From 62bace099aa9467c7e5d2d1389cb8bd81bd9394f Mon Sep 17 00:00:00 2001 From: Hadley Wickham Date: Thu, 22 Aug 2024 12:21:18 -0500 Subject: [PATCH] Port over Rmds from existing site (#165) --- .Rbuildignore | 1 + DESCRIPTION | 9 +- vignettes/.gitignore | 4 + vignettes/articles/.gitignore | 1 + vignettes/articles/cmpfun-profile1.rds | Bin 0 -> 1243 bytes vignettes/articles/cmpfun-profile2.rds | Bin 0 -> 879 bytes vignettes/articles/examples.Rmd | 216 ++++++++ vignettes/articles/faq.Rmd | 515 ++++++++++++++++++ vignettes/articles/ggplot2.rds | Bin 0 -> 52655 bytes vignettes/articles/profvis-rpubs.png | Bin 0 -> 166257 bytes vignettes/articles/profvis-shiny-demo.gif | Bin 0 -> 928860 bytes .../articles/rstudio-code-tools-menu.png | Bin 0 -> 107583 bytes .../rstudio-profile-menu-selected-lines.png | Bin 0 -> 61478 bytes .../rstudio-profile-menu-start-profiling.png | Bin 0 -> 60104 bytes vignettes/articles/rstudio-profile-view.png | Bin 0 -> 128710 bytes vignettes/articles/rstudio.Rmd | 44 ++ vignettes/articles/shinyapp.rds | Bin 0 -> 5149 bytes vignettes/profvis.Rmd | 88 +++ 18 files changed, 875 insertions(+), 3 deletions(-) create mode 100644 vignettes/.gitignore create mode 100644 vignettes/articles/.gitignore create mode 100644 vignettes/articles/cmpfun-profile1.rds create mode 100644 vignettes/articles/cmpfun-profile2.rds create mode 100644 vignettes/articles/examples.Rmd create mode 100644 vignettes/articles/faq.Rmd create mode 100644 vignettes/articles/ggplot2.rds create mode 100644 vignettes/articles/profvis-rpubs.png create mode 100644 vignettes/articles/profvis-shiny-demo.gif create mode 100644 vignettes/articles/rstudio-code-tools-menu.png create mode 100644 vignettes/articles/rstudio-profile-menu-selected-lines.png create mode 100644 vignettes/articles/rstudio-profile-menu-start-profiling.png create mode 100644 vignettes/articles/rstudio-profile-view.png create mode 100644 vignettes/articles/rstudio.Rmd create mode 100644 vignettes/articles/shinyapp.rds create mode 100644 vignettes/profvis.Rmd diff --git a/.Rbuildignore b/.Rbuildignore index 6f8fad30..4becec4f 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -14,3 +14,4 @@ ^_pkgdown\.yml$ ^pkgdown$ ^LICENSE\.md$ +^vignettes/articles$ diff --git a/DESCRIPTION b/DESCRIPTION index 9733c56b..1def2e7a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -20,8 +20,7 @@ Authors@R: c( ) Description: Interactive visualizations for profiling R code. License: MIT + file LICENSE -URL: https://r-lib.github.io/profvis/, - https://github.com/r-lib/profvis +URL: https://r-lib.github.io/profvis/, https://github.com/r-lib/profvis BugReports: https://github.com/r-lib/profvis/issues Depends: R (>= 3.0) @@ -31,9 +30,13 @@ Imports: vctrs Suggests: htmltools, + knitr, + rmarkdown, shiny, testthat (>= 3.0.0) -Config/Needs/website: tidyverse/tidytemplate +VignetteBuilder: + knitr +Config/Needs/website: tidyverse/tidytemplate, rmarkdown Config/testthat/edition: 3 Encoding: UTF-8 Roxygen: list(markdown = TRUE) diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 00000000..9e2bd63c --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,4 @@ +*.html +*.R + +/.quarto/ diff --git a/vignettes/articles/.gitignore b/vignettes/articles/.gitignore new file mode 100644 index 00000000..075b2542 --- /dev/null +++ b/vignettes/articles/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/vignettes/articles/cmpfun-profile1.rds b/vignettes/articles/cmpfun-profile1.rds new file mode 100644 index 0000000000000000000000000000000000000000..96242f14c8948c70993ec3f7e23fe4b7be226aa4 GIT binary patch literal 1243 zcmV<11SI<(iwFP!000001Kk+gZrnuFn~nG4By9?Xl9mTcO{HBycQ>bt~yY@KQO{vt+;0t)<19;+rH-yAD@QU~ZTF%%ryI!{&au3wnC|um1vw%Bb zMJTXI8?$@VAr!A7YJz$}FM|yUT}A=iUO|o{&^)03r}#{A^I--DckYHEv88CPb$Ex5 zBBMd~6E9fy%S)6ar;57`yxscnB=QQPy zqH7;Gh`*d?;utm@Oa3Ujl;q5RRm<;(=F-)v&pGq4+v`7kf3EEf^yIZ{tJ)s}ZR1xw z&|a_qm2PETl@ofFH2as6n(HXfoj-qUoTvWNdhYyve?E;bUAppn8vps--+s!7Oe&(@ zd;!m)TXCx`;Eo?GX0W5q`8wEg9zLE1ri>V79rh8i>{Yje7NcR_3ikee>UI6)lXYG)%*%Y116HV8- zHa^(bTG>HPg!PZ~Afm*o%;DSBDlo#2mr9%$ia3K2k5)QWozJ1`#L_De3!Ak#Z9$){ z>H%g^z*)4ILx^@B&1}TDi7ga4>}z874x|Y| zTg53Sias(;LTwo!2!aeqDU5tS02gG;eFPusiE!T&^#GPC87tM3v=Vn9jTF+@w<&vC zjm|V0H!?K!gGR&e>|HkLBx!9veyQ-pqv&$@(*9P7>Ilo F003XMVo3l1 literal 0 HcmV?d00001 diff --git a/vignettes/articles/cmpfun-profile2.rds b/vignettes/articles/cmpfun-profile2.rds new file mode 100644 index 0000000000000000000000000000000000000000..31fc78f96ae299349de476a9ad644c76e5bb9055 GIT binary patch literal 879 zcmV-#1Cab5iwFP!000001GQD_ZqqOnPVa4ifrR*h_~Rc%N(Jj>8)%yfnuG*H2x%Hf zAnm3!aZ=O7369ftTqNF$_uvJ155P`*(ln!pNf6a_&gWyF?