mages' blog

We released googleVis version 0.6.1 on CRAN last week. The update fixes issues with setting certain options, following the switch from RJSONIO to jsonlite.

Screen shot of some of the Google Charts

New to googleVis? The package provides an interface between R and the Google Charts Tools, allowing you to create interactive web charts from R without uploading your data to Google. The charts are displayed by default via the R internal help browser.

To lean more see the examples of googleVis charts on CRAN and read the introduction vignette.

Staying on top of new CRAN packages is quite a challenge nowadays. However, thanks to Dirk's CRANberries service I occasionally spot a new gem, such as wbstats, which appeared on CRAN last week.

Similarly to the WDI package, wbstats offers an interface to the World Bank database.

With the functions of wbstats the World Bank data can be searched and data for several indicators requested. Unlike WDI, the data is returned in a 'long' table with one column for all values and a separate column for the indicators. Additionally, the function wb allows me to specify how many most recent values (mrv) I am interested.

Thus, to recreate the famous Gapminder chart by Hans Rosling, showing the correlation between fertility, i.e. number of children per woman, and life expectancy over time by country and region, I can write (note, a Flash player is required):


If you'd like to learn more about how to create interactive charts with googleVis, then check out the free tutorial on DataCamp.

Session Info

R version 3.2.4 (2016-03-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.4 (El Capitan)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets 
[6] methods   base     

other attached packages:
[1] googleVis_0.5.10 data.table_1.9.6 wbstats_0.1     

loaded via a namespace (and not attached):
[1] httr_1.1.0        R6_2.1.2          rsconnect_0.4.2.1
[4] tools_3.2.4       curl_0.9.7        RJSONIO_1.3-0    
[7] jsonlite_0.9.19   chron_2.3-47 

We released googleVis version 0.5.8 on CRAN last week. The update is a maintenance release for the forthcoming release of R 3.2.0.

Screen shot of some of the Google Charts

New to googleVis? The package provides an interface between R and the Google Charts Tools, allowing you to create interactive web charts from R without uploading your data to Google. The charts are displayed by default via the R internal help browser.

Visit the examples of all googleVis charts on CRAN and review the vignettes.

Version 0.5.6 of googleVis was released on CRAN over the weekend. This version fixes a bug in gvisMotionChart. Its arguments xvar, yvar, sizevar and colorvar were not always picked up correctly.

Thanks to Juuso Parkkinen for reporting this issue.

Example: Love, or to love

A few years ago Martin Hilpert posted an interesting case study for motion charts. Martin is a linguist and he researched how the usage of words in American English changed over time, e.g. some words were more often used as nouns in the past and then became more popular as a verb. Do you talk about love, or do you tell someone that you love her/him? Visit his motion chart web page for more information and details!

R code

Session Info

R version 3.1.1 (2014-07-10)
Platform: x86_64-apple-darwin13.1.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base     

other attached packages:
[1] googleVis_0.5.6

loaded via a namespace (and not attached):
[1] RJSONIO_1.3-0 tools_3.1.1

The Google Charts API is quite powerful and via googleVis you can access it from R. Here is an example that demonstrates how you can zoom into your chart.

Earlier this week we released googleVis 0.5.5 on CRAN. The package provides an interface between R and Google Charts, allowing you to create interactive web charts from R. This is mainly a maintenance release, updating documentation and minor issues.

Screen shot of some of the Google Charts

New to googleVis? Review the examples of all googleVis charts on CRAN.

Perhaps the best known example of the Google Chart API is the motion chart, popularised by Hans Rosling in his 2006 TED talk.

Recently we released googleVis 0.5.3 on CRAN. The package provides an interface between R and Google Charts, allowing you to create interactive web charts from R.

Screen shot of some of the Google Charts

Although this is mainly a maintenance release, I'd like to point out two changes:

• Default chart width is set to 'automatic' instead of 500 pixels.
• Intervals for columns roles have to end with the suffix ".i", with "i" being an integer. Several interval columns are allowed, see the roles demo and vignette for more details.

