📊 streamline your analyses linking R to sas and more: the workfloweR experiment đŸ–„

📊 streamline your analyses linking R to sas and more: the workfloweR experiment đŸ–„
we all know R is the first choice for statistical analysis and data visualisation, but what about big data munging? tidyverse (or we’d better say hadleyverse 😏) has been doing a lot in this field, nevertheless it is often the case this kind of activities being handled from some other coding language. Moreover, sometimes you get as an input pieces of analyses performed with other kind of languages or, what is worst, piece of databases packed in proprietary format (like .dta .xpt and other). So let’s assume you are an R enthusiast like I am, and you do with R all of your work, reporting included, wouldn’t be great to have some nitty gritty way to merge together all these languages in a streamlined workflow?

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Over 50 practical recipes for data analysis with R in one book

Over 50 practical recipes for data analysis with R in one book
Ah, writing a blog post! This is a pleasure I was forgetting, and you can guess it looking at last post date of publication: it was around january... you may be wondering: what have you done along this long time? Well, quite a lot indeed:
  • changed my job ( I am now working @ Intesa Sanpaolo Banking Group on Basel III statistical models)
  • became dad for the third time (and if you are guessing, it’s a boy!)
  • fixed some issues with the updateR package
  • and I wrote a book!
Hope this pretty long list will help you forgive me for my long silence. I am actually pretty proud of all of them, but let’s talk about the book now. I think it is an useful contribution to the R community. But first of all, the title:

RStudio for R Statistical Computing Cookbook

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how to list loaded packages in R: ramazon gets cleaver

how to list loaded packages in R: ramazon gets cleaver

It was around midnight here in Italy:
I shared the code on Github, published a post on G+, Linkedin and Twitter and then went to bed.

In the next hours things got growing by themselves, with pleasant results like the following:

The R community found ramazon a really helpful package.

And I actually think it is: Amazon AWS is nowadays one of the most common tools for online web applications and websites hosting.

The possibility to get your Shiny App on it just running a function make it even more desirable for the amusing R people.

Therefore, even if I developed ramazon for personal purposes , I now feel some kind of responsibility to further develop it and take it updated.

Continue reading “how to list loaded packages in R: ramazon gets cleaver”

ramazon: Deploy your Shiny App on AWS with a Function

ramazon: Deploy your Shiny App on AWS with a Function

THIS IS AN OUTDATED VERSION OF THE POST. YOU CAN FIND THE UPDATED AND MAINTAINED ONE AT http://www.andreacirillo.com/2015/08/18/deploy-your-shiny-app-on-aws-with-a-function/

Because Afraus received a good interest, last month I override shinyapps.io free plan limits.

That got me move my Shiny App on an Amazon AWS instance.

Well, it was not so straight forward: even if there is plenty of tutorials around the web, every one seems to miss a part: upgrading R version, removing shiny-server examples… And even having all info it is still quite a long, error-prone process.

All this pain is removed by ramazon, an R package that I developed to take care of everything is needed to deploy a shiny app on an AWS instance. An early disclaimer for Windows users: only Apple OS X is supported at the moment.

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Introducing Afraus: an Unsupervised Fraud Detection Algorithm

Introducing Afraus: an Unsupervised Fraud Detection Algorithm
The last Report to the Nation published by ACFE, stated that on average, fraud accounts for nearly the 5% of companies revenues.
on average, fraud accounts for nearly the 5% of companies revenues

Tweet: on average, fraud accounts for nearly the 5% of companies revenues. http://ctt.ec/u5E6x+

ACFE Infographic: typical organization loses 5% of their revenues for fraud
Projecting this number for the whole world GDP, it results that the “fraud-country” produces something like a GDP 3 times greater than the Canadian GDP.

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How to add a live chat to your Shiny app

How to add a live chat to your Shiny app
As I am currently working on a Fraud Analytics Web Application based on Shiny (currently on beta version, more later on this blog) I found myself asking: wouldn’t be great to add live chat support to my Web Application visitors?
It would indeed!
an ancient example of chatting - Camera degli Sposi, Andrea Mantegna 1465 -1474
an ancient example of chatting – Camera degli Sposi, Andrea Mantegna 1465 -1474
But how to do it?
Unfortunately, looking on Google didn’t give any useful result.
Therefore I had to find it out by myself.

Continue reading “How to add a live chat to your Shiny app”

Catching Fraud with Benford’s law (and another Shiny App)

Catching Fraud with Benford’s law (and another Shiny App)

In the early ‘900 Frank Benford observed that ‘1’ was more frequent as first digit in his own logarithms manual.

More than one hundred years later, we can use this curious finding to look for fraud on populations of data.

just give a try to the shiny app

What ‘Benford’s Law’ stands for?

Around 1938 Frank Benford, a physicist at the General Electrics research laboratories, observed that logarithmic tables were more worn within first pages: was this casual or due to an actual prevalence of numbers near 1 as first digits?

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Querying Google With R

Querying Google With R
If you have a blog you may want to discover how your website is performing for given keywords on Google Search Engine. As we all know, this topic is not a trivial one.
Problem is that the analogycal solution would be quite time-consuming, requiring you to search your website for every single keyword, on many many pages.
Feeling this way?
“Pain and fear, pain and fear for me” – Oliver Twist
I was.

Continue reading “Querying Google With R”