Yu-Wei Chiu - David Chiu [Yu-Wei Chiu - David Chiu] - R for Data Science Cookbook
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- Book:R for Data Science Cookbook
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- Publisher:Packt Publishing
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- Year:2016
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Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques
About This Book
- 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
Who This Book Is For
This 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 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
In 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. 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.
Style and approach
This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Yu-Wei Chiu - David Chiu [Yu-Wei Chiu - David Chiu]: author's other books
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- acf function /
- add_tooltip function /
- aesthetics
- adding, to plot /
- aesthetics mapping
- modifying /
- agglomerative hierarchical clustering /
- Akaike Information Criterion (AIC) /
- AmeliaView /
- ANOVA
- about /
- reference /
- Apriori rule
- associations, mining with /
- area under curve (AUC) /
- argument binding mechanism /
- arguments
- matching /
- ARIMA model
- selecting /
- creating /
- forecasting with /
- stock prices, predicting with /
- arrange function /
- arules package /
- association rules
- visualizing /
- associations
- displaying /
- mining, with Apriori rule /
- axes
- controlling /
- basic plots
- creating, with ggplot2 /
- basic syntax
- about /
- Bayesian Information Criterion (BIC) /
- best-fitted regression model
- selecting, with stepwise regression /
- binomial random variates
- generating /
- biplot
- used, for visualizing multivariate data /
- boot.ci function /
- box.test function /
- Brownian motion /
- BSDA package
- z.test function, finding in /
- chi-squared distribution
- sample, generating from /
- classification model
- building, with recursive partitioning trees /
- closure
- about /
- creating, in function /
- passing, to other function /
- clustering
- about /
- silhouette information, extracting from /
- clustering methods
- comparing /
- clusters
- tree, cutting into /
- code chunk
- global option, controlling of /
- columns
- selecting, with dplyr /
- complexity parameter (CP) /
- confidence intervals
- obtaining /
- confusion matrix
- model performance, measuring with /
- cont command /
- coord_polar function /
- copy function
- about /
- CRAN (Comprehensive R Archive Network) /
- cSPADE
- frequent sequential patterns, mining with /
- CSV file
- text data, reading from /
- data, scanning from /
- CSV files
- reading /
- writing /
- cutree function
- about /
- data
- scanning, from CSV file /
- reading, from databases /
- accessing, from Facebook /
- reading, from Twitter /
- filtering /
- dropping /
- merging /
- sorting /
- reshaping /
- missing data, detecting /
- missing data, imputing /
- managing, with data.table /
- subsetting, with dplyr /
- slicing, with dplyr /
- sampling, with dplyr /
- summarizing, with dplyr /
- merging, with dplyr /
- transforming, into transactions /
- clustering, with hierarchical clustering /
- clustering, with k-means method /
- clustering, with density-based method /
- data.frame
- enhancing, with data.table /
- data.table
- about /
- data.frame, enhancing with /
- data, managing with /
- fast aggregation, performing with /
- large datasets, merging with /
- databases
- data, reading from /
- dataset
- sampling from /
- data types
- converting /
- data variable
- renaming /
- date format
- working with /
- dbscan package /
- debugging function
- about /
- density-based clustering method
- used, for recognizing digits /
- density-based method
- data, clustering with /
- desc function /
- descriptive statistics
- about /
- diagnostic plot
- used, for diagnosing regression model /
- digits
- recognizing, density-based clustering method used /
- dimension reduction
- about /
- performing, with Principal Component Analysis (PCA) /
- dimnames function /
- distance functions, for similarity measurement
- single-linkage /
- complete-linkage /
- average-linkage /
- ward-method /
- divisive hierarchical clustering /
- document options
- editing /
- download.file function /
- dplyr
- about /
- data, subsetting with /
- data, slicing with /
- data, sampling with /
- columns, selecting with /
- operations, chaining in /
- rows, arranging with /
- duplicated rows, eliminating with /
- new columns, adding with /
- data, summarizing with /
- data, merging with /
- duplicated rows
- eliminating, with dplyr /
- Eclat
- frequent itemsets, mining with /
- empirical cumulative distribution function (ECDF) /
- environments
- working with /
- Eps parameter /
- error message
- catching /
- errors
- handling, in function /
- exact binomial tests
- conducting /
- Excel
- about /
- Excel files
- working with /
- f (finish) command /
- Facebook
- reference /
- data, accessing from /
- facet function /
- faceting /
- fast aggregation
- performing, with data.table /
- feature extraction
- about /
- feature selection
- about /
- force function /
- fpp package /
- fread function
- about /
- frequent itemsets
- mining, with Eclat /
- frequent sequential patterns
- mining, with cSPADE /
- function
- closure, creating in /
- errors, handling in /
- Gaussian distribution /
- Gaussian model
- applying, for generalized linear regression /
- generalized linear model (GLM) /
- generalized linear regression
- Gaussian model, applying for /
- geometric objects
- about /
- creating, in ggplot2 /
- Geospatial Data Abstraction Library (GDAL)
- about /
- reference /
- ggplot2
- basic plots, creating with /
- geometric objects, creating in /
- ggtsdisplay function /
- ggvis
- interactive graphics, creating with /
- plots, creating with /
- ggvis plot
- interactivity, adding into /
- global option
- controlling, of code chunk /
- grammar
- about /
- graphics package /
- grid.arrange function /
- gridExtra package /
- H0(null hypothesis) /
- H1(alternative hypothesis) /
- hclust /
- help command /
- help function /
- hierarchical clustering
- data, clustering with /
- Hmisc package, within stat_summary
- mean_cl_normal() /
- mean_sdl() /
- mean_cl_boot() /
- median_hilow() /
- HTML (Hypertext Markup Language) /
- Hypertext Transfer Protocol (HTTP) /
- inferential statistics
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