Course Schedule

Part I - Using R and RStudio

An Introduction to R

Modules

• Module 01 - Getting Started with R
• Module 02 - Getting Started with RStudio
• Module 03 - Extending the Functionality of R
• Module 04 - Fundamentals of the R Language

Topics

• History of R
  • Relation to other languages and statistics software
• Installing R and RStudio
• Using R and RStudio through the cloud with Posit
• Setting up your RStudio workspace
  • Panels: Source, Console, Environment/History, Other Tabs
    • Configuration and customization
  • Setting the working directory
  • Saving workspaces
• R documentation and getting help
  • The ? command
  • Vignettes
  • Stack Overflow
• R Basics
  • Using R interactively
  • Variables and assignment
  • Packages
    • Installing and updating
    • Dependencies
  • R objects
    • Object types - Vectors, simple functions, and environments
    • Classes and attributes of objects
    • Scripting and sourcing scripts

Required Readings

• Introduction to Data Science
  • Chapter 1 - Introduction
  • Chapter 2 - R Basics
• R in Action, Second Edition
  • Chapter 1 - Getting Started
  • Chapter 2 - Creating a Dataset

Other Useful Readings

• The Book of R
  • Chapter 1 - Getting Started
  • Chapter 2 - Numerics, Arithmetic, Assignment, and Vectors
• R Programming for Data Science
  • Chapter 3 - History and Overview of R
  • Chapter 5 - R Nuts and Bolts
• Statistics: An Introduction Using R
  • Chapter 1 - Fundamentals
  • Appendix: Essentials of the R Language
• Advanced R, First Edition
  • Chapter 2 - Data Structures
• Modern Data Science with R
  • Appendix B: An Introduction to R and RStudio

Version Control and Reproducibility

Modules

• Module 05 - Basics of Version Control
• Module 06 - Reproducible Research Using RStudio
• Some recommendations on Programming Style

Topics

• Good programming practices
  • Version control with git and GitHub
  • Data workflow with R projects using local and remote repositories
  • Reproducible research using Quarto or RMarkdown documents and notebooks
  • Programming conventions and style

Required Readings

• Introduction to Data Science
  • Chapter 39 - Git and GitHub
• Essentials of Data Science
  • Chapter 11 - R with Style

Other Useful Readings

• Happy Git and GitHub for the useR
• Introduction to Data Science
  • Chapter 37 - Accessing the terminal and installing Git
  • Chapter 38 - Organizing with Unix
  • Chapter 40 - Reproducible projects with RStudio and Quarto/RMarkdown

Data Science Preliminaries

Modules

• Module 07 - Additional Data Structures in R
• Module 08 - Getting Data into R

Topics

• Working with data
  • The Tao of text
  • More object types - matrices, n-dimensional arrays, lists, data frames, and other tabular structures (e.g., data tables and “tibbles”)
  • Subsetting and filtering data structures
    • Single bracket ([]) notation
    • Double bracket ([[]]) notation
    • $ notation
  • 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} and others
    • From Dropbox, Box, and other cloud file storage
    • From other online resources - {curl}
    • From databases - {RMySQL}, {RSQLite}, {RPostgreSQL} and others

Required Readings

• The Book of R
  • Chapter 3 - Matrices and Arrays
  • Chapter 5 - Lists and Data Frames
• R in Action
  • Chapter 4 - Basic Data Management

Other Useful Readings

• The Book of R
  • Chapter 4 - Non-Numeric Values
  • Chapter 6 - Special Values, Classes, and Coercion
  • Chapter 8 - Reading and Writing Files
• Advanced R
  • Chapter 4 - Subsetting
• R for Data Science
  • Chapter 7 - Data Import

Exploratory Data Analysis

Modules

• Module 09 - Exploratory Data Analysis

Topics

• Summarizing and visualizing data
  • Basic descriptive statistics
  • Tidying and reshaping data with {tidyr}
  • Simple plotting (boxplots, histograms, scatterplots) with {base} R, {ggplot2}, and others

