In this post we will explore how to set up R package development on github focusing on implementing an automatic Travis and codecoverage check.
I set up a sample repo that will include a very basic configuration: TravisR
Travis Travis is a great You can easily sign up by connecting your github account:
You will need to select the repository want to have checked and save this in a file called travis.
The following gives an overview of the most basic concepts found in python. It serves as a quick reminder when not having coded in python for a while.
Source: Download text file or Fork me on GitHub
Main if __name__ == '__main__': main() List <list> = <list>[from_inclusive : to_exclusive : step_size] <list>.append(<el>) <list>.extend(<collection>) <list> += [<el>] <list> += <collection> <list>.sort() <list>.reverse() <list> = sorted(<collection>) <iter> = reversed(<list>) sum_of_elements = sum(<collection>) elementwise_sum = [sum(pair) for pair in zip(list_a, list_b)] sorted_by_second = sorted(<collection>, key=lambda el: el[1]) sorted_by_both = sorted(<collection>, key=lambda el: (el[1], el[0])) flatter_list = list(itertools.
Using Power BI and R Tutorial here: Run R scripts in Power BI Desktop
The only twist that I want to add is an idea on how to enable users without admin access to run R code. This can be achieved by storing a portable r installation on a mountable file storage.
R Download the necessary R installation in order to be able to execute R code.
R Portable Portable R Studio Please note that running RStudio in the cloud gets really slow.
Conda How to set up a virtual environments using conda for the Anaconda Python distribution
A virtual environment is a named, isolated, working copy of Python that that maintains its own files, directories, and paths so that you can work with specific versions of libraries or Python itself without affecting other Python projects. Virtual environmets make it easy to cleanly separate different projects and avoid problems with different dependencies and version requiremetns across components.
Columnstore A columnstore index can provide a very high level of data compression, typically by 10 times, to significantly reduce your data warehouse storage cost. For analytics, a columnstore index offers an order of magnitude better performance than a btree index. Columnstore indexes are the preferred data storage format for data warehousing and analytics workloads. Starting with SQL Server 2016 (13.x), you can use columnstore indexes for real-time analytics on your operational workload.
