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

Selecting columns with dplyr

In the last recipe, we introduced how to use the filter and slice functions to subset and slice data by rows. In this recipe, we will present how to select particular columns from the dataset using the select function.

Getting ready

Ensure that you completed the Enhancing a data.frame with a data.table recipe to load and as both data.frame and data.table into your R environment.

You also need to make sure that you have a version of R higher than 3.1.2 installed on your operating system.

How to do it…

Perform the following steps to select columns from the dataset:

  1. First, let's select the Quantity and Price columns from the dataset:

    > select.quantity.price <-  select(order.dt, Quantity, Price)
    > head(select.quantity.price, 3)
       Quantity Price
    1:        1  1069
    2:        1  1680
    3:        1   285
  2. Alternatively, we can rule out the Price column by placing a minus sign in front:

    > select.not.price <-  select(order.dt...