Book Image

Introduction to R for Business Intelligence

By : Gendron
Book Image

Introduction to R for Business Intelligence

By: Gendron

Overview of this book

Explore the world of Business Intelligence through the eyes of an analyst working in a successful and growing company. Learn R through use cases supporting different functions within that company. This book provides data-driven and analytically focused approaches to help you answer questions in operations, marketing, and finance. In Part 1, you will learn about extracting data from different sources, cleaning that data, and exploring its structure. In Part 2, you will explore predictive models and cluster analysis for Business Intelligence and analyze financial times series. Finally, in Part 3, you will learn to communicate results with sharp visualizations and interactive, web-based dashboards. After completing the use cases, you will be able to work with business data in the R programming environment and realize how data science helps make informed decisions and develops business strategy. Along the way, you will find helpful tips about R and Business Intelligence.
Table of Contents (19 chapters)
Introduction to R for Business Intelligence
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
References
R Packages Used in the Book
R Code for Supporting Market Segment Business Case Calculations

Loading data into business systems for analysis


You have imported and transformed data. It resides within your R environment as a data frame. Now you will need to provide it to marketing. The following are two common ways to export data from R into a file for use elsewhere in an organization:

  • Writing data to a CSV file

  • Writing data to a tab-delimited text file

These methods are similar, but they produce different results. Knowing about them and their differences will help you decide the format you would like to use.

Writing data to a CSV file

CSV files are common among data applications. Other data applications, such as Excel, can read these types of file. CSV files are also useful because database systems can typically import them into their environment, just as you imported a CSV into the R environment. The write.csv() function is used to write a data frame to a CSV file. In this example, the input parameters include report and the name of the output file, revenue_report.csv:

write.csv(report, "revenue_report.csv", row.names = FALSE) 

You also used a row.names = FALSE parameter. Very often, your dataset will not contain row names. This parameter prevents R from adding a column of numerical identifiers to the CSV file. There are many other parameters you can use with write.csv(). Learn more about them by typing ?write.csv in the R console.

Writing data to a tab-delimited text file

There may be times when you would like to have your data read by a data application that does not import CSV files. Recall that in the Extracting data from sources section, that read.csv() had a more flexible counterpart, read.table(). The write.table() function provides you with greater flexibility on how the final file is composed:

write.table(report, "revenue_report.txt", row.names = FALSE, sep = "\t") 

The write.table() function uses a syntax that is very similar to write.csv(). You see the addition of sep = "\t". This tells R to separate data with the tab character when creating the text file. There are many other parameters you can use with write.table(). Learn more about them by typing ?write.table in the R console.