Are there any tools for feature engineering?
Very interesting question (+1). While I am not aware of any software tools that currently offer comprehensive functionality for feature engineering, there is definitely a wide range of options in that regard. Currently, as far as I know, feature engineering is still largely a laborious and manual process (i.e., see this blog post). Speaking about the feature engineering subject domain, this excellent article by Jason Brownlee provides a rather comprehensive overview of the topic.

Ben Lorica, Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media Inc., has written a very nice article, describing the state-of-art (as of June 2014) approaches, methods, tools and startups in the area of automating (or, as he put it, streamlining) feature engineering.

I took a brief look at some startups that Ben has referenced and a product by Skytree indeed looks quite impressive, especially in regard to the subject of this question. Having said that, some of their claims sound really suspicious to me (i.e., "Skytree speeds up machine learning methods by up to 150x compared to open source options"). Continuing talking about commercial data science and machine learning offerings, I have to mention solutions by Microsoft, in particular their Azure Machine Learning Studio. This Web-based product is quite powerful and elegant and offers some feature engineering functionality (FEF). For an example of some simple FEF, see this nice video.

Returning to the question, I think that the simplest approach one can apply for automating feature engineering is to use corresponding IDEs. Since you (me, too) are interested in R language as a data science backend, I would suggest to check, in addition to RStudio, another similar open source IDE, called RKWard. One of the advantages of RKWard vs RStudio is that it supports writing plugins for the IDE, thus, enabling data scientists to automate feature engineering and streamline their R-based data analysis.

Finally, on the other side of the spectrum of feature engineering solutions we can find some research projects. The two most notable seem to be Stanford University's Columbus project, described in detail in the corresponding research paper, and Brainwash, described in this paper.