Belgium corona virus overview
Kaushika Gayathri
7/17/2020
1.Brief introduction of country belgium
Belgium, officially the Kingdom of Belgium, is acountry in Western Europe. It is a country bordered to the Netherlands to the north, Germany to the east, Luxembourg to the southeast, France to the southwest, & the North Sea to the northwest. It covers an area of 30,689 \(km^2\) & has a population of more than 11.5 million,making it the 22 nd most densely populated country in the world & the 6 th most densely populated country in Europe, with the density of 376 per square kilometre \((970/sq mi)\). The capital & the largest city is Brussels;other major cities are Antwerp, Ghent, Charleroi &Lie`ge
1.Visualization of location
library(ggplot2)
library(maptools)
library(tibble)
library(tidyverse)
library(ggrepel)
library(png)
library(grid)
library(sp)
library(coronavirus)
library(magrittr)
belgium_corona<-coronavirus%>%filter(country=="Belgium")
data("wrld_simpl")
p <-ggplot()+
geom_polygon(
data = wrld_simpl,
aes(x=long ,y=lat ,group=group) ,fill="gray",color="white"
)+
coord_cartesian(xlim = c(-180,180),ylim = c(-90,90))+
scale_x_continuous(breaks = seq(-180,180,120))+
scale_y_continuous(breaks = seq(-90,90,100))
p+
geom_point(
data = belgium_corona, aes(x=long, y=lat), color="red", size
=1
)
Location of Belgium
2.Exploratory Data Analysis
1.visualization of confirmed cases, recovered cases & deaths
library(ggplot2)
library(coronavirus)
library(magrittr)
library(tidyverse)
library(dplyr)
library(tidyr)
library(tibble)
library(knitr) belgium_corona<-coronavirus%>%filter(country=="Belgium")imputing the negative vales
#imputing the negative values
belgium_corona_new<-belgium_corona%>%mutate(cases=replace(cases,which(cases<0),NA))
summary(belgium_corona_new) date province country lat
Min. :2020-01-22 Length:336 Length:336 Min. :50.83
1st Qu.:2020-02-18 Class :character Class :character 1st Qu.:50.83
Median :2020-03-17 Mode :character Mode :character Median :50.83
Mean :2020-03-17 Mean :50.83
3rd Qu.:2020-04-14 3rd Qu.:50.83
Max. :2020-05-12 Max. :50.83
long type cases
Min. :4 Length:336 Min. : 0.0
1st Qu.:4 Class :character 1st Qu.: 0.0
Median :4 Mode :character Median : 30.0
Mean :4 Mean : 227.8
3rd Qu.:4 3rd Qu.: 291.5
Max. :4 Max. :2454.0
NA's :1 which(is.na(belgium_corona_new$cases))[1] 283 belgium_corona_new$cases[283]= mean(c(belgium_corona$cases[282],belgium_corona$cases[284]))
length(belgium_corona_new$cases)[1] 336 belgium_corona_new$col <-as.factor(c(rep("black",281),rep("red",2),rep("black",336-283)))
summary(belgium_corona_new) date province country lat
Min. :2020-01-22 Length:336 Length:336 Min. :50.83
1st Qu.:2020-02-18 Class :character Class :character 1st Qu.:50.83
Median :2020-03-17 Mode :character Mode :character Median :50.83
Mean :2020-03-17 Mean :50.83
3rd Qu.:2020-04-14 3rd Qu.:50.83
Max. :2020-05-12 Max. :50.83
long type cases col
Min. :4 Length:336 Min. : 0.0 black:334
1st Qu.:4 Class :character 1st Qu.: 0.0 red : 2
Median :4 Mode :character Median : 30.5
Mean :4 Mean : 227.5
3rd Qu.:4 3rd Qu.: 290.8
Max. :4 Max. :2454.0 ggplot(belgium_corona_new,
aes(x=date,y=cases,fill=type,col=type))+
geom_line()+geom_jitter(aes(col=type))+
facet_wrap(~type,ncol = 3)+coord_flip()
#### summary of Belgium corona
library(knitr)
belgium_summary<-as.tibble(cbind(total_active,total_recovered,total_death))
kable(belgium_summary)| total_active | total_recovered | total_death |
|---|---|---|
| 31286 | 13732 | 8761 |
calculating the precentages of Belgium
library(coronavirus)
library(magrittr)
library(tidyverse)
library(dplyr)
library(tidyr)
library(tibble)
#####calculating the %
belgium_corona<-coronavirus%>%filter(country=="Belgium")
belgium_corona
