mypack <- function(package){ new.package <- package[!(package %in% installed.packages()[, "Package"])] if (length(new.package)) install.packages(new.package, dependencies = TRUE) sapply(package, require, character.only = TRUE) } #create a vector of required R packages packages = c("scales", "RColorBrewer", "ggthemes", "lubridate", "ggrepel", "reshape", "gridExtra", "maps", "stringr", "ggcorrplot", "viridis") # mypack(packages) # Uncomment this if you want install all the libraries

library(tidyverse) library(scales) library(RColorBrewer) library(ggthemes) library(lubridate) library(ggrepel) library(reshape) library(gridExtra) library(maps) library(stringr) library(ggcorrplot) library(viridis)

options(warn = -1) options(scipen = 10000) options(repr.plot.width = 13.8, repr.plot.height = 9.2) annotate <- ggplot2::annotate theme_abz <- theme(legend.position = "top", legend.direction = "horizontal", axis.text = element_text(size = 18), plot.caption = element_text(color = "gray65", face = "bold", size = 10), legend.text = element_text(size = 15.4), axis.title = element_text(size = 15.9, face = "bold", color = "gray25"), legend.title = element_text(size = 15.4), axis.line = element_line(size = 0.4), plot.title = element_text(size = 19.5), plot.subtitle = element_text(size = 15.9), strip.text = element_text(size = 14.4, face = "bold"))

data <- read.csv("covid-world-vaccination-progress/country_vaccinations.csv") data <- data[,c("country", "total_vaccinations", "date", "people_vaccinated", "daily_vaccinations_raw", "people_vaccinated_per_hundred", "daily_vaccinations_per_million", "vaccines")] data$date <- as.Date(data$date) data$total_vaccinations[is.na(data$total_vaccinations)==T] <- 0 data$people_vaccinated[is.na(data$people_vaccinated)==T] <- 0 data$daily_vaccinations_raw[is.na(data$daily_vaccinations_raw)==T] <- 0 data$people_vaccinated_per_hundred[is.na(data$people_vaccinated_per_hundred)==T] <- 0 data$daily_vaccinations_per_million[is.na(data$daily_vaccinations_per_million)==T] <- 0 head <- data[sample(1:nrow(data),5), ] head[order(head$date),] data$month <- month(data$date) data$weekday <- weekdays(data$date) data$percent_people <- data$people_vaccinated_per_hundred/100

full1 <- data %>% group_by(date, country) %>% filter(as.Date(date) > "2020-12-18") pkr1 <- data %>% group_by(date, country) %>% filter(country == "Pakistan") %>% filter(as.Date(date) > "2020-12-18") full1$daily_vaccinations_raw <- full1$daily_vaccinations_raw/1000000 pkr1$daily_vaccinations_raw <- pkr1$daily_vaccinations_raw/1000000 ggplot()+ geom_line(data = full1, aes(date, daily_vaccinations_raw, group = country), size = 0.8, colour = "gray80")+ geom_line(data = pkr1, aes(date, daily_vaccinations_raw), size = 0.9, colour = "green2")+ geom_text(data = pkr1, aes(x = max(pkr1$date), y = pkr1$daily_vaccinations_raw[pkr1$date==max(pkr1$date)], label = country), hjust = 0.8, vjust = -0.9, size = 5.9, color = "green2")+ scale_x_date(date_labels = "%d %b %y", date_breaks = "28 days")+ labs(x = "Date", y = "Vaccinations (in milions)", title = "Daily vaccinations", subtitle = "per country, since 19-12-2020", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz

