[This article was first published on RStudioDataLab, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Beyond just plotting points on a chart, how can you transform a simple ggplot dotplot into a powerful narrative tool that reveals the hidden stories within your data's distribution, all while avoiding the common pitfalls of visual clutter and misinterpretation?A ggplot dotplot is a powerful data visualisation tool within the R programming language, specifically using the ggplot2 package. It represents individual data points as dots, stacking them in bins to show the distribution of a continuous variable. Unlike histograms, which aggregate data into bars, the geom_dotplot function allows you to visualise the frequency and spread of individual observations, making it invaluable for exploring small to moderate-sized datasets and comparing distributions across multiple groups. Its true power lies in its customizability, allowing you to control dot size, colour, and stacking to create precise and insightful graphics.Table of ContentsKeyPointsSee Every Single Data Point. Forget averages that hide the truth. A dot plot allows you to visualise every individual customer, score, or sale. This helps you spot the true story, like clusters or gaps in your data, that other charts might miss.Create Your First Plot in Seconds. Getting started is easier than you think. With just one line of R code, you can turn a column of data into an insightful visualization. This simple command is your first step to mastering the ggplot dotplot.ggplot(df, aes(x = CreditScore)) + geom_dotplot()Customise Your Plot to Stand Out. Don't settle for the default look. A few simple tweaks to the fill colour or it dotsize can make your chart much clearer and more professional. It’s how you go from a basic plot to a great one.Easily Compare Groups Side-by-Side. Want to see how different groups compare? Just add fill = YourGroup to your code. This is the most powerful feature of the dot plot, allowing you to instantly compare distributions for different categories, like policy types.ggplot(df, aes(x = Age, fill = PolicyType)) + geom_dotplot()Add Pro-Level Context with Layers. Tell the whole story by adding layers of summary statistics. You can place a transparent overlay geom_boxplot() over your dots to show both the individual data points and the overall summary in one powerful chart.Beyond the Bar Chart with ggplot2 When you need to see the real story behind your data, a simple bar chart doesn't always cut it. While useful, they often hide important details about the distribution of your numbers. This is where the ggplot2 R package comes in. It gives you the power to create a better kind of plot, one that shows every single data point. Moving to a visualisation like the dot plot is a game-changer for analysis. dot plot helps you see not just the “how much” but also the “how it's spread out.” This guide will walk you through creating a ggplot dotplot, a crucial skill for anyone serious about data analysis in R. It’s a tool that brings clarity and depth to your work, allowing you to present findings with confidence and precision.Read More »To leave a comment for the author, please follow the link and comment on their blog: RStudioDataLab.R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.Continue reading: ggplot dotplot using R | geom_dotplot, dot plot using ggplot2