1069 search results for "how to import image file to r"

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

April 18, 2013
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Examples for sjPlotting functions, including correlations and proportional tables with ggplot #rstats

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 … Weiterlesen →

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Reinhart & Rogoff: Everyone makes coding mistakes, we need to make it easy to find them + Graphing uncertainty

April 17, 2013
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Reinhart & Rogoff: Everyone makes coding mistakes, we need to make it easy to find them + Graphing uncertainty

You may have already seen a lot written on the replication of Reinhart & Rogoff’s (R &amp R) much cited 2010 paper done by Herndon, Ash, and Pollin. If you haven’t, here is a round up of some of some of what has been written: Konczal, Yglesias, Krugman, Cowen, Peng,

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Mapping the GDELT data (and some Russian protests, too)

April 15, 2013
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Mapping the GDELT data (and some Russian protests, too)

(This article was first published on Quantifying Memory, and kindly contributed to R-bloggers) In this post I show how to select relevant bits of the GDELT data in R and present some introductory ideas about how to visualise it as a network map. I've included all the code used to generate the illustrations. Because of this, if you here...

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Datasets handpicked by students

April 14, 2013
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I’m often on the hunt for datasets that will not only work well with the material we’re covering in class, but will (hopefully) pique students’ interest. One sure choice is to use data collected from the students, as it is … Continue reading →

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Win Your Snake Draft: Calculating “Value Over Replacement” using R

April 14, 2013
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Win Your Snake Draft: Calculating “Value Over Replacement” using R

In prior posts, I have demonstrated how to download, calculate, and compare fantasy football projections from ESPN, CBS, and NFL.com and how to calculate players' risk levels. In this post, I will demonstrate how...

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Changing figure options mid-chunk (in a loop) using the pander package.

April 9, 2013
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Changing figure options mid-chunk (in a loop) using the pander package.

I wrote already about changing figure options mid-chunk in reproducible research. This can be important  e.g. if you are looping through a dataset to produce a graphic for each variable but the figure width or height need to depend on properties of the variables, e.g. if you are producing histograms and want the figures to

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Halo Effects vs. Intention-Laden Ratings: Separating Baby and Bathwater

April 8, 2013
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Halo Effects vs. Intention-Laden Ratings: Separating Baby and Bathwater

Are halo effects real or illusory?  Much has been written arguing that rating scales contain extensive amounts of measurement bias.  Some tells us to avoid ratings altogether (What do customers really want?).  Others warn against the use of ratings scales without major adjustments (e.g., overcoming scale usage heterogeneity with the R package bayesm).  Obviously, by including the...

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Starting Analysis and Visualisation of Spatial Data with R

April 8, 2013
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Starting Analysis and Visualisation of Spatial Data with R

Last week I ran an introductory workshop on the analysi

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Venue Recommendation – A Simple Use Case Connecting R and Neo4j

April 7, 2013
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Venue Recommendation – A Simple Use Case Connecting R and Neo4j

Last month I attended the CeBIT trade fair in Hannover. Besides the so called “shareconomy” there was also another main topic across all expedition halls - Big Data. This subject is not completely new and I think that a lot of you also have experiences with some of the tools associated with Big Data. But due to the great...

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Quickly Profiling Compiled Code within R on the Mac

April 3, 2013
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Quickly Profiling Compiled Code within R on the Mac

This is a quick note on profiling your compiled code on the mac. It is important not to guess when figuring out where the bottlenecks in your code are, and for this reason, the R manual has several suggestions on how to profile compiled code running within R. All of the methods are platform dependent, with linux requiring command line tools

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