full2 <- data %>% group_by(date, country) %>% filter(people_vaccinated>0) %>% filter(as.Date(date) > "2020-12-18") pkr2 <- data %>% group_by(date, country) %>% filter(country == "Pakistan") %>% filter(people_vaccinated>0) %>% filter(as.Date(date) > "2020-12-18") full2$people_vaccinated <- full2$people_vaccinated/1000000 pkr2$people_vaccinated <- pkr2$people_vaccinated/1000000 ggplot()+ geom_line(data = full2, aes(date, people_vaccinated, group = country), size = 0.8, colour = "gray80")+ geom_line(data = pkr2, aes(date, people_vaccinated), size = 0.9, colour = "green2")+ geom_text(data = pkr2, aes(x = max(pkr2$date), y = pkr2$people_vaccinated[pkr2$date==max(pkr2$date)], label = country), hjust = 0.9, vjust = -0.7, size = 5.9, color = "green2")+ scale_x_date(date_labels = "%d %b %y", date_breaks = "28 days")+ labs(x = "Date", y = "Vaccinations (cumulated, in milions)", title = "Number of people vaccinated at least one time", subtitle = "per country, since 19-12-2020", caption = "© Made by Abid")+ theme_classic()+ theme_abz

ggplot()+ geom_line(data = full2, aes(date, percent_people, group = country), size = 0.8, colour = "gray80")+ geom_line(data = pkr2, aes(date, percent_people), size = 0.9, colour = "green2")+ geom_text(data = pkr2, aes(x = max(pkr2$date), y = pkr2$percent_people[pkr2$date==max(pkr2$date)], label = country), hjust = 0.9, vjust = -0.7, size = 5.9, color = "green2")+ scale_x_date(date_labels = "%d %b %y", date_breaks = "28 days")+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))+ labs(x = "Date", y = "Share of vaccinated people nationwide (%)", title = "Number of vaccinations in proportion to the number of citizens", subtitle = "per country, since 19-12-2020", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz

world_map <- map_data("world") %>% filter(between(lat, 20, 47)) %>% filter(between(long, 40, 100)) country <- data %>% filter(date == "2021-06-01")%>% group_by(country) %>% select(country, people_vaccinated_per_hundred) names(country)[1] <- "region" world_map <- world_map %>% left_join(country, by = "region") world_map2 <- world_map %>% select(long, lat, region, people_vaccinated_per_hundred) %>% group_by(region) %>% summarise(long = mean(long), lat = mean(lat), people_vaccinated_per_hundred = mean(people_vaccinated_per_hundred), .groups = 'drop') world_map2$pakistan <- ifelse(world_map2$region=="Pakistan","green2","gray80") ggplot()+ geom_polygon(data = world_map, aes(x = long, y = lat, group = group), fill = "white", colour = "gray70")+ geom_point(data = world_map2, aes(x = long, y = lat, size = people_vaccinated_per_hundred), shape = 21, fill = world_map2$pakistan)+ geom_text_repel(data = world_map2[is.na(world_map2$people_vaccinated_per_hundred)==F,], aes(x = long, y = lat, label = paste0(region, ",", round(people_vaccinated_per_hundred,0), "%")), size = 6, hjust = 0.3, vjust = 0.1)+ scale_size(range = c(0, 18))+ scale_fill_manual(values = c("green2", "gray80"))+ labs(x = "Latitude", y = "Longitude", title = "Share of vaccinated inhabitants among countries near Pakistan", size = "Share of\nvaccinated (in %)", subtitle = "per country, in 12-06-2021", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme(legend.position = "right", legend.direction = "vertical")

full3 <- data %>% filter(as.Date(date) > "2020-12-18") %>% select(weekday, country, daily_vaccinations_per_million) %>% group_by(weekday, country) %>% summarise(sr = mean(daily_vaccinations_per_million), .groups = 'drop') full3$pakistan <- ifelse(full3$country=="Pakistan","Pakistan", "Rest of World") full3pkr <- full3 %>% filter(pakistan=="Pakistan") full3row <- full3 %>% filter(pakistan=="Rest of World") full3row <- full3row %>% group_by(weekday) %>% summarise(sr = mean(sr), .groups = 'drop') full3row$country <- "Rest of World" full3pkr <- full3pkr[,1:3] full3row <- full3row[,c(1,3,2)] full3pkr <- as.data.frame(full3pkr) full3pkr$sr <- full3pkr$sr/sum(full3pkr$sr) full3row <- as.data.frame(full3row) full3row$sr <- full3row$sr/sum(full3row$sr) full3 <- rbind(full3pkr, full3row) ggplot(full3, aes(weekday, sr, fill = country))+ geom_bar(stat = "identity", width = 0.75, position=position_dodge(), col = "gray30")+ scale_x_discrete(limit = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"))+ scale_fill_manual(values = c("green2","gray80"))+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))+ labs(x = "Weekday", y = "Average share of a day in weekly vaccinations (in%)", title = "Daily vaccinations per million inhabitants by weekday", fill = "Country", subtitle = "per country, since 19-12-2020", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical", axis.title.y = element_text(size = 15))

