The students will learn to generate, test, and graphically represent basic hypotheses on data distributions using large datasets.

The students will practice identifying the appropriate basic statistical tests when given a scenario and learn how to run and interpret those statistical tests in R.

This swirl lesson will show you how to rearrange your data within R from one that shows a multiple factor design (2 x 2) to a single factor (4 levels) to allow for data analyses that require non-parametric statistics.

Students will learn how to set up a population matrix model in R and use it for demographic analysis of a population, including projecting population growth, determining lambda and the stable age distribution, and conducting an elasticity analysis.

Students will use swirl to understand Hardy-Weinberg equilibrium. The lesson starts with observed numbers of individuals for each genotype, and students will work through a number of steps to assess whether or not the population is in equilibrium.

We discuss what tests are appropriate to analyze the data from nutrient simulation experiments. They will conduct one-way ANOVAs and a post hoc test using a dummy dataset and interpret the results.

The overall goal is to help students learn common data cleaning procedures on a dataset once they’ve collected measurements and before they are able to start their analysis. This swirl lesson is designed specifically to deal with missing data.

By the end of this lesson, students should be able to load FASTA files into R as DNAStringSets and use width() and alphabetFrequency(), combined with other functions like sum() and mean(), to evaluate genome assembly quality and nucleotide frequencies. 

In this lesson, students will have the opportunity to work through a chi-squared test of independence between two categorical variables.

The students will learn to estimate population sizes and consider assumptions of mathematical models and their applicability to the ecology of an organism/population

This lesson helps students know some of the options for how to graph grouped continuous data (such as those involved in doing a t-test or ANOVA) and how to choose the best option.

This lesson offers an introduction to ANOVA, including 1) how this statistical test can be differentiated from others and 2) a step-by-step guide to conducting and interpreting ANOVA results in R, including assumption testing and post-hoc analysis.

Interpreting One-Factor Analysis of Variance (ANOVA)

How to use the "three-prong" approach to check for normality

One-way analysis of variance (ANOVA), following the text in Chapter 5 of Beckerman et al. (2017) Getting started with R: an introduction for biologists, 2nd edition.

The students will practice the basic code to test MacArthur and Wilson’s (1967) Island Biogeography model, focusing on how island size, distance, and perturbation affect species numbers.

The students will practice manipulating data to add to or extract subsets of specific values, rows, columns, or subsets of data contained in existing data files.

This lesson centers around the marginal value theorem (MVT, Charnov 1976), which describes how animals should forage in patches. It serves as a pre-lab to teach MVT basics, vectors, ANOVA, and basic plotting.

The goal of this lesson is to learn how to import datasets into R, understand variable types, make adjustments to variables, perform basic calculations, and begin data visualization. The exercise uses an over 100 year time series of climate data.

There are two complementary lessons. The first covers how sampling distributions are made and ID's their key properties. The second covers how null distributions and how the test statistic of a dataset and its p-value are visualized on this distribution.

This short swirl lesson on importing data is designed to 1) get a basic understanding in how tables are read into R and some common issues and 2) develop an individual set of instructions for students to use later to import a table on their own.

The lesson is designed to 1) teach students about the concept of logistic population growth, growth rates, and carrying capacity and 2) provide a basic introduction to ecological modeling and quantitative thinking in ecology (i.e., theoretical ecology)

This Swirl course uses ecological data of bird counts and habitat variables to estimate inter-observer variability and the relationship of birds with habitat variables.

Students work with example data to generate a length-weight plot and use lm() to obtain regression coefficients. Base plot functions are used to plot both the data and the regression line.

Uses swirl to walk students through how to use R and analyze their own data.

This lesson focused on introducing students to binomial data analysis and reinforcing concepts of confidence intervals, p-value interpretation, and one- vs two-sided tests.

In this lesson, students will learn the necessary statistical terms to understand two-way ANOVA, how to visualize their data, how to perform two-way ANOVA and interpret their results, and how to check for the ANOVA assumptions.

This lesson, created for an introductory ecology course, focuses on helping novice R users to import a data file, apply base R plotting functions, and use R Markdown to generate a reproducible report.