While the topics covered are broad, each week will feature different examples from genetics, ecology, molecular, and evolutionary biology highlighting uses of each individual set of techniques.

W&S = Whitlock and Schluter, W&G = Wickham and Grolemund, U/P for linked pdfs = biol607

Change .html to .Rmd in your browser to get the markdown

Turning in Homework: All homework should be completed using RMarkdown. You’ll freely mix answers in text and code there. Please submit both the .Rmd and .html output of your homework. If there are data files associated with your homework, when working on it, please make sure you are using the

  |— markdown
  |— data

directory structure, so that all data is in ../data/ relative to where your homework markdown and outputs are. Please standardize filenames as follows: number_lastName_firstName_2018.Rmd where number is the homework number (you’ll see it in the homework assignment’s filename - and make sure to include the 0s for numbers like 01), and your last and first names - well, you should know them!

To submit homework, go to https://www.dropbox.com/request/MgLy0OxePC4QN8ZDgQ0X and upload the files.

Week 1.

Lecture: How do we use data to understand how the world works?
Lab: Intro to R. Matrices, Lists and Data Frames. Introduction to Markdown
Reading: W&G Preface, Intro, Workflow basics, Vectors, and Markdown Chapters Cheat Sheets: RMarkdown Cheat Sheet, RMarkdown Options Guide
In Class Code: Code from Lab
Install R: Go to https://cloud.r-project.org/ and get the right version of R for you. Then, go to https://www.rstudio.com/products/rstudio/download/#download and install Rstudio.
Etherpad: https://etherpad.wikimedia.org/p/607-intro-2018

Week 2.

Lecture: Sampling and Simulation for Estimation. Descriptive statistics, and the creation of good observational sampling designs.
Lab Topic: Sampling and simulation. Libraries in R. Dplyr.
Reading: W&S 1,3-4, W&G Chapters on data transformation and pipes, Dplyr cheat sheet
Optional Reading: Cumming et al. 2007 on SDs, SEs, and CIs, Simpler Coding with Pipes, Managing Data Frames with the Dplyr package
Etherpad: https://etherpad.wikimedia.org/p/sampling-2018
Packages for the Week: dplyr - install.packages("dplyr")
In Class Code: Code from Lab
Homework: R, Dplyr, and Sampling

Week 3.

Lecture: Data visualization, Data Creation
Lab Topic: Data import, libraries, factors and forcats and Introduction to ggplot2. Data for lab here.
Reading: W&S Chapter 2, Unwin 2008, W&G Chapters on Data Vizualization and Graphics for Communication, DC Starting with Data, Data organization in spreadsheets
Optional Reading: Friendly 2008 on History of Data Viz, Fundamentals of Data Visualization - note, this is a whole book, but scan it - it’s amazing
Etherpad: https://etherpad.wikimedia.org/p/dataviz-2018
Packages used this Week: ggplot2, ggridges, forcats, readr, readxl, tibble, lubridate - install.packages(c("ggplot2", "ggridges", "forcats", "readr", "readxl", "tibble", "lubridate"))
Cheat Sheets: Reading data into R. Ggplot2 cheat sheet, Choosing a good chart cheat sheet
In Class Code: Intro to data and ggplot2
Homework: ggplot2 homework

Week 4.

Lecture: Frequentist Hypothesis Testing, NHST, Z-Tests, and Power
Lab Topic: Distributions in R, Frequentist Hypothesis testing via simulation
Reading: W&S 5-7, W&G Chapter 7, 16, Abraham Lincoln and Confidence Intervals and links therein
Etherpad: https://etherpad.wikimedia.org/p/607-hypotheses-2018
Quiz: http://tinyurl.com/hyp-pre-quiz
In Class Code: Distributions and Power
Homework: hypothesis testing and power

Week 5.

Lecture: T tests, χ2 tests, and p
Lab Topic: Statistical analysis functions for t and \(\chi^2\) in R, data
Reading: W&S 8-12, W&G Chapter 10, 20
Discussion Reading: ASA Statement on P-Values, And choose one of the accompanying rejoinders (sign up in here) (also feel free to read them all)
Additional Readings on P-Values: Peaceful negotiation in the face of so-called ‘methodological terrorism’, P-value madness: A puzzle about the latest test ban (or ‘don’t ask, don’t tell’), The Paradox of Replication, and the vindication of the P-value (but she can go deeper)
Etherpad: https://etherpad.wikimedia.org/p/607-t_tests-2018
In Class Code: t and chi square
Homework: Chi Square and T Tests

Week 6.

