- 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!
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.
THIS IS AN OUTDATED VERSION OF THE POST.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/
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.
on average, fraud accounts for nearly the 5% of companies revenues
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.
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?
I’ve been recently asked to analyze some Board entertainment expenditures in order to acquire sufficient assurance about their nature and responsible.
In response to that request I have developed a little Shiny app with an interesting reactive Bubble chart.
The plot, made using ggplot2 package, is composed by:
a categorical x value, represented by the clusters identified in the expenditures population
A numerical y value, representing the total amount expended
Points defined by the total amount of expenditure in the given cluster for each company subject.
Morover, point size is given by the ratio between amount regularly passed through Account Receivable Process and total amount of expenditure for that subject in that cluster.
The advantage i found in developing such a plot is the possibility to get a lot of information at a glance.
Reactivity is given by two sliders that let you decide the period and the range you want to analyze.
Full code (with italian comments, sorry about that) is given on GitHub, but let me underline some details:
Labels are made pasting subjects names and the amount show, in order to give you both information at once.
Colours are automatically assigned by ggplot function simply by specifying
Point size is simply obtained by putting
Hope you will find at least colorful the job.
Going on talking about colours: does anyone knows how to make point solid in this case?
Any feedback will be appreciated, but please be kind!