blocks|key|2757288|text|一些(粗略的)想法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2757289|2757290|在浏览器中分析应用程序可能会有所帮助。我有一个使用navbarPage的大型应用程序，页面构建速度变慢了。使用Chrome+(开发人员工具)中的分析来确定“罪魁祸首”。一个修复/改进是在你的应用程序的代码窗口中的works+https://github.com/rstudio/shiny/issues/381#issuecomment-33750794|ordered-list-item|offset|length|2757291|Run分析器。使用shinyAce包(https://github.com/trestletech/shinyAce)，我可以编辑(和运行)代码，包括应用程序内部的分析器(即，调用reactives等)。请参阅下面的链接(R+>代码)。注意，代码评估在服务器上是停用的，但是应用程序的源代码在github上，如果你想尝试一下(参见关于页)，|2757292|将你的代码写在由反应函数调用的常规R函数中。我正在重写我的应用程序，这样它就可以使用can进行“可重现的研究”(R+>+Report)。这种重组使得无需启动应用程序即可从R(studio)使用性能分析库变得更加容易。|2757293|Rselenium是Selenium的R接口，web应用程序的测试工具(https://github.com/johndharrison/RSelenium)。我刚刚开始使用它，但您也许可以将它与system.time之类的东西一起使用，以比较不同组件的速度。|2757294|2757295|http://vnijs.rady.ucsd.edu:3838/marketing/|2757296|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/rstudio/shiny/issues/381#issuecomment-33750794|1|https://github.com/trestletech/shinyAce|2|https://github.com/johndharrison/RSelenium|3^0|0|0|34|1T|0|0|J|13|1|0|0|10|16|2|0|0|0|16|3|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|14|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|15|8|@]|9|@$F|16|G|17|1|18]]|A|$]]|$1|H|3|I|5|E|7|19|8|@]|9|@$F|1A|G|1B|1|1C]]|A|$]]|$1|J|3|K|5|E|7|1D|8|@]|9|@]|A|$]]|$1|L|3|M|5|E|7|1E|8|@]|9|@$F|1F|G|1G|1|1H]]|A|$]]|$1|N|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1J|8|@]|9|@$F|1K|G|1L|1|1M]]|A|$]]|$1|Q|3|-4|5|6|7|1N|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|X]]|Y|$5|T|U|V|A|$W|Z]]|10|$5|T|U|V|A|$W|11]]|12|$5|T|U|V|A|$W|P]]]]

A few (rough) ideas:

<ol>
<li>Profiling the app in the browser might help. I have a largish app that uses navbarPage and the page build speed was getting slow. Using profiling in Chrome (developer tools) identified the 'culprit'. A fix/improvement is in the works <a href="https://github.com/rstudio/shiny/issues/381#issuecomment-33750794" rel="noreferrer">https://github.com/rstudio/shiny/issues/381#issuecomment-33750794</a></li>
<li>Run the profiler from a code window in your app. Using the shinyAce package (<a href="https://github.com/trestletech/shinyAce" rel="noreferrer">https://github.com/trestletech/shinyAce</a>) I can edit (and run) code, including profilers from within the app (i.e., call reactives etc.). See link below (R > Code). Note that code evaluation is deactivated on the server but the source code for the app is on github if you want to try this out (see About page)</li>
<li>Write your code in regular R functions that are called by reactive functions. I am in the process of rewriting my app so that it can use knitr for 'reproducible research' (R > Report). This restructuring makes it easier to use profiling libraries from R(studio) without starting the app.</li>
<li>Rselenium is an R interface to Selenium, testing tools for web-apps (<a href="https://github.com/johndharrison/RSelenium" rel="noreferrer">https://github.com/johndharrison/RSelenium</a>). I have only just started using this but you perhaps you could use this with something like system.time to compare speeds for different components.</li>
</ol>

<a href="http://vnijs.rady.ucsd.edu:3838/marketing/" rel="noreferrer">http://vnijs.rady.ucsd.edu:3838/marketing/</a>

