In order to avoid repeating code we will use the following function to plot two Bessel functions in R ( J_0(x) and J_2(x)): plotl <- function(. In the following sections we will explain how to customize the most common arguments of the function. Recall that there are even more arguments you can use, but we listed the most common, so type args(legend), ?legend or help(legend) for additional information. Horiz = FALSE # Horizontal (TRUE) or vertical (FALSE) legend Pch, # Add pch symbols to legend lines or boxesīty = "o", # Box type (bty = "n" removes the box)īg = par("bg") # Background color of the legendīox.lwd = par("lwd"), # Legend box line widthīox.lty = par("lty"), # Legend box line typeīox.col = par("fg"), # Legend box line color Legend, # Vector with the name of each groupįill, # Creates boxes in the legend with the specified colorsĬol = par("col"), # Color of lines or symbolsīorder = "black", # Fill box border color The summarized syntax of the function with the most common arguments is described in the following block: legend(x, y, # Coordinates (x also accepts keywords) If we've missed any of your favorite R data viz libraries in this short list, let us know! Tweet at us 22 update: R has been upgraded to version 4.2.The legend function allows you to add a legend to a plot in base R. 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. Time series chart with range selector ( RStudio ) 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 ) Mode R Notebooks support three libraries on this list - ggplot2, Lattice, and Plotly - and more than 60 others that you can explore on our Notebook support page. To provide one path through the labyrinth, we’re giving an overview of 9 useful interdisciplinary R data visualization packages. This means there are packages for practically any data visualization task you can imagine, from visualizing cancer genomes to graphing the action of a book.įor new R coders, or anyone looking to hone their R data viz chops, CRAN's repository may seem like an embarrassment of riches-there are so many data viz packages out there, it's hard to know where to start. If you've visited the CRAN repository of R packages lately, you might have noticed that the number of available packages has now topped a dizzying 18,000+. What changes to geomtext do I need to make to fix this. Instead of adding the country name, it is adding the row number. 22 update: R has been upgraded to version 4.2.0 and popular R libraries have been updated in Mode’s notebook I am having trouble adding labels to points on a scatter plot using ggplot.
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