Python has a fairly famous design principle (from “PEP 20 — The Zen of Python”):
There should be one– and preferably only one –obvious way to do it.
R (especially once you add many packages) there is usually more than one way. As an example we will talk about the common
head(), and the
glimpse(). Continue reading There is usually more than one way in R
Our next "R and big data tip" is: summarizing big data.
We always say "if you are not looking at the data, you are not doing science"- and for big data you are very dependent on summaries (as you can’t actually look at everything).
Simple question: is there an easy way to summarize big data in
The answer is: yes, but we suggest you use the
replyr package to do so.
I have new short screencast up: using R and RStudio to install and experiment with Apache Spark.
More material from my recent Strata workshop Modeling big data with R, sparklyr, and Apache Spark can be found here.
replyr is an
R package that contains extensions, adaptions, and work-arounds to make remote
dplyr data sources (including big data systems such as
Spark) behave more like local data. This allows the analyst to more easily develop and debug procedures that simultaneously work on a variety of data services (in-memory
Spark2 currently being the primary supported platforms).
Are you attending or considering attending Strata / Hadoop World 2017 San Jose? Are you interested in learning to use
R to work with
h2o? Then please consider signing up for my 3 1/2 hour workshop soon. We are about half full now, but I really want to fill the room, while making sure that people who really want to go get in.
The links to the event are below. To make sure you get to participate please sign up soon!
Modeling big data with R, sparklyr, and Apache Spark (by RStudio and Win-Vector LLC)
03/14/2017 1:30pm – 5:00pm PDT (210 minutes)
Strata & Hadoop World West, San Jose Convention Center, CA; Room: LL21 C/D
link, materials (including slides)
Win-Vector LLC’s John Mount will teach how to use R to control big data analytics and modeling. In depth training to prepare you to use
This is going to be hands-on exercises with R, sparklyr, and h2o using RStudio Server Pro (generously provided by RStudio!).
Sponsored by RStudio and
- Office Hour with John Mount (Win-Vector LLC) 03/15/2017 2:40pm – 3:20pm PDT (40 minutes) Strata & Hadoop World West, San Jose Convention Center, CA; Room: Table B link Come and ask me questions about data science, machine learning, R, statistics, or whatever you like.
I am happy to announce a couple of exciting upcoming Win-Vector LLC public speaking engagements.
- BARUG Meetup Tuesday, Tuesday February 7, 2017 ~7:50pm, Intuit, Building 20, 2600 Marine Way, Mountain View, CA. Win-Vector LLC’s John Mount will be giving a “lightning talk” (15 minutes) on R calling conventions (standard versus non-standard) and showing how to use our
replyrpackage to greatly improve scripting or programming over
dplyr. Some articles on
replyrcan be found here.
- Strata & Hadoop World West, Tuesday March 14, 2017 1:30pm–5:00pm, San Jose Convention Center, CA, Location: LL21 C/D. Win-Vector LLC’s John Mount will teach how to use R to control big data analytics and modeling. In depth training to prepare you to use
rsparkling. In partnership with RStudio.
Hope to see you there!
Consider the common following problem: compute for a data set (say the infamous
iris example data set) per-group ranks. Suppose we want the rank of
Sepal.Lengths on a per-
Species basis. Frankly this is an “ugh” problem for many analysts: it involves all at the same time grouping, ordering, and window functions. It also is not likely ever the analyst’s end goal but a sub-step needed to transform data on the way to the prediction, modeling, analysis, or presentation they actually wish to get back to.
Iris, by Diliff – Own work, CC BY-SA 3.0, Link
In our previous article in this series we discussed the general ideas of “row-ID independent data manipulation” and “Split-Apply-Combine”. Here, continuing with our example, we will specialize to a data analysis pattern I call: “Grouped-Ordered-Apply”. Continue reading Organize your data manipulation in terms of “grouped ordered apply”
Statisticians and data scientists want a neat world where data is arranged in a table such that every row is an observation or instance, and every column is a variable or measurement. Getting to this state of “ready to model format” (often called a denormalized form by relational algebra types) often requires quite a bit of data manipulation. This is how
data.frames describe themselves (try “
str(data.frame(x=1:2))” in an
R-console to see this) and is part of the tidy data manifesto.
SQL (structured query language) and
dplyr can make the data arrangement process less burdensome, but using them effectively requires “index free thinking” where the data are not thought of in terms of row indices. We will explain and motivate this idea below. Continue reading The case for index-free data manipulation