mages' blog

New R package to access World Bank data

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 

Notes from 2nd Bayesian Mixer Meetup

Last Friday the 2nd Bayesian Mixer Meetup (@BayesianMixer) took place at Cass Business School, thanks to Pietro Millossovich and Andreas Tsanakas, who helped to organise the event.

Bayesian Mixer at Cass

First up was Davide De March talking about the challenges in biochemistry experimentation, which are often characterised by complex and emerging relations among components.

The very little prior knowledge about complex molecules bindings left a fertile field for a probabilistic graphical model. In particular, Bayesian networks can help the investigator in the definition of a conditional dependence/independence structure where a joint multivariate probability distribution is determined. Hence, the use of Bayesian network can lead to a more efficient way of designing experiments.

Davide De March: Bayesian Networks to design optimal experiments

The second act of the night was Mick Cooney, presenting ideas of using growth curves to estimate the ultimate amounts paid in insurance by some cohort of policies.

The talk showed a model for these curves, discussed the implementation in Stan and how posterior predictive checks can be used to assess the output of the model.

Mick Cooney: Bayesian Modelling for Loss Curves in Insurance

Thanks again to everyone who helped to make the event a success, particularly our speakers and Jon Sedar of Applied AI.

We are planning to run another event in mid-June. Please get in touch via our Meetup site with ideas and talk proposals.