Course Overview

Modules

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Insurmountable Coding Problems


Week 01 (2021-09-03)


An Introduction to R

Topics
  • History of R
    • Relation to other languages and statistics software
  • Installing R and RStudio
  • Setting up your workspace
    • Panels: Source, Console, Environment/History, Other Views
    • Setting the working directory
    • Setting up R projects
    • Saving workspaces
  • R Basics
    • Using R interactively
    • Variables and assignment
    • Packages
    • R objects
      • Object types - Vectors and functions
      • Classes and attributes of objects
      • Scripting
  • Setting up GitHub
    • Interfacing between R and GitHub
{swirl} Tutorials
  • R Programming
Required Readings
The Book of R
  • Chapter 1 - Getting Started
  • Chapter 2 - Numerics, Arithmetic, Assignment, and Vectors
R in Action
  • Chapter 1 - Getting Started
  • Chapter 2 - Creating a Dataset
Statistics: An Introduction Using R
  • Chapter 1 - Fundamentals
Teamwork-Related Readings
Useful But Optional Readings
R Programming for Data Science
  • Chapter 3 - History and Overview of R
  • Chapter 5 - R Nuts and Bolts
Statistics: An Introduction Using R
  • Appendix: Essentials of the R Language

Week 02 (2021-09-10)


Homework 1 assigned - DUE 5pm 2021-09-16

Data Science Preliminaries

Topics

  • Good programming practices
    • Version control
    • The Tao of text
    • Reproducible research using Rmarkdown and {knitr}
  • Working with data
    • More object types - arrays, matrices, lists, and data frames
    • Subsetting and filtering data structures
    • Factors
    • Class coercion and conversion
    • Special data values - NA, NaN, Inf
  • Getting data in and out of R
    • From csv files - {readr}
    • From Excel - {readxl}, {XLConnect} and others
    • From Dropbox - {rdrop2}
    • From other repositories - {curl}
    • From databases - {RMySQL}, {RSQLite}, {RPostgreSQL} and others
  • Summarizing data and exploratory data analysis
    • Basic descriptive statistics
    • Simple plotting (boxplots, histograms, scatterplots) - {ggplot2} and others

{swirl} Tutorials

  • Getting and Cleaning Data:
    1. Manipulating Data with {dplyr}
    2. Grouping and Chaining with {dplyr}
    3. Tidying Data with {tidyr}

Required Readings

The Book of R
  • Chapter 3 - Matrices and Arrays
  • Chapter 5 - Lists and Data Frames
R in Action
  • Chapter 3 - Getting Started with Graphs
  • Chapter 4 - Basic Data Management
R Programming for Data Science
  • Chapter 6 - Getting Data In and Out of R
  • Chapter 7 - Using the {readr} Package
Teamwork-Related Readings

Useful But Optional Readings

The Book of R
  • Chapter 4 - Non-Numeric Values
  • Chapter 6 - Special Values, Classes, and Coercion
  • Chapter 8 - Reading and Writing Files
R Programming for Data Science
  • Chapter 9 - Interfaces to the Outside World

Week 03 (2021-09-17)


Homework 2 assigned - DUE 5pm 2021-09-30

Statistics Fundamentals - Exploratory Data Analysis, Central Tendency, and Variance

Topics

  • Populations and samples
  • More on summarizing data
    • Percentiles, quantiles, confidence intervals
    • Basic visualization techniques for exploratory data analysis
      • Box plots
      • Histograms
  • Beginning R programming
    • Functions
    • Loops
    • Conditional statements and flow control

{swirl} Tutorials

  • Getting and Cleaning Data (all exercises)

Required Readings

The Book of R
  • Chapter 9 - Calling Functions
  • Chapter 10 - Conditions and Loops
  • Chapter 11 - Writing Functions
R in Action
  • Chapter 5 - Advanced Data Management
  • Chapter 6 - Basic Graphs
  • Chapter 7 - Basic Statistics
    • 7.1 Descriptive Statistics
    • 7.2 Frequency and Contingency Tables
Statistics: An Introduction Using R
  • Chapter 3 - Central Tendency
  • Chapter 4 - Variance
Teamwork-Related Readings

Useful but Optional Readings

The Book of R
  • Chapter 13 - Elementary Statistics
  • Chapter 14 - Basic Data Visualization

Week 04 (2021-09-24)


Statistics Fundamentals - Probability and Distributions

Topics

  • Probability and conditional probability
  • Random variables - dicrete and continuous
  • Probability mass functions, probability density functions
  • Cumulative probability function
  • Useful distributions and their properties
    • density (d), cumulative probability (p), quantile (q), and random (r) functions
    • Discrete
      • Bernoulli
      • Poisson
      • Binomial
    • Continuous
      • Beta
      • Uniform
      • Normal
  • Q-Q Plots
  • Standard errors and confidence intervals

Required Readings

The Book of R
  • Chapter 15 - Probability
  • Chapter 16 - Common Probability Distributions
Project-Related Readings

Useful but Optional Readings

R Programming for Data Science

Week 05 (2021-10-01)


Homework 3 assigned - DUE 5pm 2021-10-14

IMPORTANT! Today is the deadline for choosing a paper from the primary literature to replicate analyses.

