
R vs. Python for Data Science: Summary of Modern Advances - lizeds
https://elitedatascience.com/r-vs-python-for-data-science
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vii
Both R and Python have gained libraries for SQL syntax queries on their
datasets. These are powered by sqllite. As much of the data that gets into R
or Python is obtained from a SQL query (or SparkQL etc.) it's awesome to be
able to query, aggregate and even join internal datasets in the same way!
[https://github.com/ggrothendieck/sqldf](https://github.com/ggrothendieck/sqldf)
is game changing.

The wealth of statistical packages available for R is astounding. Packages
like GLMNet are made available by the top researchers in the field. In
comparison, Python libraries tend to be relatively unsophisticated and less
robust.

~~~
rz2k
Sqldf is very tempting, but at least in the past it was so much, much slower
than dplyr that the time to learn dplyr's syntax could pay off in a single
project, or possibly even a single large operation.

------
carlosgg
It covers many of the libraries discussed here:

[https://news.ycombinator.com/item?id=13110230](https://news.ycombinator.com/item?id=13110230)

