API and R Nowadays, it’s pretty much expected that software comes with an HTTP API interface. Every programming language out there offers a way to expose APIs or make GET/POST/PUT requests, including R. In this post, I’ll show you how to create an API using the plumber package. Plus, I’ll give you tips on how to make it more production ready - I’ll tackle scalability, statelessness, caching, and load balancing. You’ll even see how to consume your API with other tools like python, curl, and the R own httr package.
The post below is a collection of useful (but kind of random) thoughts about R and SQL. It should serve more like a starting point, rather than exhaustive discussion of the presented topics. Note that it mostly describes SQLite - many concepts will be similar for different databases, but they might not be identical. You’ve been warned! R, SQL, dbplyr and ORM When we are talking about accessing SQL from a programming language, it is good to know a little bit about an ORM concept.
Some intuitions behind the Information Gain, Gain ratio and Symmetrical Uncertain calculated by the FSelectorRcpp package, that can be a good proxy for correlation between unordered factors. I a big fan of using FSelectorRcpp in the exploratory phase to get some overview about the data. The main workhorse is the information_gain function which calculates… information gain. But how to interpret the output of this function? To understand this, you need to know a bit about entropy.
I just started exploring the ‘active learning’ topic. It’s a very handy tool when the number of data points to build a model is limited and labelling new points is costly. It allows to determine which points should be labelled next to bring the most gain in model performance. In this post I will cover some of my small experiments in this area. Caution! If you’re interested in ready-to-use tools for active learning, this post might not be for you - I don’t cover any framework here.
Some notes based on two videos describing Apache Spark concepts. https://www.youtube.com/watch?v=AoVmgzontXo - Spark SQL: A Compiler from Queries to RDDs: Spark Summit East talk by Sameer Agarwal https://www.youtube.com/watch?v=vfiJQ7wg81Y - Top 5 Mistakes When Writing Spark Applications. Spark SQL: A Compiler from Queries to RDDs: Spark Summit East talk by Sameer Agarwal https://youtu.be/AoVmgzontXo?t=641 - an example of the optimization done by Catalyst (better to watch the whole video to get better understanding of the whole context).
Reproducibility is a severe issue. Writing code usually helps, because the code is like a journal of your work, especially if you combine it with literate programming techniques, which in R’s world is so easy to do (Rmarkdown, knitr). However, there’s one thing, which can cause some problems - the packages versions. Some of the old code might not work, because there were changes in the API or in the behavior of the packages (I’m looking at you - dplyr).
The new version of my customLayout package is on CRAN. It now supports working with PowerPoint slides using layouts created in R. For more information please read the vignette here. It also extends the idea of adjusting the font size for the flextables (see this post) - check the phl_adjust_table function. I also created a simple roadmap which describes my next steps. Please note that this package is still under development.
Warning! This post describes some intuitions behind the idea of the functional boxplots. I think that it is a very useful technique, but all statistical tools should be used with caution. Reading only one blog post might be not enough to apply them in practice. At the end of the post, I added an information about useful resources covering this topic in a more rigid way. A classical boxplot is an excellent tool for the quick summary of the data.
I recently watched the “Tidy eval: Programming with dplyr, tidyr, and ggplot2” video. It’s an excellent introduction to the concept of the tidy evaluation, which is the core concept for programming with dplyr and friends. In this video, Hadley showed on the slide the grouped_mean function (12:48). An attempt to implement this functions might be a good exercise in tidy evaluation, and an excellent opportunity to compare this approach with standard evaluation rules provided by the seplyr package.
rJava is an essential package because it allows accessing rich Java world. There are at least dozens of packages on CRAN which depends on Java (e.g., the excellent rscala for calling scala code from R). However, sometimes installing rJava might be quite problematic. In this post, I’ll focus on the pitfalls found on Linux/Ubuntu, but if you are on Windows following instructions from here, should solve your problem. R CMD javareconf One of the first thing that you should try if you have a problem with rJava is to check if you have java installed on your system, by running java -version in the console.