8/16/2023 0 Comments Ggplot2 pie chart![]() If you have many values to display, you can also consider a lollipop plot that is a bit more elegant in my opinion. The barplot is the best alternative to pie plots. And often made even worseĮven if pie charts are bad by definition, it is still possible to make them even worse by adding other bad features: Ggplot(data, aes( x=name, y=value, fill=name)) + geom_bar( stat = "identity") + scale_fill_viridis( discrete = TRUE, direction= - 1) + scale_color_manual( values= c( "black", "white")) + theme_ipsum() + theme(Īs you can see on this barplot, there is a heavy difference between the three pie plots with a hidden pattern that you definitely don’t want to miss when you tell your story. Also, try to figure out what is the evolution of the value among groups. Once more, try to understand which group has the highest value in these 3 graphics. If you’re still not convinced, let’s try to compare several pie plots. Ggplot(data, aes( x= "name", y=value, fill=name)) + geom_bar( width = 1, stat = "identity") + coord_polar( "y", start= 0, direction = - 1) + scale_fill_viridis( discrete = TRUE, direction= - 1) + geom_text( aes( y = vec, label = rev(name), size= 4, color= c( "white", rep( "black", 4)))) + scale_color_manual( values= c( "black", "white")) + theme_ipsum() + theme( # Getting the coordinates of each countryĬountry_lookup <- read.csv(paste0( "./Data/ ", "countries.csv "), stringsAsFactors = F) Pivotted_data <- pivot_by_country( grouped_data) A simple google search should come up with lots of arguments against pie charts. Because pie charts are possibly the worst way to visualize categorical data (or any data for that matter). ![]() It can be used to provide a lot of aesthetic mappings to the plotted graphs. There is a good reason why most visualizing libraries in R don't have inbuilt support for pie charts. Names( grouped_data) <- c( "country ", "count ", "death_cause ") The ggplot2 package is a powerful and widely used package for graphic visualization. Grouped_data <- rbind( grouped_data1, grouped_data2) Grouped_data2 $ death_cause2 <- "Others " Summarise( totalcount = sum( count)) % >%įilter ( death_cause %in% top_ten_causes)įilter ( ! death_cause %in% c( top_ten_causes, "All causes ")) Then, we will load it to our work environment as below.įilter ( ! sex = sex_filter, record_type = record_filter) % >% In this case, we need the data for the death causes per country and, the coordinates of each country. The first thing that we need to do is to make sure that our data is complete, that we have what we need prior to plotting. I managed to acquire a decent data about death causes in the world per country from which will be used in our illustrations and simulations. ![]() “Are there differences in the distribution of causes of death per country?” With that, we will be answering a specific question using real-world data as we go along these steps. ![]() ![]() I struggled so much on doing my first map plot, that’s why I’ll be sharing to you the step-by-step ways on how to do it using ggplot, to save you from all the google searches and trial-and-error. “How am I supposed to do this thing when I’m not even good in R programming, in fact, a newbie?” You’re probably asking this to yourself right now, or you already did. This image probably scared you as much as it did to me when I realized I need to create something the same as this. Plot showing the leading causes of death in the year 2014 for various countries In this post, we would go through the steps to plot pie charts on a world map, just like the one below. ![]()
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