|
|
|
|
|
|
by DataJunkie
2194 days ago
|
|
|
Agreed, but it might be useful for a full stack data scientist that is forced to work in a Go systems environment. That's why Python+PyData has had so much success. There are packages to support data science, but the language itself can also be used to implement a system, so integration is rather seamless. That's not true for, say, R. |
|
|
If you’re trying to create ETL pipelines that integrate with BigQuery, Mongo, or whatever other database, I think it’s fair to say that the Python packages are generally better documented than their R counterparts.
For most other things, IMO it’s hard to really separate the two languages. Is standing up a Flask API really easier than in plumber?
For dashboarding, it’s is as quick (if not much quicker) to create a decent prototype with Shiny vs Plotly Dash or bokeh.
For simple linear and logistic model training, R’s built-in stats package has much more interpretable outputs vs sklearn, and directly inspired statsmodel. Wes McKinney has acknowledged that pandas draws heavily from R’s native dataframe. And so on and so on.
EDIT:
Also forgot to mention that with R packages like reticulate, you can also directly run Python code within an R environment now. So if there happens to be some Python package that doesn’t have an R equivalent, you can still work in R (though I’ve found the opposite situation to be far more common).