How to use functions in base R and the dplyr package to construct a new dataframe with summary statistics.

For the beginnings of your R adventures. Does not cover any analysis, Only the basics of loading packages and data.

Plotting using both base R and the package ggplot2. Includes barplot (with error bars), boxplot, scatterplot, and interaction plot. A starting point for many flexible and sensible plots.

Intro to nonparametric tests including the Wilcoxon (Mann-Whitney U) and Kruskal-Wallis tests. Also a Nemenyi post-hoc test.

Conducting a Multiple Linear Regression including assumptions testing, running the test, model comparison using AIC, and calculating Variable Inflation Factors (VIFs).

How to conduct a simple linear regression including assumptions testing, running the test, and basic plotting.

How to efficiently explore your data including options to view data, data structure and summaries, scatterplots, scatterplot and correlation matrices, histograms, and quantile (q-q) plots.

Basic data management in R including summarizing data by groups (e.g. means for each group), creating matrices (by average value, abundance, and presence/absence), and subsetting using base R.

ANCOVA (ANalysis of COVAriance) including assumptions testing, running an ANCOVA, and post-hoc comparisons.

One and two-way ANOVA and t-test including assumptions testing, running the tests, and post-hoc comparisons.

Using `dplyr` functions to subset data (and create new datasets) from a master dataset.

Full set of steps to go from community composition data to complete NMDS analysis and plotting.

Steps to calculating species averages for traits.

Sample code for calculating trait CWMs using species abundance as weights.

This tutorial uses the tidyr package to help users convert long form data to wide form data. For example, stacked to matrix style.