More than thirty R users and a lot of enthusiasm!

You can download presentations and view pictures of the meeting.

If you attended the meeting, please leave a comment.

Stay connected! Visit the Milano R net website to be updated with future events organized by Milano R net community and its sponsors.

See you at the next Milano R net event.

Fiori Oscuri Bistrot & Bar (www.fiorioscuri.it)
Via Fiori Oscuri, 3 - Milano (Zona Brera)

• 18.00 - 18.15
  Registration
• 18.15 - 18.30
  Welcome presentation
  Andrea Spanò, Partner at Quantide
• 18.30 - 19.00
  Digit recognition
  Machine learning: A case of digit recognition based on a shallow neural network implemented in R
  Michele Vitali, Statistical consultant
• 19.00 - 19.30
  Chess betting odds
  How to develop a shining web app, scraping the web and building a model to calculate the odds for the chess team events, in no time with R, RStudio and Shiny.
  Alberto Santini, Banca Monte dei Paschi di Siena
• 19.30 - 21.00
  Open bar and discussion

Milano R net meeting sponsored by

How to attend?

Places are limited so, if you would like to attend to the third Milano R net meeting, please register to this page. Meeting we’ll be free of charge, our sponsors will provide the open bar session.

Fiori Oscuri Bistrot & Bar (www.fiorioscuri.it)
Via Fiori Oscuri, 3 - Milano (Zona Brera)

• 18.00 - 18.15
  Registration
• 18.15 - 18.30
  Welcome presentation
  Andrea Spanò, Partner at Quantide
• 18.30 - 19.00
  Digit recognition
  Machine learning: A case of digit recognition based on a shallow neural network implemented in R
  Michele Vitali, Statistical consultant
• 19.00 - 19.30
  Chess betting odds
  How to develop a shining web app, scraping the web and building a model to calculate the odds for the chess team events, in no time with R, RStudio and Shiny.
  Alberto Santini, Banca Monte dei Paschi di Siena
• 19.30 - 21.00
  Open bar and discussion

Milano R net meeting sponsored by

How to attend?

Places are limited so, if you would like to attend to the third Milano R net meeting, please register to this page. Meeting we’ll be free of charge, our sponsors will provide the open bar session.

Fiori Oscuri Bistrot & Bar
Via Fiori Oscuri, 3
Milano

Further details will be published shortly. Stay connected!

Course description
This two days course aims to provide an overview of the basic R environment and its applications. This course is intendended as a starting point for any future development with R.
At the end of the course, you will be able to gain awareness of the basic R language, importing/exporting data and manipulate data using R. You will do exploratory data analysis, perform basic statistics and build plots using R.

Who should attend this course
Anyone who is new to R and willing to learn it. Anyone who has some previous knowledge of R and wants to organize it in a more structured way.

Course outline
Introduction to R: overview
R Data Objects
Data Import And Export
Using R functions
R Graphics
Writing a function
Introduction to Statistical Models with R
Tables with R

Course location
Hotel Michelangelo; Via Scarlatti, 33; Milano (near Central Railway Station)

Course language
Course will be held in Italian. Course material will be in English.

Course price
Commercial: 680,00 EUR (plus VAT). No profit and academic: 480,00 EUR (plus VAT)
Price includes coffee breaks and lunches.
10% discounts for 2 participants of the same company (commercial attendees only)
20% discounts for 3 participants of the same company (commercial attendees only)
For 4 or more participants of the same company or no profit institution, please contact us

Course time
February 21-22, 2013 from 9.00 to 18.00.

Course material
The course material will be provided during the course.

Information and bookings: training@quantide.com

"Learning RStudio for R Statistical Computing" will teach you how to quickly and efficiently create and manage statistical analysis projects, import data, develop R scripts, and generate reports and graphics. R developers will learn about package development, coding principles, and version control with RStudio.

This book will help you to learn and understand RStudio features to effectively perform statistical analysis and reporting, code editing, and R development.

The book starts with a quick introduction where you will learn to load data, perform simple analysis, plot a graph, and generate automatic reports. You will then be able to explore the available features for effective coding, graphical analysis, R project management, report generation, and even project management.

Book review

"Learning RStudio for R Statistical Computing" is not another book about R. It shows how to use proficiency R through RStudio capabilities.

First three chapters may be useful for new RStudio users: they describe how to install R and RStudio, the features of the R Console and of the Editor, the object browser and the plot window. I think I didn't learned something new by reading this first chapters. By the way, their reading wasn't useless: I reorganized my information about RStudio in a more structured way, while tables with shortcuts are fantastic!

