(color = 'rgb(139,69,19)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Italy, name = "Italy", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,100,0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~`United Kingdom`, name = "UK", type = 'scatter', mode = 'lines', line = list(color = 'rgb(25,25,112)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~US, name = "USA", type = 'scatter', mode = 'lines', line = list(color = 'rgb(105,105,105)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~China, name = "China", type = 'scatter', mode = 'lines', line = list(color = 'rgb(222,41,16)', width = 2.5)) %>%
layout(xaxis = list(title = "Number of Days after 100th Confirmated Case"),
yaxis = list(title = "Number of Cases"),
title = "Evolution of Confirmed Cases after 100th Confirmed Case")
```
### Log 10
```{r}
confirmed %>%
filter(!Confirmed==0,
Country %in% top15$Country) %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed), Confirmed_day = sum(Confirmed_day)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after = row_number(),
day_after_100 = if_else(Confirmed>100,1,0)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after_100 = cumsum(day_after_100)) %>%
filter(day_after_100 > 0) %>%
mutate(
Confirmed = if_else((Country=="United Kingdom") & (Date == as.Date("2020-03-15")),1391,Confirmed),
Confirmed = log10(Confirmed)) %>%
select(Country, day_after_100, Confirmed) %>%
reshape2::dcast(day_after_100 ~ Country, value.var = c("Confirmed")) %>%
plot_ly() %>%
add_trace(x=~day_after_100, y=~Portugal, name = "Portugal", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 255, 0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Spain, name = "Spain", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,215,0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~France, name = "France", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 85, 164)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Germany, name = "Germany", type = 'scatter', mode = 'lines', line = list(color = 'rgb(139,69,19)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~Italy, name = "Italy", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,100,0)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~`United Kingdom`, name = "UK", type = 'scatter', mode = 'lines', line = list(color = 'rgb(25,25,112)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~US, name = "USA", type = 'scatter', mode = 'lines', line = list(color = 'rgb(105,105,105)', width = 2.5)) %>%
add_trace(x=~day_after_100, y=~China, name = "China", type = 'scatter', mode = 'lines', line = list(color = 'rgb(222,41,16)', width = 2.5)) %>%
layout(xaxis = list(title = "Number of Days after 100th Confirmated Case"),
yaxis = list(title = "Number of Cases"),
title = "Evolution of Confirmed Cases after the 100th Confirmed Case")
```
Lockdown Countries
=======================================================================
Row
-----------------------------------------------------------------------
### See the diference when the Lockdown measures taken effect
```{r}
study_countries <- c("Portugal", "France", "Spain", "Italy", "China")
confirmed %>%
filter(Country %in% study_countries) %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed)) %>%
data.frame() %>%
reshape(direction = "wide", idvar="Date", timevar="Country") %>%
plot_ly() %>%
add_trace(x=~Date, y=~Confirmed.Portugal, name = "Portugal", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,255,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~Confirmed.Spain, name = "Spain", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,215,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~Confirmed.France, name = "France", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,0,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~Confirmed.Italy, name = "Italy", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,100,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~Confirmed.China, name = "China", type = 'scatter', mode = 'lines', line = list(color = 'rgb((222,41,16))', width = 2.5)) %>%
add_annotations(x = as.Date("2020-01-24"),
y = 920,
text = paste("Hubei",
"
",
"Lockdown"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = 10,
ay = -90) %>%
add_annotations(x = as.Date("2020-03-09"),
y = 9172,
text = paste("Italy",
"
",
"Lockdown"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -20,
ay = -50) %>%
add_annotations(x = as.Date("2020-03-14"),
y = 6391,
text = paste("Spain",
"
",
"Lockdown"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -35,
ay = -60) %>%
