Online Program

Outline & Objectives (302867)

I. Introduction – An introduction to R: R syntax and data structures; working interactively and in batch; alternative IDEs and GUIs; adding functionality through packages; common programming mistakes; getting unstuck – were to find answers to your questions.

II. Data Management – Importing, cleaning, and reformatting data: transforming and recoding variables; subsetting, merging, and aggregating data; control structures; user-written functions.

III. Graphics – Taking advantage of R’s powerful graphics: creating basic and advanced graphs; customizing and combining graphs; innovative methods for visualizing complex data.

IV. Statistical Analysis and Data Mining – Using R for description, prediction, and classification: descriptive statistics and multi-way tables; ANOVA variants; regression (e.g., linear, logistic, Poisson), classification trees, cluster analysis, and other multivariate methods; dealing effectively with missing data; Going further.