This article provides a gallery of ggplot examplesincluding: scatter plot, density plots and histograms, bar and line plots, error bars, box plots, violin plots and more.
Ridgeline plots are partially overlapping line plots that create the impression of a mountain range.
Q: In the 1st example, what would the code be to print as top legend : R and R2 and p? Q: In the 1st example, what would the code be to print as top legend : R and R2 and P? Scatter plot Basic scatter plot with correlation coefficient. Length, Petal. Wherever there is more points overlap, the size of the circle gets bigger. In a bubble chart, points size is controlled by a continuous variable, here qsec. Distribution Density plot Basic density plot: Basic density plot ggplot iris, aes Sepal.
Length ggplot iris, aes Sepal. Histogram Basic histograms Basic histogram with mean line ggplot iris, aes Sepal. Balloon plot Balloon plot is an alternative to bar plot for visualizing a large categorical data.
Comments 3 SFer. Kassambara — thanks for this great reference!. Thanks, SFer. Thanks, SFer San Francisco —. Thank Sfer for your feedback! Give a comment Cancel reply Want to post an issue with R?Stay up-to-date. What type of visualization to use for what sort of problem? This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. This is part 3 of a three part tutorial on ggplot2, an aesthetically pleasing and very popular graphics framework in R.
This tutorial is primarily geared towards those having some basic knowledge of the R programming language and want to make complex and nice looking charts with R ggplot2. Part 1: Introduction to ggplot2covers the basic knowledge about constructing simple ggplots and modifying the components and aesthetics. Part 2: Customizing the Look and Feelis about more advanced customization like manipulating legend, annotations, multiplots with faceting and custom layouts.
Part 3: Top 50 ggplot2 Visualizations - The Master Listapplies what was learnt in part 1 and 2 to construct other types of ggplots such as bar charts, boxplots etc. The list below sorts the visualizations based on its primary purpose.
Primarily, there are 8 types of objectives you may construct plots. So, before you actually make the plot, try and figure what findings and relationships you would like to convey or examine through the visualization.
Chances are it will fall under one or sometimes more of these 8 categories. The most frequently used plot for data analysis is undoubtedly the scatterplot.
Whenever you want to understand the nature of relationship between two variables, invariably the first choice is the scatterplot. When presenting the results, sometimes I would encirlce certain special group of points or region in the chart so as to draw the attention to those peculiar cases.
Moreover, You can expand the curve so as to pass just outside the points. The color and size thickness of the curve can be modified as well. See below example. This time, I will use the mpg dataset to plot city mileage cty vs highway mileage hwy. What we have here is a scatterplot of city and highway mileage in mpg dataset.Here is a list of the different available geoms. Click one to see an example using it.
Annotation is a key step in data visualization. It allows to highlight the main message of the chart, turning a messy figure in an insightful medium. This example also explains how to apply labels to a selection of markers. Learn how to use the annotate function to add a rectangle on a specific part of the chart. Basically the same as for a segment, with just one additional argument. Marginal plots are not natively supported by ggplot2but their realisation is straightforward thanks to the ggExtra library as illustrated in graph Add rug on X and Y axis to describe the numeric variable distribution.
Add marginal distribution around your scatterplot with ggExtra and the ggMarginal function. Using boxplots is another way to show the marginal distribution. Find more in this post. The theme function of ggplot2 allows to customize the chart appearance.
It controls 3 main types of components:. Customize ggplot2 legend: position, title, text, key symbol and more. Post is dedicated to reordering. It describes 3 different way to arrange groups in a ggplot2 chart:.
The ggtitle function allows to add a title to the chart. The following post will guide you through its usage, showing how to control title main features: position, font, color, text and more. Small multiples is a very powerful dataviz technique. It split the chart window in many small similar charts: each represents a specific group of a categorical variable.A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. However, in most cases you start with ggplotsupply a dataset and aesthetic mapping with aes. That means, by-and-large, ggplot2 itself changes relatively little. When we do make changes, they will be generally to add new functions or arguments rather than changing the behaviour of existing functions, and if we do make changes to existing behaviour we will do them for compelling reasons.
If you are new to ggplot2 you are better off starting with a systematic introduction, rather than trying to learn from reading individual documentation pages. Currently, there are three good places to start:. R for Data Science is designed to give you a comprehensive introduction to the tidyverseand these two chapters will get you up to speed with the essentials of ggplot2 as quickly as possible.
