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**R Topics:**

Twitter/Text Mining:

For text analysis, check out the series in Python, here.

1. Visualizing Twitter Tokens- Hashtags, Smileys and URLs

2. Text Mining: 1. Retrieving Text from Twitter in R Using the twitteR Package *Updated 2014/3/20*

3. Text Mining: 2. Converting Tweet Text List into a Document Corpus with Transformations

4. Text Mining: 3. Stemming Text and Building a Term Document Matrix

5. Text Mining: 4. Performing Term Associations and Creating Word Clouds

6. Text Mining: 5. Hierarchical Clustering for Frequent Terms

7. Sochi #Olympics and #Crimea Tweets in R (and Justin Bieber?!)

8. Text Mining: 6. K-Medoids Clustering of #Ukraine Tweets in R

9. Text Mining: 7. Term Network Analysis Using #Ukraine Tweets in R

10. Text Mining: 8. #Ukraine Tweet Network Analysis in R

11. #PublicHealth on Twitter in R

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Time Series Analysis:

1. Time Series: 1. Decomposition into Components-Additive Model

2. Time Series: 2. Forecasting Using Auto-Regressive Integrated Moving Averages (ARIMA)

3. World Wheat Production and Harvest Area, Part I

4. World Wheat Production Part II, Linear Filtering and Regression Forecasting

5. Up, Up, And Away: Amazon Stock Prices

6. How Fast is Fast? Comparing U.S. and South Korea Broadband Speeds

7. Visualizing Google Flu Trends in R

8. Visualizing Google Flu Trends Part 2

9. Ukraine Crisis and Palladium Prices

10. Visualizing CDC's Morbidity and Mortality Weekly Report (MMWR) on Infrequently Reported Diseases

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Classification Analysis:

1. Decision Trees and Recursive Partitioning

2. Cluster Analysis: Using K-Means

3. Cluster Analysis: Choosing Optimal Cluster Number for K-Means Analysis

4. Cluster Analysis: Hierarchical Modeling

5. Creating Random Forests

6. Classifying Handwritten Digits (MNIST) using Random Forests

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Regression Analysis:

1. Regression: Quantitative Structure-Activity Relationship (QSAR) Modeling

2. Robust Regression and Estimation of Model Performance

3. Partial Least Squares Regression, RMSEP, and Components

4. R: Comparing Multiple and Neural Network Regression

5. KDD Cup: Profit Optimization in R Part 1: Exploring Data

6. KDD Cup: Profit Optimization in R Part 2: Decision Trees

7. KDD Cup: Profit Optimization in R Part 3: Visualizing Results

8. KDD Cup: Profit Optimization in R Part 4: Selecting Trees

9. KDD Cup: Profit Optimization in R Part 5: Evaluation

10. Predicting Capital Bikeshare Demand in R: Part 1. Data Exploration

11. Predicting Capital Bikeshare Demand in R: Part 2. Regression

12. Predicting Capital Bikeshare Demand in R: Part 3. Generalized Boosted Model

Predicting Fraudulent Transactions is a series from Luis Torgo's Data Mining with R book:

13. Predicting Fraudulent Transactions in R: Part 1. Transactions

14. Predicting Fraudulent Transactions in R: Part 2. Handling Missing Data

15. Predicting Fraudulent Transactions in R: Part 3. Handling Transaction Outliers

16. Predicting Fraudulent Transactions in R: Part 4. Model Criterion, Precision & Recall

17. Predicting Fraudulent Transactions in R: Part 5. Normalized Distance to Typical Price

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Neural Networks:

1. R: Neural Network Modeling Part 1

2. Neural Network Prediction of Handwritten Digits (MNIST) in R

3. R: Comparing Multiple and Neural Network Regression

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Regular Expressions:

1. Using Regular Expressions to Analyze Baltimore Homicides in HTML, Part 1

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Distributions:

1. R1.1: Probability Distributions - Random Sampling, Combinatorics

2. R1.2: Probability Distributions - Calculations for Statistical Distributions

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Cryptography:

1. RSA Encryption and Decryption

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Sports:

1. Why LeBron James Should Leave Miami: A Look At Win Shares

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Check back for more posts on data analysis! (Homepage)

Wayne

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