Check out Ista Zahn's short list of 'Useless but Fun R Packages' here, and enjoy watching what the cow says, or having R tell your fortune. Now that you've taken our tour of 9 useful R data viz packages, you probably want to learn about some useless but fun R packages. And you can use RColorBrewer with dygraphs to choose a different color palette for your time series- check out this example to see how.Ĭreated by: Dan Vanderkam and RStudio Where to learn more: dygraphs for R It's got lots of other nifty interactivity features, like synchronization or the range selector shown above.īut dygraph's interactivity doesn't come at the expense of speed: it can handle huge datasets with millions of points without slowing its roll. What's powerful about dygraphs is that it's interactive right out of the box, with default mouse-over labels, zooming, and panning. This package provides an R interface for dygraphs, a fast, flexible JavaScript charting library for exploring time-series data sets.
#Best data visualization tools 2019 series#
Time series chart with range selector ( RStudio ) The tool MAGI is a multi view, single scale, single focus visualization (n-11, see Table 5) for population data, which shows mutation data and copy number data on sequence coordinates, as well as numerous other views for the visualization of non-sequence related data, such as meta data like age and gender. But fans argue that learning to master ggplot2 and (more generally) the tidyverse way of handling data pays huge dividends for any data scientist working in R.Ĭreated by: Hadley Wickham, available in Mode Where to learn more: ggplot2 The drawback of ggplot2 is that it may be slower than base R, and new programmers may find the learning curve to be a bit steep. With ggplot2, you can, for instance, start building your plot with axes, then add points, then a line, a confidence interval, and so on. Ggplot2 is based on The Grammar of Graphics, a system for understanding graphics as composed of various layers that together create a complete plot. In the words of its creator, ggplot2 “takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.” That's why ggplot2 was born: to make building custom plots easier.
While it's relatively easy to create standard plots in R, if you need to make a custom plot, things can get hairy fast.
Scatterplot ( Hadley Wickham / Tidyverse )