I guess you could, but it would be **significantly** more complicated than doing it entirely within Python from within your PsychoPy script.

If you maintain a list of reaction times, you can calculate the mean simply with:

```
mean_rt = sum(your_rt_list)/len(your_rt_list)
```

The `numpy`

library (distributed with PsychoPy) contains functions like `std()`

to calculate standard deviations and so on.

The `matplotlib`

library is also distributed with PsychoPy and allows you to plot graphs as required. https://pythonspot.com/matplotlib-histogram/

There are many alternative plotting packages in Python, including `seaboard`

which gives results that look a bit like R’s `ggplot2`

: https://seaborn.pydata.org/tutorial/distributions.html#distribution-tutorial

but these would probably need to be installed additionally.

I’m a great fan of using R for analysis and visualisation, but for this real-time case, keeping within Python would be much easier to do than trying to interact with R in real time. PsychoPy can draw to a second window on another monitor, so the participant doesn’t need to see what you are graphing. But it would be good to only update the graph once per trial or so rather than update it continually on every screen refresh, as that would likely impair the stimulus drawing performance.