Those changes were required to fix the following issues:

• The order of y-variables in core charts wasn't maintained. Thanks to John Taveras for reporting this bug.
• Width and height of googleVis charts were only accepted in pixels, although the Google Charts API uses standard HTML units (for example, '100px', '80em', '60', 'automatic'). If no units are specified the number is assumed to be pixels. This has been fixed. Thanks to Paul Murrell for reporting this issue.

New to googleVis? Review the demo on CRAN.

The World Cup has finally kicked off last Thursday and I have seen some fantastic games already. Perhaps the Netherlands appears to be the strongest side so far, following their 5-1 victory over Spain.

To me the question is not only which country will win the World Cup, but also which prediction model will come closest to the actual results. Here I present three teams, FiveThirtyEight, a polling aggregation website, Groll & Schauberger, two academics from Munich and finally Lloyd's of London, the insurance market.

The guys around Nate Silver at FiveThirtyEight have used the ESPN’s Soccer Power Index to predict the outcomes of games and continue to update their model. Brazil is their clear favourite.

Andreas Groll & Gunther Schauberger from the LMU Munich developed a model, which like the approach from FiveThirtyEight aims to estimate the probability of a team winning the world cup. But unlike FiveThirtyEight, they see Germany to take the trophy home.

Lloyd's chose a different approach for predicting the World Cup final. The insurance market looked at the risk aspect of the teams and ranked the teams by their insured value. Arguably the better a team the higher their insured value. As a result Lloyd's predicts Germany to win the World Cup.

Quick reminder; what's the difference between insurance and gambling? Gambling introduces risk, where none exists. Insurance mitigates risk, where risk exists.

The 10th Kölner R user meeting took place last Friday at the Institute of Sociology and to celebrate the anniversary we invited Andrie de Vries to join us from Revolution Analytics. Andrie is well known in the R community; he is the co-author of the R for Dummies book and an active contributor on stackoverflow.

Taking R to the Enterprise

Andrie de Vries: Taking R to the Enterprise. Photo: Günter Faes

Andrie talked about how R is finding its way into the enterprise. He argued that R has become the go-to tool for data and statistical analysis in many companies. He observed that over the years the commercial user base evolved from a small expert community with a love for the command line and cutting edge technology into a much wider audience. Although R is still used by the technical experts, who demand more and more power, out-of-memory computations on bigger data, etc. the number of casual R users is growing rapidly. Often the casual R user relies on code written by others as part of a bigger workflow and hence prefers a more standardised user interface.

Andrie illustrated how Revolution Analytics aims to satisfy both user groups. He started with the expert R users by demonstrating Revolution Analytics' RevoScaleR package that allows them to carry out analysis on bigger data. His example followed closely the airline analysis of Joseph Rickert's white paper. Andrie then showed how more complex R code and functions can be integrated (and to some extend hidden) into Alteryx and Tableau via Rserve.

googleVis overview & developments

Slides and code available on GitHub

I gave a brief introduction to googleVis and presented recent developments. The googleVis package provides an interface between R and the Google Chart Tools API. It allows users to create web pages with interactive charts based on R data frames and to display them either via the local R HTTP help server or within their own sites, without uploading the data to Google. The best known example is perhaps the motion chart of fertility and life expectancy data from the World Bank as first presented by Hans Rosling. Another popular example is the chart that illustrates the performance of the famous Lloyd's insurance market.

Since version 0.5.0 of googleVis many new chart types have been added to googleVis, including annotation, sankey, calendar and timeline charts. Additionally new markdown vignettes have been included to present the examples also on CRAN. The slides and rmarkdown code are available via GitHub.

Kölsch & Schnitzel

No Kölner R meeting would be complete without a few Kölsch and Schnitzel at the Lux. We were lucky with the weather so that we could sit outside to enjoy drinks, food and networking opportunities.

Kölsch and Schnitzel at the Lux. Photo: Günter Faes

Next Kölner R meeting

The next meeting is scheduled for 12 September 2014.

Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.

Thanks again to Bernd Weiß for hosting the event and Revolution Analytics for their sponsorship.

