Data Types and Structures in R
- Who should take and what will you get from this course ?
- Installing R and RStudio
- Orientation to Data Types and Structures Section
- Materials for Data Types and Structures
- Vectors: The Basic Default Data Structure in R
- Matrices, Lists and Dataframes: Other Important R Data Structures
- Manipulating Vectors in R
- Naming Vectors in R
- Creating Matrices in R
- Creating Lists in R
- Creating Lists in R (continued)
- Creating Dataframes in R
Data and File Input and Output
- Orientation to Data and File Input and Output
- Materials for Data and File Input and Output
- Reading in Data using scan() Function
- Reading in Data with scan() Function (continued)
- Using readline() Function to Prompt User for Input
- Reading in Files with read.table() and read.csv() Functions
- Writing R Session Files to Disk (Outputting Data)
- Data Input and Output Exercise
Visualizing (Getting to Know) your Data
- Solution to Data Input and Output Exercise from Section 2 (1 of 2)
- Solution to Data Input and Output Exercise from Section 2 (2 of 2)
- Materials for Visualizing your Data Section 3
- Preprocessing and Visualizing Birth Data
- Preprocessing and Visualizing Birth Data (part 2)
- Preprocessing and Visualizing Birth Data (part 3)
- Visualizing Alumni Donations
- Visualizing Alumni Donations (part 2)
- Visualizing Alumni Donations (part 3)
- Visualizing Alumni Donations (part 4)
- Visualizing (Getting to Know) your Data Section Exercise
Decision Trees and Random Forests
- Solution to Visualizing Virginia Deaths Exercise
- Introduction to Decision Trees and Random Forests
- Training Decision Trees with party Package
- Training Decision Trees with party Package (part 2)
- Bodyfat Decision Tree example with Package rpart
- Bodyfat Decision Tree example with Package rpart (part 2)
- Bagging and Random Forests with Section Exercise
Linear Modeling (Regression) and Generalized Linear Modeling (GLMs)
- Begin Decision Tree and Random Forests Exercise Solution
- Random Forests Exercise Bagging Segment Solution
- Random Forests Exercise Solution (part 3)
- Materials for Regression and GLMs Section
- Begin Regression Example
- Continue Regression Example
- Finish Regression Example
- Begin Regression and GLM Slides
- Finish Generalized Linear Modeling Slides
- Heart Data Binomial GLM Example
- Epidemic Data Poisson GLM Example
- Regression and GLMs Exercises
K-Means, K-Medoids, and Hierarchical Cluster Analysis Approaches
- Materials and End-of-Section-6 Exercise
- Regression and GLM Exercises Solutions (part 1)
- Regression and GLM Exercises Solutions (part 2)
- Regression and GLM Exercises Solutions (part 3)
- K-Means Iris Flower Example
- K-Means Exoplanets Example
- K-Medoids Iris Flower Re-Analysis Example
- Hierarchical Clustering Iris Flower Example
- Hierarchical Clustering Pottery Example
Density-Based and Agglomerative Hierarchical Clustering
- Materials for Density-Based and Hierarchical Agglomerative Clustering Section
- Density-Based and Agglomerative Clustering Introduction and Previous Exercise
- Density-Based Clustering Example
- Body Measurements and Agglomerative Hierarchical Clustering Example
- Continue Body Measurements Agglomerative Clustering Example
- Clustering Jet Fighters Example
More Cluster Analysis Examples, Graphics, and Detecting Outliers
- Materials and End-of-Section-8 Exercise
- K-Means Clustering Explained in Detail
- Clustering Crime Rates Example
- Clustering Crime Rates Example (part 2)
- Gastroenterologist Questionnaire Model-Based Clustering Eample
- Graphical Approaches to Cluster Analysis Examples
- Detecting Outliers
- Detecting Outliers (part 2)
K-Means TAM Residuals Cluster Analysis Software Case example
- Crime Data Exercise Solution
- Crime Data Exercise Solution (part 2)
- Materials for Final Data Mining Course Section
- K-Means Clustering PLS-POS Capability Implementation
- K-Means Clustering PLS-POS Capability Implementation Concepts
- Implementing K-Means Clustering for TAM Residuals Continued
- Implementing K-Means Clustering for TAM Residuals in R Software
