By Anup Nair
Through this post, I would like to share an update to my RTutoR package. The first version of this package included an R Basics Tutorial App which I published earlier at DataScience+
The updated version of this package, which is now on CRAN, includes a plotting app. This app provides an automated interface for generating plots using the ggplot2 package. Current version of the app supports 10 different plot types along with options to manipulate specific aesthetics and controls related to each plot type. The app also displays the underlying code which generates the plot and this feature would hopefully be useful for people trying to learn ggplot2. The app also utilizes the plotly package for generating interactive plots which is especially suited for performing exploratory analysis.
A video demo on how to use the app is provided at the end of the post. The app is also hosted Shinyapps.io. However, unlike the package version, you would not be able to use your own dataset. Instead, the app provides a small set of pre-defined datasets to choose from.
High level overview of ggplot2
For people who are completely new to gglot2, or have just started working on it, I provide below a quick, concise overview of the ggplot2 package. This is not meant to be comprehensive, but just covers some key aspects so that it’s easier to understand how the app is structured and to make the most of it. You also can read a published tutorial in DataScience+ for ggplot2.
The template for generating a basic plot using ggplot2 is as follows:
ggplot(data_set) + plot_type(aes(x_variable,y_variable)) #For univariate analysis, you can specify just one variable
plot_type specifies the type of plot that should be constructed. There are more than 35 different plot types in ggplot2. (The current version of the app, however, supports only 10 different …read more
Source:: r-bloggers.com