Saturday's Eurovision Song Contest (ESC) from Copenhagen was hilarious as usual with acts from all over Europe and some more or less sensible gimmicks: a circular piano, a giant hamster wheel, a sea-saw, or indeed a beard and fancy dress.

The results of the ESC were only a little different to what the bookmakers in the UK had predicted before the event started. Sweden was seen as the favourite, followed by Austria, Netherlands, Armenia and the UK.

In the end Conchita Wurst from Austria won with 290 points in front of the acts from the Netherlands with 238, Sweden with 218 and Armenia with 174 points. The UK ended on the 17th rank with only 40 points. Perhaps the reason the English bookies had put the UK in fifth place reflected the bias of their clients towards their home country.

The points were given as a combination of a public tele vote and a jury in each country. But how big was the resemblance between the votes of the juries and the public? Is there much variance between countries?

The detailed voting data are available from the Eurovision site. Out of the 37 countries that participated in the voting, two countries, San Marino and Albania, had no tele voting and one country, Georgia, had no jury. In the remaining 34 countries Austria and the Netherlands made into the top 2 of the jury and public voting results if they were treated independently. Sweden made it into the top 5 of both as well, but the other countries differed.


So, how consistent was the voting of the jury and public for the top 5 in each country?

Well, in only 11 countries out of 34, the jury agreed with the public on more than 2 of the top 5 songs. In 25 countries the jury and public agreed on at least two candidates.

In some cases the differences between public and jury were so wide, that although a candidate was voted into the top 5 of the tele rankings the act wouldn't get any points. The top public winner in Belgium (Armenia), Ireland (Poland), Montenegro (Russia) and the UK (Poland) didn't get any points at all.

Still, getting the top favourites right seems much easier then any of the followers. Or in other words, it is really difficult to produce a hit, a song/act on which many can agree. But when you hear one, it is much easier to identify it as such, something you really like and believe others would like as well.

R Code

At the end of March Google released a new version of the Chart Tools API with new options for point shapes and line brushes. The arguments are called pointShape and lineDashStyle and can be set directly via googleVis.

We published googleVis 0.5.2 on CRAN yesterday with added examples for those new options in gvisLineChart and gvisScatterChart. Note, these options can be used with most chart types as well, also in combination. Visit the Google documentation for more details.

Customising Points

Customising Lines

Session Info

R version 3.1.0 (2014-04-10)
Platform: x86_64-apple-darwin13.1.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base     

other attached packages:
[1] googleVis_0.5.2

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.1.0 

GoogleVis 0.5.1 was released on CRAN yesterday.

New Features

• New functions gvisSankey, gvisAnnotationChart, gvisHistogram,
gvisCalendar and gvisTimeline to support the new Google charts of the same names (without 'gvis').
• New demo Trendlines showing how trend-lines can be added to Scatter-, Bar-, Column-, and Line Charts.
• New demo Roles showing how different column roles can be used in core charts to highlight data.
• New vignettes written in R Markdown showcasing googleVis examples and how the package works with knitr.

Changes

• The help files of gvis charts no longer show all their options, instead a link to the online Google API documentation is given.
• Updated googleVis demo
• All googleVis output will be displayed in your default browser. In previous versions of googleVis output could also be viewed in the preview pane of RStudio. This feature is no longer available with the current version of RStudio, but is likely to be introduced again with the release of RStudio version 0.99 or higher.

I will discuss the new features in more detail at the R user group meetings in Tokyo this Thursday and in Cologne, 23 May.

After my posts on timeline, Sankey and calendar charts, this will be the last to introduce new chart types of the developer version of googleVis. Today I will give examples for the new annotation charts and histograms.

Annotation charts

Annotation charts have been part of the Google Chart tools for a long time and googleVis as well. However, in the past only a flash based version was available (gvisAnnotatedTimeLine in googleVis). With the new Google Charts Tools version also a HTML5 version was released. In googleVis it is integrated as gvisAnnotationChart. It works in exactly the same way as the Flash based version, so here is an example.



For the R code below to run you will require the developer version (≥ 0.5.0-4) of googleVis from GitHub and R ≥ 3.0.2.

Histograms

Histograms are a new chart type in Google Chart Tools API - not so in R. Still, it is nice to know that one can create those now as well.


