rud.is

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

We:

– 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:

saveWidget(rb, file="rbokeh001.html")

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.