Skip to main content
Link
Search
Menu
Expand
Document
(external link)
Kremowka
Welcome
Module 1: Introduction
Datasets
Slides
Module 2: Getting Started with R
Slides
Procedure 1: Navigate to and launch the R command line
Procedure 2: Issue commands to the R Console
Procedure 3: Set a Working Directory
Procedure 4: Run a script from the R command line
Procedure 5: Launching R Studio
Procedure 6: Identify Packages Installed
Procedure 7: Browsing and Installing Packages
Procedure 8: Review Help and Documentation
Procedure 9: Load and Unload Packages in RStudio
Procedure 10: Load Packages using Script
Procedure 11: List all Functions in a Package
Procedure 12: Use the help() function to explain a function
Procedure 13: Unloading a Package
Procedure 14: Creating a Numeric Variable by Assignment
Procedure 15: Create a string variable by assignment
Procedure 16: Create a logical variable by assignment
Procedure 17: List Variables in R
Procedure 18: Remove Variables in R
Module 3: Data Structures Introduction
Slides
Procedure 1: Create a Vector with c Function
Procedure 2: Perform Vector Arithmetic
Procedure 3: Create Vector via a Sequence
Procedure 4: Create a Vector via Repetition
Procedure 5: Selecting and Filtering from a numeric Vector
Procedure 6: Setting Vector Labels or Names
Procedure 7: Selecting and Filtering from a Character Vector
Procedure 8: Combine Vectors to make a Matrix with cbind
Procedure 9: Viewing a Matrix
Procedure 10: Combine Vectors to make a Matrix with rbind
Procedure 11: Create a Matrix of defined size with a Vector
Procedure 12: Labelling a Matrix
Procedure 13: Selecting from a Matrix
Procedure 14: Creating a Factor from a Vector
Procedure 15: Creating a Factor from a Vector with Levels and Ordering
Procedure 16: Creating a list with a variety of objects
Procedure 17: Subsetting and referencing objects with a name
Procedure 18: Create a Data Frame from Vectors
Procedure 19: Create a Data Frame from Names and stringsAsFactors
Procedure 20: Saving .Rdata to file
Procedure 21: Loading .Rdata from file
Module 4: Loading, Shaping and Merging Data
Slides
Procedure 1: Using Numeric Functions to create a Horizontal Abstraction
Procedure 2: Extracting a substring from a string, testing logically and presenting for machine learning
Procedure 3: Searching with Regular Expressions
Procedure 4: Create a Date with a specific Date and Time format
Procedure 5: Perform Date Arithmetic
Procedure 6: Extract Reporting Periods from a Date
Procedure 7: Importing a CSV file with R Studio
Procedure 8: Importing a pipe separated file
Procedure 9: Connect to an SQL Server Database
Procedure 10: Fetch an entire table from an SQL Server Database
Procedure 11: Sorting a Data Frame with the arrange() function
Procedure 12: Specifying columns of a Data Frame to return
Procedure 13: Adding Vectors or Factors to an existing Data Frame
Procedure 14: Merging a Data Frame
Procedure 15: Delete a Vector from a Data Frame
Procedure 16: Exporting a csv file
Module 5: Summary Statistics and Basic Plots in R
Slides
Procedure 1: Create a Histogram of Time Series Data in R
Procedure 2: Establish Range in R
Procedure 3: Calculate Quartiles and the Interquartile Range
Procedure 4: Establish the Mean and Median in R
Procedure 5: Create a Box Plot
Procedure 6: Navigate Plots and Export Visualisations
Procedure 7: Create the Variance and Standard Deviation
Procedure 8: Calculate a Z Score
Procedure 9: Create a Range Normalisation for a Value
Procedure 10: Create the Skewness and Kurtosis statistics
Procedure 11: Create Probabilities from a test value in a normal distribution
Module 6: ggplot2 Rapid Exploration
Slides
Procedure 1: Quickly Creating a Scatter Plot with qplot()
Procedure 2: Quickly Creating a Line Chart with qplot()
Procedure 3: Quickly Creating a Bar Chart with qplot()
Procedure 4: Quickly Creating a Histogram with qplot()
Module 7: Abstraction
Slides
