Introduction to R: R as calculator (7:52: R as calculator, assignment operators, basic arithmetic operations, Boolean (T/F)).
Introduction to R: R is vectorized (6:08: Over writing variable assignments, R is vectorized)
Introduction to R: Comparing Vectors, Making matrices (6:29: Comparing vectors, Making matrices, data structures in R, attributes of objects)
Introduction to R: introducing objects and classes in R (8:05: Objects, classes, the basics.)
Introduction to R: workspaces getting help (6:53: looking at objects in workspace, removing objects, saving workspace, help functions in R)
Introduction to R: functions in R (8:20) (writing our own functions, what script editor (IDE) might you wish to use)
Introduction to R: indexing, regular sequences (15:39)
Introduction to R: Getting your data in to R (24:37)
Control flow in R (17:16, if statements, else if, ifelse(), for loops, strsplit())
the apply family of functions in R (21:10: apply, sapply, tapply, by)
Introduction to the General Linear model in R part 1
Introduction to linear models using R part 2
Introduction to linear models using R part 3
Interpreting the parameters in models with multiple predictors
Examining colinearity among predictor variables using R
Basic diagnostics for the general linear model using R
Basic probability calculations in R
Basic probability calculations in R, part II
Where do the Chi-squared and F distributions come from
The absolute basics for doing simulations
Screencast - Generating Monte Carlo Confidence Intervals part I ~20m minutes.
Screencast - Monte Carlo simulations under null models. How to generate monte carlo simulation models under null models, and using them to get simple things like p-values (we have to start somewhere).
Screencast - Generating Monte Carlo Confidence Intervals part II
Screencast - Using the sample() function for resampling - Basics (8:05) how to use the sample function to do sampling with and without replacement.
Screencast - Introduction to Permutation tests in R Performing simple permutation tests for simple linear models.
Screencast - Introduction to the non-parametric bootstrap using R How to use simple non parametric bootstrap to construct SE, CIs and for simple hypothesis testing.
Introduction to model fitting with Maximum Likelihood estimation using R
Slightly more advanced model fitting with MLE in R
Some tutorials on fitting and intepreting mixed models in R using the lme4 library (and other tools)
Fitting linear mixed models using lmer() - This tutorial is a bit on the long side!
Fitting a longitudinal model using lmer() Based on a classic example from Douglas Bates.
Information and Model Selection Lecture part 1.
Information and Model Selection Lecture part 2
Information and Model Selection Lecture part 3
Information and Model Selection Lecture part 4
Information and Model Selection Lecture part 5
Information and Model Selection Lecture part 6- multimodel inferences.
- Screencast on using lists, lapply, do.call, unlist, etc (i.e. all of the headaches with lists).
- Screencast on data munging with base R (including reshape)
- screencast on data munging with dplyr, and reshaping with reshape library, maybe tidyr.
- functions, scopes and environments. Or just send them to here
- permutations using residuals?
Some other nice tutorials to try