R in corporate enivroments (finance depts.)
Walter Djuric
A short presentation, on the potential and real life uses of R (and RStudio) in a typical business (ie Accounting) department in a large Austrian financial institution.
Common pitfalls (mostly linked to infrastructure problems eg firewalls, non-admin user-rights, AV software, etc) are discussed and a short overview of tactics for coping with these challenges is given (eg set up a local CRAN, package building so that R newbies can utilize it, enhanced .

Self Service Data Preparation und Data Science
Peter Jeitschko
Peter presented Alteryx, a platform built for Business Analysts to master tasks like data management, data cleaning and modelling. The tool is windows only and will be ported to Linux soon. It can connect to multiple data sources and helps Business Analysts to deploy models in production. Finally, Peter also showed a Demo including data ingestion, an example of the Facebook Face API and some community features.

Deep learning with R using **mxnet**

A **sparklyr** introduction by Roland Boubela.

A brainstorming session about forecasting time series in R.

Materials from Max Leodolters talk in May covering the igraph package.
require(igraph)
require(data.table)
require(ggplot2)
Centrality - What is it?
Network Centrality gives you an idea of how important a vertex/node and edge/link in your network/graph \(N(V,E)\) respectively \(G(N,L)\) is.
Examples:
Closeness
Betweenness
Eigenvalue
degree
…
Some formulas:
Closeness Centrality
\[ C^{node}(n) = \frac{1}{\sum_{m \in N \setminus \{n\}}d(n,m)} \]
Betweenness centrality
\[ B^{node}(n) = \sum_{m\neq o \in N\backslash \{n\} } \frac{\sigma_{mo}(n)}{\sigma_{mo}} \]
Node-based Closeness for a link
\[ \ddot{C}^{link}(l_{n,m}) = \frac{C^{node}(n) + C^{node}(m)}{2} \]
Node-based Betweenness for a link
\[ \ddot{B}^{link}(l_{n,m}) = \frac{B^{node}(n) + B^{node}(m)}{2} \]
Link-based Betweenness for a link
\[ B^{link}(l) = \sum_{n \neq m \in N } \frac{\sigma_{nm}(l)}{\sigma_{nm}} \]
*\(\sigma\) is the number of traversing OD relations, and \(d(n,m)\) is the distance of the shortest route from \(n\) to \(m\)
What to select, \(\ddot{B}^{link}(l_{n,m})\) or \(B^{link}(l)\)?

The last meetup before the summer break concluded the igraph session with a talk by Rainer. Mario summed up his impressions from the R/Finance 2016 conference.

This meetup extensively covered the **igraph** package with talks by Florian Sobieczky and Max Leodolter.

This meetup included an extensive Text Mining in R session with an *Introduction to* **tm** by Ingo Feinerer and a talk about *Text Mining with Hadoop* by Stefan Theussl.

After a very nice hands-on introduction in yesterday’s Vienna R meetup meeting from Mario Annau, I created an example of textmining ending with a wordcloud. As the blog is called ViennaR, I chose to use a play strongly related to Vienna - Ödön von Horváth - Geschichten aus dem Wienerwald.