Awesome

NOTE: Clickme is no longer in active development

Clickme is an R package that lets you create interactive visualizations in the browser, directly from your R session.

That means you can minimize your use of boring static plots.

Install

Just run this in R to install Clickme:

install.packages("devtools") # you don't need to run this command if you already have the devtools package installed.

devtools::install_github("clickme", "nachocab")

If there is a new Clickme version a few days later, you can update by simply re-running that last command.

Examples

Let's take it out for a spin.

library(clickme)
clickme("points", rnorm(100)) # try zooming in and out, click the Show names button, hover over points

points

Clickme remembers the most recently used template, so you don't need to specify it again

clickme(rnorm(50))

A more interesting example

data(microarray)
clickme("points", x = microarray$significance, y = microarray$logFC,
        color_groups = ifelse(microarray$adj.P.Val < 1e-4, "Significant", "Noise"),
        names = microarray$gene_name,
        x_title = "Significance (-log10)", y_title = "Fold-change (log2)",
        extra = list(Probe = microarray$probe_name))

microarray

You can also try lines

xy_values <- list(line1 = data.frame(x = 1:4, y = 5:8),
                  line2 = data.frame(x = 1:5, y = 10:14))
clickme("lines", xy_values, radius = 5)

lines

Resources

Points template parameters
Developer guide (coming soon)

Acknowledgements

Thank you Mike Bostock. Making the D3.js library more accessible was my strongest motivation for developing Clickme.

Thank you Yihui Xie. The knitr R package has shown me the importance of building bridges across technologies, while also turning my scientific ramblings into reproducible work.

Thank you Hadley Wickam. The testthat R package has been consistently saving my butt since I started coding for a living.

There are other fine people trying to move visualization to the browser. Check out rCharts by Ramnath Vaidyanathan.