r tutorial slides

7/28/2019 R Tutorial Slides

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R TutorialCapital One Data Mining Cup

UW Statistics Club

Saturday, March 23, 2013


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Who Will Benefit From This?

Aimed at Students who

Have the statistical background but lack the (R) modelling expertise

Never taken a linear regression course (or simply forgot the one they did!)


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What We Will Be Doing Today

Walkthrough example of a statistical prediction problem using Kaggle testdata (Titanic problem)

The goal is to predict who will survive given different factors such as

Age

Ticket Fare

Sex

Cabin

Number of family aboard
http://www.kaggle.com/http://www.kaggle.com/


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R Basics

Opening R (RStudio)

Navigating to the working directory

Running commands

Installing packages

Loading packages


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Basic Guideline to Data Analysis

1. Define the question

2. Define the ideal data set

3. Determine what data you can access

4. Obtain the data

5. Clean the data

6. Exploratory data analysis

7. Statistical prediction/modelling8. Interpret results

9. Challenge results

10. Synthesize/write up results

11. Create reproducible code


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Cleaning the Data (skipped)

Fix variable names

Merge data sets

Fix missing content

Fix inconsistent data


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Exploratory Data Analysis

Make use of

Aggregation Tables

Charts

We use two different R packages here:ggplot2, plyr


8/13

Testing Your Model

Before we build our model we need to have a methodology on how we will test it.

A nave analyst would use the entire data set to build the model and then test it on the samedata set. This causes overfitting!

Instead: partition training data set into a real training set and a validation set. To createvalidation set use:

Random sub-sampling

K-fold

Leave-one-out

What measurement do we use to compare?

Adjusted 2, AIC, BIC


9/13

Building Our First Model - Simple Linear

Regression

Why is this a good starting point?

Easy to implement in R

Black box (i.e. no tuning parameters)

Easy to interpret/explain

Disadvantage: performs poorly in non-linear setting


10/13

Building Our First Model - Simple Linear

Regression

After we have run our first model we want to:

Examine Residuals plot

Examine Q-Qplot

Use the Model Testing process to pick a proper model

Using the step function in R


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Understanding Interaction (optional)


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Checking for Multicollinearity (optional)

Multiple predictor variables are highly correlated

Can be caused by:

Creating a new predictor variable from existing ones

Having multiple predictors that explain the same thing

Consequence: standard error blows up on estimate

Use R to compute correlation between all predictors. If there exists sets ofpredictors above 0.90 0.95 then either:

Remove all but one

Combine into a new composite variable


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What Next?

Taking our Simple Linear Regression to the next level

Higher order terms

Interaction terms

Data Transformations

Check for multicollinearity

Different Types of Models (not covered here but check the R Code!)

Generalized Linear Models

Trees

Random Forest

Ensemble Methods

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