AN/BI 588: Course Outline

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Course Overview
Modules
Assignments
Resources
Policies
Insurmountable Coding Problems

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Week 01 (2023-09-07)

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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

• The Programmer’s Guide to Working on a Team
• Important Steps when Building a new Team
• Hello World! A new grad’s guide to coding as a team

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

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Week 02 (2023-09-14)

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Homework 1 assigned - DUE 5pm 2023-09-20

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

• 8 Tips for Giving Great Peer Feedback
• How to Give Negative Feedback Without Sounding Like a Jerk

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

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Week 03 (2021-09-21)

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Homework 2 assigned - DUE 5pm 2023-10-04

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

• How to Handle Reviewer Comments.
• Putting comments in code: the good, the bad, and the ugly.

Useful but Optional Readings

The Book of R

• Chapter 13 - Elementary Statistics
• Chapter 14 - Basic Data Visualization

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Week 04 (2023-09-28)

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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

• King, G. 1995. Replication, Replication. PS: Political Science & Politics, 28, 444-452.
• King, G. 2006. Publication, Publication. PS: Political Science and Politics, 36(1), 119-125. - although our goal isn’t necessarily to publish our replication studies, please note many of the guidelines listed herein!

Useful but Optional Readings

R Programming for Data Science

• Chapter 22 - Simulation

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Week 05 (2023-10-05)

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Homework 3 assigned - DUE 5pm 2023-10-18

IMPORTANT! Today is the deadline for choosing a paper from the primary literature from which 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

• Turner, T.R. & Mulligan C.J. 2019. Data sharing in biological anthropology: Guiding principles and best practices. Am J Phys Anthropol, 170(1), 3-4.

Useful but Optional Readings

Statistics: An Introduction Using R

• Chapter 5 - Single Samples
• Chapter 6 - Two Samples

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Week 06 (2023-10-12)

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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

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Week 07 (2023-10-19)

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Homework 4 assigned - DUE 5pm 2022-11-02

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)

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Week 08 (2023-10-26)

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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

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Week 09 (2023-11-02)

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

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

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Week 10 (2023-11-09)

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Homework 5 assigned - DUE 5pm 2023-11-22

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

Useful but Optional Readings

• Bolker, B.M. (2008). Chapter 9. Standard statistics revisited. In: Ecological Models and Data in R. Princeton, NJ: Princeton University Press.
• Quinn, G. P., & Keough, M. J. (2002). Chapter 13. Generalized linear models and logistic regression. Experimental Design and Data Analysis for Biologists. Cambridge, UK: Cambridge University Press.
• Package descriptions for {AICcmodavg} and {MuMIn}

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Week 11 (2023-11-16)

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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.

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Week 12 (2023-11-23)

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OPTIONAL FOR THANKSGIVING BREAK

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.

Readings

• Ellison, A.M. (1996). An introduction to Bayesian inference for ecological research and environmental decision-making. Ecological Applications 6(4):1036-1046.

Useful but (also )Optional Readings

• Hadfield, J. (2016) MCMCglmm Course Notes
• Package descriptions for {MCMCglmm} and {rethinking}

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Week 13 (2023-11-30)

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Group Presentations and Vignettes

Module 22 - TBA

Module 23 - TBA

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Week 14 (2023-12-07)

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Group Presentations and Vignettes

Module 24 - TBA

Module 25 - TBA

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