Course
Overview
Modules
Assignments
Resources
Policies
Insurmountable
Coding Problems
Week 01 (20230907)
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
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
TeamworkRelated 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
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:

Manipulating Data with {dplyr}

Grouping and Chaining with {dplyr}

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
TeamworkRelated Readings
Useful But Optional Readings
The Book of R

Chapter 4  NonNumeric 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
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

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
TeamworkRelated Readings
Useful but Optional Readings
The Book of R

Chapter 13  Elementary Statistics

Chapter 14  Basic Data Visualization
Week 04 (20230928)
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

QQ Plots

Standard errors and confidence intervals
Required Readings
The Book of R

Chapter 15  Probability

Chapter 16  Common Probability Distributions
ProjectRelated Readings
Useful but Optional Readings
R Programming for Data Science
Statistical Inference and Basic Hypothesis Testing
Topics

Significance and p values

Classic hypothesis testing

One and twosample 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
ProjectRelated Readings
Useful but Optional Readings
Statistics: An Introduction Using R

Chapter 5  Single Samples

Chapter 6  Two Samples
Week 06 (20231012)
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
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 (20231026)
Basic Categorical Data Analysis and ANOVA
Topics
 Regression with categorical predictors
 One and multiplefactor ANOVA
 Type I, Type II, Type III sums of squares
 Simple categorical data analysis
 KruskalWallis tests
 ChiSquare tests of goodnessoffit and independence
 Interaction plots to visualize changes across groups
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 (20231102)
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
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}
 Checkin on group project teambuilding 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
Week 11 (20231116)
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, JS.S. (2008) Generalized linear mixed models: a
practical guide for ecology and evolution. Trends in Ecology and
Evolution 24(3):127135.
Week 12 (20231123)
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}
 Burnin, iterations, and intervals
 Model assessment
 Implementing Bayesian models in the {rethinking} package
framework
More details coming soon!
 Checkin on group project teambuilding and development.
Useful but (also )Optional Readings
Week 13 (20231130)
Group Presentations and Vignettes
Week 14 (20231207)
Group Presentations and Vignettes