data$number_of_vac <- str_count(data$vaccines, ",")+1 vacx <- data %>% filter(as.Date(date) > "2020-12-18") %>% group_by(country, number_of_vac) %>% summarise(avg = mean(number_of_vac), .groups = 'drop') %>% filter(avg>0) vacx$pakistan <- ifelse(vacx$country=="Pakistan", "Pakistan", "Rest of World") ggplot(vacx, aes(reorder(country, +avg), avg, fill = pakistan, colour = pakistan))+ geom_bar(stat = "identity", width = 0.9)+ scale_fill_manual(values = c("green2", "gray80"))+ scale_colour_manual(values = c("green2", "gray80"), guide = F)+ coord_flip()+ labs(y = "Number of suppliers", x = "Country", title = "Number of vaccine suppliers", fill = "Country", subtitle = "per country, since 19-12-2020", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical", axis.text.y = element_text(size = 0), panel.grid.major.y = element_blank())

other <- read.csv("countries-of-the-world/countries of the world.csv", stringsAsFactors = F) other <- other[,c("Country", "Population", "Area..sq..mi..", "Pop..Density..per.sq..mi..", "GDP....per.capita.", "Phones..per.1000.", "Literacy....", "Birthrate", "Deathrate", "Agriculture", "Industry", "Service")] names(other) <- c("region", "Population", "Area", "PopDensity", "GDP", "Phones", "Literacy", "Birthrate", "Deathrate", "Agriculture", "Industry", "Service") other$region <- gsub(" ", "", other$region) other$PopDensity <- as.numeric(gsub(",", ".", other$PopDensity)) other$Phones <- as.numeric(gsub(",", ".", other$Phones)) other$Literacy <- as.numeric(gsub(",", ".", other$Literacy)) other$Birthrate <- as.numeric(gsub(",", ".", other$Birthrate)) other$Deathrate <- as.numeric(gsub(",", ".", other$Deathrate)) other$Agriculture <- as.numeric(gsub(",", ".", other$Agriculture)) other$Industry <- as.numeric(gsub(",", ".", other$Industry)) other$Service <- as.numeric(gsub(",", ".", other$Service)) country <- as.data.frame(country) country$region <- as.character(country$region) #other <- other %>% # left_join(country, by = "region") other <- merge(other, country, by = "region", all.x = T, ) names(other)[13] <- "Vaccinate_Percentage" head2 <- other[sample(1:nrow(other),5), ] head2

core <- cor(other[,c(2:ncol(other))], method = "spearman", use = "complete.obs") options(repr.plot.width = 13, repr.plot.height = 11.18) ggcorrplot(core, outline.col = "gray30", type = "upper", lab = T, lab_size = 5.5, legend.title = "Strength of \ncorrelation", colors = c("#6D9EC1", "white", "#E46726"))+ labs(y = "", x = "", title = "Spearman's correlation matrix for continuous variables", subtitle = "by countries, vaccinations from 01-06-2021", caption = "© Made by deepnote.com/@abid")+ annotate("rect", xmin = 0.5, xmax = 11.5, ymin = 10.5, ymax = 11.5, alpha = 0.1, color = "green2", size = 0.85)+ guides(fill = guide_colorbar(title.position = "top", barheight = 10, barwidth = 1.7))+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical", axis.text.x = element_text(angle = 40, hjust = 1))

