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With regards to the usefulness of piping with dplyr: the post does express its admiration for data.table as the alternative for dplyr. In data.table, chaining
by phonebucket 7y ago
With regards to the usefulness of piping with dplyr: the post does express its admiration for data.table as the alternative for dplyr.
In data.table, chaining operations together is standard and as simple as a set of brackets to contain the next operation.
Both are preferable to pandas though (joke not troll).
- bsg75 7y agoThere are nuances that to me make R data frames, especially with Magrittr, simpler to work with over Pandas. Pandas indexing always seems to get in the way. I have been using Python a lot longer than R, but the Pandas syntax is taking longer to internalize. Maybe it's just me?
- wodenokoto 7y agoNo. Pandas is a powerful tool, but a poor api / user interface.
- finebalance 7y agoPandas grouping and slicing is, imo, significantly worse than R. And I so despise those multiindexes.
- phonebucket 7y agoFor those R users who aren't quite enjoying Pandas so much, I enjoyed the following blog post: https://stmorse.github.io/journal/tidyverse-style-pandas.html https://stmorse.github.io/journal/tidyverse-style-pandas.htm...
- c06n 7y agoI use piping in combination with data.table all the time. It works simply like this: my_dt %>% .[i, j, by] It's a powerful combination. Fast, very readable, extremely versatile.