The RiffynTools AddIn for JMP includes a function for tagging any data points you have selected in JMP when analyzing your Riffyn data, and then saving those tags to the experiment in Riffyn. You can use this feature for hit-picking samples, tagging bad data points, or marking runs and data points with observations.
First be sure you have downloaded the latest AddIn (version 3.00 or higher).
Using the data tagging function in the RiffynTools JMP AddIn
(Click on images to enlarge them)
This function is accessed via the Tag selected data points in Riffyn menu item on the Riffyn menu in JMP. Here's how to use it.
1. Add a tag property to your experiment or process. Your experiment must contain at least one property with 'tag' or 'Tag' in the property name. (The JMP tagging tool only writes to 'tag' properties as a precaution against accidentally overwriting primary experimental data.) If you don't have such a property, just add it using the "Modify" tab in the bottom panel, or by editing your process design.
Ensure that at least one row contains data on the property. Otherwise the column will not export. We recommend starting with "none" or "-" on all runs.
2. Download your data via the UI or the RiffynTools AddIn, and open it in JMP.
3. Analyze your data and select your points in JMP. Be sure to retain all identifier columns, in particular the process id, experiment id, run id, and eventgroup id columns are required in order for the AddIn to identify your data points correctly and save them to Riffyn.
4. Run the tagging function from the Riffyn menu.
5. Enter the value to tag your data and runs. Also chose the tag column you want to save the tag values to. Only columns with 'tag' or 'Tag' in them will be presented. Click OK.
6. Confirm you want to save the tag. You will be presented with a warning screen to allow you to cancel the action if you wish. Otherwise confirm by Clicking OK. Your tags will be written to both Riffyn and the JMP data table.
7. Check your experiment. Now your tags are visible in Riffyn. (Be sure to click off the step and back on it to refresh the display.) You can repeat this analysis, selection and tagging process in steps 3 - 6 as many times as you wish.
8. Group by the tags. Now you can group the runs (aggregate) by the tag column.
9. Work with the tagged runs. This displays all the similarly tagged runs together in a group. You can select this group and propagate the runs forward to another step or another experiment.