![]() ![]() ![]() We created a new data frame from the original dataframe to select the data points of interest and used it with geom_point() to add it as another to layer to the plot. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. In summary, we saw examples of using ggplot2 to highlight certain data points of interest in a scatter plot. Overview ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. We can see that all the highlighted points are from the country Kuwait. Let us color the highlighted data points by country.Īes(x=lifeExp,y=gdpPercap, color=country),size=3) We can also highlight by a variable/column in the dataframe to learn more about the highlighted data points. Highlight selected points with ggplot2 in R We can see that the data points above 59k for gdpPercap is highlighted in red. And in the second geom_point(), we use the new dataframe, not the original data frame. Note that we have two geom_point(), one for all the data and the other for with data only for the data to be highlighted. We can use the new data frame containing the data points to be highlighted to add another layer of geom_point(). # filter dataframe to get data to be highligheted Here we can use filter function to create a new dataframe from gapminder data. And then create a new dataframe containing only the data points we need to highlight. The way to do it is, we first make the scatter plot normally as we did before. Let us highlight the outlier data points in red using ggplot2. Also, we probably need to change the y-axis to log-scale to spread out the datapoints on y-axis. It is natural to seek out more information on the outliers. Since there are a lot of overlapping data points, let us set the transparency level to 0.3.Ī quick look at the plot suggests the gdpPercap outliers on y-axis squishes the ploints on y-axis a lot. Let us plot lifeExp on x-axis and gdpPercap on y-axis. 5.1 filter() 5.2 select() 5.3 arrange() 5.4 Chaining dplyr functions 5.5 Writing data to a file 5.6 Chaining dplyr and ggplot 5.7 mutate() 5.8 summarize. Let us use the data to make a simple scatter plot using ggplot. # country year pop continent lifeExp gdpPercap This is how our gapminder data looks like. These are easily identified by their square bracket syntax. Use R’s built in data manipulation tools. ![]() The key idea is that we use some criteria to extract a subset of rows from our data and use only those rows in subsequent analysis. Let us use the gapminder data from Carpentries website to make plots and highlight data points. We generally call this process filtering in Excel or selection in SQL. Let us first load the packages needed, we will mainly be using dplyr and ggplot2 here. Here we will see an example of highlighting specific data points in a plot. Sometimes, one might want to highlight certain data points in a plot in different color. The power of ggplot2 lies in making it easy to make great plots and in easily tweaking it to the one wants. ![]()
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