|jF0-s+mBm9xa@BvcvWkO{&p1mN8|(z*HQAeJ~%=5&|E;u zp=t^Ia?TeSK?A&{-l1QU_+EScK=fMHcfWtl_u!`$p1oV%w*r*Uus+1|kUhSHrBzd< zQnW@*A4h7@CM3#U`nIr5equ~$_(@egGM`+G$`q(!;FR&oIun7gLnamZT z3v^m6n00}Y)UNy`&AD3SZFyz{yaCj0lbc@O)tji5n?eVj`09*bvnXP{N&b=zD`UnP6BGoJ4mj9 z5OI(pv~c-YVnejiK@9VlNf$lwP*dkegOkK|0)`BN2t^ToZ*1I0+4K%9iqOiS(P+YY$tblS%ISkW)`JpK`Ra&F`!iT-7 z$$C!U*_cH=?)J{lwr2kJ`OfBpXk_uhWOnXDB|>N>$_A%IDiSHwM zckGDmdfuU+}X=m7)l1e=n#%0sB#nqXAFNTIhuiDr#V~@Wf zc6K3MQ`kPvCD8~?+a}ZnfrJPkNHdDVkckvP^U$PNOm< z;8eV=s)K(2ZUL)3?Dxa@A4pa%OS0||H)i-hRHZ&8c!n7aj>N?uWoB(c31@{W{7^@* zm=t`qN^_V!%WLl3yYTxPMLw F008X@vmyWh literal 0 HcmV?d00001 diff --git a/vignettes/articles/examples.Rmd b/vignettes/articles/examples.Rmd new file mode 100644 index 00000000..7d500f3f --- /dev/null +++ b/vignettes/articles/examples.Rmd @@ -0,0 +1,216 @@ +--- +title: "Examples" +--- + +```{r echo = FALSE} +library(profvis) +library(knitr) + +knitr::opts_chunk$set(collapse = TRUE, comment = "#>") + +# Make output a little less tall by default +registerS3method("knit_print", "htmlwidget", function(x, ...) { + # Get the chunk height + height <- knitr::opts_current$get("height") + if (length(height) > 0 && height != FALSE) + x$height <- height + else + x$height <- "450px" + + htmlwidgets:::knit_print.htmlwidget(x, ...) +}) +``` + + +Below are some examples of profvis in use. Keep in mind that R's sampling profiler is non-deterministic, and that the code in these examples is run and profiled when this knitr document is executed, so the numeric timing values may not exactly match the text. + + +### Example 1 + +In this first example, we'll work with a data frame that has 151 columns. One of the columns contains an ID, and the other 150 columns contain numeric values. What we will do is, for each numeric column, take the mean and subtract it from the column, so that the new mean value of the column is zero. + +```{r ex1} +# Generate data +times <- 4e5 +cols <- 150 +data <- as.data.frame(x = matrix(rnorm(times * cols, mean = 5), ncol = cols)) +data <- cbind(id = paste0("g", seq_len(times)), data) + +profvis({ + data1 <- data # Store in another variable for this run + + # Get column means + means <- apply(data1[, names(data1) != "id"], 2, mean) + + # Subtract mean from each column + for (i in seq_along(means)) { + data1[, names(data1) != "id"][, i] <- data1[, names(data1) != "id"][, i] - means[i] + } +}) +``` + +Most of the time is spent in the `apply` call, so that's the best candidate for a first pass at optimization. We can also see that the `apply` results in a lot of memory being allocated and deallocated. Memory "churning" is often a cause for performance problems. + +Looking at the flame graph, we can see that `apply` calls `as.matrix` and `aperm`. These two functions convert the data frame to a matrix and transpose it -- so even before we've done any useful computations, we've spent a large amount of time transforming the data. + +We could try to speed this up in a number of ways. One possibility is that we could simply leave the data in matrix form (instead of putting it in a data frame in line 4). That would remove the need for the `as.matrix` call, but it would still require `aperm` to transpose the data. It would also lose the connection of each row to the `id` column, which is undesirable. In any case, using `apply` over columns looks like it will be expensive because of the call to `aperm`. + +An obvious alternative is to use the `colMeans` function. But there's also another possibility. Data frames are implemented as lists of vectors, where each column is one vector, so we could use `lapply` or `vapply` to apply the `mean` function over each column. Let's compare the speed of these four different ways of getting column means. + +```{r