**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.