I’d like to share some new timings on a grouped in-place aggregation task. A client of mine was seeing some slow performance, so I decided to time a very simple abstraction of one of the steps of their workflow.
Continue reading New Timings for a Grouped In-Place Aggregation Task
data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases.
In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable).
In this note we share a quick study timing how long it takes to perform some simple data manipulation tasks with R data.frames.
We are interested in the time needed to select a column, alter a column, or select a row. Knowing what is fast and what is slow is critical in planning code, so here we examine some common simple cases. It is often impractical to port large applications between different work-paradigms, so we use porting small tasks as approximate stand-ins for measuring porting whole systems.
We tend to work with medium size data (hundreds of columns and millions of rows in memory), so that is the scale we simulate and study.
Continue reading Timing Working With a Row or a Column from a data.frame
Let’s try some "ugly corner cases" for data manipulation in R. Corner cases are examples where the user might be running to the edge of where the package developer intended their package to work, and thus often where things can go wrong.
Let’s see what happens when we try to stick a fork in the power-outlet.
This note is a comment on some of the timings shared in the dplyr-0.8.0 pre-release announcement.
The original published timings were as follows:
With performance metrics: measurements are marketing. So let’s dig in the above a bit.
Our interference from then environment issue was a bit subtle. But there are variations that can be a bit more insidious.
Please consider the following.
Continue reading A Better Example of the Confused By The Environment Issue
It is no great secret: I like value oriented interfaces that preserve referential transparency. It is the side of the public debate I take in R programming.
"One of the most useful properties of expressions is that called by Quine referential transparency. In essence this means that if we wish to find the value of an expression which contains a sub-expression, the only thing we need to know about the sub-expression is its value."
Please read on for discussion of a subtle bug shared by a few popular non-standard evaluation interfaces.
Continue reading A Subtle Flaw in Some Popular R NSE Interfaces
I’ve ended up (almost accidentally) collecting a number of different solutions to the “use a column to choose values from other columns in R” problem.
Please read on for a brief benchmark comparing these methods/solutions.
We recently saw a great recurring R question: “how do you use one column to choose a different value for each row?” That is: how do you use a column as an index? Please read on for some idiomatic base R, data.table, and dplyr solutions.
One thing that is sure to get lost in my long note on macros in R is just how concise and powerful macros are. The problem is macros are concise, but they do a lot for you. So you get bogged down when you explain the joke.
Let’s try to be concise.