add_annotations(x = as.Date("2020-03-17"),
y = 7699,
text = paste("France",
"
",
"Lockdown"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -20,
ay = -50) %>%
add_annotations(x = as.Date("2020-03-19"),
y = 785,
text = paste("Portugal",
"
",
"Emergency State"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -20,
ay = -40)
```
Temperature Correlation
=======================================================================
Row
-----------------------------------------------------------------------
### See the relation between the average weekly temperature and the Evolution of cases
```{r}
### temperature max and evolution
top30 <- confirmed %>%
group_by(Country) %>%
summarise(all_cases = sum(Confirmed_day)) %>%
arrange(desc(all_cases)) %>%
filter(row_number()<=30)
mean_temperature <- temperature_max %>%
filter(Country %in% top30$Country) %>%
group_by(Country, Continent, Date) %>%
summarise(temp_max = mean(TempMax)) %>%
ungroup() %>%
mutate(Week = floor_date(Date, unit="week")) %>%
group_by(Country, Continent, Week) %>%
summarise(mean_temp = mean(temp_max))
weekly_evolution <- confirmed %>%
filter(Country %in% top30$Country) %>%
group_by(Country, Continent, Date) %>%
summarise(Confirmed_cases = sum(Confirmed_day)) %>%
ungroup() %>%
mutate(Week = floor_date(Date, unit="week")) %>%
group_by(Country, Continent, Week) %>%
summarise(cases = sum(Confirmed_cases)) %>%
ungroup() %>%
group_by(Country, Continent) %>%
arrange(Week) %>%
mutate(evolution = 100*((cases-lag(cases, default = 0))/lag(cases, default = 0))) %>%
filter(!is.nan(evolution),
!is.infinite(evolution),
evolution > 0)
## join data and plot
weekly_evolution %>%
left_join(mean_temperature, by=c("Week"="Week","Country"="Country", "Continent"="Continent")) %>%
ungroup() %>%
mutate_if(is.factor, as.character) %>%
mutate(
evolution = round(evolution,2),
text = paste("Continent: ", Continent,
"\nCountry: ", Country,
"\nWeek: ", Week,
"\nNew Cases: ", cases,
"\nEvolution (%): ", evolution, sep="")) %>%
plot_ly(x=~mean_temp, y=~evolution, color=~Continent, text =~text, type = 'scatter', mode = 'markers') %>%
layout(xaxis = list(title = "Average Temperature (ºC)"),
yaxis = list(title = "Variation of Infected (%)"),
title = "Distribution of Evolution of Infected Cases with the Average Temperature (ºC)")
```
Portugal
=======================================================================
Row
-----------------------------------------------------------------------
### Distribution of Active Covid19 Cases
```{r}
plot_ly(data = portugal_data,
x = ~ data,
y = ~ confinamento_casa,
name = 'Home Confinement',
fillcolor = '#FDBBBC',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ internados,
name = "Hospitalized with Symptoms",
fillcolor = '#E41317') %>%
add_trace(y = ~internados_uci,
name = 'Intensive Care',
fillcolor = '#9E0003') %>%
layout(title = "Portugal - Distribution of Active Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Direcção Geral de Saúde"))
```
### Distribution of Covid19 Cases
```{r}
plot_ly(data = portugal_data,
x = ~ data,
y = ~ ativos,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace( y = ~ obitos,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~ recuperados,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Portugal - Distribution of Covid19 Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Direcção Geral de Saúde"))
# masculinos <- portugal_data %>%
# mutate(data = dmy(data)) %>%
# filter(data == max(data)) %>%
# select(data, obitos_0_9_m, obitos_10_19_m, obitos_20_29_m, obitos_30_39_m, obitos_40_49_m, obitos_50_59_m,
# obitos_60_69_m, obitos_70_79_m, obitos_80_plus_m) %>%
# pivot_longer(., cols = 2:10, names_to = "Age", values_to = "Confirmed") %>%
# select(-c(data)) %>%
# mutate(Gender = "Male")
#
# femininas <- portugal_data %>%
# mutate(data = dmy(data)) %>%
# filter(data == max(data)) %>%
# select(data, obitos_0_9_f, obitos_10_19_f, obitos_20_29_f, obitos_30_39_f, obitos_40_49_f, obitos_50_59_f,
# obitos_60_69_f, obitos_70_79_f, obitos_80_plus_f) %>%
# pivot_longer(., cols = 2:10, names_to = "Age", values_to = "Confirmed") %>%
# select(-c(data)) %>%
# mutate(Gender = "Female")
#
#
# masculinos %>%
# bind_rows(femininas) %>%
# mutate(abs_pop = abs(Confirmed)) %>%
# plot_ly(x= ~Confirmed, y=~Age,color=~Gender) %>%
# add_bars(orientation = 'h', hoverinfo = 'text', text = ~abs_pop) %>%
# layout(bargap = 0.1, barmode = 'overlay',
# xaxis = list(tickmode = 'array', tickvals = c(0, 10, 25, 40, 50),
# ticktext = c('0', '10', '25', '40', '50')))
```
Row
-----------------------------------------------------------------------
