R: Recipes for Analysis, Visualization and Machine Learning

Viswa Viswanathan et al.



Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning

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 Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We’ll start off with data analysis – this will show you ways to use R to generate professional analysis reports. We’ll then move on to visualizing our data – this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we’ll move into the world of machine learning – this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.

Table of Contents

1: A SIMPLE GUIDE TO R
2: PRACTICAL MACHINE LEARNING WITH R
3: ACQUIRE AND PREPARE THE INGREDIENTS – YOUR DATA
4: WHAT'S IN THERE? – EXPLORATORY DATA ANALYSIS
5: WHERE DOES IT BELONG? – CLASSIFICATION
6: GIVE ME A NUMBER – REGRESSION
7: CAN YOU SIMPLIFY THAT? – DATA REDUCTION TECHNIQUES
8: LESSONS FROM HISTORY – TIME SERIES ANALYSIS
9: IT'S ALL ABOUT YOUR CONNECTIONS – SOCIAL NETWORK ANALYSIS
10: PUT YOUR BEST FOOT FORWARD – DOCUMENT AND PRESENT YOUR ANALYSIS
11: WORK SMARTER, NOT HARDER – EFFICIENT AND ELEGANT R CODE
12: WHERE IN THE WORLD? – GEOSPATIAL ANALYSIS
13: PLAYING NICE – CONNECTING TO OTHER SYSTEMS
14: BASIC AND INTERACTIVE PLOTS
15: HEAT MAPS AND DENDROGRAMS
16: MAPS
17: THE PIE CHART AND ITS ALTERNATIVES
18: ADDING THE THIRD DIMENSION
19: DATA IN HIGHER DIMENSIONS
20: VISUALIZING CONTINUOUS DATA
21: VISUALIZING TEXT AND XKCD-STYLE PLOTS
22: CREATING APPLICATIONS IN R
23: DATA EXPLORATION WITH RMS TITANIC
24: R AND STATISTICS
25: UNDERSTANDING REGRESSION ANALYSIS
26: CLASSIFICATION (I) – TREE, LAZY, AND PROBABILISTIC
27: CLASSIFICATION (II) – NEURAL NETWORK AND SVM
28: MODEL EVALUATION
29: ENSEMBLE LEARNING
30: CLUSTERING
31: ASSOCIATION ANALYSIS AND SEQUENCE MINING
32: DIMENSION REDUCTION
33: BIG DATA ANALYSIS (R AND HADOOP)

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