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 a logistic regression analysis

In the previous examples, we have discussed how to use fit data into linear model of continuous variables. In addition, we can use the logit model in generalized linear models to predict categorical variables. In this recipe, we will demonstrate how to perform binomial logistic regression to create a classification model that can predict binary responses on a given a set of predictors.

Getting ready

Download the house rental dataset from first, and ensure you have installed R on your operating system.

How to do it…

Perform the following steps to fit a generalized linear regression model with the logit model:

  1. Read customer.csv into an R session:

    > customer = read.csv('customer.csv', header=TRUE)
    > str(customer)
    'data.frame':	100 obs. of  5 variables:
     $ CustomerID : int  1 2 3 4 5 6 7 8 9 10 ...
     $ gender     : Factor w/ 2 levels "F","M": 1 2 1 1 2 2 1 1 2 2 ...
     $ age       ...