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 basic plots with ggplot2

In this recipe, we demonstrate how to use The Grammar of Graphics to construct our very first ggplot2 chart with the superstore sales dataset.

Getting ready

First, download the superstore_sales.csv dataset from the GitHub link.

Next, you can use the following code to download the CSV file to your working directory:

> download.file('', 'superstore_sales.csv')

You will also need to load the dplyr package to manipulate the superstore_sales dataset.

How to do it…

Please perform the following steps to create a basic chart with ggplot2:

  1. First, install and load the ggplot2 package:

    > install.packages("ggplot2")
    > library(ggplot2)
  2. Import superstore_sales.csv into an R session:

    > superstore <-read.csv('superstore_sales.csv', header=TRUE)
    > superstore$Order.Date <- as.Date(superstore$Order.Date)