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.

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.

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

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))

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)

• Points template parameters
• Developer guide (coming soon)

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.