5) + labs(title = "", x = "Rank", y ="Coup Attempts", fill = "Region") + coord_flip() + scale_fill_brewer(palette = "Dark2") + theme(plot.title = element_text(size = 20), axis. y % ggplot(aes(x, y)) + geom_col(width =. Finished gganimate graphs will “zoom in” on each individual bar. The gganimate object below displays what the “whole graph” looks like when you tell R not to display the individual graphs. gganimate is an extension of the grammar of graphics, as implemented by the ggplot2 package, that adds support for declaring animations using an API familiar to users of ggplot2. For example, if you wish to display a value for each month of the year, gganimate can create 12 individual graphs for January-December and display the corresponding values. We can think about how the packages interact in the following way: gganimate will create a series of ggplot objects and then sequentially display them individually. Before jumping into the code, however, it is useful to quickly dive down into how this package works at a conceptual level.įirst, as the package’s name suggests, gganimate is designed to work with ggplot and the broader tidyverse. While certainly versatile, it is ideal for comparing ranks across time as it allows us to create animated bar charts which can smoothly transition between different time periods. In the face of such a dilemma, the gganimate package comes in to save the day. The generally preferred alternative to this is to just use a line graph, however, this is a less intuitive option in terms of ranking while it is likely the graph will become too crowded when comparing more than a few categories. While this can be an effective option, there is considerable nuance lost in aggregating time in this way. One way around this is to compare two aggregated time periods (two decades, for instance) and present them as paired bars. Most obviously, in order to present a set of ranked categories, one must choose a single time period to visualize. However, bar graphs have significant limitations because they are not designed to visualize data over time. Though rarely considered an especially elegant data visualization, bar graphs are one of the most intuitive when comparing the size or rank of various categories of things.
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