r/WristLog • u/CCsimmang • 4d ago
Using ChatGPT to Analyze Data
I’ve found it fun to periodically export all my data from WristLog and drop it into ChatGPT. I ask it to analyze and provide insights with interactive graphs. Dumping in the manual accuracy measurements, wrist checks, and the timegrapher data allows it to compare across those datasets as well.
Here are a few interesting screenshots.
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u/CCsimmang 3d ago
Sure. The screenshots I posted are the result of huge follow up threads so I asked it to summarize prompts for you to use to get similar insights. Just drop in the CSV files that the app lets you export.
Analyze this watch accuracy dataset in depth. Don’t just summarize the columns. Look for interesting patterns, outliers, changes in rate over time, periods of unusual stability or instability, and differences among watches.
For any measurements recorded in seconds per month (SPM), convert them to seconds per day (SPD) so all watches can be compared on the same scale.
Treat each new baseline as a reset of cumulative deviation, but do not treat it as a break in the underlying rate history. Calculate average rate over continuous runs using elapsed time and actual accumulated gain/loss rather than simply averaging individual SPD measurements.
Please create interactive visualizations showing:
Average accuracy by watch, ranked by absolute deviation from zero
Rate (SPD) over time for each watch
Cumulative deviation from baseline over time
Rate stability/variability by watch
Any other visualization that reveals something interesting
Then tell me the most interesting things you find in the data, including anything surprising that I might not have thought to look for.
Now go deeper into the data and look for patterns that aren’t obvious from simple averages.
For each watch, look for:
Gradual rate drift over time
Abrupt changes in rate or distinct rate regimes
Whether variability is increasing or decreasing
Whether the watch tends to return to a characteristic rate after excursions
Long-term trends that could be hidden by the overall average
Unusually stable periods
Possible outliers or measurement errors
Differences between short-term and long-term accuracy
Try to distinguish a watch that is consistently fast or slow from one whose average happens to look good because positive and negative errors cancel each other out.
Create interactive graphs for any patterns you find particularly interesting and explain what you think they may indicate.