It provides a set of recipes to solve common graphics problems. It describes the theoretical underpinnings of ggplot2 and shows you how all the pieces fit together. This book helps you understand the theory that underpins ggplot2, and will help you create new types of graphics specifically tailored to your needs. The RStudio community is a friendly place to ask any questions about ggplot2. Stack Overflow is a great source of answers to common ggplot2 questions. It is also a great place to get help, once you have created a reproducible example that illustrates your problem.
Created by DataCamp. Installation The easiest way to get ggplot2 is to install the whole tidyverse: install. Learning ggplot2 If you are new to ggplot2 you are better off starting with a systematic introduction, rather than trying to learn from reading individual documentation pages. Getting help There are two main places to get help with ggplot2: The RStudio community is a friendly place to ask any questions about ggplot2. R' 'aaa. R' 'utilities. R' 'backports. R' 'bench. R' 'coord. R' 'coord-sf.
R' 'coord-transform. R' 'facet. R' 'geom-linerange. R' 'geom-smooth. R' 'guide-colorbar. R' 'guide-legend. R' 'labeller. R' 'layout. R' 'limits.Viewing the same plot for different groups in your data is particularly difficult. The ggplot2 package is extremely flexible and repeating plots for groups is quite easy. From this perspective a pie chart is just a bar chart with a circular polar coordinate system replacing the rectangular Cartesian coordinate system. However, it is not a light read and it presents an abstract graphical syntax that is meant to clarify his concepts.
It is not a language you can use to recreate his graphs! The ggplot2 package is a simplified implementation of grammar of graphics written by Hadley Wickham for R. It is simplified only in that he uses R for data transformation and restructuring, rather than implementing that in his syntax.
Here I will review the basic examples presented in my books. The practice data set is shown here. The programs and the data they use are also available for download here. To make it easy to get started, the ggplot2 package offers two main functions: quickplot and ggplot.
Top 50 ggplot2 Visualizations - The Master List (With Full R Code)
It is particularly easy to use for simple plots. Below is an example of the default plots that qplot makes. The command that created each plot is shown in the title of each graph.
Most of them are useful except for middle one in the left column of qplot workshop, gender. A plot like that of two factors simply shows the combinations of the factors that exist which is certainly not worth doing a graph to discover. While qplot is easy to use for simple graphs, it does not use the powerful grammar of graphics.
The ggplot function does that. To understand ggplot, you need to ask yourself, what are the fundamental parts of every data graph?
Bar and line graphs (ggplot2)
They are:.It is statistics and design combined in a meaningful way to interpret the data with graphs and plots. In this ggplot2 tutorial we will see how to visualize data using gglot2 package provided by R. We see that data visualization tools help in exploring the data, as well as explaining the data.
Let us begin this blog by first looking at the types of visualization. We have a number of visualization tools to make aesthetic graphs. In any language the grammatical rules are to be kept in mind to construct meaningful sentences, such as:. The ggplot2 package is a simplified implementation of grammar of graphics written by Hadley Wickham for R.
It takes care of many of the fiddly details that make plotting a hassle like drawing legends as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics. We can easily say that the weight is in the range of by just looking at this bar plot.
The same inference can be drawn but it is much clear from this graph. Prior to the statistical analysis and model building, it is essential to visually observe the relationship between the different data elements.
This helps us in obtaining meaningful insights from the data to build better models. Already have an account? Sign in.Change Font Size of ggplot2 Plot in R (Examples) - Axis Text, Main Title & Legend
Top 10 Reasons to Learn R. Tutorial on Importing Data in R Commander. Become a Certified Professional. Recommended videos for you. Business Analytics with R Watch Now. Android Development : Using Android 5. Recommended blogs for you. Read Article.In this chapter, we will focus on creation of multiple plots which can be further used to create 3 dimensional plots.
This dataset provides fuel economy data from and for 38 popular models of cars. The dataset is shipped with ggplot2 package. It is important to follow the below mentioned step to create different types of plots.
A density plot is a graphic representation of the distribution of any numeric variable in mentioned dataset. It uses a kernel density estimate to show the probability density function of the variable. We can create the plot by renaming the x and y axes which maintains better clarity with inclusion of title and legends with different color combinations.
Box plot also called as box and whisker plot represents the five-number summary of data. The five number summaries include values like minimum, first quartile, median, third quartile and maximum. Dot plots are similar to scattered plots with only difference of dimension. In this section, we will be adding dot plot to the existing box plot to have better picture and clarity. Violin plot is also created in similar manner with only structure change of violins instead of box.
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