Trick question: is a
10,000 cell numeric
data.frame big or small?
In the era of “big data”
10,000 cells is minuscule. Such data could be fit on fewer than
1,000 punched cards (or less than half a box).
The joking answer is: it is small when they are selling you the system, but can be considered unfairly large later.
Continue reading Is 10,000 Cells Big?
For some time we have been teaching
R users "when working with wide tables on Spark or on databases: narrow to the columns you really want to work with early in your analysis."
The idea behind the advice is: working with fewer columns makes for quicker queries.
photo: Jacques Henri Lartigue 1912
The issue arises because wide tables (200 to 1000 columns) are quite common in big-data analytics projects. Often these are "denormalized marts" that are used to drive many different projects. For any one project only a small subset of the columns may be relevant in a calculation.
Continue reading How to Greatly Speed Up Your Spark Queries
A big “thank you!!!” to Microsoft for hosting our new introduction to seplyr. If you are working R and big data I think the seplyr package can be a valuable tool.
Continue reading Getting started with seplyr
We have updated the errata for Practical Data Science with R to reflect that it is no longer worth the effort to use the Java version of SQLScrewdriver as described.
We are very sorry for any confusion, trouble, or wasted effort bringing in Java software (something we are very familiar with, but forget not everybody uses) has caused readers. Also, database adapters for R have greatly improved, so we feel more confident depending on them alone. Practical Data Science with R remains an excellent book and a good resource to learn from that we are very proud of and fully support (hence errata). Continue reading Practical Data Science with R errata update: Java SQLScrewdriver replaced by R procedures and article
It is a bit of a shock when R
dplyr users switch from using a
tbl implementation based on R in-memory
data.frames to one based on a remote database or service. A lot of the power and convenience of the
dplyr notation is hard to maintain with these more restricted data service providers. Things that work locally can’t always be used remotely at scale. It is emphatically not yet the case that one can practice with
dplyr in one modality and hope to move to another back-end without significant debugging and work-arounds.
replyr attempts to provide a few helpful work-arounds.
Our new package
replyr supplies methods to get a grip on working with remote
tbl sources (SQL databases, Spark) through
dplyr. The idea is to add convenience functions to make such tasks more like working with an in-memory
data.frame. Results still do depend on which
dplyr service you use, but with
replyr you have fairly uniform access to some useful functions.
Continue reading New R package: replyr (get a grip on remote dplyr data services)
I have previously written on using containerized PostgreSQL with R. This show the steps for using containerized MySQL with R. Continue reading MySql in a container
A great number of readers reacted very positively to Nina Zumel‘s article Using PostgreSQL in R: A quick how-to. Part of the reason is she described an incredibly powerful data science pattern: using a formerly expensive permanent system infrastructure as a simple transient tool.
In her case the tools were the data manipulation grammars SQL (Structured Query Language) and dplyr. It happened to be the case that in both cases the implementation was supplied by a backing database system (PostgreSQL), but the database was not the center of attention for very long.
In this note we will concentrate on SQL (which itself can be used to implement dplyr operators, and is available on even Hadoop scaled systems such as Hive). Our point can be summarized as: SQL isn’t the price of admission to a server, a server is the fee paid to use SQL. We will try to reduce the fee and show how to containerize PostgreSQL on Microsoft Windows (as was already done for us on Apple OSX).
The Smashing Pumpkins “Bullet with Butterfly Wings” (start 2 minutes 6s)
“Despite all my rage I am still just a rat in a cage!”
(image credit). Continue reading Databases in containers