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Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout This BookGain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packagesUnderstand how to apply useful data analysis techniques in R for real-world applicationsAn easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisWho This Book Is ForThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.What You Will LearnGet to know the functional characteristics of R languageExtract, transform, and load data from heterogeneous sourcesUnderstand how easily R can confront probability and statistics problemsGet simple R instructions to quickly organize and manipulate large datasetsCreate professional data visualizations and interactive reportsPredict user purchase behavior by adopting a classification approachImplement data mining techniques to discover items that are frequently purchased togetherGroup similar text documents by using various clustering methodsIn DetailThis 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” packag

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout This BookGain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packagesUnderstand how to apply useful data analysis techniques in R for real-world applicationsAn easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisWho This Book Is ForThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.What You Will LearnGet to know the functional characteristics of R languageExtract, transform, and load data from heterogeneous sourcesUnderstand how easily R can confront probability and statistics problemsGet simple R instructions to quickly organize and manipulate large datasetsCreate professional data visualizations and interactive reportsPredict user purchase behavior by adopting a classification approachImplement data mining techniques to discover items that are frequently purchased togetherGroup similar text documents by using various clustering methodsIn DetailThis 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” packag

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout This BookGain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packagesUnderstand how to apply useful data analysis techniques in R for real-world applicationsAn easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisWho This Book Is ForThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.What You Will LearnGet to know the functional characteristics of R languageExtract, transform, and load data from heterogeneous sourcesUnderstand how easily R can confront probability and statistics problemsGet simple R instructions to quickly organize and manipulate large datasetsCreate professional data visualizations and interactive reportsPredict user purchase behavior by adopting a classification approachImplement data mining techniques to discover items that are frequently purchased togetherGroup similar text documents by using various clustering methodsIn DetailThis 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” packag

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics.This book covers the basics of R by setting up a user-friendly programming environment and performing data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationships. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimension reduction.

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R codeAbout This BookApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset, applying different classification methods.Who This Book Is ForThis book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.What You Will LearnCreate and inspect transaction datasets and perform association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsDetect and impute missing values in air quality dataPredict possible churn users with the classification approachPlot the autocorrelation function with time series analysisUse the Cox proportional hazards model for survival analysisImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on big dataIn DetailBig data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapt

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R codeAbout This BookApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset, applying different classification methods.Who This Book Is ForThis book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.What You Will LearnCreate and inspect transaction datasets and perform association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsDetect and impute missing values in air quality dataPredict possible churn users with the classification approachPlot the autocorrelation function with time series analysisUse the Cox proportional hazards model for survival analysisImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on big dataIn DetailBig data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapt

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R codeAbout This BookApply R to simplify predictive modeling with short and simple codeUse machine learning to solve problems ranging from small to big dataBuild a training and testing dataset, applying different classification methods.Who This Book Is ForThis book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.What You Will LearnCreate and inspect transaction datasets and perform association analysis with the Apriori algorithmVisualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithmCompare differences between each regression method to discover how they solve problemsDetect and impute missing values in air quality dataPredict possible churn users with the classification approachPlot the autocorrelation function with time series analysisUse the Cox proportional hazards model for survival analysisImplement the clustering method to segment customer dataCompress images with the dimension reduction methodIncorporate R and Hadoop to solve machine learning problems on big dataIn DetailBig data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapt

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout This BookGain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packagesUnderstand how to apply useful data analysis techniques in R for real-world applicationsAn easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisWho This Book Is ForThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.What You Will LearnGet to know the functional characteristics of R languageExtract, transform, and load data from heterogeneous sourcesUnderstand how easily R can confront probability and statistics problemsGet simple R instructions to quickly organize and manipulate large datasetsCreate professional data visualizations and interactive reportsPredict user purchase behavior by adopting a classification approachImplement data mining techniques to discover items that are frequently purchased togetherGroup similar text documents by using various clustering methodsIn DetailThis 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” packag