With so many people jumping on the data science bandwagon, how can you tell a great R coder from the rest?
While the debate rages about a reproducibility crisis in academia, there’s a much more common one right under our noses
By writing a function to analyze Star Wars characters, learn the powerful abstraction capabilities of R
How to test your server side output and stop unnecessary red error messages of death appearing in your user interface
Constantly manually executing SQL queries for your clients? Here’s a way to get them to help themselves.
It is now possible to build Shiny apps that can update themselves on a regular basis, pulling in refreshed data so that people are always looking at the most up to date analysis. So with one up front development sprint, an analyst can reduce their ongoing analytic workload on a particular topic by close to 100%.
As data scientists, we are all familiar with what happens when a process, package or application breaks. We dive into it with interest to try to diagnose where the unanticipated error occurred. Did something unexpected occur in the raw data? Did our code not anticipate a particular permutation of inputs?
I have pieced together ten simple questions that test how well someone knows what they are doing in R.
This is the second in a series of articles explaining the principles of graph theory for those who may use it in a data science context.
In April, I permanently deleted my Facebook presence. I joined in 2008 and was fairly active over the years. In the process of deleting my account, I downloaded all my data.