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AidanMLambert

Aidan Lambert

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GINI Coefficient by Income Share of the Lowest 20%
This visualization shows, unsurprisingly, that the GINI Coefficient tends to be smaller when the poorest 20% of the population capture a larger income share. We also see that both Population (represented by the size of each country's bubble) and the Poverty Gap (represented here by a color gradient) are distributed fairly evenly across countries with varying shares of income captured by the poorest 20%. Mouse over each bubble for more details, and use the zoom and pan tools at the top right for clearer comparisons of small countries!
Vietnam Poverty, Equality, and Population Indicators Over 25 Years
These visualizations suggest that the marked improvements we see in Vietnamese poverty indicators are being driven by improvements in Vietnam's economy as a whole, rather than solely improvement aimed at the poorest sectors. Indeed we see measures of inequality rising at the same time that poverty indicators are falling. The inclusion of population trends assures us that the improved poverty measures we observe are not due to exogenous population shocks, but actually indicate both people crossing the poverty line and improved purchasing power among the Vietnamese poor.
GINI Coefficient by Income Group 2014
Plotly plot of GINI Coefficiennts by Income Group for 2014. Stratification still evident, but lacking data for many countries.
GINI Coefficient by Income Group
Mouse over each point for more details, and use the zoom and pan tools at the top right for clearer comparisons of similar points! This interactive plot highlights a few interesting characteristics of the distribution of GINI Coefficients across countries: 1 - GINI Coefficient varies widely within each Income Group. 2 - There appears to be significant regional stratification within each Income Group. 3 - The order of the regional stratification appears relatively consistent across Income Groups, although the overall regional composition changes significantly. Note that while this plot includes 15 years of data, and so this stratification is more pronounced, it does appear in each singular year. The reason for choosing to display multiple years is due to many countries' inconsistent reporting intervals. See http://rpubs.com/AidanMLambert/301716 for a single - year example.
Cumulation Of Origin - R Markdown of Code for Senior Thesis
This is an R markdown document detailing the R code used for analysis in my undergraduate thesis, in which I examined the effects of different origin cumulation regimes on trade preference utilization rates. This was my first major foray into data analysis, and while it is evidence of a basic familiarity with R and statistical analysis, the code and vizualizations could certainly be neater. While I have used primarily SPSS and Excel in a professional capacity in recent years, I provide more recent (if simple) examples of visualizations I’ve done using R here: http://rpubs.com/AidanMLambert.
Combined %s of EEA Sectors with Utilization Rates Not Exceeding a Given Threshold
Visualization of trade preference utilization data for trade between the EU and the EEA. This was output for my undergraduate thesis and while it demonstrates at least a basic familiarity with R, it also provides an example of my early design philosophy (or lack thereof) which might be contrasted against more recent examples.
2016 TPP Member Bilateral Trade Volumes Force Network
A force network diagram of bilateral trade volumes for TPP members. Values taken from UNCOMTRADE (and VietnamPlus for Brunei-Vietnam trade). Closer and thicker links represent greater trade volumes. Variation in length is directly proportional but variation in thickness is scaled by the squared root for readability purposes. Hover over a node to highlight its trade relationships. Made in R using networkD3, a package which allows the creation of D3.js visualizations using R syntax.
2016 US - TPP Trade Flows (USD) Sankey
Sankey diagram showing 2016 US Imports from and Exports to countries which would have been party to the TPP. Hover over each link to see the value and direction of exchange. Drag each node to rearrange the countries as you see fit. Made in R using networkD3, a package which allows the creation of D3.js visualizations using R syntax.