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 Kolmogorov-Smirnov tests

We use a one-sample Kolmogorov-Smirnov test to compare a sample with reference probability. A two-sample Kolmogorov-Smirnov test compares the cumulative distributions of two datasets. In this recipe, we will demonstrate how to perform a Kolmogorov-Smirnov test with R.

Getting ready

In this recipe, we will use the ks.test function from the stat package.

How to do it…

Perform the following steps to conduct a Kolmogorov-Smirnov test:

  1. Validate whether the x dataset (generated with the rnorm function) is distributed normally with a one-sample Kolmogorov-Smirnov test:

    > x <-rnorm(50)
      Kolmogorov-Smirnov test
    data:  x
    D = 0.073034, p-value =
    alternative hypothesis: two-sided
  2. Next, we can generate uniformly distributed sample data:

    > x <- runif(n=20, min=0, max=20)
    > y <- runif(n=20, min=0, max=20)
  3. We first plot the ECDF of two generated data samples: