3 Plots In R

Ggplot2 is now over 10 years old and is used by hundreds of thousands of people to make millions of plots. 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. 3 variable plot Faceting is a valuable technique for looking at several conditional distributions at the same time. If the faceted distributions are laid out in a grid, you can consider the association between a variable and two others, one on the rows of the grid and the other on the columns.

1. 3 Plots In R T
2. Combine Three Plots In R
3. 3 Dim Plot In R
4. 3 Dimensional Plots In R

One of R's key strength is what is offers as a free platform for exploratory data analysis; indeed, this is one of the things which attracted me to the language as a freelance consultant. Given the attraction of using charts and graphics to explain your findings to others, we're going to provide a basic demonstration of how to plot categorical data in R.

Introducing the Scenario

Given the attraction of using charts and graphics to explain your findings to others, we're going to provide a basic demonstration of how to plot categorical data in R. Introducing the Scenario Imagine we are looking at some customer complaint data. Examples of Scatter plots in R Language. In the example of scatter plots in R, we will be using R Studio IDE and the output will be shown in the R Console and plot section of R Studio. The dataset we will be using is the iris dataset, which is a popular built-in data set in the R language. 3D charts R allows to build three dimensional charts, mainly thanks to the rgl package. Even if 3D is often a bad practice, it can be useful in specific situation. This section provides several examples made in R.

Imagine we are looking at some customer complaint data. This consists of a log of phone calls (we can refer to them by number) and a reason code that summarizes why they called us. Assume we have several reason codes:

• A – Bad Product
• B – Shipping
• C – Packaging
• D – Other

Now that we've defined our defect codes, we can set up a data frame with the last couple of months of complaints.

So, now that we've got a lovely set of complaints, lets do some analysis.

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How To Plot Categorical Data in R

A good starting point for plotting categorical data is to summarize the values of a particular variable into groups and plot their frequency. We're going to do that here. The one liner below does a couple of things.

• use table () to summarize the frequency of complaints by product
• Sort the table in decreasing order
• Use barplot to generate a basic plot of the distribution

Yielding the following chart

If we produced the products in similar quantities, we might want to check into what is going on with our paper tissue manufacturing lines.

How To Plot Categorical Data in R – Proportions

Another common ask is to look at the overlap between two factors. You can accomplish this through plotting each factor level separately. We're going to use the plot function below.

Which produces:

Other Common Tables and Charts for Categorical Data

These two charts represent two of the more popular graphs for categorical data. The
bar graph of categorical data is a staple of visualizations for categorical data. The spineplot heat-map allows you to look at interactions between different factors. These are not the only things you can plot using R.

You can easily generate a pie chart for categorical data in r. Look at the pie function.

Along the same lines, if your dependent variable is continuous, you can also look at using boxplot categorical data views (example of how to do side by side boxplots here).

That concludes our introduction to how To Plot Categorical Data in R. As you can see, there are number of tools here which can help you explore your data…

Going Deeper…

Interested in Learning More About Categorical Data Analysis in R? Check Out

Graphics

Tutorials

• How To Run A Chi Square Test in R (earlier article)

by Joseph Rickert

Recently, I was trying to remember how to make a 3D scatter plot in R when it occurred to me that the documentation on how to do this is scattered all over the place. Hence, this short organizational note that you may find useful.

First of all, for the benefit of newcomers, I should mention that R has three distinct graphics systems: (1) the 'traditional' graphics system, (2) the grid graphics system and (3) ggplot2. The traditional graphic system refers to the graphics and plotting functions in base R. According to Paul Murrell's authoritative R Graphics, these functions implement the graphics facilities of the S language, and up to about 2005 they comprised most of the graphics functionality of R. The grid graphics system is based on Deepayan Sarkar's lattice package which implements the functionality of Bill Cleveland's Trellis graphics. Finally, ggplot2 Hadley Wickham's package based on Wilkinson's Grammar of Graphics, took shape between 2007 and 2009 when ggplot2 Elegant Graphics for Data Analysis appeared.

There is considerable overlap of the functionality of R's three graphics systems, but each has its own strengths and weaknesses. For example, although ggplot2 is currently probably the most popular R package for doing presentation quality plots it does not offer 3D plots. To work effectively in R I think it is necessary to know your way around at least two of the graphics systems. To really gain a command of the visualizations that can be done in R, a person would have to be familiar with all three systems as well as the many packages for specialized visualizations: maps, social networks, arc diagrams, animations, time series etc.

But back to the relatively tame task of 3D plots: the generic function persp() in the base graphics package draws perspective plots of a surface over the x–y plane. Typing demo(persp) at the console will give you an idea of what this function can do.

The plot3D package from Karline Soetaert builds on on persp()to provide functions for both 2D and 3D plotting. The vignette for plot3D shows some very impressive plots. Load the package and type the following commands at the console: example(persp3D), example(surf3D) and example(scatter3D) to see examples of 3D surface and scatter plots. Also, try this code to see a cut-away view of a Torus.

The scatterplot3d package from R core members Uwe Ligges and Martin M�achler is the 'go-to' package for 3D scatter plots. The vignette for this package is shows a rich array of plots. Load this package and type example(scatterplot3d) at the console to see examples of spirals, surfaces and 3D scatterplots.

The lattice package has its own distinctive look. Once you see one lattice plot it should be pretty easy to distinguish plots made with this package from base graphics plots. Load the packate and type example(cloud) in the console to see a 3D graph of a volcano, and 3D surface and scatter plots.

rgl from Daniel Adler and Duncan Murdoch and Rcmdr from John Fox et al. both allow interactive 3D vizualizations. Load the rgl package and type example(plot3d) to see a very cool, OpenGL, 3D scatter plot that you can grab with your mouse and rotate.

For additional references, see the scatterplot page of Robert Kabacoff's always helpful Quick-R site, and Paul E. Johnson's 3D Plotting presentation.

3 Plots In R T

Combine Three Plots In R

3 Dim Plot In R

3 Dimensional Plots In R