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

Subsetting and slicing data with dplyr

In this recipe, we will introduce how to use dplyr to manipulate data. We first cover the topic of how to use the filter and slice functions to subset and slice data.

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 subset and slice data with dplyr:

  1. Let's first install and load the dplyr package:

    > install.packages("dplyr")
    > library(dplyr)
  2. Next, we can filter data by quantity number with the filter function:

    > quantity.over.3 <- filter(order.dt, Quantity >= 3)
    > head(quantity.over.3, 3)
                      Time Action       User     Product Quantity Price
    1: 2015-07-01 00:39:22  order U465146448 P0006173160        3  1076
    2: 2015-07-01 00...