Chapter 4 is about R Projects. I began using R Project some days before I read this book, so this was not a new argument to me. By the way, I learned new and useful information, particularly from the "Tips and tricks" sections. At the moment, I am not using some version control system, but I appreciate this great opportunity provided by RStudio. The book explains in a very clear way and to non-experienced users how to integrate R and SVN.

Chapter 5 is the core of the book. The title "Generating Report" hides the extended opportunities provided by RStudio and well described here. I was looking for a simple way to get a report from an R script, without encapsulate it in a Markdown or a Sweave document. I already have the solution in my RStudio, but I didn't know. Reading the book I knew Notebook, the simple way to generate a report from an R script. For more structured reports, R scripts can be encapsulated in Markdown, Rhtml or Sweave documents. The book compare this great opportunities provided by RStudio.

Chapter 6 contains some useful hints about using RStudio. If you already use RStudio, may be you already know most part of topics of this chapter. But also experienced users may find something new: I discovered Roxygen2, an R extension that helps to document functions.

In conclusion, RStudio is not a simple editor for R. It is a true IDE with a lot of useful tools. "Learning RStudio for R Statistical Computing" by Mark P.J. van der Loo and Edwin de Jonge is a useful book, both for new and experienced users, to discover these tools.

A choropleth map is a thematic map featuring regions colored or shaded according to the value assumed by the variable of interest in that particular region.

Packages used

We make use of three packages (plus their dependencies) to produce the maps in this post.

• maptools: a set of tools for reading and handling spatial objects. In particular, it will be used to read .shp files, a common format used by geographic information systems software.
• ggplot2: one of the powerful graphics engines available to R users
• ggmap: a package for spatial visualization using popular on-line mapping systems, such as GoogleMaps or OpenStreetMap.

So, let's load them into R.

> library(maptools)
> library(ggplot2)
> library(ggmap)

Data used

In order to produce the maps displayed later in this post, we used data publicly available from Eurostat.

The polygons for drawing the administrative boundaries were obtained from this link. In particular, the NUTS 2010 shapefile in the 1:60 million scale was downloaded and used. The other available scales would allow the drawing of better defined maps, but at a computational cost. The zipped file has to be extracted in a folder of choice for using it later.

Note: for an introduction on NUTS (Nomenclature of territorial units for statistics) classification, look at this Introduction on Eurostat website.

The statistic that will be drawn in the map is the the public expenditure on education as a percentage of the Gross Domestic Product (year 2009), and is one of the many indicators available for download at Eurostat's statistics portal.

In order to make the following code fully reproducible, I'm uploading below the .csv file I obtained after performing the various selections on the Eurostat portal.

csv file: educ_thexp_1_Data

With the following commands we read both the shapefile and the csv in R.

# read administrative boundaries (change folder appropriately)
> eurMap eurEdueurEdu$Value

Data preparation

The eurMap object could potentially be used as is to produce a map. In fact, a command as simple as plot(eurMap) would draw the map of Europe at the NUTS-3 level.

However, since we are going to make use of the great versatility of the ggplot2 graphics facility, we need to transform eurMap to a data frame, as that is the only type of object accepted by the ggplot() function. The ggplot2 package has the fortify() function that takes care of said conversion.

# convert shp data to data frame for ggplot
> eurMapDf

After that, we can add the data on education expenditure to the newly created data frame. Note that, since administrative borders will be plotted as paths, it's important to have them ordered as they have to be drawn.

# merge map and data
> eurEduMapDfeurEduMapDf

Finally, to avoid the plotting of European territories far away from mainland Europe, we put some coordinates limits to the data, using the geocode() function we introduced in previous posts.

# limit data to main Europe
> europe.limits  eurEduMapDf  min(europe.limits$lon) & long < max(europe.limits$lon) & lat > min(europe.limits$lat) & lat < max(europe.limits$lat))

Mapping with ggplot2

While not specifically designed for the purpose, the ggplot2 package is well suited for the display of maps. Let's first create an empty map (just showing borders,) by adding a geom_path layer to a ggplot() call.

# ggplot mapping
# data layer
> m0m1

The map in the following image is obtained by typing m1 in the R console.

Then, by adding a geom_polygon layer, we color the country area according to the education expenditure value (stored in the Value column.)

# fill with education expenditure data
> m2

And here is the result of typing m2 in the R console.

The country borders are not visible anymore because the filled polygons have been stacked on top of them. Changing the order of layers will produce a more readable map.

# inverse order (to have visible borders)
> m0  m1  m2  m2

Overlay on GoogleMaps

Polygon and path layers can be overlaid on a map obtained from GoogleMaps (or OpenStreetMaps) using the ggmap package introduced in the previous posts.

It must be noted that the overlaying will work only if the boundary files have the same coordinate system and the same projection of the underlying map. In this case, the combination of Eurostats shapefiles and GoogleMaps works fine.

