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

Performing Z-tests

When making decisions, it is important to know whether decision error can be controlled or measured. In other words, we want to prove that the hypothesis formed is unlikely to have occurred by chance, and it is statistically significant. In hypothesis testing, there are two types of hypothesis: null hypothesis and alternative hypothesis (research hypothesis). The purpose of hypothesis testing is to validate whether the experiment results are significant. However, to validate whether the alternative hypothesis is acceptable, the alternative hypothesis is deemed to be true if the null hypothesis is rejected.

A Z-test is a parametric hypothesis method that can determine whether the observed sample is statistically significantly different from a population with known standard deviation, based on standard normal distribution.

Getting ready

Ensure that you installed R on your operating system.

How to do it…

Perform the following steps to calculate the Z-score:

  1. First, collect the volume...