Introduction

Welcome to MBP intro stats 2019. This web-site contains the instructions for how to prepare for the course, and will be updated with all the slides as they become available.

Preparing for the course

You will need a laptop with R and RStudio installed for this course. There are multiple good instructions around the web for getting it installed - here’s a good one you can follow:

http://web.cs.ucla.edu/~gulzar/rstudio/

At the end of those instructions it hints at installing packages. Here are the ones you should install:

  1. tidyverse
  2. ggplot2
  3. forcats
  4. lme4
  5. ggridges
  6. knitr
  7. rmarkdown
  8. xaringan

A quick way of doing that in R (or in the Console in RStudio) is by copying and pasting the following commands:

required_packages = c("tidyverse", "forcats", "knitr", "lme4", "rmarkdown",
                      "ggplot2", "xaringan", "ggridges")
install.packages(required_packages)

You will also want the following files downloaded and somewhere handy on your computer:

mice.csv

volumes.csv

(Depending on your browser you might need to right click and select “Download as”).

Readings for the course

These articles are required reading, and will be discussed in more detail on Thursday (so make sure to have read them by then):

Why Most Published Research Findings Are False

Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability

Readings these articles for Thursday is not stricly required, but encouraged:

The garden of the forking paths

Believe it or not: how much can we rely on published data on potential drug targets?

Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015

Lastly, these text books make for good general resources, both for the course and thinking through data analysis afterwards:

R for Data Science

OpenIntro statistics

An Introduction to Statistical Learning

Statistical rethinking - sadly this one is not available for free online, but is fantastic and worth getting.

Slides

Lecture Description HTML PDF Rmd
Day 1 Intro to class; plotting, modelling html pdf Rmd
Day 2 Modelling html pdf Rmd
Day 3 Replication and truth html pdf Rmd
Day 4 From beginning to end html pdf Rmd

Data for last day: data Last day code: Rmd Last day code: html