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
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Renaming the data variable


The use of a data frame enables the user to select and filter data by row names and column names. As not all imported datasets contain row names and column names, we need to rename this dataset with a built-in naming function.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Perform the following steps to rename data:

  1. First, download employees.csv from the GitHub link https://github.com/ywchiu/rcookbook/raw/master/chapter3/employees.csv:

    > download.file("https://github.com/ywchiu/rcookbook/raw/master/chapter3/employees.csv", " employees.csv")
    
  2. Additionally, download salaries.csv from the GitHub link https://github.com/ywchiu/rcookbook/raw/master/chapter3/salaries.csv:

    > download.file("https://github.com/ywchiu/rcookbook/raw/master/chapter3/salaries.csv", "salaries.csv")
    
  3. Next, read the file into an R session with the read.csv function:

    > employees <- read.csv('employees...