Overview of a series of RPubs lessons on using PCA to analyze SNPs.

An introduction to building time series plot from 2 vectors

An introduction to computational biology, using AlphaFold2 as a case study.

PCA with vegan's rda function

Basic for loop examples, and debugging them using functions. Also an example of using them to run multiple regressions.

An introduction to writing function

Explore milk evolution data with ggplot

Exploring structure of milk evolution data with Principal Components Analysis

Load milk evolution data and clean.

Data prep. for milk evolution dataset

Take regression data from Motulsky and make a 2 panel plot in base R (plot) and ggplot (qplot)

Data set up for Skibiel et al 2013. Journal of Animal Ecology. The evolution of the nutrient composition of mammalian milks

Superscript in plots in R and RMarkdown text

Plotting data from a paper recent retracted from Cell.

We'll take the infamous rat bladder data and plot it as in Motulsky's book

Adjust where axes labels go using the mtext() function.

Make a polished effect size plot using output from t.test()

A very basic look at R objects, lists, and how to use them when plotting results of statistical tests.

Duq Lecture 1: Into to Course

Worked examples and notes for test 2 take home on t-tests and 1-way ANOVA

Lion data set used as inspiration for a paired t-test example

Simple function that plots means as individual points (not bars) & approx. 95% confidence intervals using user-supplied means & standard errors. (It can also take pre-calculated CIs). It provides some feed back via the console & on the plot itself regarding errors and how to make the plot more polished.

Takes the output from R's TukeyHSD function for post-hoc comparisons & makes a prettier plot of the output than the default.

Implements Wilson's confidence interval for binomial proportions, such as number of deaths out of number of organisms in a toxicology experiment.

Key for test

Example of how to think about and analyze data that is binary or binomial.

Full example analysis of lion data, fitting model, inferance, and diagnostics.

A very brief introduction to 2-way ANOVA using the lion data.

Lion data is re-formatted for use as a 1-way ANOVA to illustrate how to report results for you independent project.

Fitting separate lions for each population of lions

The goal of this lab is to: * Review assumptions of ANOVA/t-tests/regression * Review diagnostics plots for normality and constant variance * Introduce diagnostics plots for outliers * Investigate the role of transformation on diagnostic plots * Introduce how to model curved (non-linear) data with x^2 terms

Shows how to do diagnostics for a 2-sample t-test. Uses reformated lion data as an example.

An example of how to format analyses & plots for a scientific paper, using data on the relationship between the amount of pigment on lion snouts & their age from Whitman et al (2004). These data are featured in Ch 17 of Whitlock & Shulter’s Analysis of Bio Data, 2nd ed. The original data was presented in Whitman et al. Sustainable trophy hunting of African lions. Nature.

This is a good function when the x axis is time and you are plotting a seperate line for each thing that is changing over time (the mean of a group, observations for individuals)

Setting up lion data as a 2-sample t-test, then getting the residuals and doing diagnostics.

Very brief introduction to 2-way ANOVA (aka factorial ANOVA). The lion data is set up to do this.

How to write up results for you independet project if you are doing a 1-way ANOVA. The lion data gets converted to be used w/ a 1-way ANOVA followed by Tukey's HSD.

Regression diagnostics and other topics. R2 section was not covered.

Example of format for a scientific paper, using data from Whitman, K, AM Starfield, HS Quadling and C Packer. 2004. Sustainable trophy hunting of African lions. Nature.

Introduction to 1-way ANOVA using data on the effects of nutrition on deer antler growth

Uses data from Table 17.9-1 of Whitlock & Shulter

A walkthrough of a regression analysis

Basic regression example with Whitlock & Shulter's lion data from chapter 17

The is a simple function that plots means as individual points (not bars) & approximate 95% confidence intervals using user-supplied means & standard errors. It provides some feed back via the console & on the plot itself regarding errors and how to make the plot more polished.

tapply() doesn't have a "data = ..." argument and so needs to be told explicitly where to find your data.

Code needed to generate the different antler datasets for analysis w/ANOVA (mass, basal circumference, main beam, etc)

Takes the output from R's TukeyHSD function for post-hoc comparisons & makes a prettier plot of the output than the default.

Whitlock and Shulter paired t-test example

2nd Ed Chapter 12 Question 18 Whitlock & Shulter Analysis of Biological Data

Brief introduction to data transformations (log and square root)

A brief walk through of two equivalent ways to conduct a paired t-test, depending on how the data are organized.

Using the factor() command to designate a column as a categorical / grouping variable

Practice loading data and plotting means

Illustrates impact of sample size on precision using data from Barro Colorado Island

Intro to t-tests and review of plotting grouped data. Uses Ex 12.4 in Whitlock and Shulter's Analysis of Biological Data, 2nd Ed.

Data from Whitlock & Schulter Chapter 3 question 28

Split up grouped data using subset() and tapply()

Basic function to plot 2 means w/ their 95% Confidence Intervals.

Notes on using the Bayesian ordination and modeling package boral, with particular reference to plotting

General notes on rarefaction for confused pop. ecologists, and comparison of Chao's iNEXT package to vegan.

Plotting using the Hmisc::errbar() function when the means are already calculated.

test

Practice calculating the var, SD, and SE by hand.

Source code for Frank E Harrell Jr Hmsic:errbar function.

Based on Chapter 14 of Whitlock & Shulter

Plotting means with error bars

The Iris data Loading data: iris -data(iris) Loading packages in base R: MASS -library(MASS) Loading packages from CRAN: doBy Boxplots w/ iris data -anatomy of an R function -xlab, ylab -cex -lw Histograms w/Iris data -hist Scatterplots w/iris data

Displaying & Describing Data. Based on Chapters 2 & 3 in Whitlock & Shulter The Analysis of Biological Data

A first run through of R using data on bald eagles

Lecture 3 ENS 494

Draft/outline of introduction to loading nasty data into R

A rough draft of a walk through of a paired t-test

A walk thru of basic data manipulation, graphing and t-tests with a small data set.

In this script I annotate the code for mark-recapture analysis in the R package marked provide in the paper Laake, Johnson and Conn 2013. marked: an R package for maximumlikelihood and Markov ChainMonte Carlo analysis of capture–recapture data. Methods in Ecology and Evolution 4:885–890 and join it with other code produced by the authors in previous publications.