I set aside a small bit of time to give [rbokeh](https://github.com/bokeh/rbokeh) a try and figured I’d share a small bit of code that shows how to make the “same” chart in both ggplot2 and rbokeh.
#### What is Bokeh/rbokeh?
rbokeh is an [htmlwidget](http://htmlwidgets.org) wrapper for the [Bokeh](http://bokeh.pydata.org/en/latest/) visualization library that has become quite popular in Python circles. Bokeh makes creating interactive charts pretty simple and rbokeh lets you do it all with R syntax.
#### Comparing ggplot & rbokeh
This is not a comprehensive introduction into rbokeh. You can get that [here (officially)](http://hafen.github.io/rbokeh/). I merely wanted to show how a ggplot idiom would map to an rbokeh one for those that may be looking to try out the rbokeh library and are familiar with ggplot. They share a very common “grammar of graphics” base where you have a plot structure and add layers and aesthetics. They each do this a tad bit differently, though, as you’ll see.
First, let’s plot a line graph with some markers in ggplot. The data I’m using is a small time series that we’ll use to plot a cumulative sum of via a line graph. It’s small enough to fit inline:
library(ggplot2) library(rbokeh) library(htmlwidgets) structure(list(wk = structure(c(16069, 16237, 16244, 16251, 16279, 16286, 16300, 16307, 16314, 16321, 16328, 16335, 16342, 16349, 16356, 16363, 16377, 16384, 16391, 16398, 16412, 16419, 16426, 16440, 16447, 16454, 16468, 16475, 16496, 16503, 16510, 16517, 16524, 16538, 16552, 16559, 16566, 16573), class = "Date"), n = c(1L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 4L, 2L, 3L, 2L, 5L, 5L, 1L, 1L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 7L, 1L, 2L, 6L, 7L, 1L, 1L, 1L, 2L, 2L, 7L, 1L)), .Names = c("wk", "n"), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -38L)) -> by_week events <- data.frame(when=as.Date(c("2014-10-09", "2015-03-20", "2015-05-15")), what=c("Thing1", "Thing2", "Thing2"))
The ggplot version is pretty straightforward:
gg <- ggplot() gg <- gg + geom_vline(data=events, aes(xintercept=as.numeric(when), color=what), linetype="dashed", alpha=1/2) gg <- gg + geom_text(data=events, aes(x=when, y=1, label=what, color=what), hjust=1.1, size=3) gg <- gg + geom_line(data=by_week, aes(x=wk, y=cumsum(n))) gg <- gg + scale_x_date(expand=c(0, 0)) gg <- gg + scale_y_continuous(limits=c(0, 100)) gg <- gg + labs(x=NULL, y="Cumulative Stuff") gg <- gg + theme_bw() gg <- gg + theme(panel.grid=element_blank()) gg <- gg + theme(panel.border=element_blank()) gg <- gg + theme(legend.position="none") gg
– setup a base ggplot object
– add a layer of marker lines (which are the 3 `events` dates)
– add a layer of text for the marker lines
– add a layer of the actual line – note that we can use `cumsum(n)` vs pre-compute it
– setup scale and other aesthetic properties
That gives us this:
Here’s a similar structure in rbokeh:
figure(width=550, height=375, logo="grey", outline_line_alpha=0) %>% ly_abline(v=events$when, color=c("red", "blue", "blue"), type=2, alpha=1/4) %>% ly_text(x=events$when, y=5, color=c("red", "blue", "blue"), text=events$what, align="right", font_size="7pt") %>% ly_lines(x=wk, y=cumsum(n), data=by_week) %>% y_range(c(0, 100)) %>% x_axis(grid=FALSE, label=NULL, major_label_text_font_size="8pt", axis_line_alpha=0) %>% y_axis(grid=FALSE, label="Cumulative Stuff", minor_tick_line_alpha=0, axis_label_text_font_size="10pt", axis_line_alpha=0) -> rb rb
Here, we set the `width` and `height` and configure some of the initial aesthetic options. Note that `outline_line_alpha=0` is the equivalent of `theme(panel.border=element_blank())`.
The markers and text do not work exactly as one might expect since there’s no way to specify a `data` parameter, so we have to set the colors manually. Also, since the target is a browser, points are specified in the same way you would with CSS. However, it’s a pretty easy translation from `geom_[hv]line` to `ly_abline` and `geom_text` to `ly_text`.
The `ly_lines` works pretty much like `geom_line`.
Notice that both ggplot and rbokeh can grok dates for plotting (though we do not need the `as.numeric` hack for rbokeh).
rbokeh will auto-compute bounds like ggplot would but I wanted the scale to go from 0 to 100 in each plot. You can think of `y_range` as `ylim` in ggplot.
To configure the axes, you work directly with `x_axis` and `y_axis` parameters vs `theme` elements in ggplot. To turn off only lines, I set the alpha to 0 in each and did the same with the y axis minor tick marks.
Here’s the rbokeh result:
NOTE: you can save out the widget with:
and I like to use the following `iframe` settings to include the widgets:
<iframe style="max-width=100%" src="rbokeh001.html" sandbox="allow-same-origin allow-scripts" width="100%" height="400" scrolling="no" seamless="seamless" frameBorder="0"></iframe>
#### Wrapping up
Hopefully this helped a bit with translating some ggplot idioms over to rbokeh and developing a working mental model of rbokeh plots. As I play with it a bit more I’ll add some more examples here in the event there are “tricks” that need to be exposed. You can find the code [up on github](https://gist.github.com/hrbrmstr/a3a1be8132530b355bf9) and please feel free to drop a note in the comments if there are better ways of doing what I did or if you have other hints for folks.