(this all prefaced with a massive thank you for tidyverse, without which R is very crusty).
I love R for interactive work and quick analyses, but I'm currently trying to integrate various bits of R code into a large document-building pipeline and wishing I could use Python for it:
- Exception handling and error processing seem a pain in R. Maybe I'm doing it wrong, but if feels like a mess and not nearly as ergonomic as python. Trycatch seems to have gotchas related to scope because the error handling is in a function. The distinction between warning, stop etc seems odd. The option to stop on warnings isn't useful because older packages seem to abuse warnings as messages. I have just discovered `safely` which is helpful, but then you have to unwrap lists in pipelines which feels clunky.
- Related, I _really_ wish we could just drop model objects or other tibbles as single objects directly into a tibble cell rather than as list(df). Unpacking lists and checking objects inside them exist is much more of a pain (e.g. can't just do `filter(!is.na(df_col))`)
- I really miss defaultdict from python, and dictionaries generally.
- Passing variable names as strings to dynamically generate things seems clunky compared with python. Again, it may be because I'm doing to wrong but I end up having to wrap things in !!sym the whole time and the nse semantics seem hard to remember (I only use R about 20% of the time). I liked cur_data() for passing a df row to a function but this now seems deprecated.
- String formatting -- fstrings are just great. Glue is OK, but escaping special characters seems more tricksy. Jinjar is OK, not quite jinja.
- purrr is nice, but furrr just isn't a drop-in replacement. Making http requests in parallel seems non-trivial compared to doing it with python. Is there an easy way to do it without creating multiple processes? Why can't I just do something like `. %>% mutate_parallel(response=GET(url), workers=10) %>% ...`?