Shiny App for the Wittgenstein Centre Population Projections

A few weeks ago a new version of the the Wittgenstein Centre Data Explorer was launched. The data explorer is intended to disseminate the results of a recent global population projection exercise which uniquely incorporates level of education (as well as age and sex) and the scientific input of more than 500 population experts around the world. Included are the projected populations used in the 5th assessment report of the Intergovernmental Panel on Climate Change (IPCC).

Over the past year or so I have been working (on and off) with the data lab team to create a shiny app, on which the data explorer is based. All the code and data is available on my github page. Below are notes to summarise some of the lessons I learnt:

1. Large data

We had a pretty large amount of data to display (31 indicators based on up to 7 scenarios x 26 time periods x 223 geographical areas x 21 age groups x 2 genders x 7 education categories)… so somewhere over 8 million rows for some indicators. Further complexity was added by the fact that some indicators were by definition not available for some dimensions of the data, for example, population median age is not available by age group. The size and complexity meant that data manipulations were a big issue. Using read.csv to load the data didn’t really cut the mustard, taking over 2 minutes when running on the server. The fantastic saves package and ultra.fast=TRUE argument in the loads function came to the rescue, alongside some pre-formatting to avoid as much joining and reshaping of the data on the server as possible. This cut load times to a couple of seconds at most, and allowed the app to work with the indicator variables on the fly as demanded by the user selections. Once the data was in, the more than awesome dplyr functions finished the data manipulations jobs in style. I am sure there is some smarter way to get everything running a little bit quicker than it does now, but I am pretty happy with the present speed, given the initial waiting times.

2. googleVis and gvisMerge

It’s a demographic data explorer, which means population pyramids have to pop-up somewhere. We needed pyramids that illustrate population sizes by education level, on top of the standard age and sex breakdown. Static versions of the education pyramids in the explorer have previously been used by my colleagues to illustrate past and future populations. For the graphic explorer I created some interactive versions, for side-by-side comparisons over time and between countries, and which also have some tool tip features. These took a little while to develop. I played with ggvis but couldn’t get my bar charts to go horizontal. I also took a look at some other functions for interactive pyramids but I couldn’t figure out a way to overlay the educational dimension. I found a solution by creating gender specific stacked bar charts from gvisBarChart in the googleVis package and then gvisMerge to bring them together in one plot. As with the data tables, they take a second or so render, so I added a withProgress bar to try and keep the user entertained.

I could not figure out a way in R to convert the HTML code outputted by the gvisMerge function to a familiar file format for users to download. Instead I used a system call to the wkhtmltopdf program to return PDF or PNG files. By default, wkhtmltopdf was a bit hit and miss, especially with converting the more complex plots in the maps to PNG files. I found setting --enable-javascript --javascript-delay 2000 helped in many cases.

3. The shiny user community

I asked questions using the shiny tag on stackoverflow and the shiny google group a number of times. A big thank you to everyone who helped me out. Browsing through other questions and answers was also super helpful. I found this question on organising large shiny code particularly useful. Making small changes during the reviewing process became a lot easier once I broke the code up across multiple .R files with sensible names.

4. Navbar Pages

When I started building the shiny app I was using a single layout with a sidebar and tabbed pages to display data and graphics (using tabsetPanel()), adding extra tabs as we developed new features (data selection, an assumption data base, population pyramids, plots of population size, maps, FAQ’s, etc, etc.). As these grew, the switch to the new Navbar layout helped clean up the appearance and provide a better user experience, where you can move between data, graphics and background information using the bar at the top of page.

5. Shading and link buttons

I added some shading and buttons to help navigate through the data selection and between different tabs. For the shading I used cssmatic.com to generate the colour of a fluidRow background. The code generated there was copy and pasted into a tags$style element for my defined row myRow1, as such;

library(shiny)
runApp(list(
  ui = shinyUI(fluidPage(
    br(),
    fluidRow(
      class = "myRow1", 
      br(),
      selectInput('variable', 'Variable', names(iris))
    ),
    tags$style(".myRow1{background: rgba(212,228,239,1); 
                background: -moz-linear-gradient(left, rgba(212,228,239,1) 0%, rgba(44,146,208,1) 100%);
                background: -webkit-gradient(left top, right top, color-stop(0%, rgba(212,228,239,1)), color-stop(100%, rgba(44,146,208,1)));
                background: -webkit-linear-gradient(left, rgba(212,228,239,1) 0%, rgba(44,146,208,1) 100%);
                background: -o-linear-gradient(left, rgba(212,228,239,1) 0%, rgba(44,146,208,1) 100%);
                background: -ms-linear-gradient(left, rgba(212,228,239,1) 0%, rgba(44,146,208,1) 100%);
                background: linear-gradient(to right, rgba(212,228,239,1) 0%, rgba(44,146,208,1) 100%);
                filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#d4e4ef', endColorstr='#2c92d0', GradientType=1 );
                border-radius: 10px 10px 10px 10px;
                -moz-border-radius: 10px 10px 10px 10px;
                -webkit-border-radius: 10px 10px 10px 10px;}")
  )),
  server = function(input, output) {
  }
))

I added some buttons to help novice users switch between tabs once they had selected or viewed their data. It was a little tougher to implement than the shading, and in the end I need a little help. I used bootsnipp.com to add some icons and define the style of the navigation buttons (using the tags$style element again).

