Our goal in creating a new package of C++ rolling functions is to build up a suite of functions useful in environmental time series analysis. We want these functions to be available in a neutral environment with no underlying data model. The functions are as straightforward to use as is reasonably possible with a target audience of data analysts at any level of R expertise.Continue reading
Beautiful Maps with MazamaSpatialPlots
Many of us have become addicted to The NY Times COVID maps — maps of US state or county level data colored by cases, vaccinations, per capita infections, etc. While recreating maps like these in R is possible, it is disappointingly difficult. The just released MazamaSpatialPlots R package takes a first stab at remedying this situation.Continue reading
Using R — Packaging a C library in 15 minutes
Yes, this post condenses 50+ hours of learning into a 15 minute tutorial. Read ’em and weep. (That is, you read while I weep.)Continue reading
Using R — Calling C code with Rcpp
In two previous posts we described how R can call C code with .C() and the more complex yet more robust option of calling C code with .Call(). Here we will describe how the Rcpp package can be used to greatly simplify your C code without forcing you to become expert in C++.Continue reading
Using R – .Call(“hello”)
In an introductory post on R APIs to C code, Calling C Code ‘Hello World!’, we explored the .C() function with some ‘Hello World!’ baby steps. In this post we will make a leap forward by implementing the same functionality using the .Call() function.Continue reading
Using R – Calling C code ‘Hello World!’
One of the reasons that R has so much functionality is that people have incorporated a lot of academic code written in C, C++, Fortran and Java into various packages. Libraries written in these languages are often both robust and fast. If you are using R to support people in a particular field, you may be called upon to incorporate some outside code into your R environment. Unfortunately, much of the documentation on how to do this is written at a very high level. In this post we will distil some of the available information on calling C code from R into three “Hello World” examples.Continue reading
Ten UNIX commands every data manager should know
Working with data from varied sources can be frustrating — some data will be in CSV format; some in XML; some available as HTML pages; other data as relational databases or MS Excel spreadsheets.
This post will cover the UNIX tools that every data manager needs to be familiar with in order to work with varied data sources.Continue reading
Data Structures – Tabular vs. Relational
With enough effort it is possible to fit a square peg into a round hole. But we have all learned — sometimes more than once — that it is much easier if peg and hole have the same shape.Continue reading
Logging and error handling in operational systems
Operational systems, by definition, need to work without human input. Systems are considered “operational” after they have ben thoroughly tested and shown to work properly with a variety of input.
However, no software is perfect and no real-world system operates with 100% availability or 100% consistent input. Things occasionally go wrong – perhaps intermittently. In a situation with occasional failures it is vitally important to have good logging and error handling. The MazamaCoreUtils R package helps with these tasks.Continue reading
Despite what they say, size does matter.
Successful data management is all about finding the proper tools and formats for dealing with your data. There is no one-size-fits-all solution. The very first question you should be asking yourself is: “How much data are we talking about?”Continue reading