Module 8: Linear Regression
Slides
Procedure 1: Scanning Scatter Plots for Relationships
Procedure 2: Creating a Scatter Plot for Closer Inspection with ggplot2
Procedure 3: Create a Correlation Matrix using Spearman and Pearson
Procedure 4: Ranking Correlation by Absolute Strength
Procedure 5: Adding a Trend Line to a Scatter Plot
Procedure 6: Creating a One Way Linear Regression Model
Procedure 7: Deploying a One Way Linear Regression Manually with vector arithmetic
Procedure 8: Using the predict function for a one way linear regression one
Procedure 9: Identifying Confidence Intervals
Procedure 10: Create a Stepwise Linear Regression Model
Procedure 11: Heat Map Correlation Matrix
Module 9: Logistic Regression
Slides
Procedure 1: Pivot a Categorical Variable for Regression Analysis
Procedure 2: Create an Abstraction Deviation Independent Vector
Procedure 3: Fit a one-way Log Curve on a Plot
Procedure 4: Forward Stepwise Logistic Regression
Procedure 5: Recalling a Logistic Regression Model
Procedure 6: Activating Logistic Regression and Creating a Confusion Matrix
Procedure 7: Output Logistic Regression Model as Probability
Procedure 8: Creating a ROC Curve
Procedure 9: Grading the ROC Performance with AUC
Module 10: Splits, Probability and Decision Trees
Slides
Procedure 1: Create a Decision Tree using rpart
Procedure 2: Visualise a rpart Decision Tree
Procedure 3: Recalling a rpart() Decision Tree
Procedure 4: Creating a C5 Decision Tree object
Procedure 5: Recalling a C5 Decision Tree
Procedure 6: Creating a Confusion Matrix for a C5 Decision Tree
Procedure 7: Visualising a C5 Decision Tree
Procedure 8: Expressing Business Rules from C5
Procedure 9: Boosting and Recalling in C5
Procedure 10: Creating a Gradient Boosting Machine
Procedure 11: Recalling a Gradient Boosting Machine
Module 11: Naive Bayesian Classifiers and Laplace Estimator
Slides
Procedure 1: Converting Continuous Data to Categorical Data
Procedure 2: Training a Naive Bayesian Classifier
Procedure 3: Recalling a Naive Bayesian Classifier for P
Procedure 4: Recalling a Naive Bayesian Classifier for Classification
Procedure 5: Create a Naive Bayesian Network with a Laplace Estimator
Module 12: Norsys Netica and Bayesian Analysis
Slides
Procedure 1: Create a New Canvas, add a Dependent Variable and an Independent Variable
Procedure 2: Set States attributed to the Dependent and Independent Variables
Procedure 3: Link Variables as causes consequence
Procedure 4: Enter subjective probabilities for each consequence
Procedure 5: Manually setting node states to predict and explain
Procedure 6: Netica Discretisation of Continuous Variable
Procedure 7: Learn node probabilities
Procedure 8: Test Classification Accuracy of a Bayesian Network
Procedure 9: Add Nodes Automatically to a Canvas
Procedure 10: Learn TAN Structure to Link Nodes Automatically
Module 13: Neural Networks
Procedure 1: Train a Neural Network
Procedure 2: Plotting a Neural Network
Procedure 3: Recalling a Neural Network with compute() and understanding performance
Procedure 4: Training a Deeper Neural Network
Procedure 5: Training a Classification Model
Module 14: Deep Learning with H20
Procedure 1: Install H2O package, instantiate and browse to the Flow User Interface
Procedure 2: Loading Data into H2O with Flow
Procedure 3: Creating a Logistic Regression model in H2O (GLM)
Procedure 4: Recalling a Logistic Regression model with Flow
Procedure 5: Loading Data into h2O with R
Procedure 6: Creating a Neural Network with R
Procedure 7: Recalling a Neural Network with R
Module 15: Monte Carlo Model Simulation
Slides
Procedure 1: Create Discrete Vectors with triangle for each model parameter
Procedure 2: Process Random Data Frame against Neural Network Model
Procedure 3: Filter Data Frame for Activations and Produce Summary Statistics to prescribe
Module 16: R Plumber
Slides
Welcome
Module 6: ggplot2 Rapid Exploration
Slides
