The plotly package. A godsend for interactive documents, dashboard and presentations. For such documents, there is no doubt that anyone would prefer a plot created in plotly rather than ggplot2. Why? Using plotly gives you neat and crucially interactive options at the top, whereas ggplot2 objects are static. In an app we have been developing here at Jumping Rivers, we found ourselves asking the question would it be quicker to use
plot_ly() or wrapping a ggplot2 object in
ggplotly()? I found the results staggering.
Throughout we will be using the packages: dplyr, tidyr, ggplot2, plotly and microbenchmark. The data in use is the
birthdays dataset in the mosaicData package. This data sets contains the daily birth count in each state of the USA from 1969 – 1988. The packages can be installed in the usual way (remember you can install packages in parallel)
install.packages(c("mosaicData", "dplyr", "tidyr", "ggplot2", "plotly", "microbenchmark"))
library("mosaicData") library("dplyr") library("tidyr") library("ggplot2") library("plotly") library("microbenchmark")
Let’s load and take a look at the data.
data("Birthdays", package = "mosaicData") head(Birthdays) ## state year month day date wday births ## 1 AK 1969 1 1 1969-01-01 Wed 14 ## 2 AL 1969 1 1 1969-01-01 Wed 174 ## 3 AR 1969 1 1 1969-01-01 Wed 78 ## 4 AZ 1969 1 1 1969-01-01 Wed 84 ## 5 CA 1969 1 1 1969-01-01 Wed 824 ## 6 CO 1969 1 1 1969-01-01 Wed 100
First, we’ll create a very simple scatter graph of the mean births in every year.
meanb = Birthdays %>% group_by(year) %>% summarise(mean = mean(births))
Wrapping this as a ggplot object inside
ggplotly() we obtain this…
ggplotly(ggplot(meanb) + geom_point(aes(y = mean, x = year, colour = year)))
plot_ly() give us this…
plot_ly(data = meanb, y = ~mean, x = ~year, color = ~year, type = "scatter")
Both graphs are, identical, bar styling, yes?
Now let’s use
microbenchmark to see how their timings compare (for an overview of timing R functions, see our previous blog post).
time = microbenchmark::microbenchmark( ggplotly = ggplotly(ggplot(meanb) + geom_point(aes(y = mean, x = year, colour = year))), plotly = plot_ly(data = meanb, y = ~mean, x = ~year, color = ~year, type = "scatter"), times = 100, unit = "s") time ## Unit: seconds ## expr min lq mean median uq max neval cld ## ggplotly 0.050139 0.052229 0.070750 0.054760 0.056785 1.56652 100 b ## plotly 0.002475 0.002527 0.003017 0.002571 0.002674 0.03061 100 a
Now I thought nesting a ggplot object within
ggplotly() would be slower than using
plot_ly(), but I didn’t think it would be this slow. On average
ggplotly() is approximately 23 times slower than
Let’s take it up a notch. There we were plotting only 20 points, what about if we plot over 20,000? Here we will plot the min, mean and max births on each day.
date = Birthdays %>% group_by(date) %>% summarise(mean = mean(births), min = min(births), max = max(births)) %>% gather(birth_stat, value, -date)
Wrapping this a ggplot2 object inside
ggplotly() we obtain this graph…
ggplotly(ggplot(date) + geom_point(aes(y = value, x = date, colour = birth_stat)))
plot_ly() we obtain…
plot_ly(date, x = ~date, y = ~value, color = ~birth_stat, type = "scatter")
Again, both plots are identical, bar styling.
time2 = microbenchmark( ggplotly = ggplotly( ggplot(date) + geom_point(aes(y = value, x = date, colour = birth_stat)) ), plotly = plot_ly(date, x = ~date, y = ~value, color = ~birth_stat, type = "scatter"), times = 100, unit = "s") time2 ## Unit: seconds ## expr min lq mean median uq max neval cld ## ggplotly 0.335823 0.355301 0.389759 0.365353 0.378502 0.54746 100 b ## plotly 0.002472 0.002534 0.002719 0.002585 0.002675 0.01179 100 a
ggplotly() is 143 times slower than
plot_ly(), with the max run time being 0.547 seconds!
I’m going to level with you. Using
ggplotly() in interactive mode isn’t a problem. Well, it’s not a problem until your shiny dashboard or your markdown document has to generate a few plots at the same time. With only one plot, you’ll probably go with the method that gives you your style in the easiest way possible and you’ll do this with no repercussions. However, let’s say you’re making a shiny dashboard and it now has over 5 interactive graphs within it. Suddenly, if you’re using
ggplotly(), the lag we noticed in the analysis above starts to build up unnecessarily. That’s why I’d use
Thanks for chatting!
R.version.string ##  "R version 3.4.2 (2017-09-28)" packageVersion("ggplot2") ##  '2.2.1' packageVersion("plotly") ##  '4.7.1'