I use R and the Tidyverse extensively. Exploratory data analysis is definitely clunkier in Julia - you need the `@pipe` macro to patch up some limitations in na

I use R and the Tidyverse extensively. Exploratory data analysis is definitely clunkier in Julia - you need the `@pipe` macro to patch up some limitations in native pipes, there's no `dbplyr` equivalent that I know of, and despite Julia's better metaprogramming in general, the lack of built-in equivalents to scoped `select`/`mutate`/`summarize` is a real drag. But Julia's type system, substantially better date/time system and utilities, explicit vectorization with `.`, and the use of functions instead of scoped expressions in functions like filter are all real benefits over R. If you write a lot of Rcpp, Julia's performance without dropping down into a lower-level language is also a significant advantage. It's easy, bordering on trivial, to performantly implement a generic join (i.e. `join(f, df1, df2)`) in Julia; `dplyr` still doesn't have those at all, `data.table` only sort of does, and I believe the canonical R implementation (AFAIK) in the `fuzzyjoin` package requires holding the Cartesian product of the dataframes in memory, which is obviously not great.