535 search results for "boxplot"

Updated R & BLAS Timings

March 30, 2016
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Updated R & BLAS Timings

With the recent releases of R 3.2.4 and OpenBLAS 2.17, I decided it was time to re-benchmark R speed. I’ve settled on a particular set of tests, based on my experience as well as some of Simon Urbanek’s work which I separated into two groups: those focusing on BLAS-heavy operations and those which do not. Read the full...

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Election tRends: An interactive US election tracker (using Shiny and Plotly)

March 29, 2016
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Guest post by Jonathan Sidi Introduction The US primaries are coming on fast with almost 120 days left until the conventions. After building a shinyapp for the Israeli Elections I decided to update features in the app tried out plotly in the shiny framework. As a casual voter trying to gauge the true temperature of … Continue reading...

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Margin of Error by Geography in the American Community Survey (ACS)

March 14, 2016
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Margin of Error by Geography in the American Community Survey (ACS)

Today I will demonstrate how the margin of error in American Community Survey (ACS) estimates grow as the size of the geography decreases.  The final chart that we’ll create is this: The way I interpret the above chart is this: The ACS is very confident about its state-level estimates. It’s a bit less confident about county-level estimates. But The post

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An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity

March 3, 2016
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The Flint River in Flint, Michigan, USA, in the late 1970s. By U.S. Army Corps of Engineers, photographer unknown via Wikimedia Commons Introduction As many have heard recently residents of Flint Michigan have been rightly outraged due to the high presence of toxic chemicals including lead in their drinking water. The question arises how did

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From spss to R, part 3

March 1, 2016
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From spss to R, part 3

In this post we will start with a build-in dataset and some basic ggplot graphics. In the next post we will combine dplyr and ggplot to do awesome stuff with the Dutch University student counts from the previous lessons. We will work with the build-in dataset mtcars. There are many datasets in r library(help = "datasets") but in many examples online...

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rbokeh Version 0.4.1 Released

February 29, 2016
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The rbokeh package version 0.4.1 was recently released. The most major addition in 0.4 is support for javascript callbacks for custom interactivity, which I’ll provide an example of below and will be posting about in more detail soon. Thanks to i...

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more e’s [and R’s]

February 21, 2016
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more e’s [and R’s]

Alex Thiéry suggested debiasing the biased estimate of e by Rhee and Glynn truncated series method, so I tried the method to see how much of an improvement (if any!) this would bring. I first attempted to naïvely implement the raw formula of Rhee and Glynn with a (large) Poisson distribution on the stopping rule

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Interactive plotting with rbokeh

February 17, 2016
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Hello everyone! In this post, I will show you how you can use rbokeh to build interactive graphs and maps in R. What is bokeh? Bokeh is a popular python library used for building interactive plots and maps, and now it is also available in R, thanks to Ryan Hafen. It is a very powerful

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Why I use ggplot2

February 12, 2016
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Why I use ggplot2

If you’ve read my blog, taken one of my classes, or sat next to me on an airplane, you probably know I’m a big fan of Hadley Wickham’s ggplot2 package, especially compared to base R plotting. Not everyone agrees. Among the anti-ggplot2 crowd is JHU Professor Jeff Leek, who yesterday wrote up his thoughts on the Simply Statistics...

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Clustering French Cities (based on Temperatures)

February 11, 2016
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Clustering French Cities (based on Temperatures)

In order to illustrate hierarchical clustering techniques and k-means, I did borrow François Husson‘s dataset, with monthly average temperature in several French cities. > temp=read.table( + "http://freakonometrics.free.fr/FR_temp.txt", + header=TRUE,dec=",") We have 15 cities, with monthly observations > X=temp > boxplot(X) Since the variance seems to be rather stable, we will not ‘normalize’ the variables here, > apply(X,2,sd) Janv Fevr Mars...

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