You can find instructions for the replication assignment here!

Statistical Inference and Basic Hypothesis Testing

Topics

  • Significance and p values
  • Classic hypothesis testing
    • One- and two-sample T and Z tests
  • Type I and Type II error
  • Statistical power, effect sizes

Required Readings

The Book of R
  • Chapter 17 - Sampling Distributions and Confidence
  • Chapter 18 - Hypothesis Testing (through section 18.3)
Statistics Done Wrong
  • Chapter 1 - An Introduction to Statistical Significance
Project-Related Readings

Useful but Optional Readings

Statistics: An Introduction Using R
  • Chapter 5 - Single Samples
  • Chapter 6 - Two Samples

Week 06 (2021-10-08)


Introduction to Linear Regression

Topics

  • Correlation and covariation
  • Introduction to linear modeling
    • Continuous random predictor and reponse variables
    • Simple linear regression (1 predictor and 1 reponse variable)
    • Estimating and interpreting regression coefficients
    • Model I and Model II regression
    • The lm() function
    • Interference in regression
      • Confidence intervals and prediction intervals
    • Residuals

{swirl} Tutorials

  • Regression Models (all exercises)

Required Readings

The Book of R

  • Chapter 20 - Simple Linear Regression

R in Action

  • Chapter 8 - Regression (through section 8.2)

Useful but Optional Readings

Statistics: An Introduction Using R

  • Chapter 7 - Regression

Week 07 (2021-10-15)


Homework 4 assigned - DUE 5pm 2021-10-28

Seminar will be held on Tuesday, in observance of Indigenous Peoples’ Day.

Elements of Regression Analysis

Topics

  • Inference in regression
    • Estimating standard errors for regression cofficients
  • Model checking
  • Partitioning of variance in linear models
  • Data transformations

Required Readings

The Book of R

  • Chapter 20 - Simple Linear Regression
  • Chapter 18 - Hypothesis Testing (section 18.4)

R in Action

  • Chapter 8 - Regression (sections 8.3 through 8.8)

Week 08 (2021-10-22)


Basic Categorical Data Analysis and ANOVA

Topics

  • Regression with categorical predictors
  • One- and multiple-factor ANOVA
  • Type I, Type II, Type III sums of squares
  • Simple categorical data analysis
    • Kruskal-Wallis tests
    • Chi-Square tests of goodness-of-fit and independence
  • Interaction plots to visualize changes across groups

{swirl} Tutorials

  • ANOVA

Required Readings

The Book of R

  • Chapter 18 - Hypothesis Testing (section 18.4)
  • Chapter 19 - Analysis of Variance

R in Action

  • Chapter 9 - Analysis of Variance

Useful but Optional Readings

Statistics: An Introduction Using R

  • Chapter 8 - Analysis of Variance

Week 09 (2021-10-29)


Multiple Regression and ANCOVA

Topics

  • Generating mock data with a defined correlation structure
  • Regression with multiple predictors
    • More than one continuous predictor
    • Combinations of continuous and categorical predictors
  • Visualizing linear models with more than one predictor
  • Confidence intervals and prediction in multiple regression
  • Interactions between predictors

Required Readings

The Book of R

  • Chapter 21 - Multiple Linear Regression

Useful but Optional Readings

Statistics: An Introduction Using R

  • Chapter 9 - Analysis of Covariance

Week 10 (2021-11-05)


Homework 5 assigned - DUE 5pm 2021-11-11

IMPORTANT! Deadline for choosing your team and topic for group statistical methods presentation and vignette

Model Selection, Generalized Linear Modeling, and Mixed Effects Modeling

Topics

  • Model simplification and selection
    • The Akaike Information Criterion (AIC) and others
      • {stats} step()
      • {MASS} stepwise()
      • {AICcmodavg}
  • Generalized Linear Modeling
    • Dealing with other response variables types - counts, binary responses
  • Introduction to mixed effects modeling and nonlinear models
    • Assessing model fit for GLMs and mixed models using {MuMIn}
  • Check-in on group project team-building and development.

Required Readings

R in Action

  • Chapter 8 - Regression (section 8.6)
  • Chapter 13 - Generalized Linear Models

Statistics: An Introduction Using R

  • Chapter 12 - Other Response Variables

Week 11 (2021-11-12)


Same readings as last week, plus:

Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, M.H.H., White, J-S.S. (2008) Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution 24(3):127-135.


Week 12 (2021-11-19)


A Very Brief Introduction to Bayesian Methods

Topics

  • Bayesian vs. frequentist statistics
    • Basics and Bayes’ Theorem
    • Priors (when they matter and when they don’t)
  • Implementing Bayesian (regression) models in R
    • Using {MCMCglmm}
    • Burn-in, iterations, and intervals
    • Model assessment
  • Implementing Bayesian models in the {rethinking} package framework

More details coming soon!

  • Check-in on group project team-building and development.

Useful but Optional Readings


Week 13 (2021-11-26)


NO CLASSES, THANKGIVING BREAK


Week 14 (2021-12-03)


Group Presentations and Vignettes

Module 22 - TBA

Module 23 - TBA


Week 15 (2021-12-10)


Group Presentations and Vignettes

Module 24 - TBA

Module 25 - TBA