1662 search results for "Twitter"

Resources for getting started with R

June 18, 2013
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Resources for getting started with R

As we believe you may know, we are having a webinar tomorrow (June 19th, 2013) on Predictive Analytics. During this webinar, you are going to be introduced to R, learn how to build a predictive model and also how to carry insightful analysis through visualization. As learning a new language can be a really difficult

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Stashing and playing with raw data locally from the web

June 17, 2013
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It is getting easier to get data directly into R from the web. Often R packages that retrieve data from the web return useful R data structures to users like a data.frame. This is a good thing of course to make things user friendly.

However, what if you want to drill down into the data that's returned from a query...

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A list of R packages, by popularity

June 14, 2013
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A list of R packages, by popularity

R package developer (and R-bloggers editor) Tal Galili just published the answers to a question many R users have asked: which are the most popular R packages? He wrote some R code to rank the top 100 packages by number of downloads. Here's the top 10: The source data are the download logs from the RStudio CRAN mirror, whose...

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Visualizing densities of spatial processes

June 11, 2013
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Visualizing densities of spatial processes

We recently uploaded on http://hal.archives-ouvertes.fr/hal-00725090 a revised version of our work, with Ewen Gallic (a.k.a. @3wen) on Visualizing spatial processes using Ripley’s correction: an application to bodily-injury car accident location In this paper, we investigate (and extend) Ripley’s circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical...

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In case you missed it: May 2013 Roundup

June 10, 2013
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In case you missed them, here are some articles from May of particular interest to R users: Billions of geotagged Tweets create a beautiful map of the world when plotted with the ggmap package. A review of Ryan Sheftel's talk at R/Finance, on how he uses R on the trading desk at Credit Suisse. Also, a quick take on...

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Better Neighborhoods with R: Exploring and Analyzing SeeClickFix Data (part 1)

Better Neighborhoods with R: Exploring and Analyzing SeeClickFix Data (part 1)

Better Neighborhoods with R: Exploring and Analyzing SeeClickFix Data (part 1) The ‎ National Day of Civic Hacking took place …

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How old is the oldest person you know?

June 4, 2013
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How old is the oldest person you know?

Last week, we had a discussion with some colleagues about the fact that – in order to prepare for the SOA exams – we did not have time (so far) to mention results on extreme values in our actuarial program. I did gave an introduction in my nonlife actuarial models class, but it was only an introduction, in three...

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Value at Risk and Expected Shortfall, and other upcoming events

June 4, 2013
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Value at Risk and Expected Shortfall, and other upcoming events

Highlighted Value at Risk and Expected Shortfall A two-day course exploring Value at Risk and Expected Shortfall, and their role in risk management. 2013 June 25 & 26, London. Lead by Patrick Burns. Details at the CFP Events site. New Events Thalesians — San Francisco 2013 June 5. Jesse Davis on “Risk Model Imposed Manager-to-Manager … Continue reading...

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Getting started with twitteR in R

June 3, 2013
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Getting started with twitteR in R

I have asked by a few people lately to help walk them through using twitter API in R, and I’ve always just directed them to the blog post I wrote last year during the US presidential debates not knowing that Twitter had changed a few things. Having my interest peaked through a potential project at

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Understanding the value of Predictive Analytics on Web Data

June 3, 2013
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Understanding the value of Predictive Analytics on Web Data

In this blogpost, I will be talking briefly about Predictive Analytics and why it holds value from a web analytics perspective. Broadly speaking, Predictive Analytics is a set of methodologies that assist us in anticipating customer behavior. The customer behavior of interest could be anything ranging from spend, buying habits, page views, response to a

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