### See the comprison between the number of tests performed and the number of infected
```{r}
portugal_data %>%
select(data, confirmados, suspeitos) %>%
plot_ly() %>%
add_trace(x=~data, y=~suspeitos, name = "Tests Performed", type = "scatter", mode = "lines", line = list(color = 'rgb(0, 255, 0)', width = 2.5)) %>%
add_trace(x=~data, y=~confirmados, name = "Confirmed Cases", type = "scatter", mode = "lines", line = list(color = 'rgb(255, 0, 0)', width = 2.5)) %>%
layout(title = "Portugal - Comparison Between the Number of Tests and Confirmed Cases",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Direcção Geral de Saúde"))
```
### Distribution of Infected cases by District
```{r}
portugal <- rnaturalearth::ne_states(country = "Portugal", returnclass = "sf") %>%
select(name, geometry)
madeira <- c("Calheta", "Câmara de Lobos", "Funchal",
"Machico", "Ponta do Sol", "Porto Moniz",
"Porto Santo", "Ribeira Brava","Santa Cruz",
"Santana", "São Vicente")
pt_cont <- concelhos %>%
left_join(excel_2, by=c("Concelho"="Concelho")) %>%
mutate(Distrito = if_else(Concelho %in% madeira,"Madeira",Distrito),
Distrito = if_else(is.na(Distrito),"Azores",Distrito)) %>%
group_by(Distrito) %>%
summarise(Casos = sum(Casos))
mapview::mapviewOptions(vector.palette = colorRampPalette(c("yellow", "orange", "red")),
na.color = "grey")
portugal %>%
left_join(pt_cont, by=c("name"="Distrito")) %>%
mutate(Casos = replace_na(Casos,0)) %>%
filter(name != c("Madeira", "Azores")) %>%
mapview::mapview(zcol = "Casos")
```
Exponential Forecasting
=======================================================================
### Exponential Forecasting
```{r}
today_date <- as.Date(Sys.time())
### confirmed since day one
evolution_of_confirmed <- confirmed %>%
filter(!Confirmed==0)
countries <- c("China","Italy","Spain","Portugal","US", "United Kingdom", "Germany", "France")
evolution_of_confirmed_v2 <- evolution_of_confirmed %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed), Confirmed_day = sum(Confirmed_day)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after = row_number(),
day_after_10 = if_else(Confirmed>10,1,0)) %>%
ungroup() %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after_10 = cumsum(day_after_10),
Confirmed = if_else((Country=="United Kingdom") & (Date == as.Date("2020-03-15")),1391,Confirmed))
today_cases <- evolution_of_confirmed_v2 %>%
filter(day_after_10 > 0 & !(Country %in% c("Portugal", "France", "US", "Germany"))) %>%
bind_rows(evolution_of_confirmed_v2 %>%
filter(Country == "Portugal")) %>%
bind_rows(evolution_of_confirmed_v2 %>%
filter(Date > as.Date("2020-02-23") & Country %in% c("France", "US", "Germany"))) %>%
filter(Country %in% countries,
Date < today_date) %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after = row_number()) %>%
ungroup() %>%
select(Country, Date, Confirmed)
n_days <- 3
for(i in 1:length(countries)) {
country_selected <- countries[i]
for(j in 1:n_days) {
country_row <- today_cases %>%
filter(Country == country_selected & Date > today_date) %>%
tibble::add_row(Country = country_selected, Date = today_date+(j-1), Confirmed = NA)
if(j == 1) country_test <- country_row else country_test <- country_test %>% bind_rows(country_row)
}
country_test <- country_test %>%
bind_rows(today_cases %>%
filter(Country == country_selected))
if(i == 1 ) today_cases_v2 <- country_test else today_cases_v2 <- today_cases_v2 %>% bind_rows(country_test) %>% arrange(Country, Date)
}
## add day_after column (again)
today_cases_v3 <- today_cases_v2 %>%
group_by(Country) %>%
arrange(Date) %>%
mutate(day_after = row_number()) %>%
na_if(0) %>%
filter(Country != "China")
### for each country create an exponential model
countries_exp <- unique(today_cases_v3$Country)
for(n in 1:length(countries_exp)) {
train_data <- today_cases_v3 %>%
filter(Country == countries_exp[n])
exponential.model <- lm(log(Confirmed) ~ day_after, data = train_data)
predicted_data <- data.frame(exp(predict(object = exponential.model, newdata = train_data))) %>%
rename(predict = 1) %>%
bind_cols(train_data) %>%
select(Country, Date, day_after, Confirmed, predict) %>%
mutate(predict = floor(predict))
if(n == 1) predicted_cases <- predicted_data else predicted_cases <- predicted_cases %>% bind_rows(predicted_data)
}
predicted_cases %>%
select(-c(day_after)) %>%
bind_rows(confirmed %>%
select(Country, Date, Confirmed) %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed)) %>%
ungroup() %>%
filter(Country %in% countries,
Date >= today_date,
Date < today_date+3)) %>%
mutate(predict = replace_na(predict, 0)) %>%
group_by(Country, Date) %>%
