EBOOK
About
Key Features
Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages
Understand how to apply useful data analysis techniques in R for real-world applications
An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis
Book Description
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.
What you will learn
Get to know the functional characteristics of R language
Extract, transform, and load data from heterogeneous sources
Understand how easily R can confront probability and statistics problems
Get simple R instructions to quickly organize and manipulate large datasets
Create professional data visualizations and interactive reports
Predict user purchase behavior by adopting a classification approach
Implement data mining techniques to discover items that are frequently purchased together
Group similar text documents by using various clustering methods
Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages
Understand how to apply useful data analysis techniques in R for real-world applications
An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis
Book Description
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.
What you will learn
Get to know the functional characteristics of R language
Extract, transform, and load data from heterogeneous sources
Understand how easily R can confront probability and statistics problems
Get simple R instructions to quickly organize and manipulate large datasets
Create professional data visualizations and interactive reports
Predict user purchase behavior by adopting a classification approach
Implement data mining techniques to discover items that are frequently purchased together
Group similar text documents by using various clustering methods