Syllabus
BLOCK I - GETTING STARTED:
Introduction to R and RStudio (Course info, R basics)
Creating a dataset (Intro, Data structures, Data input)
Basic data management (Basic data management)
Advanced data management (Math & Stats functions, Data-management challenge, Control-flow and others)
Getting started with graphs (ggplot basics, ggplot details)
BLOCK II - BASIC METHODS:
Basic graphs (Bar charts & tree maps, Histograms, kernels, box plots)
Basic statistics (Descriptive statistics, Contingency tables, Correlations, Parametric and non-parametric tests)
BLOCK III - INTERMEDIATE METHODS:
Regression basics (Types of regression, Picking the "best" model, Cross-validation)
Analysis of variance (Terminology, One-way ANOVA, Other ANOVA types)
Intermediate graphs (Scatter plots, Line charts, correlograms, mosaic plots)
Annotation
An introductory course to data analysis in R. The course covers the basics of practical programming in the R environment, including data structures, data manipulation, graphs, graphical outputs, basic statistics, variance analysis, test powers, bootstrapping, and component and factor analysis.