#Task 2 question 1
install.packages("openxlsx")
library(openxlsx)
install.packages("tidyverse")
library(tidyverse)

name_sheet = 1:2

covidData= map(name_sheet, ~ read.xlsx("D:/rrr/covid.xlsx",sheet=.))# Read the path to the xlsx file

China<-covidData[[1]]
UK<-covidData[[2]]



#Task 2 question 2


i<-c(China[10,5],China[39,5],China[70,5],China[100,5],China[131,5],China[161,5],China[192,5],China[223,5],China[253,5],China[284,5],China[314,5],China[345,5],China[376,5],China[404,5],China[435,5],China[465,5],China[496,5],China[526,5],China[557,5],China[588,5],China[618,5],China[649,5])
i

k<-c(UK[1,5],UK[30,5],UK[61,5],UK[91,5],UK[122,5],UK[152,5],UK[183,5],UK[214,5],UK[244,5],UK[275,5],UK[305,5],UK[336,5],UK[367,5],UK[395,5],UK[426,5],UK[456,5],UK[487,5],UK[517,5],UK[548,5],UK[579,5],UK[609,5],UK[640,5])
k
g<-seq(1,22,1)
g
a<-c(China[10,8],China[39,8],China[70,8],China[100,8],China[131,8],China[161,8],China[192,8],China[223,8],China[253,8],China[284,8],China[314,8],China[345,8],China[376,8],China[404,8],China[435,8],China[465,8],China[496,8],China[526,8],China[557,8],China[588,8],China[618,8],China[649,8])
a
b<-c(0,0,UK[61,8],UK[91,8],UK[122,8],UK[152,8],UK[183,8],UK[214,8],UK[244,8],UK[275,8],UK[305,8],UK[336,8],UK[367,8],UK[395,8],UK[426,8],UK[456,8],UK[487,8],UK[517,8],UK[548,8],UK[579,8],UK[609,8],UK[640,8])
b


# Give the chart file a name.
png(file = "D:/rrr/linechart.jpg")  #Path to generate chart

# Plot the bar chart.
plot(g,i,type = "o",col = "red",pch=3, xlab = "Months elapsed", ylab = "amount", 
     main = "total_cases and total_deaths both in China and UK")
axis(1,1:22, 1:22)
lines(k, type = "o",pch=2, col = "blue")
lines(a, type = "o",pch=1,col = "green")
lines(b, type = "o",pch=4,col = "black")
legend("topright", c("Chinatotal_cases", "UKtotal_case","Chinatotal_deaths","UKtotal_deaths"),pch = c(3,2,1,4),col=c("red","blue","green","black"),bg ="white")
# Save the file.
dev.off()





#Task 2 question 3
#Partition data
d<-c((China[(71:100),6]))
e<-c((China[(254:284),6]))
f<-c((China[(436:465),6]))
g<-c((China[(619:649),6]))
d1<-c((UK[(62:91),6]))
e1<-c((UK[(245:275),6]))
f1<-c((UK[(427:456),6]))
g1<-c((UK[(610:640),6]))
a<-c(min(g1),max(g1))
a
area<-c("China","UK")
#generate mean, standard deviation, median, max, min, and range
April2020newcasestotal<-c(sum(d),sum(d1))
April2020newcasesmean<-c(mean(d),mean(d1))
April2020newcasesstandarddeviation<-c(sd(d),sd(d1))
April2020newcasesmedian<-c(median(d),median(d1))
April2020newcasesmax<-c(max(d),max(d1))
April2020newcasesmin<-c(min(d),min(d1))
April2020newcasesrange<-c("3-353","3473-5505")

October2020newcasestotal<-c(sum(e),sum(e1))
October2020newcasesmean<-c(mean(e),mean(e1))
October2020newcasesstandarddeviation<-c(sd(e),sd(e1))
October2020newcasesmedian<-c(median(e),median(e1))
October2020newcasesmax<-c(max(e),max(e1))
October2020newcasesmin<-c(min(e),min(e1))
October2020newcasesrange<-c("7-47","6929-26707")

April2021newcasestotal<-c(sum(f),sum(f1))
April2021newcasesmean<-c(mean(f),mean(f1))
April2021newcasesstandarddeviation<-c(sd(f),sd(f1))
April2021newcasesmedian<-c(median(f),median(f1))
April2021newcasesmax<-c(max(f),max(f1))
April2021newcasesmin<-c(min(f),min(f1))
April2021newcasesrange<-c("6-33","-4787-4565")

October2021newcasestotal<-c(sum(g),sum(g1))
October2021newcasesmean<-c(mean(g),mean(g1))
October2021newcasesstandarddeviation<-c(sd(g),sd(g1))
October2021newcasesmedian<-c(median(g),median(g1))
October2021newcasesmax<-c(max(g),max(g1))
October2021newcasesmin<-c(min(g),min(g1))
October2021newcasesrange<-c("5-91","29520-51719")

CovidSummary<-data.frame(area,April2020newcasestotal,April2020newcasesmean,April2020newcasesstandarddeviation,April2020newcasesmedian,April2020newcasesmax,April2020newcasesmin,April2020newcasesrange,October2020newcasestotal,October2020newcasesmean,October2020newcasesstandarddeviation,October2020newcasesmedian,October2020newcasesmax,October2020newcasesmin,October2020newcasesrange,April2021newcasestotal,April2021newcasesmean,April2021newcasesstandarddeviation,April2021newcasesmedian,April2021newcasesmax,April2021newcasesmin,April2021newcasesrange,October2021newcasestotal,October2021newcasesmean,October2021newcasesstandarddeviation,October2021newcasesmedian,October2021newcasesmax,October2021newcasesmin,October2021newcasesrange)
CovidSummary#Generate data frame



#Task 2 question 4
t.test(e1,g1)
#Between October 2020 and October 2021, the number of new cases in the UK first decreased significantly and then increased significantly


#Task 2 question 5

aov(formula, data = d)
TukeyHSD(fit)


library(multcomp)
tuk <- glht(fit, linfct = mcp(trt = ¡°Tukey¡±))
plot(cld(tuk, level = 0.05), col = ¡°lightgrey¡±)