Logging framework dedicated for complex shiny apps.
R is great for data analysis and Shiny is great for interactive data visualisation, but could we use R&Shiny for efficient declarative data collection? Moreover, how can we develop web data products in R&Shiny, that are based on real-time declarative …
The second eRum was organized this year in Budapest (Hungary) and gathered ~500 participants (mostly, but not only, from Europe). It was a great event and a worthy successor of the first eRum organized in Poznań (Poland) two years ago.
In the Workskop Day I participated in two workshops:
Efficient R programming by Colin Gillespie (author of the Efficient R Programming book by O’Reilly) Building a package that lasts by Colin Fay.
During my research visit at Notre Dame University I had the pleasure to participate in Hadley Wickham’s lecture Welcome to the Tidyverse and meet Hadley in person. Hadley’s talks are always well-structured and worth listening.
Hadley Wickham has been a prime mover in releasing R upon the masses, enabling hordes of unsuspecting would-be researchers to process and visualize data in ways they never dreamed of. The tidyverse, the culmination of years of effort in the R language, is a universe of packages that facilitate a grammar of data, graphics, and modeling that allows even beginners to speak the language of data science fluently.
UPDATE: The online static version of the presentation is available here: http://rpubs.com/kalimu/erum. Thank you for all the feedback!
In two days, from 12 to 14 October, 2016 in Poznan (Poland) will take place the conference “eRum 2016: european R users meeting“. I will give a talk about new R package: LimeRick. LimeRick enables close connection between two important open-source projects: R and LimeSurvey. LimeSurvey is the most advanced open-source system for on-line survey and Computer Aided Web Interviewing (CAWI) and together with R they offer advanced ecosystem for gathering and analyzing declarative data.
In recent years, there has been increased interest in methods for gender prediction based on first names that employ various open data sources. These methods have applications from bibliometric studies to customizing commercial offers for web users. …
The last update of this post: 2019-01-23.
Below is a checklist that I wrote for my own purposes. I helps me to be sure that I did what I should have done before release a new version of R package to CRAN. Maybe it will also help others.
See what new Hadley recommends:
Update R & packages:
RStudio (stable or preview version).
Update your package:
Check unit tests.
The R package for gender prediction based on first names.