blocks|key|162815|text|根据我的经验：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|162816|162817|Plugin+print()在函数中，您可以找到哪个函数占用的时间最多。例如：|ordered-list-item|162818|162819|mydebug+<-+function(msg="[DEBUG]")+{+++DEBUG+<-+FALSE+++if+(DEBUG)+{+++++print(sprintf("%25s+-+%25s+-+%25s",+msg1,+as.character(Sys.time()),+as.character(deparse(sys.calls()[[sys.nframe()-1]]))))+++}+}+f+<-+function()+{+++mydebug()+++##+your+original+function+definitions+.....+++mydebug()+++return(...)+##+the+returned+value+needs+to+be+after+mydebug()+}|offset|length|style|CODE|162820|162821|使用铬火焰图分析|162822|162823|你可以获得一个火焰来找出时间花费在哪里(例如，哪个JS函数？这是由于布局吗？)。|162824|​|162825|📷|atomic|162826|162827|有关详细信息，请参阅：https://developers.google.com/web/tools/chrome-devtools/profile/rendering-tools/analyze-runtime?hl=en|162828|entityMap|0|LINK|mutability|MUTABLE|url|https://ask.qcloudimg.com/http-save/yehe-900000/42bd9da1ad74714100ab7e290674da8e.png|1|IMAGE|IMMUTABLE|imageUrl|imageAlt|2|https://developers.google.com/web/tools/chrome-devtools/profile/rendering-tools/analyze-runtime?hl=en^0|0|0|0|0|0|9P|2Y|1|0|0|0|0|0|0|0|0|1|1|0|0|B|2T|2|0^^$0|@$1|2|3|4|5|6|7|1F|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|C|3|D|5|E|7|1H|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|1J|8|@$I|1K|J|1L|K|L]]|9|@$I|1M|J|1N|1|1O]]|A|$]]|$1|M|3|-4|5|6|7|1P|8|@]|9|@]|A|$]]|$1|N|3|O|5|E|7|1Q|8|@]|9|@]|A|$]]|$1|P|3|-4|5|6|7|1R|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1S|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1T|8|@]|9|@]|A|$]]|$1|U|3|V|5|W|7|1U|8|@]|9|@$I|1V|J|1W|1|1X]]|A|$]]|$1|X|3|T|5|6|7|1Y|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|1Z|8|@]|9|@$I|20|J|21|1|22]]|A|$]]|$1|10|3|-4|5|6|7|23|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|17]]|18|$5|19|14|1A|A|$1B|17|1C|-4]]|1D|$5|13|14|15|A|$16|1E]]]]

From my experiences:

<ol>
<li>Plugin print() in the functions
You can find out which function takes most of the time.
For example:</li>
</ol>

<code>
mydebug &lt;- function(msg="[DEBUG]") {
 DEBUG &lt;- FALSE
 if (DEBUG) {
 print(sprintf("%s - %s - %s", msg<a href="https://i.stack.imgur.com/tQM2M.png" rel="nofollow noreferrer">1</a>, as.character(Sys.time()), as.character(deparse(sys.calls()[[sys.nframe()-1]]))))
 }
}
f &lt;- function() {
 mydebug()
 ## your original function definitions .....
 mydebug()
 return(...) ## the returned value needs to be after mydebug()
}
</code>

<ol start="2">
<li>Use Chrome flame graph to profile </li>
</ol>

You can obtain a flame to find out where the time spent (e.g., which JS function? Is it due to layout?).

<a href="https://i.stack.imgur.com/tQM2M.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tQM2M.png" alt="enter image description here"></a>

For details, refer to: <a href="https://developers.google.com/web/tools/chrome-devtools/profile/rendering-tools/analyze-runtime?hl=en" rel="nofollow noreferrer">https://developers.google.com/web/tools/chrome-devtools/profile/rendering-tools/analyze-runtime?hl=en</a>

