This assignment had the goal of creating a multivariate data visualization using one of the discussed visualization software/features we've seen in this class. I decided to use the "swiss" data set in R that reports the fertility rates and infant mortality of the Swiss populations within its 47 French-speaking provinces in 1888 in addition to socio-economic factors. The likely problem for someone conducting an investigation on this data set would be about the variables contributing to the infant-mortality rates. The factors used were: agriculture, fertility, examination, education, and catholic population density. Using this data, I chose to explore the relationship between fertility rates and infant mortality, and incorporated teh other variables into a plot using the ggplot package.
The resulting plot:
This plot adheres to the basic principles of design in a number of ways. The first, alignment, is already encapsulated by the organized and cohesive capability of ggplot and other software. The plot is readable, coherent, and through the use of labels, gradient, and color there it is evident that the interactions between variables and the plot itself are significant--these factors contribute to the overall repetition and contrast of the visualization. Color and shape add a sense of contrast and a better sense of contrast between the data points and the goal of the visualization. Balance is accomplished through the use of labels and scaling. The data itself is asymmetrical, but the labels on all four sides aid in a sense of balance across the model. Additionally, there is no abundance of space without being too cramped.
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