**Pre-course instructions**
---------------------------
> Please come to Cumberland Lodge with a **draft outline of a research study** of your own that you would like to pre-register.
>
> Due to potentially slow Wifi at Cumberland Lodge, please check under each of the components ***Bishop*** through to ***Whitaker*** and download materials for use during the workshop.
>
> These materials may be updated up until **3 Jan 2020**, so please remember to check again.
>
>Also feel free to ask in the **#Repro20** channel on Slack about what is required.
Attendees will need to have the following software installed on their personal laptops.:
- R
- R studio
- Git, and a GitHub account
This is free software and instructions for its installation can be found below:
- Visit https://github.com/mikecroucher/ISBE_Symposium
- Follow the first 2 steps in the tutorial, which includes installation of **R**, and **R studio** (also covered in detail further down this page), and also covers **Git** and the creation of a **GitHub account**.
**Install R (free software)**
- Open an internet browser and go to www.r-project.org.
- Click the "**download R**" link in the middle of the page under "Getting Started."
- Click on the link for a CRAN location close to you
- **Mac users:**
- Click on the "Download R for (Mac) OS X" link at the top of the page.
- Click on the file containing the latest version of R under "Files."
- Save the .pkg file, double-click it to open, and follow the installation instructions.
- **Windows users:**
- Click on the "Download R for Windows" link at the top of the page.
- Click on the "install R for the first time" link at the top of the page.
- Click "Download R for Windows" and save the executable file somewhere on your computer.
- Run the .exe file and follow the installation instructions.
**Install R studio** – a friendly interface for R
- Go to www.rstudio.com and click on the "Download RStudio" button.
- Click on "Download RStudio Desktop."
- **Mac users:**
- Click on the version recommended for your system, or the latest Mac version, save the .dmg file on your computer, double-click it to
open, and then drag and drop it to your applications folder.
- **Windows users:**
- Click on the version recommended for your system, or the latest Windows version, and save the executable file. Run the .exe file and follow the installation instructions.
Once **R studio** is installed, you need not open the original **R** software: instead, you access **R** by opening the **R studio** application
**Install packages**
- Open R studio.
- On the menu bar at the top, select **Tools > Install Packages**
- Select packages by typing the name in the box that pops up, and clicking Install after selecting the one you want.
- For each package you install, you will see text flashing by on your console as it is loaded.
For **Dorothy Bishop's workshop session**, the first 3 packages listed here are required:
1. **MASS** - used for generating multivariate normal random numbers (among other things)
2. **Hmisc** - useful for computing correlations
3. **yarrr** *(Note that yarrr has three Rs!)* - used to create a nice kind of plot called a pirate plot.
4. **corrr** - BONUS package *(again 3 Rs!)* is a nice package for doing and visualising correlations. If you are an R expert and want a challenge beyond the elementary content of the exercises, you could install that as well and see what it can do.
For **Daniel Lakens' workshop session**, 2 packages are required:
1. **metafor**
2. **truncnorm**
**Download some introductory scripts**
- From the Open Science Framework site download the following scripts found in the **Bishop** component (https://osf.io/skz3j/):
1. Simulation_ex1_intro.R
2. Simulation_ex1_multioutput.R
3. Simulation_ex2_correlations.R
**Optional: R for total novices**
If you have no background in R, you may find it helpful to gain some basic familiarity with it to get the most out of this course.
A very friendly and basic interface for learning R is here:
https://swirlstats.com/
There are various online courses on R from Data Carpentry and Software Carpentry that you can find by Googling: these are geared to different disciplines, and you may find it helpful to browse to find one most suited to your field.
This one, although designated for Social Sciences, is a good introduction to the ‘tidyverse’ – a set of functions in R that make data manipulation relatively easy:
https://datacarpentry.org/r-socialsci/
It also has a good first lesson to just help you find your way around R studio.