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Format: 1 online resource (452 pages)

Edition: 1st ed.

ISBN: 9781784392048

Content: About 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 For R for Data Science Cookbook is intended 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 the 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 togetherIn Detail 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. 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, and so on. 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

Content: data analysis

Note: Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Functions in R -- Introduction -- Creating R functions -- Matching arguments -- Understanding environments -- Working with lexical scoping -- Understanding closure -- Performing lazy evaluation -- Creating infix operators -- Using the replacement function -- Handling errors in a function -- The debugging function -- Chapter 2: Data Extracting, Transforming, and Loading -- Introduction -- Downloading open data -- Reading and writing CSV files -- Scanning text files -- Working with Excel files -- Reading data from databases -- Scraping web data -- Accessing Facebook data -- Working with twitteR -- Chapter 3: Data Preprocessing and Preparation -- Introduction -- Renaming the data variable -- Converting data types -- Working with the date format -- Adding new records -- Filtering data -- Dropping data -- Merging data -- Sorting data -- Reshaping data -- Detecting missing data -- Imputing missing data -- Chapter 4: Data Manipulation -- Introduction -- Enhancing a data.frame with a data.table -- Managing data with a data.table -- Performing fast aggregation with a data.table -- Merging large datasets with a data.table -- Subsetting and slicing data with dplyr -- Sampling data with dplyr -- Selecting columns with dplyr -- Chaining operations in dplyr -- Arranging rows with dplyr -- Eliminating duplicated rows with dplyr -- Adding new columns with dplyr -- Summarizing data with dplyr -- Merging data with dplyr -- Chapter 5: Visualizing Data with ggplot2 -- Introduction -- Creating basic plots with ggplot2 -- Changing aesthetics mapping -- Introducing geometric objects -- Performing transformations -- Adjusting scales -- Faceting -- Adjusting themes -- Combining plots -- Creating maps , Chapter 6: Making Interactive Reports -- Introduction -- Creating R Markdown reports -- Learning the markdown syntax -- Embedding R code chunks -- Creating interactive graphics with ggvis -- Understanding basic syntax and grammar -- Controlling axes and legends -- Using scales -- Adding interactivity to a ggvis plot -- Creating an R Shiny document -- Publishing an R Shiny report -- Chapter 7: Simulation from Probability Distributions -- Introduction -- Generating random samples -- Understanding uniform distributions -- Generating binomial random variates -- Generating Poisson random variates -- Sampling from a normal distribution -- Sampling from a chi-squared distribution -- Understanding Student's t-distribution -- Sampling from a dataset -- Simulating the stochastic process -- Chapter 8: Statistical Inference in R -- Introduction -- Getting confidence intervals -- Performing Z-tests -- Performing student's T-tests -- Conducting exact binomial tests -- Performing Kolmogorov-Smirnov tests -- Working with the Pearson's Chi-squared tests -- Understanding the Wilcoxon Rank Sum and Signed Rank tests -- Conducting one-way ANOVA -- Performing two-way ANOVA -- Chapter 9: Rule and Pattern Mining with R -- Introduction -- Transforming data into transactions -- Displaying transactions and associations -- Mining associations with the Apriori rule -- Pruning redundant rules -- Visualizing association rules -- Mining frequent itemsets with Eclat -- Creating transactions with temporal information -- Mining frequent sequential patterns with cSPADE -- Chapter 10: Time Series Mining with R -- Introduction -- Creating time series data -- Plotting a time series object -- Decomposing time series -- Smoothing time series -- Forecasting time series -- Selecting an ARIMA model -- Creating an ARIMA model -- Forecasting with an ARIMA model , Predicting stock prices with an ARIMA model -- Chapter 11: Supervised Machine Learning -- Introduction -- Fitting a linear regression model with lm -- Summarizing linear model fits -- Using linear regression to predict unknown values -- Measuring the performance of the regression model -- Performing a multiple regression analysis -- Selecting the best-fitted regression model with stepwise regression -- Applying the Gaussian model for generalized linear regression -- Performing a logistic regression analysis -- Building a classification model with recursive partitioning trees -- Visualizing a recursive partitioning tree -- Measuring model performance with a confusion matrix -- Measuring prediction performance using ROCR -- Chapter 12: Unsupervised Machine Learning -- Introduction -- Clustering data with hierarchical clustering -- Cutting tree into clusters -- Clustering data with the k-means method -- Clustering data with the density-based method -- Extracting silhouette information from clustering -- Comparing clustering methods -- Recognizing digits using the density-based clustering method -- Grouping similar text documents with k-means clustering methods -- Performing dimension reduction with Principal Component Analysis (PCA) -- Determining the number of principal components using a scree plot -- Determining the number of principal components using the Kaiser method -- Visualizing multivariate data using a biplot -- Index

Additional Edition: Print version Chiu), Yu-Wei, Chiu (David R for Data Science Cookbook Birmingham : Packt Publishing, Limited,c2016

Language: English

Keywords: Electronic books ; Electronic books

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URL: FULL  ((OIS Credentials Required))