@rstats Maybe you know this list of free already, but it is still a great collection:

"29 Excellent Free Books to Learn all about R", April 24, 2019, Erik Karlsson

linuxlinks.com/excellent-free-

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@gerald_leppert
That looks good, thanks. Is there a link to a full list?
@rstats

@koantig
@rstats

Just try this search:
link.springer.com/search?showA

There is no single place for R books on SpringerLink, thus you need to experiment a bit using different search terms, e.g. data science, econometrics, statistics,...

It is just important to *not* tick the field "Include Preview-Only content"

@askans

In my opinion, is very suitable for . With R, machine learning can be easily integrated into usual data analysis workflows. provide access to virtually all relevant machine learning algorithms like , Support Vector machines (), , Extreme Gradient Boosting (), algorithms, etc.

Does anyone of the @rstats group have further recommendations?

See reply for sources: 4 books on machine learning.

@askans @rstats

Four books on with :

Dinov, I.D. (2018): Data Science and Predictive Analytics: Biomedical and Health Applications using R. link.springer.com/book/10.1007

Irizarry, R.A. (2019): Introduction to Data Science. rafalab.github.io/dsbook/

James, G. et al. (2014): An Introduction to Statistical Learning. With Applications in R. link.springer.com/book/10.1007

Lantz, B. (2013): Machine Learning with R. subscription.packtpub.com/book

@askans @gerald_leppert @rstats Tabular ML, absolutely.

LightGBM is simpler in R than it is in Python, and it works pretty well. Caret is miles better than other options in Python AFAICT.
BSTS _only_ exists in R & C++.

Working with anything that doesn't fit easily into data.frames/tables, it's just not well suited because of the issues with speed, and R is fastest with matrix & data.frame calculations.

NNs & Keras etc, it's better working in Python.

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