For more information and installation instructions see the googleVis project site and Google documentation.

Session Info

R version 3.0.3 (2014-03-06)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base     

other attached packages:
[1] googleVis_0.5.0-4

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.3 

My little series of posts about the new googleVis charts continues with calendar charts.

Google's calendar charts are still in beta, but they provide already a nice heat map visualisation of calendar year data. The current development version of googleVis supports this new function via gvisCalendar. Here is an example displaying daily stock price data.


For the code below to run you will require the developer version (≥ 0.5.0-4) of googleVis from GitHub and R ≥ 3.0.2.

I suppose the biggest current drawback is that the colours of the cells cannot be defined by the user. However, this should change with future versions of the Google Chart Tools. For more information and installation instructions see the googleVis project site and Google documentation.

Interestingly, the calendar chart looks very similar to the visualisation R. Wicklin and R. Allison from SAS used for the winning poster at the Data Expo 2009. Paul Bleicher created a function in R, based on lattice that creates a very similar output. You may recall David Smith's blog post about this.

Session Info

R version 3.0.3 (2014-03-06)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] grid stats graphics grDevices utils datasets  methods  
[8] base     

other attached packages:
[1] googleVis_0.5.0-4 chron_2.3-45 lattice_0.20-24  

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.3

Sankey diagrams are great for visualising flows from one set of data values to another. Although named after Irish Captain Matthew Henry Phineas Riall Sankey, who used this type of diagram in 1898 to show the energy efficiency of a steam engine, the best know Sankey diagram is probably Charles Minard's Map of Napoleon's Russian Campaign of 1812, which he actually produced in 1869.

Thomas Rahlf: Datendesign mit R

The above example from Thomas Rahlf's book Datendesign mit R shows that Minard's plot can be reproduced with base graphics in R. Aaron Berdanier posted in 2010 the SankeyR function and January Weiner published the river plot package on CRAN that allows users to create static Sankey charts as well.

Interactive Sankey diagram can be generated with rCharts and now also with googleVis (version >= 0.5.0). For my a first example I use UK visitor data from VisitBritain.org. The following diagram visualises the flow of visitors in 2012; where they came from and which parts of the UK they visited. This example illustrates the key concept already. I need a data frame with three columns that explains the flow of data from a source to a target and the strength or weight of the connection.




My next example uses a graph data set that I visualise in the same way again, but here I start to play around with the various parameters of the Google API.




As stated by Google, the Sankey chart may be undergoing substantial revisions in future Google Charts releases.

For more information and installation instructions see the googleVis project site and Google documentation.

Session Info

R version 3.0.3 (2014-03-06)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets  methods  
[7] base     

other attached packages:
[1] googleVis_0.5.0-4 igraph_0.7.0     

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.3 

Last year at the Google I/O conference Mitchell Foley presented new developments of the Google Chart Tools API and one of the new features he mentioned were timeline charts (about 6 min into the talk).


Timeline charts are a great way of visualising different dates/events over time and are now also supported by googleVis from version 0.5.0 onwards (currently only available from GitHub). Here is an example, showing classroom allocation in the afternoon. The exact times and durations are given when you hover over the bars.



I can swap around the bar and row labels to show the rooms by languages:



Here is another example, inspired by Jason Bryer's timeline package, showing the US presidents and UK prime ministers during World War II. For gvisTimeline I have to remove the line breaks in Jason's data.



And finally a more complex example from the Google Chart Tools API documentation showing the terms of the first US presidents with various options set to change the colours and fonts:

After nearly 4 years of developing googleVis on Google Code with SVN we decided to move to GitHub. The main reason was that Google stopped the facility of hosting pre-CRAN builds of the package for user testing. The devtools package on the other hand makes it really easy to install packages from source hosted on GitHub. Additionally, we hope that GitHub will make collaboration with others more effective. Thus, bookmark http://github.com/mages/googleVis.

Screen shot of some of the new features in googleVis 0.5.0-1.

