jerid.francom + package   18

Project Environments • renv
The renv package helps you create reproducible environments for your R projects. Use renv to make your R projects more:

Isolated: Installing a new or updated package for one project won’t break your other projects, and vice versa. That’s because renv gives each project its own private package library.

Portable: Easily transport your projects from one computer to another, even across different platforms. renv makes it easy to install the packages your project depends on.

Reproducible: renv records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go.
package  r  packrat  renv  reproducible  research 
september 2019 by jerid.francom
R packages
Extensive documentation on how to create and distribute R packages
r  package  development  creation 
november 2017 by jerid.francom
re2r
RE2 is a primarily DFA based regexp engine from Google that is very fast at matching large amounts of text.
r  package  regular-expressions  visualization 
november 2017 by jerid.francom
Twitter's new R package for anomaly detection
For Twitter, finding anomalies — sudden spikes or dips — in a time series is important to keep the microblogging service running smoothly. A sudden spike in shared photos may signify an "trending" event, whereas a sudden dip in posts might represent a failure in one of the back-end services that needs to be addressed. To detect such anomalies, the engineering team at Twitter created the AnomalyDetection R package, which they recently released as open source. (Late last year Twitter released a separate but related R package to detect "breakouts" in time series.) Finding spikes and dips is relatively easy...
r  twitter  package 
january 2015 by jerid.francom
jbryer/qualtrics · GitHub
install_github('qualtrics', 'jbryer')
r  qualtrics  package 
april 2013 by jerid.francom
plyr
plyr is a set of tools for a common set of problems: you need to split up a big data structure into homogeneous pieces, apply a function to each piece and then combine all the results back together. For example, you might want to:

fit the same model to subsets of a data frame
quickly calculate summary statistics for each group
perform group-wise transformations like scaling or standardising
r  plyr  package  data  transformation  statistics 
january 2012 by jerid.francom

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