That is about it for the moment. I might add a few more notes to this post as they occur to me… I would encourage anyone who is tempted to learn shiny to take the plunge. I did not know JavaScript or any other web languages before I started, and I still don’t… which is the great beauty of the shiny package. I started with the RStudio tutorials, which are fantastic. The R code did not get a whole lot more complex than what I learnt there, even though the shiny app is quite large in comparisons to most others I have seen.

Any comments or suggestions for improving website are welcome.

2014 World Cup Squads

I have been having a go in R at visualising player movements for the World Cup. I wanted to use similar plots to those used to visualise international migration flows in the recent Science paper that I co-authored. In the end I came up with two plots. The first, and more complex one, is based on a non-square matrix of leagues system of players clubs by their national team.

You can zoom in and out if you click on the image.

Colours are based on the shirt of each team in the 2014 World Cup. Lines represent the connections between the country in which players play their club football (at the lines base) and their national teams (at the arrow head). Line thickness represent number of players. It’s a little cluttered, but shows nicely how many players in the English, Italian, Spanish and French leagues are involved in the world cup. It also highlights well some countries where almost all the players are at clubs abroad, for example most of the players in the African squads.

Whilst the first plot gave a lot of detail, I wanted to visualise the broader interactions, so I aggregated over leagues systems and national squads by regional confederations. This gives a square matrix:

> m
          squad
league     AFC CONCACAF CONMEBOL CAF UEFA
  AFC       49        2        1   3    1
  CONCACAF   0       13        0   0    0
  CONMEBOL   2        0       54  11    0
  CAF        0        0        0  36    0
  UEFA      41       99       37  86  296

The plot of which looks like:

This type of aggregation works really well to show how few European national players play elsewhere (only Zvjezdan Misimovic in all the European World Cup squads). It also provides a way to compare the share of non-European players plying their trade in the European leagues to those in more local leagues within their confederation.

I scraped the data from the provisional squads on Wikipedia, and then created the images with the circlize package. All the code to reproduce the plots + scraping the Wikipedia squad pages are on the my github.

Demo file for the fanplot package

Post moved to the fanplot pkgdown site

 

Circular Migration Flow Plots in R

Post deleted. See the migest pkgdown site for easier ways to create chord diagrams for directional origin-destination data.

Forecasting Environmental Immigration to the UK

A couple of months ago, a paper I worked on with co-authors from the Centre of Population Change was published in Population and Environment. It summarised work we did as part of the UK Government Office for Science Foresight project on Migration and Global Environmental Change. Our aim was to build expert based forecasts of environmental immigrants to the UK. We conducted a Delphi survey of nearly 30 migration experts from academia, the civil service and non-governmental organisations to obtain estimates on the future levels of immigration to the UK in 2030 and 2060 with uncertainty. We also asked them what proportion of current and future immigration are/will be environmental migrants. The results were incorporated into a set of model averaged Bayesian time series models through prior distributions on the mean and variance terms.

The plots in the journal article got somewhat butchered during the publication process. Below is the non-butchered version for the future immigration to the UK alongside the past immigration data from the Office of National Statistics.

At first, I was a bit taken aback with this plot. A few experts thought there were going to be some very high levels of future immigration which cause the rather striking large upper tail. However, at a second glance, the central percentiles show a gentle decrease where these is only (approximately) a 30% chance of an increase in future migration from the 2010 level throughout the forecast period.

The expert based forecast for total immigration was combined with the responses to questions on the proportion of environmental migrants, to obtain an estimate on both the current level of environmental migration (which is not currently measured) and future levels:

As is the way with these things, we came across some problems in our project. The first, was with the definition of an environmental migrant, which is not completely nailed on in the migration literature. As a result the part of the uncertainty in the expert based forecasts are reflective of not only the future level but also of the measure itself. The second was with the elicitation of uncertainty. We used a Likert type scale, which caused some difficulties even during the later round of the Delphi survey. If I was to do over, then this I reckon problem could be much better addressed by getting experts to visualise their forecast fans in an interactive website, perhaps creating a shiny app with the fanplot package. Such an approach would result in smoother fans than those in the plots above, which were based on interpolations from expert answers at only two points of time in the future (2030 and 2060).

Publication Details:

Abel, G.J., Bijak, J., Findlay, A.M., McCollum, D. and Wiśniowski, A. (2013). Forecasting environmental migration to the United Kingdom: An exploration using Bayesian models. Population and Environment. 35 (2), 183–203

Over the next 50 years, the potential impact of environmental change on human livelihoods could be considerable, with one possible consequence being increased levels of human mobility. This paper explores how uncertainty about the level of immigration to the United Kingdom as a consequence of environmental factors elsewhere may be forecast using a methodology involving Bayesian models. The conceptual understanding of forecasting is advanced in three ways. First, the analysis is believed to be the first time that the Bayesian modelling approach has been attempted in relation to environmental mobility. Second, the paper considers the expediency of this approach by comparing the responses to a Delphi survey with conventional expectations about environmental mobility in the research literature. Finally, the values and assumptions of the expert evidence provided in the Delphi survey are interrogated to illustrate the limited set of conditions under which forecasts of environmental mobility, as set out in this paper, are likely to hold.