22 October 2014

test

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test Post Blog Entry 5 January 2014

test at noon Baghdad

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Rchievement of the day #3: Bloggin’ from R

ASCIImoose: William K. Morris’s Blog

I have become a complete knitr addict of late and have been using it in combination with RStudio’s R markdown support on a regular basis. In fact I wrote this post using it! It then dawned on me how great it would be if I could upload the post directly from R/RStudio. It turned out that wasn’t too hard at all. Here’s how.


How to update your WordPress.com blog from R

Installing the RWordPress package

First I need to install (if I haven’t already done so) and load the “RWordPress” package from www.omegahat.org. The “RWordPress” package uses XML-RPC to connect to the WordPress blogging engine.

 

Connecting to WordPress.com

Next I set my username, password and website url as R global options.

That was not my real password by the way!

The “RWordpress” package provides a bunch of functions (see ?RWordPress) based on methods from the WordPress

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Shiny 0.8.0 released; Yihui Xie joins RStudio

RStudio Blog

We’re very pleased to announce Shiny 0.8.0 (which actually went up on CRAN about two weeks ago). This release features a vastly better way to display tabular data, and new debugging tools that make it much easier to fix errors in your app.

DataTables support

datatables

We now support much more attractive and powerful displays of tabular data using the popular DataTables library. Our DataTables integration features pagination, searching/filtering, sorting, and more. Check out this demo to see it in action, and learn more about how to use it in your own apps by visiting the tutorial’s chapter on DataTables.

Debugging tools

In version 0.8.0 of the Shiny package, we’ve greatly improved the set of debugging tools you can use with your Shiny apps. It’s now much easier to figure out what’s happening when things go wrong, thanks to two new features:

  • Integration with the new visual debugger that’s available…

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Star Wars Arc Diagram

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Examples for sjPlotting functions, including correlations and proportional tables with ggplot #rstats

Füge hier deine Gedanken hinzu… (optional)

Strenge Jacke!

Sometimes people ask me how the examples of my plotting functions I show here can be reproduced without having a SPSS data set (or at least, without having the data set I use because it’s not public yet). So I started to write some examples that run “out of the box” and which I want to present you here. Furthermore, two new plotting functions are introduced: plotting correlations and plotting proportional tables on a percentage scale.

As always, you can find the latest version of my R scripts on my download page.

Following plotting functions will be described in this posting:

  • Plotting proportional tables: sjPlotPropTable.R
  • Plotting correlations: sjPlotCorr.R
  • Plotting frequencies: sjPlotFrequencies.R
  • Plotting grouped frequencies: sjPlotGroupFrequencies.R
  • Plotting linear model: sjPlotLinreg.R
  • Plotting generalized linear models: sjPlotOdds.R

Please note that I have changed function and parameter names in order to have consistent, logical names across all functions!

At the end of this…

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Tutorials on git/github and GNU make

A much needed tutorial

The stupidest thing...

If you’re not using version control, you should be. Learn git.

If you’re not on github, you should be. That’s real open source.

To help some colleagues get started with git and github, I wrote a minimal tutorial. There are lots of git and github resources available, but I thought I’d give just the bare minimum to get started; after using git and github for a while, other resources make a lot more sense and seem much more worthwhile.

And for R folks, note that it’s easy to install R packages that are hosted on github, using Hadley Wickham‘s devtools package. For example, to install Nacho Caballero‘s clickme package:

Having written that git/github tutorial, I thought: I should write more such!

So I immediately wrote a similar short tutorial on GNU make, which I think is the most important tool for reproducible research.

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