ABSTRACT
This chapter outlines how several different types of data visualizations may be carried out in Julia. This includes very well-known approaches such as histograms and boxplots as well as perhaps lesser-known, but very useful, approaches such as violin plots and hexbin plots. The chapter explains exploratory graphics through the lens of the capabilities of GadFly.jl. Gadfly.jl has a simple plotting interface and prefers that its data is consumed as Julia dataframes, although other formats are possible. Histograms are another common plot method for summarizing univariate data. Data scientists often want to investigate the distributions within data. An alternative way of visualizing distributions of data is the empirical cumulative distribution function plot. Frequently data scientists want to graphically display a three-dimensional surface. In many data science problems, it is desirable to summarize model parameters and the uncertainty associated with these parameters.