Lecture: Least Squares Linear Regression: Correlation and Regression, Fit and Power
Lab Topic: Linear regression, diagnostics, visualization, and data
Reading: W&S 16-17, W&G on model basics, model building
Etherpad: https://etherpad.wikimedia.org/p/607-lm-2018
In Class Code: lm
Homework: Correlation and Linear Models

Week 7.

Lecture: Likelihood, Fitting a line with Likelihood
Lab Topic: Likelihood and linear models, data
Reading: W&S 20, W&G Chapter Iteration
Etherpad: https://etherpad.wikimedia.org/p/607-likelihood-2018
In Class Code: linear models with likelihood
Packages for The Week: install.packages(c("MASS", "profileModel", "bbmle"))
Homework:pufferfish and likelihood

Week 8.

Lecture: Bayesian Inference, Fitting a line with Bayesian techniques
Lab Topic: Bayesian computation in R, Fitting a line with Bayesian techniques
Reading: Ellison 1996, Statistical Rethinking Chapter 1 and Chapter 2, R Users will Now Inevitably Become Bayesians, bayesplot cheat sheet
Additional Reading: how to choose a prior, bayesian t-tests, regression models with brms, rethinking with brms (many very cool examples), brms tutorials, How to use rstanarm, Linear Models in rstanarm, Bayesian basics with R
Packages for The Week: install.packages(c("brms", "rstanarm", "arm", "rethinking")) and devtools::install_github("mjskay/tidybayes")
Etherpad: https://etherpad.wikimedia.org/p/607-bayes-2018
In Class Code: Bayesian Data Analysis

Week 9.

Lecture: Joins, Tidy data
Data: Hemlock
Reading: 10 Commandments for Good Data Managament, W&G Chapters on tidy data, Strings, and Dates
Packages: install.packages(c("reprex", "datapasta", "lubridate", "tidyr"))
Etherpad: http://etherpad.wikimedia.org/p/607-tidy-2018
In Class Code: Tidy, markdown options

Week 10.

Lecture: Experimental design and ANOVA part 1, part 2, Cottingham discussion
Lab Topic: One-Way ANOVA, Midterm work session
Lab Data: Multiple Files
Reading: W&S Chapter 14-15, Cottingham et al. 2005
Optional Reading: Ecological applications of multilevel analysis of variance with appendices
Packages for The Week: install.packages(c("car", "emmeans", "multcompView", "contrast")) Etherpad: https://etherpad.wikimedia.org/p/607-anova-2018 In Class Code: lots of anova

Week 11.

Lectures: Experimental Design in a Multicausal World - Multiway ANOVA, Factorial ANOVA
Lab Topic: Discussion of Hurlbert, Cottingham, Factorial ANOVA
Lab Data: Multiple Files
Reading: W&S 18, Hurlbert 1984
Optional Reading: Analysis of variance with unbalanced data: an update for ecology & evolution
In Class Code: lots of anova

Week 12.

Lecture: The General Linear Model: ANCOVA, Multiple Regression, and Interaction Effects, Information Theoretic Approaches
Lab Topic: Multiple Regression, Multimodel Inference - data files
Readings: Symonds and Moussalli 2010, Simple means to improve the interpretability of regression coefficients, Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change
Optional Readings: Centring in regression analyses: a strategy to prevent errors in statistical inference, Model selection for ecologists: the worldviews of AIC and BIC, The whole Ecology Special Section on P Values is incredible reading.
Etherpad: https://etherpad.wikimedia.org/p/607-mlr-2018
Packages for the Week: install.packages(c("AICcmodavg", "MuMIn", "modelr"))
In Class Code: ancova, mlr, and aic

Week 13.

Lecture: Generalized Least Squares and Heteroscedasticity, Entering a non-normal world - Modeling count data with Genearlized linear models.
Data: GLS Data, GLM Data
Lab Topic: Generalized Linear Models
Packages for the Week: install.packages(c("MASS", "readxl", "betareg", "DHARMa"))
Reading: O’Hara 2009 through section on GLMs, O’Hara and Kotze 2010, Wharton and Hui 2011, Hartig DHARMa vignette

Week 14.

Lecture: Iteration with purrr, Class’s Choice
Data: gapminder
Reading: Murtaugh 2007, iteration in R4DS, functionals in adv-r
Cheat Sheets: Apply Functions with purrr Cheat Sheet
Lab Topic: Class’s Choice, Final Presentation Open Lab
In Class Code: spread and gather

Week 15.

Lecture: Final Presentations