342 search results for "PCA"

In case you missed it: August 2015 roundup

September 14, 2015
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In case you missed them, here are some articles from August of particular interest to R users. Creating interactive time series charts of financial data in R. Many R books have been translated into Chinese. A tutorial on visualizing current-events geographic data with choropleths. Revolution R Enterprise 7.4.1 is now available on Windows and Linux servers and in the...

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xkcd survey and the power to shape the internet

September 2, 2015
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xkcd survey and the power to shape the internet

The xkcd survey If you’ve never heard of xkcd, it’s “ webcomic of romance, sarcasm, math, and language” created by Randall Munroe. Also, if you’ve never heard of xkcd, be prepared for losing at least a day’s worth of productivity reading the comics and the excellent what if column where Randall answers hypothetical questions with physics. Randall...

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Modern Honey Network Machinations with R, Python, phantomjs, HTML & JavaScript

August 23, 2015
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Modern Honey Network Machinations with R, Python, phantomjs, HTML & JavaScript

This was (initially) going to be a blog post announcing the new mhn R package (more on what that is in a bit) but somewhere along the way we ended up taking a left turn at Albuquerque (as we often do here at ddsec hq) and had an adventure in a twisty maze of Modern Honey Network...

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Why I Don’t Like Jupyter (FKA IPython Notebook)

August 22, 2015
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Don’t get me wrong, it’s certainly a great tool for presenting your code or even reporting, but everytime I use it for explorative, interactive data science, I keep switching to other tools quite quickly and wonder why I am still even trying to use it. I just mostly end up with messy, broken, “ungitable” and unreadable analyses and I...

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Ofuro, start H2O on Hadoop from R

August 21, 2015
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Ofuro, start H2O on Hadoop from R

tl;dr I made a simple functionality to start H2O on hadoop from R. You can easily start H2O on hadoop, run your analytics and close all the processes without occuppying Hadoop nodes and memory all the time. I like to take a bath. Fill a bath and warm up in there is a perfect refreshment after a hard working day. I...

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5 New R Packages for Data Scientists

August 20, 2015
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5 New R Packages for Data Scientists

by Joseph Rickert One great beauty of the R ecosystem, and perhaps the primary reason for R’s phenomenal growth, is the system for contributing new packages. This, coupled to the rock solid stability of CRAN, R’s primary package repository, gives R a great advantage. However, anyone with enough technical knowhow to formulate a proper submission can contribute a package...

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R in big data pipeline

August 15, 2015
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R in big data pipeline

R is my fabovite tool for research. There are still quite a few things that only R can do or quicker/easier with R. But unfortunately a lot of people think R becomes less powerful at production stage where you really need to make sure all the functionalities run as you planned against incoming big data. Personally, what makes R special in...

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R News From JSM 2015

August 13, 2015
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R News From JSM 2015

by Joseph Rickert We can declare 2015 the year that R went mainstream at the JSM. There is no doubt about it, the calculations, visualizations and deep thinking of a great many of the world's statisticians are rendered or expressed in R and the JSM is with the program. In 2013 I was happy to have stumbled into a...

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Matrix Factorization Comes in Many Flavors: Components, Clusters, Building Blocks and Ideals

August 6, 2015
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Matrix Factorization Comes in Many Flavors: Components, Clusters, Building Blocks and Ideals

Unsupervised learning is covered in Chapter 14 of The Elements of Statistical Learning. Here we learn about several data reduction techniques including principal component analysis (PCA), K-means clustering, nonnegative matrix factorization (NMF) ...

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Peeling of group layers.

August 6, 2015
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Peeling of group layers.

As an experienced dplyr user since almost day one, I thought I knew every aspect of it. But when my new colleague, who is learning dplyr from scratch, asked me to explain the peeling of group layers with summarise, I was like, what? Turns out this actually is a thing. Let me show the example from the dplyr introduction: library(dplyr) library(nycflights13) daily...

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