Using R for statistics
The Basics
Data Tables
— A good guide to getting started with data.tables

Making and Using Lists
— Everything I could find about lists in R.

Read Files Function
— a short function I wrote that will read all files in a directory and give them the same name in the global environment.

Creating New Variables
— Creating variables, categories, binary variables, factors. Cutting data into those categories.

Editing Text Variables
— Using grep, grepl, gsub, sub, and strsplit to modify character strings.

Managing Data Frames with dplyr
— Subset and reshape data using dplyr

Merging Data
— How to merge two data sets using merge and plyr

The apply series: apply, lapply, sapply, tapply
— Using the apply series to run a function across a matrix.

Reshape
— Using Reshape to transform data and plot it.

Dates and times: POSIXct & POSIXlt
— Dates and times using POSIXct and POSIXlt

Working With Dates
— Using date() and lubridate to create dates.

Filling In An Area Under A Curve
— Using ggplot2 to plot a curve and then color in a section of the plot.

Multiplot
— Using the multiplot function with ggplot2

Reinstall R packages during R reinstall
— An easy way to reinstall R packages when reinstalling R.

Finding Peak Values For a Density Distribution
— Use density and other tricks to find the high and low points in a distribution

Central Limit Theorem
— What is the Central Limit Theorem. Sample Means. Other stuff.

Coin Toss Probability
— The probability of winning 19 out of 25 coin tosses.

Confidence Intervals When Sigma Is Known
— Confidence Intervals and Sample Size

Confidence Intervals Part 2: Using TDistributions When Sigma Is Unknown
— Confidence Intervals TDistributions, Beer.

Confidence Intervals Part 3: Confidence Intervals With Proportions
— Proportons and Sample Size

Confidence Intervals Part 4: Using the ChiSquared Distribution
— Sample Size and Confidence Intervals for Standard Deviation and Variance

Binomial Distribution
— use the binom series of commands and plot binomial distributions with the base graphing package and ggplot2

Hypergeometric Distribution
— Calculating distributions without replacement.

Hypothesis Testing Part 1
— Testing the difference between two samples.

Linear Regression
— Finding Linear Regressions, using cor, cor.test, lm, predict, and confint.

Multinomial Distribution
— Distributions with more than two outcomes.

Normal Approximation to the Binomial Distribution
— Find a binomial probability with a continuous distribution

Normal Distribution
— Finding zscores, using norm and tigerstats functions

Permutations and Combinations
— Using R to create permutations and combinations

Poisson Distribution
— Working with poisson distributions.

Poisson Distribution Example
— An example with horses

Two Way ANOVA
— Two Way ANOVA by hand and using R

PM 2.5 Emissions
— Using the maps package to display emissions data on a map.

PM 2.5 Emissions  Part 2
— Using the maps package to display emissions data on a map.