My first COVID-19 Dashboard

#these libraries need to be loaded

#read the dataset sheet for the daily case numbers in the EU into “R”. The dataset will be called "covid19".
covid19 <- read.csv("", na.strings = "", fileEncoding = "UTF-8-BOM")

str(covid19) # check if the data has been loaded correctly
head(covid19) # show the to 6 lines of the data frame

#read the dataset sheet for the cases in the different age groups in the EU into “R”. The dataset will be called "covid19_age".
covid19_age<-read.csv("", na.strings = "", fileEncoding = "UTF-8-BOM")

str(covid19_age) # check if the data has been loaded correctly
head(covid19_age) # show the to 6 lines of the data frame

# load package
library(tidyverse) #to reshape and convert data
#load packages
library(ggplot2) #to make nice figures
library(tidyquant) #to quickly produce a rolling 7 day average

# convert dateRep to class date and rename certain columns
covid19 <- covid19 %>% 
  mutate(dateRep = as.Date(dateRep, format = "%d/%m/%Y")) %>% rename(country = "countriesAndTerritories", continent = "continentExp")
covid19$prop.dead<-covid19$deaths/covid19$cases #calculate proportion of deaths per day
covid19 <- filter(covid19, dateRep > as.Date("2021-03-31")) #set the start date for your report to 31st March 2021

str(covid19) #have quick look at your data if everything is there as planned

# create a data set only for Germany
covid19Germany <- filter(covid19, country=="Germany")

#produce a tibble with covid19 case counts for EU country including CFR and confidence intervals
covid19_counts<-covid19 %>% group_by(country) %>% summarise(case_count=sum(cases,na.rm=T),death_count=sum(deaths,na.rm=T))

covid19_counts$recovered<-covid19_counts$case_count-covid19_counts$death_count #calculate the number of recovered
covid19_counts$CFR<-round(covid19_counts$death_count/covid19_counts$case_count*100,digits=3) #calculate CFR per country
covid19_counts$lower_CI<-round((100/covid19_counts$case_count)*(covid19_counts$death_count - (1.96*sqrt(covid19_counts$death_count))),digits=3) #calculate upper confidence interval
covid19_counts$upper_CI<-round((100/covid19_counts$case_count)*(covid19_counts$death_count + (1.96*sqrt(covid19_counts$death_count))),digits=3) #calculate lower confidence interval

#have a look if all variables are in the tibble table

Column {data-width=500}

Daily case numbers for Germany and 7 day rolling average in 2021

# create the ggplot for the number of cases and deaths in Germany from 31st March to today

cols <- c("Cases"="lightblue","Deaths"="red") #cols defines the colours in the legend that we want to draw. This is a special case here, because we need to specify colour and fill in the aesthetics to be able to draw a legend. However, we also have two geom_col() functions. So in order not to "confuse" ggplot() we have to tell it with the help of cols that Cases need to be lightblue and Deaths need to be red.

ggplot(data = covid19Germany)+ #tell ggplot() which data set to use, don't forget to link the different lines of code for your plot with a "+", otherwise not all commands will be executed
   mapping = aes(x = dateRep, y = cases, colour="Cases", fill="Cases"),
   width = 1)+                #geom_col produces bars for the aesthetics specified in the brackets. Here for daily counts, set width = 1 to avoid white space between bars
   mapping = aes(x = dateRep, y = deaths, colour="Deaths", fill="Deaths"),
   width = 1)+                #now the same for the deaths
  tidyquant::geom_ma(aes(x = dateRep, y = cases), # plot moving average
      n = 7,           
      size = 1,
      colour = "darkblue")+
  scale_fill_manual(name="",values=cols)+    #define fill colours
  scale_colour_manual(name="",values=cols)+  #define border colours, at the same time ggplot draws a legend
  # labels for x-axis
    date_breaks = "1 month",       #labels every month 
    date_minor_breaks = "1 month", #gridlines every month
    date_labels = '%b')+           #labelled by month with the year below

  theme_minimal()+                 #theme style, the theme is the overall look of the figure, try other themes such as theme_classic() or others:
  labs(x = "Date of report",       #x-axis title
    y = "Number of cases/deaths")

14-day incidence rate per age group in the EU

#load package
library(scales) #to convert scientific numbers to decimal numbers

covid19_age_incidence_means<-covid19_age %>% group_by(year_week,age_group) %>% summarise(mean_14d_incidence=mean(rate_14_day_per_100k,na.rm=T)) # create a tibble with mean 14 day incidence for each age group across all EU countries

ggplot(data = covid19_age_incidence_means)+
   mapping = aes(x = year_week, y = mean_14d_incidence, fill=age_group),
   width = 1)+
# create facets
  facet_wrap(                 # split your plot into several plots to show the epi curves for each age group
    ~age_group,               # define for which variable you want to produce facets
    ncol = 3,                 # number of columns for the arrangement of each small plot 
    strip.position = "top")+  # define where the age group should be added to each small plot

# rotate labels for x-axis
  theme(axis.text.x = element_text(angle = 90, size = 5))+
# make y-axis numbers non-scientific
  labs(x = "Date of report", 
    y = "Number of cases per 100'000 inhabitants",
    fill="Age group")

Column {data-width=500}

COVID-19 cases, deaths and CFRs for each country in the EU

knitr::kable(covid19_counts) #unfortunately flextable() and flexdashboard do not work nicely together.