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.

Reading Data

• 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.

Subsetting & Transforming Data

• 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

• Dates and times: POSIXct & POSIXlt

  — Dates and times using POSIXct and POSIXlt

• Working With Dates

  — Using date() and lubridate to create dates.

Plotting

• 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

Extras

• 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

Statistics

• 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 T-Distributions When Sigma Is Unknown

  — Confidence Intervals T-Distributions, Beer.

• Confidence Intervals Part 3: Confidence Intervals With Proportions

  — Proportons and Sample Size

• Confidence Intervals Part 4: Using the Chi-Squared 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 z-scores, 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

Project

• 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.