There are some exciting new features in the development version of 0.5.0-1 of googleVis, reflecting the enhanced Google Chart Tools API:

New Features

• New functions gvisSankey, gvisAnnotationChart, gvisHistogram, gvisCalendar and gvisTimeline to support the new Google charts of the same names (without 'gvis').
  
• New demo Trendlines showing how trend-lines can be added to Scatter-, Bar-, Column-, and Line Charts.
  
• New demo Roles showing how different column roles can be used in core charts to highlight data.
  
• New vignettes written in R Markdown showcasing googleVis examples and how the package works with knitr.

Changes

• The help files of gvis charts no longer show all their options, instead a link to the online Google API documentation is given.
  
• All googleVis output will be displayed in your default browser. In previous versions of googleVis output could also be displayed in the preview pane of RStudio. This feature is no longer available with the current version of RStudio, but is likely to be introduced again with the release of RStudio version 0.99 or higher.
  

I will post about the new features and changes in the coming weeks. Please feel free to test the development version already. Visit our GitHub project page for installation instructions and further details.

For the impatient (you will require R >= 3.0.2):

install.packages(c("devtools","RJSONIO", "knitr", "shiny", "httpuv"))
library(devtools)
install_github("mages/googleVis")

In my previous post, I presented a preview version of googleVis that provided an integration with RStudio's Viewer pane (introduced with version 0.98.441).

Over 80% in my little survey favoured the new default output mechanism of googleVis within RStudio. Hence, I uploaded googleVis 0.4.7 on CRAN over the weekend.

However, there were also some thoughtful comments, which suggested that the RStudio Viewer pane is not always the best option. Indeed, Flash charts and gvisMerge output will still be displayed in your default browser, but also if you work on larger charts and with smaller screen, then the browser might still be the better option compared to the Viewer pane - of course you can launch the browser from the Viewer pane as well.

Hence, googleVis gained a new option 'googleVis.viewer' that controls the default output of the googleVis plot method. On package load it is set to getOption("viewer") and if you use RStudio, then its viewer pane will be used for displaying non-Flash and un-merged charts. You can set options("googleVis.viewer" = NULL) and the googleVis plot function will open all output in the default browser again. Thanks to J.J. from RStudio for the tip.

The screen shot below shows a geo chart within the RStudio Viewer pane of the
devastating typhoon track of Haiyan that hit Southeast Asia last week.

Session Info

RStudio v0.98.456 and R version 3.0.2 (2013-09-25)

Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base     

other attached packages:
[1] googleVis_0.4.7 XML_3.95-0.2   

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.2  

The preview version 0.98.441 of RStudio introduced a new viewer pane to render local web content and with that it allows me to display googleVis charts within RStudio rather than in a separate browser window.


I think this is a rather nice feature and hence I have updated the plot method in googleVis to use the RStudio viewer pane as the default output. If you use another editor, or if the plot is using one of the Flash based charts, then the browser is still the default display.

The behaviour can also be controlled via the option viewer. Set options("viewer"=NULL) and googleVis will plot all output in the browser again.

Of course shiny apps can also run in the viewer pane. Here is the example of the renderGvis help page of googleVis. For more information about the new viewer pane see the online RStudio documentation.


For the time being you can get the next version 0.4.6 of googleVis from our project site only. Please get in touch if you find any issues or bugs with this version, or add them to our issues list.

Is this a step in the right direction? Please use the voting buttons below.

Session Info

R Under development (unstable) (2013-10-25 r64109)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] googleVis_0.4.6

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.1.0 

Changing the plotting width in bar-, column- and combo-charts of googleVis works identical and is defined by the bar.groupWidth argument. The dot in the argument means that it has to be split in R into bar="{groupWidth:'10%'}".

Example

library(googleVis)
cc <- gvisColumnChart(head(Population,10), 
                      xvar="Country", yvar="Population",
                      options=list(seriesType="bars", legend="top",
                                   bar="{groupWidth:'10%'}",
                                   width=500, height=450),
                      chartid="thincolumns")
plot(cc)

Session Info

R version 3.0.1 (2013-05-16)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] googleVis_0.4.5

loaded via a namespace (and not attached):
[1] RJSONIO_1.0-3 tools_3.0.1