I don't want to say "advantage", so much as preference. But a few things come to mind.

- Lots of high quality statistical libraries, for one thing.

- RStudio's RMarkown is great; I prefer it to Jupyter Notebook.

- I personally found the syntax more intuitive, easier to pick up. I don't usually find myself confused about the structure of the objects I'm looking at. For whatever reason, the "syntax" of pandas doesn't square well (in my opinion) with python generally. I certainly want to just use python. But, shrug.

- The tidyverse package, especially the pipe operator %>%, which afaik doesn't have an equivalent in Python. E.g.

    with_six_visits <- task_df %>%
      group_by(turker_id, visit) %>%
      summarise(n_trials = n_distinct(trial_num)) %>%
      mutate(completed_visit = n_trials>40) %>%
      filter(completed_visit) %>%
      summarise(n_visits = n_distinct(visit)) %>%
      mutate(six_visits = n_visits >= 6) %>%
      filter(six_visits) %>%
      ungroup()

Here I'm filtering participants in an mturk study by those who have completed more than 40 trials at least six times across multiple sessions. It's not that I couldn't do the same transformation in pandas, but it feels very intuitive to me doing it this way.

- ggplot2 for plotting; its really powerful data visualization package.

Truthfully, I often do my data text parsing in Python, and then switch over to R for analysis, E.g. python's JSON parsing works really well.