R
Course Contents
page
Session 1: R
Introduction and References
Notation Conventions used throughout the course Starting R for Windows
Getting Help
The R Console
The Menu bar
The Toolbar
Practical Session 1
1. Objective of this exercise Starting R
Accessing built-in datasets A simple analysis
Viewing output
Printing and saving output Leaving R
2. Creating your own R Data File Using the Data Editor
The Data
Defining the Variables Entering the Data Saving the Data
Some Descriptive Statistics Exiting R

Session 2: Importing Data Files from Text Formats Reading a raw data file
Importing Text Files
A note about variable names in R
Notes on the Data Editor: Adjusting Column widths Missing Values
Importing an Excel file to R Practical Session 2
1. Reviewing the demonstration
2. Importing an ASCII file, defining labels & missing values
British Social Attitudes Survey, 1987:Coding Sheet

Session 3: Data and Language Structures R as a calculator
Vectors and assignment
Generating Sequences
Character Vectors
Arithmetic Operators
Comparison Operators
Extracting Data by using an index  3-7
Simple and Complex Objects  3-9
Loops and Conditional Execution  3-10
1. Conditional Statement: If Statement  3-10
2. Iteration: for, while  3-12
Practical Session 3  3-13
 1 . Reviewing the demonstration  3-13
2. Operators and Subscripts  3-13
3. Loops and Conditional Execution  3-14

Session 4: Crosstabulation and Recode
Crosstabulation in R  4-2
Recoding  4-4
How to recode  4-5
Recoding into a different variable  4-5
Recoding AGE into age groups (Recode Ranges)  4-6
Missing values and recoding  4-7
Variable labels  4-8
Practical Session 4  4-9
1. Reviewing the demonstration  4-10
2. Age and gender and government spending  4-10
3. Political identification and age  4-11

Session 5: Computing New Variables
Numerical Expressions  5-2
Missing values and compute  5-5
Other types of variables  5-6
More on data frames  5-8
The function tapplyt)  5-9
Arrays and matrices  5-11
Index arrays  5-13
Manipulation of Arrays and Matrices  5-15
Practical Session 5  5-18
1. Reviewing the demonstration  5-18
2. Computing new variables/ producing bar charts  5-18
3. Computing new matrices  5-19

Session 6: Selecting, Sampling Cases and Functions
Selecting Cases  6-2
Selecting the whole set of cases again  6-4
Sampling Cases  6-4
Split Data Frame Command  6-5
Functions  6-7
Practical Session 6  6-9
1. Reviewing the demonstration  6-9
2. Mobility Tables  6-9
3. Subdividing by sex  6-10
4. Writing functions  6-10
Session 7: Numerical and Graphical Summaries of Data
Summary Statistics  7-2

Histograms Box-Plots
Bar Charts
Scatter Plots Practical Session 7
1. Reviewing the demonstration
2. Further Graphs
3. Exploring the age of the respondents
4. Investigating the education of respondent and family

Session 8: R-Commander, Two-Sample T-Tests and ANOVA

R Commander Example
Running R Commander automatically
Running Scripts in R
Two-Sample t-test- Comparing Two Independent Means Paired t-test for Means - Comparing Two Dependent Means One-Way ANOVA
Pairwise Comparisons

Very simple linear regression Practical Session 8
1. Reviewing the demonstration
2. Analysis of the STATLABA data
3. Consolidation

Session 9: The General linear model and graphics An example: block designs
Model simplification
More on linear regression More on graphics Practical Session 9

Session 10: Additional topics 1. The Chi-Squared (Z2) test Cross-tabulation
Observed and Expected Frequencies
Comparing Observed and Expected Frequencies the x2 Test
Degrees of Freedom
2. Logistic regression The glmO function Modelling proportions
Binary logistic regression - model selection
3. Survival Analysis
Functions used in Survival analysis
Kaplan Meier estimate of the Survival Function Cox Proportional hazards model
Parametric models

yahoocom  金币 +1  奖励 2009-4-26 21:54:58