This is an R script designed to batch analyse raw data files output by Zantiks behavioural research equipment Y-maze protocol using zebrafish (Danio rerio). Please read the instructions below before use. If you use this script to analyse data that goes on to be published please cite this work appropriately.
- Download and install R from https://www.r-project.org/
- Download and install RStudio from https://www.rstudio.com/
- On this github repository, go to the top of the page and click the green "Clone or download" button and then click "Download Zip".
- On your local machine, navigate to where
"ZANTIKS_YMaze_Analysis_Script-master.zip"
was downloaded and extract the contents. - Launch RStudio.
- Install dependencies: Inside the R console window, type
install.packages(c("tidyverse", "lubridate"))
and press enter.
- Inside a filebrowser on your local machine open the
"ZANTIKS_YMaze_Analysis_Script-master"
directory. - You will find example data in the
"data"
directory inside the main"ZANTIKS_YMaze_Analysis_Script-master"
directory. Feel free to use this to test the script. Once you have finished testing, please move the example data out of the"data"
directory. - Copy and paste the data that you would like to analyse into the
"data"
directory. - Inside RStudio click
File > Open File
- Navigate to the
"ZANTIKS_YMaze_Analysis_Script-master"
directory, selectscript.R
and press Open. - Inside the script window in RStudio, change the path of input and output to the location of the
"data"
and"output"
directories, which are found inside the main"ZANTIKS_YMaze_Analysis_Script-master"
directory. - Run the script.
- Your analysed data will be located in the
"output"
directory inside the main"ZANTIKS_YMaze_Analysis_Script-master"
directory.
- Data must be in .csv format
- Fish IDs can only contain numbers
Fontana, B. D., Cleal, M., Clay, J. M., & Parker, M. O. (2019). Zebrafish (Danio rerio) behavioral laterality predicts increased short-term avoidance memory but not stress-reactivity responses. Animal Cognition. https://doi.org/10.1007/s10071-019-01296-9