summarise(Confirmed = sum(Confirmed), predict = sum(predict)) %>%
data.frame() %>%
reshape(direction = "wide", idvar="Date", timevar="Country") %>%
arrange(Date) %>%
plot_ly() %>%
add_trace(x=~Date, y=~predict.Portugal, name = "Prediction Portugal", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 255, 0)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.Portugal, name = "Real Portugal", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0, 255, 0)', width = 2.5)) %>%
add_trace(x=~Date, y=~predict.Spain, name = "Prediction Spain", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,215,0)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.Spain, name = "Real Spain", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255,215,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~predict.France, name = "Prediction France", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255, 0, 0)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.France, name = "Real France", type = 'scatter', mode = 'lines', line = list(color = 'rgb(255, 0, 0)', width = 2.5)) %>%
add_trace(x=~Date, y=~predict.Germany, name = "Prediction Germany", type = 'scatter', mode = 'lines', line = list(color = 'rgb(139,69,19)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.Germany, name = "Real Germany", type = 'scatter', mode = 'lines', line = list(color = 'rgb(139,69,19)', width = 2.5)) %>%
add_trace(x=~Date, y=~predict.Italy, name = "Prediction Italy", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,100,0)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.Italy, name = "Real Italy", type = 'scatter', mode = 'lines', line = list(color = 'rgb(0,100,0)', width = 2.5)) %>%
add_trace(x=~Date, y=~`predict.United Kingdom`, name = "Prediction UK", type = 'scatter', mode = 'lines', line = list(color = 'rgb(25,25,112)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~`Confirmed.United Kingdom`, name = "Real UK", type = 'scatter', mode = 'lines', line = list(color = 'rgb(25,25,112)', width = 2.5)) %>%
add_trace(x=~Date, y=~predict.US, name = "Prediction USA", type = 'scatter', mode = 'lines', line = list(color = 'rgb(105,105,105)', width = 2.5, dash = 'dot')) %>%
add_trace(x=~Date, y=~Confirmed.US, name = "Real USA", type = 'scatter', mode = 'lines', line = list(color = 'rgb(105,105,105)', width = 2.5)) %>%
layout(xaxis = list(title = "Date"),
yaxis = list(title = "Number of Cases"),
title = "Prediction of future cases (using exponential curve) \nPrediction between March 31 to April 02")
```
About
=======================================================================
**The Coronavirus Dashboard**
This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic with information focused on the evolution of this pandemic in the European Continent with some studies about the exponential evolution of Infected cases. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/joaosilvagmr/covid19_dashboard)
**Data**
The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/CSSEGISandData/COVID-19)
The raw tempoerature data is pulled from the imants [repository](https://github.com/imantsm/COVID-19) with obtained the data from the [Dark Sky Api](https://darksky.net/dev)
**Packages**
* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package.
* Visualization - the [plotly](https://plot.ly/r/) package for the plots
* Read Data - the [readr](https://readr.tidyverse.org/)
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), [tidyr](https://tidyr.tidyverse.org/), [lubridate](https://lubridate.tidyverse.org/)
* To obtain the continents information - [sp](https://github.com/edzer/sp/) [rworldmap](https://github.com/AndySouth/rworldmap/)
* Tables - the [DT](https://rstudio.github.io/DT/) package
**Deployment and reproducibly**
The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:
* Fork the dashboard [repository](https://github.com/joaosilvagmr/covid19_dashboard), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)
For any question or feedback, you can either open an [issue](https://github.com/joaosilvagmr/covid19_dashboard/issues).
**Contribution**
The base code for this dashboard was provided by Rami Krispin, principally the first page, where the code is written by him.
You can visit the diverse Coronavirus packages that he is developing.
* Package with [Coronavirus Dataset](https://github.com/RamiKrispin/coronavirus)
* Package with [Coronavirus Italy](https://github.com/RamiKrispin/covid19Italy)
* Code for the [Coronavirus Dashboard](https://github.com/RamiKrispin/coronavirus_dashboard)
Thanks Rami for the work developed!