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 student's T-tests

In a Z-test, we can determine whether two mean values are significantly different if the standard deviation (or variance) is known. However, if the standard deviation is unknown and the sample size is fairly small (less than 30), we can perform a student's T-test instead. A one sample T-test allows us to test whether two means are significantly different; a two sample T-test allows us to test whether the means of two independent groups are different. In this recipe, we will discuss how to conduct a one sample T-test and a two sample T-test using R.

Getting ready

Ensure that you installed R on your operating system.

How to do it…

Perform the following steps to calculate the t-value:

  1. First, we visualize a sample weight vector in a boxplot:

    > weight <- c(84.12,85.17,62.18,83.97,76.29,76.89,61.37,70.38,90.98,85.71,89.33,74.56,82.01,75.19,80.97,93.82,78.97,73.58,85.86,76.44)
    >boxplot(weight, main="A boxplot of weight")
    >abline(h=70,lwd=2, col="red")