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Statistical Application Development with R and Python

You're reading from Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

Product type Paperback

Published in Aug 2017

Publisher

ISBN-13 9781788621199

Length 432 pages

Edition 2nd Edition

Languages

Python

Concepts

Application Development

Author (1):

Prabhanjan Narayanachar Tattar

View More author details

Table of Contents (12) Chapters

Preface

1. Data Characteristics FREE CHAPTER

2. Import/Export Data

3. Data Visualization

4. Exploratory Analysis

5. Statistical Inference

6. Linear Regression Analysis

7. Logistic Regression Model

8. Regression Models with Regularization

9. Classification and Regression Trees

10. CART and Beyond

Index

Using utils and the foreign packages

Data is generally available in an external file. The types of external files are certainly varied and it is important to learn which of them may be imported into R. The probable spreadsheet files may exist in a comma separated variable (CSV) format, XLS or XLSX (Microsoft Excel) form, or ODS (OpenOffice/LibreOffice Calc) ones. There are more possible formats but we restrict our attention to these described previously. A snapshot of two files, Employ.dat and SCV.csv, in gedit and MS Excel are given in the following screenshot. The brief characteristics of the two files are summarized in the following list:

The first row lists the names of the variables of the dataset
Each observation begins on a new line
In the DAT file, the delimiter is a tab (\t), whereas for the CSV file, it is a comma (,)
All three columns of the DAT file are numeric in nature
The first five columns of the CSV file are numeric while the last column is character
Overall, both the files...

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