Labs
Lab 1
Introduction to R and RStudio
Introduction to R and Posit Cloud, focusing on basic calculations, functions, and understanding R’s console output.
Lab 2
Workflow and Data Types
Working with data frames in R using the palmerpenguins dataset to practice data inspection, data types, and simple summaries.
Lab 3
Importing Data
Reading external data into R, with emphasis on file structure, missing values, and correct specification of categorical variables.
Lab 4
Graphing with ggplot
An introduction to creating scatterplots in R using ggplot2 and a layered approach to graphing.
Lab 5
ggplot2 Visualization Challenge
An introduction to creating scatterplots in R using ggplot2 and a layered approach to graphing.
Lab 6
Describing Data with Summary Statistics
Using descriptive statistics and graphs to summarize, compare, and interpret biological data.
Lab 7
Analyzing proportions
Estimate population proportions, construct confidence intervals, test hypotheses about proportions, and interpret the results using biological datasets in R.
Lab 8
Transforming Data
Use tidyverse tools to subset, reshape, join, and transform biological datasets into formats appropriate for analysis and visualization.
Lab 9
Contingency Analysis
Analyze relationships between categorical variables using contingency tables, relative risk, odds ratios, and chi-square tests with NHANES health data.
Lab 10
t-tests
Compare means using one-sample, two-sample, and paired statistical tests while evaluating assumptions, selecting appropriate methods, and interpreting results in biological contexts.
Lab 11
ANOVA
Compare means among multiple groups using analysis of variance, evaluate model assumptions with residual plots, and identify differences among groups using post hoc tests.