None! But see FAQs below for some tips and advice.
Download your data from the lab's database by entering your Experiment ID number on the
View Runs tab. You can find your ID number on the
We analyze data in our
RStudio Cloud . If you are a grad student, post-doc, or friend of the lab, AND you prefer to analyze on your local machine, skip to
Login to the R Studio Cloud with GitHub — this is important to allow you access to shared code. You can ping Katie in basecamp to request an access link to the lab's cloud.
Once inside the project, configure git by entering the following commands in the terminal, replacing [email protected] with your email address and Your Name with your own name.
git config --global user.email "[email protected]"git config --global user.name "Your Name"
This helps us keep track of who made what changes to the code in the github repo.
Since these are collaborative projects, you'll want to pull in any changes your collaborator has made. Navigate to the git tab and click the blue down arrow (next to Commit) to do so.
Next you'll want to upload the .csv file you exported in step 1. Click the data folder, then upload, then choose file.
.csv file, each row is a run of the experiment. The
data column includes the JSON data (from jsPsych) for that run. Usually you'll want to make each trial in the JSON it's own row — you can do this using the
tidyjson library in R.
# load required librarieslibrary(tidyverse)library(tidyjson)# read in your data filedata <- read.csv("data/name-of-your-data-file.csv") %>%# expand the json so each trial is a rowas.tbl_json(json.column="data") %>% gather_array %>% spread_all
participant_id 0 to indicate test runs of the experiment. When testing on prolific (via their preview feature), prolific grabs our lab's prolific ID
5cc08aa4d923cf0016ea55a5. Remove this data by adding a filter, as in line 6 below.
# read in your data filedata <- read.csv("data/name-of-your-data-file.csv") %>%# expand the json so each trial is a rowas.tbl_json(json.column="data") %>% gather_array %>% spread_all %>%# remove tester data, participant_id 0 and lab's prolific IDfilter(!(participant_id %in% c(0, "5cc08aa4d923cf0016ea55a5")))
Now you are free to analyze however you like!
To allow us to work collaboratively on the code, you'll need to push your changes to the GitHub repository. There are 3 steps:
Save your changes
Navigate to the Git tab and stage the files you edited and saved
Commit those files and push them to the repo
How do I get access to the lab's RStudio Cloud?
Send a message to Katie or Ariel in basecamp to request access.
Can I use my local RStudio to analyze?
Yes if you are a grad student or postdoc! If you are an undergrad working with Katie, you need to use the lab's RStudio Cloud.
I don't see a project for my experiment in the lab's RStudio Cloud.
Either one doesn't exist or you don't have access to it. Send Katie a message in basecamp to request access.
I want to try some random stuff, but don't want to mess with my official project. Is there some place to do that in the lab's RStudio Cloud?
Yes! You want your personal Workspace. Click
New Project to create an empty R Project and go nuts! Do whatever you want there — it's for you.
I don't know how to program in R, any advice?
Yes! The lab's RStudio Cloud has tutorials built right in. Click
Primers under Learn in the sidebar to access the tutorials. I recommend doing (1) The Basics, (2) Tidy your data, and (3) Visualize data in that order!