1249 search results for "tutorial"

Tables from R into Word

February 5, 2013
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Tables from R into Word

A good looking table matters! This tutorial is on how to create a neat table in Word by combining knitr and R Markdown. I'll be using my own function, htmlTable, from the Gmisc package. Background: Because most journals that I submit to want...

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analyze the survey of income and program participation (sipp) with r

February 4, 2013
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if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp).  it's giant.  it's rich with variables.  it's monthly.  it follows households over three, four, now five year panels.  the congressional budget office uses it for their health insurance simulation.  analysts read that sipp has...

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General Navigation in R

February 1, 2013
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General Navigation in R

So you’ve finally managed to install the pesky environment but have no idea what you are looking at when you open the program. This tutorial is for you. (Again, here is a version with screenshot pictures). When you open R, it might look different than the screenshots in the picture version of the tutorial. This

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Scatterplot Matrices

January 31, 2013
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Scatterplot Matrices

Scatterplot matrices are a great way to roughly determine if you have a linear correlation between multiple variables. This is particularly helpful in pinpointing specific variables that might have similar correlations to your genomic or proteomic data. If you already have data with multiple variables, load it up as described here. If not, no worries

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How to install packages on R + screenshots

January 31, 2013
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How to install packages on R + screenshots

Have no fear, the screenshots are here! (For the original tutorial, click here) Method 1 (less typing) Part 1-Getting the Package onto Your Computer Open R via  your preferred method (icon on desktop, Start Menu, dock, etc.) Click “Packages” in the top menu then click “Install package(s)”.  Choose a mirror that is closest to your geographical location. Now

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The three-dots construct in R

January 30, 2013
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There is a mechanism that allows variability in the arguments given to R functions.  Technically it is ellipsis, but more commonly called “…”, dots, dot-dot-dot or three-dots. Basics The three-dots allows: an arbitrary number and variety of arguments passing arguments on to other functions Arbitrary arguments The two prime cases are the c and list The post The...

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Strata’s Data Driven Business Day

January 29, 2013
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The tagline for O'Reilly Strata conference series — Making Data Work — has meant that it's always been popular with practitioners, primarily data scientists working with Big Data in real-world environments. Recent Strata events have also attracted more business-oriented attendees, with events focused more on processes and outcomes than on the implementation details. On Tuesday February 26, Strata Santa...

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The components garch model in the rugarch package

January 28, 2013
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The components garch model in the rugarch package

How to fit and use the components model. Previously Related posts are: A practical introduction to garch modeling Variability of garch estimates garch estimation on impossibly long series Variance targeting in garch estimation The model The components model (created by Engle and Lee) generally works better than the more common garch(1,1) model.  Some hints about … Continue reading...

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I thought R was a letter…intro/installation

January 27, 2013
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I thought R was a letter…intro/installation

I will make a confession. This past summer, I didn’t spend my spare time watching relentlessly addicting TV shows nor clubbing in San Francisco. Instead, I checked out figures. No, not the sort of figures you’re probably thinking about. The ones that are included in research papers and have the potential to be beautiful works of

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Shiny 0.3.0 released

January 25, 2013
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Shiny 0.3.0 released

Version 0.3.0 of Shiny is now available on CRAN. This version of Shiny has several new features and bug fixes. Some of the changes are under the hood: for example, Shiny now uses a more efficient algorithm for scheduling the execution of reactive functions. There are also some user-facing changes: for example, the new runGitHub()

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