June 2019

In the first Data Voyage article, I introduced my Book of Names as my record-keeping document for all the people I met. Given the great diversity of nationalities I found, in this article, I explore a potential explanator of this diversity, and if there is a relationship between it and other sources (please, refer to citations).

Working Holiday Visas

I came to New Zealand with a Working Holiday Visa (WHV) and, given the places where I had stayed and worked, I frequently met people who were also on this scheme. The New Zealand government offers a different number of visas for many countries, while some nations have no limitation.
The following plot compares the number of WHV granted to each country to a comparative scale of nationalities in my Book of Names.

This bar chart attempts to compare the number of WHV offered annually for every country against a proportional distribution of nationalities in my Book of Names (the red bars). While it does tell about with which nationalities I had more contact during my trip, there doesn’t seem to be a noticeable pattern related to visas allowance.

Distance

Another potential explanation might be related to the distance to each country.
The length of the bars spanning from the central axis represents the distance from each country to New Zealand, while the colour of the bar indicates on a comparative scale the number of people. The dashed circle represents a 10,000 kilometres milestone.
Distances are calculated as the linear geographical interval between the capital cities, for instance, between Wellington and Buenos Aires.

Economic relationships

What if the number of WHV granted is associated with New Zealand’s commercial partnership? The graphs so far suggested no relationship between external factors and the origin of my acquaintances, but it might be interesting to explore how New Zealand distributes its WHV.
The first bubble plot explores the relationship between total commerce with New Zealand and the number of WHV; the size of each bubble represents the number of acquaintances in my Book of Names.

Most countries hold a total trade value under 5 billion New Zealand dollars. Unlimited visas show a slightly higher commercial exchange, which are in turn dwarfed by an outlier to the right-most edge of the chart - presumably, China. The blue line is a linear regression model, describing a null relationship.
Similarly, the chart below replaces the commercial relationship with a World Bank GDP Per Capita measure from the year 2014 - the latest one with widely available data.

In this chart, the correlation shows even neater than in the last one. There is a positive linear relationship between GDP Per Capita and the number of visas granted to every given country. However, it looks like I met people from both high and low GDP Per Capita countries.

Wrap-up

Citations

“My Voyage in Data” is a personal project to share my data analysis and visualization skills with R and combine it in a story-telling format. For those interested in the code I used on this publication, feel free to contact me.
Concerning the WHV quota, I did a cursory Internet search, and all I could find about why some countries have more visas than others was that these are “reciprocal agreements”, but could not find details on the rationale behind these. There could be many reasons, from commercial partnership to international security; but it is not readily available or disclosed.
Below are the citations of technologies and external resources I used in this article.

## R Core Team (2018). _R: A Language and Environment for Statistical
## Computing_. R Foundation for Statistical Computing, Vienna,
## Austria. <URL: https://www.R-project.org/>.
## 
## Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2018). _maps: Draw
## Geographical Maps_. R package version 3.3.0, <URL:
## https://CRAN.R-project.org/package=maps>.
## 
## Hijmans R (2019). _geosphere: Spherical Trigonometry_. R package
## version 1.5-10, <URL:
## https://CRAN.R-project.org/package=geosphere>.
## 
## Vaidyanathan R, Xie Y, Allaire J, Cheng J, Russell K (2018).
## _htmlwidgets: HTML Widgets for R_. R package version 1.3, <URL:
## https://CRAN.R-project.org/package=htmlwidgets>.
## 
## Wickham H (2017). _httr: Tools for Working with URLs and HTTP_. R
## package version 1.3.1, <URL:
## https://CRAN.R-project.org/package=httr>.
## 
## Wickham H (2019). _rvest: Easily Harvest (Scrape) Web Pages_. R
## package version 0.3.4, <URL:
## https://CRAN.R-project.org/package=rvest>.
## 
## Wickham H, Hester J, Ooms J (2018). _xml2: Parse XML_. R package
## version 1.2.0, <URL: https://CRAN.R-project.org/package=xml2>.
## 
## Wickham H (2018). _stringr: Simple, Consistent Wrappers for Common
## String Operations_. R package version 1.3.1, <URL:
## https://CRAN.R-project.org/package=stringr>.
## 
## Wickham H, Henry L (2018). _tidyr: Easily Tidy Data with
## 'spread()' and 'gather()' Functions_. R package version 0.8.2,
## <URL: https://CRAN.R-project.org/package=tidyr>.
## 
## Garnier S (2018). _viridis: Default Color Maps from 'matplotlib'_.
## R package version 0.5.1, <URL:
## https://CRAN.R-project.org/package=viridis>.
## 
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## 'matplotlib' (Lite Version)_. R package version 0.3.0, <URL:
## https://CRAN.R-project.org/package=viridisLite>.
## 
## Rudis B, Gandy D (2017). _waffle: Create Waffle Chart
## Visualizations in R_. R package version 0.7.0, <URL:
## https://CRAN.R-project.org/package=waffle>.
## 
## Wickham H, François R, Henry L, Müller K (2019). _dplyr: A Grammar
## of Data Manipulation_. R package version 0.8.3, <URL:
## https://CRAN.R-project.org/package=dplyr>.
## 
## Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_.
## Springer-Verlag New York. ISBN 978-3-319-24277-4, <URL:
## http://ggplot2.org>.