# over a GoogleMap (not working if not correctly projected)
> map m0 m1m2

As a final touch, let's put text on the map. Using the summaryBy() funcion of the doBy package, we calculate average values for multiple variables. The solution of using the average coordinates of all boundary points as the position where to place the text is a lazy one, as appropriate ways to calculate the center of mass of a polygon should be used instead.

# add text
> library(doBy)
> txtValm3

Where to find Shapefiles

If you are looking for shapefiles, your best choice is probably googling for them. However, here are a few places where you can find them.

• The Eurostat page on Administrative and statistical units mentioned at the beginning of this post.
• ISTAT's cartography page for Italian administrative units. Also contains geocoded data points of ports, police stations and many other things.
• The Global Administrative Areas website has worldwide boundaries at various levels in several formats.
• CShapes features historical boundaries going back to 1946, thus useful for displaying older data. They can be downloaded as shapefiles, but chsapes also comes as an R package.

View (and download) the full code:

library(maptools)
library(ggplot2)
library(ggmap)
 
# read administrative boundaries (change folder appropriately)
eurMap <- readShapePoly(fn="NUTS_2010_60M_SH/Shape/data/NUTS_RG_60M_2010")
 
# read downloaded data (change folder appropriately)
eurEdu <- read.csv("educ_thexp_1_Data.csv", stringsAsFactors = F)
eurEdu$Value <- as.double(eurEdu$Value) #format as numeric
 
# merge map and data
eurEduMapDf <- merge(eurMapDf, eurEdu, by.x="id", by.y="GEO")
eurEduMapDf <- eurEduMapDf[order(eurEduMapDf$order),]
 
#limit data to main Europe
europe.limits <- geocode(c("Cape Fligely, Rudolf Island, Franz Josef Land, Russia", "Gavdos, Greece", "Faja Grande, Azores", "Severny Island, Novaya Zemlya, Russia"))
 
eurEduMapDf <- subset(eurEduMapDf, long > min(europe.limits$lon) & long < max(europe.limits$lon) & lat > min(europe.limits$lat) & lat < max(europe.limits$lat))
 
# ggplot mapping
# data layer
m0 <- ggplot(data=eurEduMapDf)
# empty map (only borders)
m1 <- m0 + geom_path(aes(x=long, y=lat, group=group), color='gray') + coord_equal()
 
# fill with education expenditure data
m2 <- m1 + geom_polygon(aes(x=long, y=lat, group=group, fill=Value))
 
# inverse order (to have visible borders)
m0 <- ggplot(data=eurEduMapDf)
m1 <- m0 + geom_polygon(aes(x=long, y=lat, group=group, fill=Value)) + coord_equal()
m2 <- m1 + geom_path(aes(x=long, y=lat, group=group), color='black')
m2
 
# over a GoogleMap (not working if not correctly projected)
map <- get_map(location = 'Europe', zoom=4)
m0 <- ggmap(map)
m1 <- m0 + geom_polygon(aes(x=long, y=lat, group=group, fill=Value), data=eurEduMapDf, alpha=.9)
m2 <- m1 + geom_path(aes(x=long, y=lat, group=group), data=eurEduMapDf, color='black')
 
# add text
library(doBy)
txtVal <- summaryBy(long + lat + Value ~ id, data=eurEduMapDf, FUN=mean, keep.names=T)
m3 <- m2 + geom_text(aes(x=long, y=lat, label=Value), data=txtVal, col="yellow", cex=3)

Course description
This two days course aims to provide an overview of the basic R environment and its applications. This course is intendended as a starting point for any future development with R.
At the end of the course, you will be able to gain awareness of the basic R language, importing/exporting data and manipulate data using R. You will do exploratory data analysis, perform basic statistics and build plots using R.

Who should attend this course
Anyone who is new to R and willing to learn it. Anyone who has some previous knowledge of R and wants to organize it in a more structured way.

Course outline
Introduction to R: overview
R Data Objects
Data Import And Export
Using R functions
R Graphics
Writing a function
Introduction to Statistical Models with R
Tables with R

Course location
Hotel Michelangelo; Via Scarlatti, 33; Milano (near Central Railway Station)

Course language
Course will be held in Italian. Course material will be in English.

Course price
Commercial: 680,00 EUR (plus VAT). No profit and academic: 480,00 EUR (plus VAT)
Price includes coffee breaks and lunches.
10% discounts for 2 participants of the same company (commercial attendees only)
20% discounts for 3 participants of the same company (commercial attendees only)
For 4 or more participants of the same company or no profit institution, please contact us

Course time
February 21-22, 2013 from 9.00 to 18.00.

Course material
The course material will be provided during the course.

Information and bookings: training@quantide.com