If you want to automate your own workflow around a series of NetCDF data sources then tidync provides all the flexibility and power required with as little pain as possible. A NetCDF source may be a file or an online URL. A NetCDF data source typically stores one or more arrays of data, along with metadata that describe the data array space (grid), and any metadata describing array coordinates, units, and interpretation. NetCDF is a very widely used system for storing and distributing scientific array data. We then discuss some limitations and future work, and then (most importantly) reflect on the rOpenSci process of package review. This is followed by sections on how tidync sees metadata and coordinates in NetCDF, how we can slice a dataset and control the format of the output. Then there is a detailed illustration of a raster data set in R including some of the challenges faced by R users. There is a section about the NetCDF data model itself.

The tidync package is focussed on efficient data extraction for developing your own software, and this somewhat long post takes the time to explain the concepts in detail. This overview is enough to read if you just want to try out the package on your own data. Here we provide a quick overview of the typical workflow with some pseudo-code for the main functions in tidync. In May 2019 version 0.2.0 of tidync was approved by rOpenSci and accepted to CRAN.

Reading cdf files r