Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

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.

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R for Data Science Cookbook (n)
Yu-Wei, Chiu (David Chiu)
Jul, 2016 | 452 pages
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Table of Contents (19 chapters)
R for Data Science Cookbook
R for Data Science Cookbook
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Functions in R
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
2
Data Extracting, Transforming, and Loading
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
3
Data Preprocessing and Preparation
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
4
Data Manipulation
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
5
Visualizing Data with ggplot2
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
6
Making Interactive Reports
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
7
Simulation from Probability Distributions
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
8
Statistical Inference in R
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
9
Rule and Pattern Mining with R
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
10
Time Series Mining with R
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
11
Supervised Machine Learning
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
12
Unsupervised Machine Learning
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
Index

Introduction

R is the mainstream programming language of choice for data scientists. According to polls conducted by KDnuggets, a leading data analysis website, R ranked as the most popular language for analytics, data mining, and data science in the three most recent surveys (2012 to 2014). For many data scientists, R is more than just a programming language because the software also provides an interactive environment that can perform all types of data analysis.

R has many advantages in data manipulation and analysis, and the three most well-known are as follows:

  • Open Source and free: Using SAS or SPSS requires the purchase of a usage license. One can use R for free, allowing users to easily learn how to implement statistical algorithms from the source code of each function.

  • Powerful data analysis functions: R is famous in the data science community. Many biologists, statisticians, and programmers have wrapped their models into R packages before distributing these packages worldwide through CRAN (Comprehensive R Archive Network). This allows any user to start their analysis project by downloading and installing an R package from CRAN.

  • Easy to use: As R is a self-explanatory, high-level language, programming in R is fairly easy. R users only need to know how to use the R functions and how each parameter works through its powerful documentation. We can easily conduct high-level data analysis without having knowledge of the complex underlying mathematics.

R users will most likely agree that these advantages make complicated data analysis easier and more approachable. Notably, R also allows us to take the role of just a basic user or a developer. For an R user, we only need to know how a function works without requiring detailed knowledge of how it is implemented. Similarly to SPSS, we can perform various types of data analysis through R's interactive shell. On the other hand, as an R developer, we can write their function to create a new model, or they can even wrap implemented functions into a package.

Instead of explaining how to write an R program from scratch, the aim of this book is to cover how to become a developer in R. The main purpose of this chapter is to show users how to define their function to accelerate the analysis procedure. Starting with creating a function, this chapter covers the environment of R, and it explains how to create matching arguments. There is also content on how to perform functional programming in R, how to create advanced functions, such as infix operator and replacement, and how to handle errors and debug functions.

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