Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer

Creating interactive graphics with ggvis

In order to interact with the reports figures, one can create an interactive graphic with ggvis. In this recipe, we demonstrate how to build our first interactive plot from the real estate dataset.

Getting ready

Before starting this recipe, you should download the RealEstate.csv dataset from the following GitHub link:

How to do it…

Please perform the following steps to create an interactive plot with ggvis:

  1. Install and load the ggvis package:

    > install.packages("ggvis")
    > library(ggvis)
  2. Import RealEstate.csv into an R session:

    > house <- read.csv('RealEstate.csv', header=TRUE)
    > str(house)
    'data.frame': 781 obs. of  8 variables:
     $ MLS        : int  132842 134364 135141 135712 136282 136431 137036 137090 137159 137570 ...
     $ Location   : Factor w/ 54 levels " Arroyo Grande",..: 21 44 44 39 50 42 50 50 39 22 ...
     $ Price      : num  795000 399000 545000 909000 109900 ...