blocks|key|162841|text|我认为这个问题需要一点更新，因此我添加了另一个答案……|type|unstyled|depth|inlineStyleRanges|entityRanges|data|162842|您也可以使用profvis包来分析闪亮的应用程序。它会直接给出你的R代码的火焰图。也就是说，不需要使用Chrome的火焰图并猜测瓶颈在哪里。您将确切地知道在何处更改代码。|offset|length|style|CODE|162843|以下是的使用方法：|BOLD|162844|162845|使用您的闪亮应用通过Profvis|ordered-list-item|162846|Interact运行闪亮应用|162847|Close+browser|162848|Stop+Console通过停止按钮|162849|Load+profile|162850|如果步骤5失败，请尝试此操作:如果需要，请转换为html+(内存问题)|162851|162852|某些步骤的详细信息添加如下：|162853|第1步:运行profvis|162854|library(profvis)
profvis({+runApp('directory_of_shiny_app')+}++
++++,+prof_output+=+'/directory_to_save_profile')|code-block|syntax|javascript|162855|第5步:加载您的配置文件|162856|profvis(prof_input+=+'/path_to_save_output/random_name.Rprof')+|162857|注意:+Profvis会随机给你的文件起一个名字。因此您需要相应地更改输入路径|162858|第6步:转换为html|162859|如果你有一个巨大的应用程序并且火焰图变得有点长，那么这一步可能是必要的。您可能会收到错误"Pandoc:...+memory“|162860|p+<-+profvis(prof_input+=+'/path_to_save_output/file108f93bff877b.Rprof')
htmlwidgets::saveWidget(p,+"/path_to_save_output/profile.html")|162861|然后在浏览器中打开html文件。|162862|entityMap^0|0|6|7|0|3|6|0|0|0|0|0|0|0|0|0|0|0|D|0|0|0|C|0|0|0|0|B|0|0|0|0^^$0|@$1|2|3|4|5|6|7|1O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1P|8|@$D|1Q|E|1R|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|1S|8|@$D|1T|E|1U|F|J]]|9|@]|A|$]]|$1|K|3|-4|5|6|7|1V|8|@]|9|@]|A|$]]|$1|L|3|M|5|N|7|1W|8|@]|9|@]|A|$]]|$1|O|3|P|5|N|7|1X|8|@]|9|@]|A|$]]|$1|Q|3|R|5|N|7|1Y|8|@]|9|@]|A|$]]|$1|S|3|T|5|N|7|1Z|8|@]|9|@]|A|$]]|$1|U|3|V|5|N|7|20|8|@]|9|@]|A|$]]|$1|W|3|X|5|N|7|21|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|22|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|23|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|24|8|@$D|25|E|26|F|J]]|9|@]|A|$]]|$1|13|3|14|5|15|7|27|8|@]|9|@]|A|$16|17]]|$1|18|3|19|5|6|7|28|8|@$D|29|E|2A|F|J]]|9|@]|A|$]]|$1|1A|3|1B|5|15|7|2B|8|@]|9|@]|A|$16|17]]|$1|1C|3|1D|5|6|7|2C|8|@]|9|@]|A|$]]|$1|1E|3|1F|5|6|7|2D|8|@$D|2E|E|2F|F|J]]|9|@]|A|$]]|$1|1G|3|1H|5|6|7|2G|8|@]|9|@]|A|$]]|$1|1I|3|1J|5|15|7|2H|8|@]|9|@]|A|$16|17]]|$1|1K|3|1L|5|6|7|2I|8|@]|9|@]|A|$]]|$1|1M|3|-4|5|6|7|2J|8|@]|9|@]|A|$]]]|1N|$]]

I think this question needs a little update, therefore I am adding another answer to it...

You can use the package <code>profvis</code> to profile shiny apps as well. It will give flame graphs directly for your R code. I.e. no need to use Chrome's flame graphs and guess where the bottleneck is. You will know exactly where to change your code.

Here is how to do it:

<ol>
<li>Run shiny app via Profvis</li>
<li>Interact with your shiny app</li>
<li>Close browser</li>
<li>Stop Console via Stop button</li>
<li>Load profile</li>
<li>If Step 5 fails, try this: Convert to html if needed (memory problems)</li>
</ol>

Details for certain steps are added below:

Step 1: Run profvis

<pre><code>library(profvis)
profvis({ runApp('directory_of_shiny_app') } 
 , prof_output = '/directory_to_save_profile')
</code></pre>

Step 5: Load your profile

<pre><code>profvis(prof_input = '/path_to_save_output/random_name.Rprof') 
</code></pre>

N.B. Profvis gives a random name to your file. So you need to change the input path accordingly

Step 6: Convert to html

This step might be needed, if you have a huge app and flame graph gets a little bit longer. You might get an error "Pandoc:... memory"

<pre><code>p &lt;- profvis(prof_input = '/path_to_save_output/file108f93bff877b.Rprof')
htmlwidgets::saveWidget(p, "/path_to_save_output/profile.html")
</code></pre>

Then open the html file in your browser.

For an R Shiny web app, what are some good ways to run code profiling that show the parts of the Shiny code that are taking the most processing time? 

I've got a big, fat, complex Shiny app, and I'd like to figure out where in this labyrinth of code I'm slowing my Shiny app down the most. I've tried out Rprof and profr but haven't gotten much insight from them.

Code profiling for Shiny app?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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对于一个R闪亮的web应用程序，有什么好方法可以运行代码分析来显示闪亮的代码部分，这些部分占用了最多的处理时间？我有一个大的，胖的，复杂的闪亮的应用程序，我想找出在这个错综复杂的代码中，我在哪里放慢了我的闪亮的应用程序的速度。我尝试过Rprof和profr，但没有从他们那里获得太多的洞察力。

问闪亮应用的代码分析？
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