Nothing has changed about the chart – the only difference is that the legend now says “Blue”. Library(ggplot2) p <- ggplot(data=diamonds, aes(x=carat, y=price)) p + geom_point(aes(colour=“Blue”)) It all becomes clear if we try replace red with a different colour. Why is there a legend – we only wanted one colour, so there should not be a need for a legend. The other unexpected thing you might notice is the legend on the right. The first thing that stands out is that the red doesn’t look exactly like red. Library(ggplot2) p <- ggplot(data=diamonds, aes(x=carat, y=price)) p + geom_point(aes(colour=“Red”))
#MAP WILL NOT SHOW IN PLANEPLOTTER CODE#
How would the code for such plot look? Maybe like this: Let’s say we don’t want to use colour to help us see different categories – instead, we just want all of the observations to be red. But let’s now look at a slightly different example. Indeed, all we did in that last bit of code is mapped the colour aesthetic of the plot to the clarity variable in our dataset. “What’s was so complicated about this?” – you might ask. This makes sense, because IF stands for internally flawless – only about 3% of diamonds ever receive this grading. Here we are using colour to categorize the observations by the clarity of each diamond:īeautiful! As we see, ggplot2 has even automatically created a legend for us on the right.Įven without knowing anything about diamonds we can tell that the IF-graded clarity rocks sell at the highest price at a given size. Library(ggplot2) p <- ggplot(data=diamonds, aes(x=carat, y=price)) p + geom_point(aes(colour=clarity)) How would we do that? Perhaps by adding a bit of code into the last line like this: Already an interesting peek into our data, however let’s now say that we want to make this visual even more insightful by adding colour to it. This chart illustrates the sales prices (y axis) for over 50,000 diamonds over their weight in carats (x axis). Running the code creates the following chart: Here we’re visualizing the diamonds dataset, which comes with ggplot2. Library(ggplot2) p <- ggplot(data=diamonds, aes(x=carat, y=price)) p + geom_point()
![map will not show in planeplotter map will not show in planeplotter](https://i.ytimg.com/vi/3jWmq8xAJiw/maxresdefault.jpg)
Today we will talk about one of these specific challenges: mapping vs setting aesthetics. Though ggplot2 is extremely logical, and therefore easy to learn, there are certain challenges associated with getting your head even around this package. Many R users are familiar with the ggplot2 package by Hadley Wickham. In this quick article I wanted to share with you a valuable tip we cover off in my course R Programming A-Z™ on Udemy (click here to get a 30% discount on the course). My name is Kirill Eremenko and I teach R on Udemy.