/IGP_biostatistics

Course site for DGP 484 (Quantitative Biology: Statistics and Data Analysis for Life Scientists) - Fall 2021

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Quantitative Biology: Statistics and Data Analysis for Life Scientists

This repository contains all the course materials for IGP-484 (2021). You can find both the lecture slides and the code to generate all plots and statistical analyses in the designated date folder. All analyses are performed in R and require previous installation of tidyverse. If you don't have tidyverse, you can install the package with install.packages("tidyverse") or you can install the specific packages required for this class: install.packages(c("dplyr", "tidyr", "ggplot2")).

Final Exam: Tuesday, December 7 @1:30 (Hughes Auditorium)

Syllabus

Link to the syllabus

Study guide for Final Exam:

here

Possible data sources for the final project:

Not familiar with R? Follow these steps

  1. Download R here
  2. Download RStudio here
  3. Download the package swirl (link to swirl):
install.packages("swirl")
  • Begin swirl (interactive module to get familiar with R) by typing swirl::swirl() and selecting the module 1: R Programming: The basics of programming in R when prompted.
  • You can exit and later resume progress at the end of each section if you wish.

Data analysis with the Tidyverse

Looking for help with basic data wrangling in R? New to the "Tidyverse"? Check out the course materials from my workshop for NUIT for step-by-step help and lots of examples and practice questions.

  • Additionally, check out the swirl course Getting and Cleaning Data for more Tidyverse intro

Questions?

For all questions, contact Katie at kathryn.evans@northwestern.edu (or on the biostats-484 slack)

General R links for more help

  • swirl (here)
  • Data types and structures (here)
  • Base R vs. Tidyverse functions (here)

More Stats help

  • Great YouTube channel - has videos for probably every topic (and more!): here
  • Brief but thorough overview on statistics: here
  • Choosing the right statistical test: here
  • Odds ratio and relative risk: here
  • Two-way ANOVA: here
  • Comparing one- and two-way ANOVA here