The first argument ( x) is the object to be plotted. With the matrix prepared, we can create the heatmap. gmdistmat <- dist(select(gmun, -country)) In this case we’ll use the dist() function to create distance matrix between each country using the continuous variables calculated above. Sdgdppercap = sd(gdpPercap)) gmun countryĭ3heatmap() is essentially an interactive extension of base R’s heatmap(), and also accepts a matrix as its primary argument. # read data from github repository using readr read_csv() function Nb Russia is a member of the UN Security Council but does not appear in the gapminder data set library(d3heatmap) The code below reads in the raw data, identifies security council countries, filters the data to only include these countries, aggregates and calculates the mean and standard deviation of each variable of interest. For our purposes, we’ll look at current UN Security Council member nations 9 and their similarity based on life expectancy and GDP per capita. But since there are 142 countries, this may be easier to visualize on a subset. By aggregating on country, one calculate historical summary statistics about each nation and then visualize these side-by-side. One question that this data could address is the “similarity” of countries. The data of interest below is the gapminder data set, which includes life expectancy, GDP per capita, and population, every five years, from 1952 to 2007 for 142 countries as a data frame 8. For this tutorial we’ll focus on single D3 feature: interactive heatmapping 7. So far, individual D3 plotting methods have been distributed across multiple R packages. D3 can produce everything from choropleths 5 to scatter plots to dygraphs to network visualizations 6 … and beyond. In use since 2011, D3 is a staple of many interactive graphs featured on media outlets like the New York Times. D3 (Data Driven Documents) is one of the most well-known JavaScript visualization libraries.

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