434 search results for "boxplot"

ERGM Tutorial

May 10, 2016
By
ERGM Tutorial

This was originally for a talk to the UC Davis Network Science group on using statnet to manage, visualize, and model networks with a focus on exponential random graph models (ERGM). I have cleaned it up a little so that it hopefully stands on its own. If anything is unclear, feel free to leave questions in the comments.

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Identify, describe, plot, and remove the outliers from the dataset

April 30, 2016
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Identify, describe, plot, and remove the outliers from the dataset

In statistics, a outlier is defined as a observation which stands far away from the most of other observations. Often a outlier is present due to the measurements error. Therefore, one of the most important task in data analysis is to identify and (if is necessary) to remove the outliers. There are different methods to Related Post

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Tufte style visualizations in R using Plotly

April 19, 2016
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2016-04-20 18_36_20-Clipboard

This post is inspired by Lukasz Piwek’s awesome Tufte in R post. We’ll try to replicate Tufte’s visualization practices in R using Plotly. You can read more about Edward Tufte here. One easy way to replicate the graphs showcased on Lukasz’s blog would be to simply use ggplotly() on his ggplot2() code. We’ll use plot_ly()

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How to sort a list of dataframes

April 13, 2016
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A method to gather data from different sources, sort them and keep a reference to the origin of each subset, plus some efficiency considerations The post How to sort a list of dataframes appeared first on MilanoR.

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Book Review: Graphical Data Analysis with R

April 7, 2016
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Book Review: Graphical Data Analysis with R

by Joseph Rickert Basically, there are two kinds of graphics or plots you can make from a data set: (1) those that allow you to see what is going on with the data, and (2) those you make to communicate what you have found to someone else. When making the first kind, you want to select plots that will...

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The Pirate Plot (2.0) – The RDI plotting choice of R pirates

April 5, 2016
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The Pirate Plot (2.0) – The RDI plotting choice of R pirates

  Plain vanilla barplots are as uninformative (and ugly) as they are popular. And boy, are they popular. From the floors of congress, to our latest scientific articles, barplots surround us. The reason why barplots are so popular is because they are so simple and easy to understand. However, that simplicity also carries costs — namely, ...

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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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