It all started January 26 ^th this year when I signed up to volunteer asa reviewer for R packages submitted to rOpenSci. My main motivation forwanting to volunteer was to learn something new and tocontribute to the R open source community. If you are wondering why thepeople behind rOpenSci are doing this, you can read How rOpenSci uses Code Review to Promote Reproducible Science.

We have started working on a new rOpenSci package called writexl. This package wraps the very powerful libxlsxwriter library which allows for exporting data to Microsoft Excel format. The major benefit of writexl over other packages is that it is completely written in C and has absolutely zero dependencies. No Java, Perl or Rtools are required. Getting Started The write_xlsx function writes a data frame to an xlsx file.

The new rOpenSci spelling package provides utilities for spell checking common document formats including latex, markdown, manual pages, and DESCRIPTION files. It also includes tools especially for package authors to automate spell checking of R documentation and vignettes. Spell Checking Packages The main purpose of this package is to quickly find spelling errors in R packages.

At rOpenSci, we create and curate software to help scientists with the data life cycle. These tools access, download, manage, and archive scientific data in open, reproducible ways. Early on, we realized this could only be a community effort. The variety of scientific data and workflows could only be tackled by drawing on contributions of scientists with field-specific expertise. With the community approach came challenges.

Are you thinking about submitting a package to rOpenSci’s open peer software review? Considering volunteering to review for the first time? Maybe you’re an experienced package author or reviewer and have ideas about how we can improve. Join our Community Call on Wednesday, September 13th . We want to get your feedback and we’d love to answer your questions!

As you might remember from my blog post about ropenaq, I work as a data manager and statistician for an epidemiology project called CHAI for Cardio-vascular health effects of air pollution in Telangana, India. One of our interests in CHAI is determining exposure, and sources of exposure, to PM2.5 which are very small particles in the air that have diverse adverse health effects.

The package FedData has gone through software review and is now part of rOpenSci. FedData includes functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from six datasets: The National Elevation Dataset (NED) digital elevation models (1 and 1/3 arc-second;

Contributing to an open-source community without contributing code is an oft-vaunted idea that can seem nebulous. Luckily, putting vague ideas into action is one of the strengths of the rOpenSci Community, and their package onboarding system offers a chance to do just that.

This is a phrase that comes up when you first get a dataset. It is also ambiguous. Does it mean to do some exploratory modelling? Or make some histograms, scatterplots, and boxplots? Is it both? Starting down either path, you often encounter the non-trivial growing pains of working with a new dataset.

Last week we released an update of the tesseract package to CRAN. This package provides R bindings to Google’s OCR library Tesseract. install.packages("tesseract") The new version ships with the latest libtesseract 3.05.01 on Windows and MacOS. Furthermore it includes enhancements for managing language data and using tesseract together with the magick package.