https://github.com/datalowe/synr
An R package for handling synesthesia consistency test data. Explore, validate and summarize data.
https://github.com/datalowe/synr
data-cleaning synesthesia
Last synced: 4 months ago
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An R package for handling synesthesia consistency test data. Explore, validate and summarize data.
- Host: GitHub
- URL: https://github.com/datalowe/synr
- Owner: datalowe
- License: other
- Created: 2020-05-18T18:22:02.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2024-01-13T13:24:34.000Z (over 2 years ago)
- Last Synced: 2025-12-09T12:20:15.766Z (7 months ago)
- Topics: data-cleaning, synesthesia
- Language: R
- Homepage: https://datalowe.github.io/synr/
- Size: 7.94 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# synr 
[](https://app.travis-ci.com/datalowe/synr)
This is an [R](https://www.r-project.org/) package for working with data resulting from [grapheme-color synesthesia](https://en.wikipedia.org/wiki/Grapheme%E2%80%93color_synesthesia)-related [consistency tests](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118597/#S4title). synr provides tools for exploring test data, including visualizing a single participant's data, and applying summarizing functions such as calculating [color variation/consistency scores](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118597/#S4title) or classifying participant data as valid or invalid.
## Installation
synr is available [on CRAN](https://cran.r-project.org/package=synr), meaning you can simply:
```r
install.packages('synr')
```
Note that this will also install packages that synr depends on unless you already have them (`dbscan`, `data.table` and `ggplot2`).
## Usage
Once data are in an appropriately formatted data frame/tibble ('long format' - see vignettes for more information), everything starts with rolling up participant data into a 'ParticipantGroup' object with `create_participantgroup_widedata`:
```r
library(synr)
pgroup <- create_participantgroup(
formatted_df, # data frame/tibble to use, with data in 'long format'
n_trials_per_grapheme=3, # number of trials that grapheme was used for
participant_col_name="participant_id", # name of column which holds participant ID's
symbol_col_name='symbol', # name of column which holds grapheme symbol strings
color_col_name='color', # rname of column which holds response color HEX codes
color_space_spec = "Luv" # color space to use for all calculations with participant group
)
```
Using the resulting object (`pgroup`), you can call various methods. A few examples follow.
### Example group-level method: get_mean_consistency_scores
`pgroup$get_mean_consistency_scores(symbol_filter=LETTERS)` would return a vector of [CIELUV](https://en.wikipedia.org/wiki/CIELUV)-based consistency scores, using only data from trials involving capital letters.
### Example group-level method: check_valid_get_twcv_scores
`pgroup$check_valid_get_twcv_scores(symbol_filter=0:9)` would return a data frame which describes classifications of all participant data, where each data set is classified as 'invalid' or 'valid', based largely on [DBSCAN clustering](https://en.wikipedia.org/wiki/DBSCAN). This may be used to identify participants who varied their responses too little, e. g. by responding with an orange color on every trial.
### Example participant-level method: get_plot
`pgroup$participants[[1]]$get_plot(symbol_filter=LETTERS)` would produce a bar plot of per-grapheme consistency scores for a single participant, using only data from trials involving capital letters. You can see an example below.
### Detailed usage information
More details on required data format and how to use the above functions and more can be found in the [package's vignettes](https://datalowe.github.io/synr/articles/), some of which are also included in the package itself (run `help(synr)` to find them). Additional information is available in the following article:
> Wilsson, L., van Leeuwen, T.M. & Neufeld, J. synr: An R package for handling synesthesia consistency test data. Behav Res 55, 4086–4098 (2023). https://doi.org/10.3758/s13428-022-02007-y
## Feedback
If you have any suggestions on improvements you are very welcome to directly raise issues or commit code improvements to the github repository at https://github.com/datalowe/synr.