Required Readings

• Introduction to Data Science
  • Chapter 5 - The {tidyverse}
• R in Action
  • Chapter 6 - Basic Graphs
  • Chapter 7 - Basic Statistics

Other Useful Readings

• The Book of R
  • Chapter 13 - Elementary Statistics
  • Chapter 14 - Basic Data Visualization
• R for Data Science
  • Chapter 5 - Data Tidying

Data Wrangling and Programming

Modules

• Module 10 - Data Wrangling with {dplyr}
• Module 11 - Functions and Flow Control

Topics

• Manipulating data
  • {dplyr} functions - select(), filter(), arrange(), rename(), mutate(), group_by(), summarize()
  • Chaining and piping data with pipe operators (e.g., |>, &>%)
• R programming practices
  • Writing functions
    • Argument lists
    • Default values
  • Program flow control
    • Conditional statements (e.g., if () { } else { })
    • for() loops
    • while() loops

Required Readings

• Introduction to Data Science
  • Chapter 4 - Programming Basics

Other Useful Readings

• The Book of R
  • Chapter 9 - Calling Functions
  • Chapter 10 - Conditions and Loops
  • Chapter 11 - Writing Functions
• R for Data Science
  • Chapter 10 - Relational Data with {dplyr}
• R in Action
  • Chapter 5 - Advanced Data Management

Part II - Statistics and Inference

Beginning Statistics

Modules

• Module 12 - Beginning Statistics

Topics

• Populations and samples, parameters and statistics
• Describing central tendency, spread, and skew
• Standard errors and quantiles

Required Readings

• Modern Data Science with R
  • Chapter 7 - Statistical Foundations
• Introduction to Data Science
  • Chapter 15 - Random Variables
• Statistical Inference via Data Science
  • Chapter 7 - Sampling

Other Useful Readings

• Statistics: An Introduction Using R
  • Chapter 3 - Central Tendency
  • Chapter 4 - Variance

Probability and Distributions

Modules

• Module 13 - Probability and Distributions

Topics

• Probability and conditional probability
• Random variables - discrete and continuous
• Probability mass functions, probability density functions
• Cumulative probability function
• Some useful distributions and their properties
  • Distribution functions
    • Density (d)
    • Cumulative probability (p)
    • Quantile (q)
    • Random (r)
  • Discrete distributions
    • Bernoulli
    • Poisson
    • Binomial
  • Continuous distributions
    • Beta
    • Uniform
    • Normal
• Q-Q plots

Required Readings

• Introduction to Data Science
  • Chapter 14 - Probability
• The Book of R
  • Chapter 15 - Probability
  • Chapter 16 - Common Probability Distributions

Confidence Intervals

Modules

• Module 14 - Confidence Intervals

Topics

• Standard errors and confidence intervals
  • CIs based on a theoretical distribution
  • The Central Limit Theorem
  • CIs based on bootstrapping
  • CIs for proportions

Required Readings

• Introduction to Data Science
  • Chapter 16 - Statistical Inference
• The Book of R
  • Chapter 17 - Sampling Distributions and Confidence

Other Useful Readings

• R Programming for Data Science
  • Chapter 22 - Simulation
• Statistical Inference via Data Science
  • Chapter 8 - Bootstrapping and Confidence Intervals

Hypothesis Testing

Modules

• Module 15 - Classical Hypothesis Testing
• Module 16 - Permutation and Randomization Tests
• Module 17 - Error, Power, and Effect Size

Topics

• p values and “significance”
• Classic null hypothesis significance testing (NHST)
  • One- and two-sample \(T\) and \(Z\) tests
• Permutation and randomization tests
• Type I and Type II error
• Statistical power, effect sizes

Required Readings

• The Book of R
  • Chapter 18 - Hypothesis Testing
• Statistical Inference via Data Science
  • Chapter 9 - Hypothesis Testing
• Legendre & Legendre (2012). Chapter 1.2. Statistical testing by permutation. Numerical Ecology, 3rd Edition. Elsevier.

