Profiling with RStudio and profvis

缺乏、安全感 2022-08-22 15:24 167阅读 0赞

“How can I make my code faster?” If you write R code, then you’ve probably asked yourself this question. A profiler is an important tool for doing this: it records how the computer spends its time, and once you know that, you can focus on the slow parts to make them faster.

The [preview releases][] of RStudio now have integrated support for profiling R code and for visualizing profiling data. R itself has long had a built-in profiler, and now it’s easier than ever to use the profiler and interpret the results.

To profile code with RStudio, select it in the editor, and then click on**Profile -> Profile Selected Line(s)**. R will run that code with the profiler turned on, and then open up an interactive visualization.

[![profile1.gif_w_912_h_786][]][profile1.gif_w_912_h_786 1]

In the visualization, there are two main parts: on top, there is the code with information about the amount of time spent executing each line, and on the bottom there is a *flame graph*, which shows R was doing over time. In the flame graph, the horizontal direction represents time, moving from left to right, and the vertical direction represents the *call stack*, which are the functions that are currently being called. (Each time a function calls another function, it goes on top of the stack, and when a function exits, it is removed from the stack.)

![profile.png][]

The **Data** tab contains a call tree, showing which function calls are most expensive:

[![Profiling data pane][]][Profiling data pane 1]

Armed with this information, you’ll know what parts of your code to focus on to speed things up!

The interactive profile visualizations are created with the [profvis][]package, which can be used separately from the RStudio IDE. If you use profvis outside of RStudio, the visualizations will open in a web browser.

To learn more about interpreting profiling data, check out the[profvis website][profvis], which has interactive demos. You can also find out more about [profiling with RStudio][] there.

[preview releases]: https://www.rstudio.com/products/rstudio/download/preview/
[profile1.gif_w_912_h_786]: /images/20220722/0e7c76045b91424aa4ddedaf8f673907.png
[profile1.gif_w_912_h_786 1]: https://rstudioblog.files.wordpress.com/2016/05/profile1.gif
[profile.png]: /images/20220722/3491407251c24a28b896e36bdbf3d8df.png
[Profiling data pane]: /images/20220722/65237b1ee2d54fb095454919ed3610ef.png
[Profiling data pane 1]: https://rstudioblog.files.wordpress.com/2016/05/data1.png
[profvis]: https://rstudio.github.io/profvis/
[profiling with RStudio]: http://rstudio.github.io/profvis/rstudio.html