Book Image

Statistical Application Development with R and Python - Second Edition

Book Image

Statistical Application Development with R and Python - Second Edition

Overview of this book

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.
Table of Contents (19 chapters)
Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
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 have...