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


To handle the uncertainty of real-world events, we can use probability to measure the likelihood of whether an event will occur. By definition, probability is quantified with a number between 0 and 1; the higher the probability (closer to 1), the more certain we are that an event will occur.

As statistical inference is used to deduce the properties of a given population, knowing the probability distribution of a given population becomes essential. For example, if you find that the data selected for prediction does not follow the exact assumed probability distribution in experiment design, the results should be refuted. In other words, probability provides justification for statistical inference.

In this chapter, we focus on the topic of probability distribution and simulation. We first discuss how to generate random samples, before covering how to use R to generate samples from various distributions such as normal, uniform, Poisson, chi-squared, and Student's t-distribution. We...