other$pakistan <- ifelse(other$region=="Pakistan","Pakistan", "Rest of World") options(repr.plot.width = 13.8, repr.plot.height = 9.2) ggplot(other, aes(Area, Population/1000000, fill = pakistan, size = PopDensity))+ geom_point(shape = 21)+ scale_fill_manual(values = c("green2", "gray80"), guide = F)+ scale_size(range = c(0.2, 14))+ scale_y_log10()+ scale_x_log10()+ annotate(geom = "curve", xend = 700000, y = 500, x = 100000, yend = 200, curvature = -0.25, arrow = arrow(length = unit(3.5, "mm")))+ annotate("text", x = 30000, y = 600, label = "Pakistan", size = 5.5, colour = "green2", fontface = 2)+ labs(y = "Population (in milions, logarithmic scale)", x = "Area (in km2, logarithmic scale)", title = "Population, area and density of population", fill = "Country", subtitle = "by countries", size = "Population Density\n(per square mile)", caption = "© Made by deepnote.com/@abid")+ # guides(fill = guide_legend(override.aes = list(size = 4)))+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical")

ggplot(other, aes(Birthrate, Deathrate, fill = pakistan))+ geom_point(shape = 21, size = 3.5, alpha = 0.85)+ scale_fill_manual(values = c("green2", "gray80"), guide = F)+ annotate(geom = "curve", xend = 29, y = 11.7, x = 24, yend = 9, curvature = -0.25, arrow = arrow(length = unit(3.5, "mm")))+ annotate("text", x = 23, y = 12.5, label = "Pakistan", size = 5.5, colour = "green2", fontface = 2)+ labs(y = "Deathrate", x = "Birthrate", title = "Birthrate and deathrate", fill = "Country", subtitle = "by countries", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical")

ggplot(other, aes(Phones, Literacy/100, fill = pakistan))+ geom_point(shape = 21, size = 3.5, alpha = 0.85)+ scale_fill_manual(values = c("green2", "gray80"), guide = F)+ scale_x_log10()+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))+ annotate(geom = "curve", xend = 40, y = 0.76, x = 800, yend = 0.46, curvature = -0.35, arrow = arrow(length = unit(3.5, "mm")))+ annotate("text", x = 800, y = 0.8, label = "Pakistan", size = 5.5, colour = "green2", fontface = 2)+ labs(y = "Literacy (in %)", x = "Phones (per 1000 inhabitants, logarithmic scale)", title = "Literacy and number of phones", fill = "Country", subtitle = "by countries", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical")

ggplot(other, aes(reorder(region, + GDP), GDP, fill = pakistan, size = pakistan))+ geom_bar(stat = "identity", width = 0.9)+ scale_fill_manual(values = c("green2", "gray80"))+ scale_colour_manual(values = c("green2", "gray80"))+ scale_size_manual(values = c(7,2), guide = F)+ coord_flip()+ labs(y = "GDP per capita (in $)", x = "Country", title = "GDP per capita", fill = "Country", subtitle = "by countries", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical", axis.text.y = element_text(size = 0), panel.grid.major.y = element_blank())

gosp <- other %>% select(region, Agriculture, Industry, Service) %>% filter(is.na(Agriculture)==F) %>% filter(is.na(Industry)==F) %>% filter(is.na(Service)==F) %>% melt(., id.vars = "region") gosp <- gosp[gosp$region %in% c("Pakistan", "Bangladesh", "Nepal", "Iran", "Afghanistan", "UnitedArabEmirates", "Spain", "NewZealand", "Qatar","Greece", "Cyprus", "Kuwait", "Slovenia", "Portugal", "Korea,South", "Oman", "Bahrain", "Finland", "CzechRepublic", "Hungary", "UnitedKingdom","India"),] gosp$pakistan <- ifelse(gosp$region=="Pakistan","Pakistan", "Rest of World") a <- ifelse(gosp$region=="Pakistan","green2", "gray25") ggplot(gosp, aes(region, color = pakistan, weight = value))+ geom_bar(aes(fill = variable), width = 0.75)+ scale_fill_brewer(palette = "Spectral", direction = -1)+ scale_colour_manual(values = c("green2", "gray45"), guide = F)+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))+ coord_flip()+ labs(x = "Country", y = "Share of the sector in the employment structure", title = "Agriculture, industry and service", colour = "Country", fill = "Employment\nstructure", subtitle = "by countries", caption = "© Made by deepnote.com/@abid")+ theme_classic()+ theme_abz+ theme(legend.position = "right", legend.direction = "vertical", axis.text.y = element_text(colour = a))