This is a cheatsheet about parallel programming in R. The heart of this cheatsheet is based on the Parallel Computing in R course from DataCamp by Hana ¦ev?íková.

Data science is one of the most exciting and fastest growing careers in the world. The goal of this series is to help people with no background and limited resources transition into data science. It would be helpful to have already taken our Introduction, Organizing Data Science Projects, and Version Control courses. We guide you through the rest!

In this installment of the Learn R series, we’re going to start to have a look at data types, dataframes and what on earth you do with them!

In my current project on Long-term care at some point we were required to use a regression model with multinomial responses. I was very surprised that in contrast to well-covered binomial GLM for binary response case, multinomial case is poorly described. Surely, there are half-dozen packages overlapping each other, however, there is no sound tutorial or vignette. Hopefully, my post will improve the current state.

In this blogpost, I’ll highlight how I gradually improved on my plumber building/testing workflow and eventually automated my testing steps.

This book describes the algorithms and procedures used to fit statistical models to data. The material covered is taught in the Advanced Statistical Computing course in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

Clustering analysis is used in many contexts to group similar samples. One problem when conducting this kind of analysis is how many clusters to use. This is usually controlled by a parameter provided to the clustering algorithm, such as k for k-means clustering.

One my primary uses of twitter favorites is to bookmark interesting content. The trouble is they easily accumulate and the twitter search interface does not make it easy to re-find that one tweet from 3 months ago. In the following blog post I show how you can use the rtweet and DT packages to make yourself a searchable twitter favorites dashboard in the style of https://jsta.rbind.io/tweets.

Given your data, someone else can reproduce your exact results

The workflowr R package helps researchers organize their analyses in a way that promotes effective project management, reproducibility, collaboration, and sharing of results. Workflowr combines literate programming (knitr and rmarkdown) and version control (Git, via git2r) to generate a website containing time-stamped, versioned, and documented results. Any R user can quickly and easily adopt workflowr.

replacement for gather/spread

Everything seemed fine, until I opened the spreadsheet and realized that the correct answers were highlighted in bold, and the position of the correct answer for each question was already randomized. I’ve written about not using formatting this way (and so have many others before me, see here) , but I personally hadn’t had to deal with this kind of dataset before.

The foolbox package implements functionality for static analysis of R functions and for manipulating functions by rewriting the components they consist of. The package was written to collect similar functionality from the pmatch and tailr packages, that both have functions for rewriting other functions, but is a general framework for static analysis and function rewriting.

@adr @tranlib also works with RStudio and other interfaces :) For example

Missing V8/JavaScript integration, all good otherwise.