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    Evolutionary Ecophysiology swirl lesson
    Uses swirl to walk students through how to use R and analyze their own data.
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    Binomial test
    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.
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    An introduction to population matrix models: a swirl lesson
    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...
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    Genome Sequence Data in R using Biostrings (Swirl Lesson)
    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...
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    Histograms and Boxplots
    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.
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    Hardy-Weinberg Equilibrium: a swirl resource
    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...
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    Cleaning Data with R and the Tidyverse in Swirl
    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...
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    Importing Data into R
    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...
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    Population Demography in Swirl
    The students will learn to generate, test, and graphically represent basic hypotheses on data distributions using large datasets.
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    Island Biogeography
    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.
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    Two-Way ANOVA
    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...
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    Tree Biomass and Phenology
    This lesson centers around understanding the connection of tree biomass and phenology with climate change. It uses both swirl and LearnR lessons in the R programming language to help students make...
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    R Subsetting Tutorial
    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.
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    REMNet Tutorial, Excel Part 4: Focusing on Taxonomy 4.26.19
    Video on analyzing microbiome data in Excel from the Research Experiences in Microbiomes Network
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    Sampling Distributions and Null Distributions: two swirl lessons in R
    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...
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    Basic Statistics
    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.
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    Exploring the functionality of R
    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.
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    Single Factor Analysis of Variance (ANOVA)
    This swirl lesson introduces students to theoretical and practical concepts about single-factor ANOVA analysis and an approach to conduct this type of analysis using R.

    Keywords: ANOVATukey HSD

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    Making Predictions with Linear Models: A Murder Mystery Case Study
    This Swirl lesson will introduce two main concepts to students: 1) the idea of uncertainty around linear slopes versus individual values and 2) predicting values using linear models.
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