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Introduction to R for Business Intelligence

You're reading from   Introduction to R for Business Intelligence Profit optimization using data mining, data analysis, and Business Intelligence

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785280252
Length 228 pages
Edition 1st Edition
Languages
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Author (1):
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Jay Gendron Jay Gendron
Author Profile Icon Jay Gendron
Jay Gendron
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Table of Contents (13) Chapters Close

Preface 1. Extract, Transform, and Load FREE CHAPTER 2. Data Cleaning 3. Exploratory Data Analysis 4. Linear Regression for Business 5. Data Mining with Cluster Analysis 6. Time Series Analysis 7. Visualizing the Datas Story 8. Web Dashboards with Shiny A. References
B. Other Helpful R Functions C. R Packages Used in the Book
D. R Code for Supporting Market Segment Business Case Calculations

Chapter 2 - Data Cleaning

The following table describes some other useful functions for Chapter 2, Data Cleaning:

Function

Description

as.character(); as.factor(); as.logical()

These are the functions to perform type conversion, such as as.numeric() seen in Chapter 2, Data Cleaning.

class()

This is used on an R object to determine the data type that the object is stored as.

encoding()

This reveals the encoding scheme that is used in storing the object.

grepl(); grep()

These are the functions in the base R package that use regular expressions to find instances of a search string.

gsub()

This is the function in the base R package that allows for global substitution of a string with another.

impute(x, fun = mean)

This is a function to impute a vector of values based on the average of existing values. This is used with x[is.na(x)] to isolate missing values.

install.packages()

This downloads and installs packages that are not included in Base R from CRAN-like...

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