And this is West Africa
# pays <- filter(data,V2 =="US")
# pays <- t(pays)
# us <- as.numeric(tail(pays,2))
# us_nc <- us[2] - us[1]
# us <- us[2]
pays <- filter(data,V2 =="Benin")
pays <- t(pays)
be <- as.numeric(tail(pays,2))
be_nc <- be[2] - be[1]
be <- be[2]
pays <- filter(data,V2 =="Burkina Faso")
pays <- t(pays)
bf <- as.numeric(tail(pays,2))
bf_nc <- bf[2] - bf[1]
bf <- bf[2]
pays <- filter(data,V2 =="Gambia")
pays <- t(pays)
gm <- as.numeric(tail(pays,2))
gm_nc <- gm[2] - gm[1]
gm <- gm[2]
pays <- filter(data,V2 =="Ghana")
pays <- t(pays)
gh <- as.numeric(tail(pays,2))
gh_nc <- gh[2] - gh[1]
gh <- gh[2]
pays <- filter(data,V2 =="Guinea")
pays <- t(pays)
gu <- as.numeric(tail(pays,2))
gu_nc <- gu[2] - gu[1]
gu <- gu[2]
pays <- filter(data,V2 =="Guinea-Bissau")
pays <- t(pays)
gb <- as.numeric(tail(pays,2))
gb_nc <- gb[2] - gb[1]
gb <- gb[2]
pays <- filter(data,V2 =="Cote d'Ivoire")
pays <- t(pays)
ci <- as.numeric(tail(pays,2))
ci_nc <- ci[2] - ci[1]
ci <- ci[2]
pays <- filter(data,V2 =="Liberia")
pays <- t(pays)
li <- as.numeric(tail(pays,2))
li_nc <- li[2] - li[1]
li <- li[2]
pays <- filter(data,V2 =="Mali")
pays <- t(pays)
ml <- as.numeric(tail(pays,2))
ml_nc <- ml[2] - ml[1]
ml <- ml[2]
pays <- filter(data,V2 =="Mauritania")
pays <- t(pays)
mr <- as.numeric(tail(pays,2))
mr_nc <- mr[2] - mr[1]
mr <- mr[2]
pays <- filter(data,V2 =="Niger")
pays <- t(pays)
nr <- as.numeric(tail(pays,2))
nr_nc <- nr[2] - nr[1]
nr <- nr[2]
pays <- filter(data,V2 =="Nigeria")
pays <- t(pays)
ng <- as.numeric(tail(pays,2))
ng_nc <- ng[2] - ng[1]
ng <- ng[2]
pays <- filter(data,V2 =="Senegal")
pays <- t(pays)
sn <- as.numeric(tail(pays,2))
sn_nc <- sn[2] - sn[1]
sn <- sn[2]
pays <- filter(data,V2 =="Sierra Leone")
pays <- t(pays)
sl <- as.numeric(tail(pays,2))
sl_nc <- sl[2] - sl[1]
sl <- sl[2]
pays <- filter(data,V2 =="Togo")
pays <- t(pays)
tg <- as.numeric(tail(pays,2))
tg_nc <- tg[2] - tg[1]
tg <- tg[2]
cases <- data.frame(Cases = c(be,
bf,gm,gh,gu,gb,ci,
li,ml,mr,nr,ng,sn,sl,tg))
new_cases <- data.frame(New_Cases = c(be_nc,
bf_nc,gm_nc,gh_nc,
gu_nc,gb_nc,ci_nc,
li_nc,ml_nc,mr_nc,
nr_nc,ng_nc,sn_nc,sl_nc,tg_nc))
West_Africa <- cbind(West_Africa,cases,new_cases)
# Find rank for cases
rank <- West_Africa
rank <- arrange(rank,desc(Cases))
rank$rank <- row.names(rank)
rank <- arrange(rank,Country)
West_Africa <- rank
rm(rank)
country_pop <- data.frame(Popuation= c(12.1,20.8,2.4,31,13.1,2,26.3,5,20.2,4.6,24.1,
205.4,16.7,8,8.3))
West_Africa<- cbind(West_Africa,country_pop)
West_Africa$Cases_per_100000 <-
round((West_Africa$Cases/West_Africa$Popuation)*0.1,1)
rank_cpht <- West_Africa
rank_cpht <- arrange(rank_cpht,desc(Cases_per_100000))
rank_cpht$rank_cpht <- row.names(rank_cpht)
top4 <- head(rank_cpht,4)
bottom4 <- tail(rank_cpht,4)
rank_cpht <- arrange(rank_cpht,Country)
West_Africa <- rank_cpht
West_Africa$Country <- as.character(West_Africa$Country)
top4$Country <- as.character(top4$Country)
top_total <- inner_join(w_africa, top4, by = "Country")
bottom4$Country <- as.character(bottom4$Country)
bottom_total <- inner_join(w_africa, bottom4, by = "Country")
wa_total <- inner_join(w_africa, West_Africa, by = "Country")