time_belgium<-belgium_corona %>% separate(date,into=c("year","month","day"),sep="-")
newtime_belgium<-time_belgium%>%
mutate(month=if_else(month=="01","January",(
if_else(month=="02","February",
if_else(month=="03","March",
if_else(month=="04","April","May"))
))))library(knitr)
rate<-cbind(r1,r2,r3)
myrate<-as.data.frame(rate)
kable(myrate)| month | confirmed | confirmed_rate | month | recovered | recovered_rate | month | death | death_rate |
|---|---|---|---|---|---|---|---|---|
| January | 0 | 0.0000000 | January | 0 | 0.0000000 | January | 0 | 0.000000 |
| February | 1 | 0.0018595 | February | 1 | 0.0018595 | February | 0 | 0.000000 |
| March | 12774 | 23.7527659 | March | 1695 | 3.1517879 | March | 705 | 1.310921 |
| April | 35744 | 66.4646051 | April | 9880 | 18.3714833 | April | 6889 | 12.809833 |
| May | 5260 | 9.7807694 | May | 2156 | 4.0089998 | May | 1167 | 2.169992 |
*cumilative sum of belgium confirmed cases visualization
2.comparison between the neighbouring countries
unique(coronavirus$country)netherlands_corona<-coronavirus%>%filter(country=="Netherlands")
germany_corona<-coronavirus%>%filter(country=="Germany")
belgium_corona<-coronavirus%>%filter(country=="Belgium")
france_corona<-coronavirus%>%filter(country=="France")Craeting a data frame of neightbouring countries & Belgium
countries_corona<-as.data.frame(rbind(netherlands_corona,germany_corona,belgium_corona,france_corona))Comparison of covid-19 deaths between the neighbouring countries of Belgium
countries_death<-countries_corona%>%filter(type=="death")
ggplot(countries_death,
aes(x=date,y=cases,fill=type,col=country))+
geom_line()+geom_point()+
facet_wrap(~country,ncol = 4)+coord_flip()+
ggtitle("Comparison of covid-19 deaths between the neighbooring countries")
#### Recovesed cases of neighbouring countries of Belgium
countries_recovered<-countries_corona%>%filter(type=="recovered")Comparison between the recovered of covid-19 patients between the neighbooring countries of Belgium
ggplot(countries_recovered,
aes(x=date,y=cases,fill=type,col=country))+
geom_line()+geom_point()+
facet_wrap(~country,ncol = 4)+coord_flip()+
ggtitle("Comparison between the recovered of covid-19 patients
between the neighbooring countries")
countries_cases<-countries_corona%>%filter(type=="confirmed")Comparison between the confirmed covid-19 patients between the neighbooring countries of Belgium
ggplot(countries_cases,
aes(x=date,y=cases,fill=type,col=country))+
geom_line()+geom_point()+
facet_wrap(~country,ncol = 4)+coord_flip()+
ggtitle("Comparison between the confirmed covid-19
patients between the neighbooring countries")
3.Conclusions and Discussion
In the belgium corona virus data set there was a negative value of confirmed cases,but it is not significant so i have do conduct a data preprocessing step. Here i have used imputing missing data & color it in a red color for the identification. For imputing missing value i have used mean imputation interpolation method.Because of data entering erroes or changing of the methodology may occured those negative values. And also the province was not available in the belgium coronavirus data set. thus we cannot conduct a provincewise analysis of the patients. When we are comapared to the other neightbouring countries belgium has low number of deaths compared to france & germany.The government and the National security council & the health sector has given the adequate rules to control the virus spread with in the country.
4.References
\(\underline\{This\ https://en.m.wikipedia.org/wiki/COVID-19_pandemic_in_Belgium\ is\ underlined}\) \(\underline\{This\ https://github.com/thiyangt/CoronaSriLanka/blob/master/coronaSLDashboard.Rmd\ is\ underline}\)
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