The numbers don’t seem to be right since the life expectancy is close to 100 for all countries - we will fix this later. To build a ggplot, we first use the ggplot() function to specify the default data source and aesthetic mappings: Let’s also make “year” a factor, since it is a discrete variable: Base plot Let’s restrict the data to the countries and years we are interested in, and save this new dataset as data_graph. We can use the following code to install and load packages. We need to install the following packages: Now that we know what we need to include in the graph, let’s move on to writing code. Groups on the x-axis - we want to group countries by continent.for India, we want one bar for the life expectancy in 1952 and another bar for 2007 Type of visualization - we want one bar per country per year e.g.Axes - we want country name on the x-axis and life expectancy on the y-axis.Dataset - for us, this is a subset of the gapminder data that includes only the countries and years in question.Using our rough sketch as a guide, we know that our components are: The first step to building the graphic is to identify the components. For now, what we need to understand is that we will build a graphic by adding components one after the other, like layers. It has specialized terminology to refer to the elements of a graph, and I’ll introduce and explain new terms as we encounter them. Ggplot2 is based on the “grammar of graphics”, which provides a standard way to describe the components of a graph (the “gg” in ggplot2 refers to the grammar of graphics). We also want to colour the bars differently based on the continent. Note that we want two bars per country - one of these should be the life expectancy in 1952 and the other in 2007. Here is a rough sketch to get us started on what we can do: We will use a bar plot to communicate this information graphically because we can easily see the levels of the life expectancy variable, and compare values over time and across countries. We also want to group the countries by continent. Specifically, we want to see the life expectancy in each of these countries in 19. We would like to show the change in life expectancy from 1952 to 2007 for 11 (arbitrarily-selected) countries: Bolivia, China, Ethiopia, Guatemala, Haiti, India, Kenya, Pakistan, Sri Lanka, Tanzania, Uganda. This dataset is an excerpt from the GapMinder data, and it shows the life expectancy, population and GDP per capita of various countries over 12 years between 1952 to 2007. We will be using the GapMinder dataset that comes pre-packaged with R. All code is commented so this should be straightforward to follow even if you have not used dplyr before. I also use the dplyr package to clean data. This post assumes basic familiarity with the following R concepts: It can be difficult for a beginner to tie all this information together. Geom_text(aes(y=label_ypos, label=len), vjust=1.There is a wealth of information on the philosophy of ggplot2, how to get started with ggplot2, and how to customize the smallest elements of a graphic using ggplot2 - but it’s all in different corners of the Internet. If you want to place the labels at the middle of bars, you have to modify the cumulative sum as follow : df_cumsum <- ddply(df_sorted, "dose", Geom_text(aes(y=label_ypos, label=len), vjust=1.6, Ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) + Head(df_cumsum) # supp dose len label_ypos # 6 VC D2 33.0 # Calculate the cumulative sum of len for each dose Calculate the cumulative sum of the variable len for each dose.Sort the data by dose and supp : the package plyr is used.Position = position_dodge(0.9), size=3.5)+Īdd labels to a stacked barplot : 3 steps are required Geom_text(aes(label=len), vjust=1.6, color="white", Geom_bar(stat="identity", position=position_dodge())+ Add labels to a dodged barplot : ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
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