Felipe Flores
First of all, let's import the necessary packages:
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1 ✔ purrr 0.2.4
## ✔ tibble 1.4.2 ✔ dplyr 0.7.4
## ✔ tidyr 0.8.0 ✔ stringr 1.3.0
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(rjson)
colors <- rjson::fromJSON(file = "colors.json")Rjson is only needed for my color palette, which I created as a JSON from here. I also imported my color palette as the vector named colors. Next, since I want to be able to produce as many as these plots as I want, I created a function that would let me avoid redundance. The function first checks whether we're computing for the entire world or just a country, then does the necessary tidying up and plotting. Of course, in order to create the function I first explored the dataset. This is just the finalized product.
plotting_function <-
function(
data,
yearOfInterest,
countryName,
title = paste("Share of death by cause, ", countryName, ", ", yearOfInterest, sep = ""),
subtitle = "",
caption = "Source: IHME, Global Burden of Disease"
){
if (countryName == "World") {
data <- data %>%
filter(year == yearOfInterest) %>%
select(-c(country, country_code, year)) %>%
summarize_all(funs("sum"))
} else {
data <- data %>%
filter(year == yearOfInterest, country == countryName) %>%
select(-c(country, country_code, year))
}
data %>%
gather(key = "disease", value = "deaths") %>%
mutate(deaths = deaths / sum(deaths)) %>%
ggplot(aes(x = reorder(disease, deaths), y = deaths, fill = disease))+
geom_bar(stat = "identity")+
geom_text(aes(label = paste(round(100 * deaths, 2), "%")), hjust = -0.1)+
scale_y_continuous(labels = scales::percent, limits = c(0, 0.35))+
scale_fill_manual(values = colors)+
guides(fill = FALSE)+
coord_flip()+
xlab("")+
ylab("")+
theme_classic()+
labs(title = title, subtitle = subtitle, caption = caption)+
theme(
panel.grid.major.x = element_line(linetype = "dotted", color = "#5043484A")
)
}So let's try it out on the dataset! First we import the data with readxl and use stringr to remove the percentage signs from the variable names. I also decided to omit NA's, which I think is why my numbers are slightly different from those in the article. Oh well shrug
data <- readxl::read_xlsx("global_mortality.xlsx") %>%
rename_all(funs(stringr::str_remove_all(., "[(%)]"))) %>%
na.omit() Finally, let's use the function on the world, the US, and my beloved Chile for 2016.
plotting_function(data = data, yearOfInterest = 2016, countryName = "World")
plotting_function(data = data, yearOfInterest = 2016, countryName = "United States")
plotting_function(data = data, yearOfInterest = 2016, countryName = "Chile")
Nice! Of course, we could input any country and year and get the same kind of plot. That's all friends!
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