Analytic administrator is a role that data scientists assume when they onboard new tools, deploy solutions, support existing standards, or train other data scientists. It is a role that works closely with IT to maintain, upgrade, and scale analytic environments. Analytic admins have a multiplier effect - as they go ...

This week, members of the Bay Area useR Group (BARUG) celebrated the group’s one hundred and first meetup with beer, pizza and three outstanding presentations at the cancer diagnostics company GRAIL. Pete Mohanty began the evening with the talk Did “Communities in Crisis” Elect Trump?: An Analysis with Kernel ...

Last week, I had the opportunity to talk to a group of Master’s level Statistics and Business Analytics students at Cal State East Bay about R and Data Science. Many in my audience were adult students coming back to school with job experience writing code in Java, Python and ...

Here are my picks for the “Top 40” new packages submitted to CRAN in April 2017. These selections, which were culled from 208 submissions, are organized into four categories: Data, Finance, Statistics and Utilities. The number of entries in the Data and Utilities categories reflect the initiatives of R developers to connect to ...

One of the most valuable things I have learned working on Data for Democracy’s Medicare drug spending project has been the value of collaborative tools. It has been my first in-depth experience using Github collaboratively, for one, but it has also introduced me to data.world. data.world is ...

In a previous post, I introduced DataFest and how one can streamline the organization of this event using Google Forms and tools from the tidyverse. In this post, I’ll walk through building a Shiny app that demonstrates the growth of DataFest over the years, both in terms of host ...

In the crowded market space of data science and R language books, Lovelace and Gillespie’s Efficient R Programming (2016) stands out from the crowd. Over the course of ten comprehensive chapters, the authors address the primary tenets of developing efficient R programs. Unless you happen to be a member of ...

Current State Using databases is unavoidable for those who analyze data as part of their jobs. As R developers, our first instinct may be to approach databases the same way we do regular files. We may attempt to read the data either all at once or as few times as ...

Shiny Apps are becoming ubiquitous as a way for data scientists to present the results of an analysis, and also to engage with information consumers who may not be coders. The trend I see is that the greater the variety of skills and interests of the information consumers for any ...

The 2017 New York R Conference was held last weekend in Manhattan. For the third consecutive year, the organizers - a partnership including Lander Analytics, The New York Meetup and Work-Bench - pulled off a spectacular event. There was a wide range of outstanding talks, some technical and others more philosophical, ...

With roots dating back to at least 1662 when John Graunt, a London merchant, published an extensive set of inferences based on mortality records, Survival Analysis is one of the oldest subfields of Statistics [1]. Basic life-table methods, including techniques for dealing with censored data, were known before 1700 [2]. In the early eighteenth ...

R’s startup behavior is incredibly powerful. R sets environment variables, loads base packages, and understands whether you’re running a script, an interactive session, or even a build command. Most R users will never have to worry about changing R’s startup process. In fact, for portability and reproducibility ...

R’s startup behavior is incredibly powerful. R sets environment variables, loads base packages, and understands whether you’re running a script, an interactive session, or even a build command. Most R users will never have to worry about changing R’s startup process. In fact, for portability and reproducibility ...