Other Useful Readings

• Statistics Done Wrong
  • Chapter 1 - An Introduction to Statistical Significance
• Statistics: An Introduction Using R
  • Chapter 5 - Single Samples
  • Chapter 6 - Two Samples

Relevant Data Camp Material

• Foundations of Inference - Introduction to Ideas of Inference
• Foundations of Inference - Confidence Intervals
• Foundations of Inference - Completing a Randomization Test
• Foundations of Inference - Hypothesis Testing Errors

Introduction to Linear Modeling

Modules

• Module 18 - Introduction to Linear Regression

Topics

• Correlation and covariation
• Basic linear modeling
  • Continuous random predictor and response variables
  • Simple linear regression (1 predictor and 1 response variable)
  • Estimating and interpreting regression coefficients
  • Model I versus Model II regression
  • The lm() function

Required Readings

• The Book of R
  • Chapter 20 - Simple Linear Regression

Other Useful Readings

• Statistics: An Introduction Using R
  • Chapter 7 - Regression
• Statistical Inference via Data Science
  • Chapter 5 - Basic Regression

Relevant Data Camp Material

• Correlation and Regression in R - Visualizing Two Variables
• Correlation and Regression in R - Correlation
• Correlation and Regression in R - Simple Linear Regression
• Correlation and Regression in R - Interpreting Regression Models

Elements of Regression Analysis

Modules

• Module 19 - Elements of Regression Analysis

Topics

• Inference in regression
  • Estimating standard errors for regression cofficients
  • Confidence intervals and prediction intervals
  • Residuals
• Model checking
• Partitioning of variance in linear models
• Data transformations

Required Readings

• Statistical Inference via Data Science
  • Chapter 10 - Inference for Regression

Other Useful Readings

• Gotelli, N.J. & Ellison, A.M. (2012). Chapter 9. Regression. A Primer of Ecological Statistics, 2nd Edition. Sinauer Associates, Inc.

Extending Linear Regression

Modules

• Module 20 - Categorical Data Analysis
• Module 21 - Multiple Regression and ANCOVA

Topics

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

Required Readings

• The Book of R
  • Chapter 19 - Analysis of Variance
  • Chapter 21 - Multiple Linear Regression

Other Useful Readings

• Gotelli, N.J. & Ellison, A.M. (2012). Chapter 10. The analysis of variance. A Primer of Ecological Statistics, 2nd Edition. Sunderland, Sinauer Associates, Inc.
• Statistics: An Introduction Using R
  • Chapter 8 - Analysis of Variance
  • Chapter 9 - Analysis of Covariance

Model Selection

Modules

• Module 22 - Model Selection in Linear Regression

Topics

• Model simplification and selection
  • Partial F tests for comparing models
  • Forward and backward selection
  • Information criteria considerations for comparing models
• The Akaike Information Criterion (AIC) and others
  • {stats} step()
  • {MASS} stepwise()
  • {AICcmodavg}

Required Readings

• The Book of R
  • Chapter 22 - Linear Model Selection and Diagnostics

Other Useful Readings

• Package descriptions for {AICcmodavg} and {MuMIn}

Relevant Data Camp Material

• Correlation and Regression in R - Model Fit

Linear and Mixed Effects Modeling

Modules

• Module 23 - Generalized Linear Models
• Module 24 - Mixed Effects or Multilevel Modeling

Topics

• Generalized linear models
  • Other response variables types (e.g., counts, binary responses)
  • Logistic regression, multiple logistic regression
  • Log-linear modeling
  • Likelihood ratio tests
• Introduction to mixed effects modeling
  • Combining fixed and random factors
• Assessing model fit for GLMs and mixed models

Required Readings

• R in Action
  • Chapter 13 - Generalized Linear Models
• 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: 127-135.

Other Useful Readings

• Statistics: An Introduction Using R
  • Chapter 12 - Other Response Variables
• 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.