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
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.