ex1_apply} +profvis({ + data1 <- data + # Four different ways of getting column means + means <- apply(data1[, names(data1) != "id"], 2, mean) + means <- colMeans(data1[, names(data1) != "id"]) + means <- lapply(data1[, names(data1) != "id"], mean) + means <- vapply(data1[, names(data1) != "id"], mean, numeric(1)) +}) +``` + +`colMeans` is about 3x faster than using `apply` with `mean`, but it looks like it's still using `as.matrix`, which takes a significant amount of time. `lapply`/`vapply`are faster yet -- about 10x faster than `apply`. `lapply` returns the values in a list, while `vapply` returns the values in a numeric vector, which is the form that we want, so it looks like `vapply` is the way to go for this part. + +(If you want finer-grained comparisons of code performance, you can use the [microbenchmark](https://cran.r-project.org/web/packages/microbenchmark/index.html) package. There's more information about microbenchmark in the [profiling](http://adv-r.had.co.nz/Profiling.html#code-organisation) chapter of Hadley Wickham's book, *Advanced R*.) + +You can also see that the faster methods also result in less memory allocation and deallocation. This is not a coincidence -- allocating and deallocating memory can be expensive. + +Let's take the original code and replace `apply` with `vapply`: + +```{r ex1_for} +profvis({ + data1 <- data + means <- vapply(data1[, names(data1) != "id"], mean, numeric(1)) + + for (i in seq_along(means)) { + data1[, names(data1) != "id"][, i] <- data1[, names(data1) != "id"][, i] - means[i] + } +}) +``` + +Our code is about 3x faster than the original version. Most of the time is now spent on line 6, and the majority of that is in the `[<-.data.frame` function. This is usually called with syntax `x[i, j] <- y`, which is equivalent to `` `[<-`(x, i, j, y)``. In addition to being slow, the code is ugly: on each side of the assignment operator we're indexing into `data1` twice with `[`. + +In this case, it's useful to take a step back and think about the broader problem. We want to normalize each column. Couldn't we we apply a function over the columns that does both steps, taking the mean and subtracting it? Because a data frame is a list, and we want to assign a list of values into the data frame, we'll need to use `lapply`. + +```{r ex1_norm} +profvis({ + data1 <- data + + # Given a column, normalize values and return them + col_norm <- function(col) { + col - mean(col) + } + + # Apply the normalizer function over all columns except id + data1[, names(data1) != "id"] <- lapply(data1[, names(data1) != "id"], col_norm) +}) +``` + +Now we have code that's not only about 6x faster than our original -- it's shorter and more elegant as well. Not bad! The profiler data helped us to identify performance bottlenecks, and understanding of the underlying data structures allowed us to approach the problem in a more efficient way. + +Could we further optimize the code? It seems unlikely, given that all the time is spent in functions that are implemented in C (`mean` and `-`). That doesn't necessarily mean that there's no room for improvement, but this is a good place to move on to the next example. + + +### Example 2 + +This example addresses some more advanced issues. This time, it will be hard to directly see the causes of slowness, but we will be able to see some of their side-effects, most notably the side-effects from large amounts of memory allocation. + +Suppose you have a data frame that contains a column for which you'd like to take a cumulative sum (and you don't know about R's built-in `cumsum` function). Here's one way to do it: + +```{r ex2} +profvis({ + data <- data.frame(value = runif(5e4)) + + data$sum[1] <- data$value[1] + for (i in seq(2, nrow(data))) { + data$sum[i] <- data$sum[i-1] + data$value[i] + } +}) +``` + +This takes about 2.5 seconds to calculate the cumulative sum of 50,000 items. That's pretty slow for a computer program. Looking at the profvis visualization, we can see a number of notable features: + +Almost all the time is spent in one line of code, line 6. Although this is just one line of code, many different functions that are called on that line. + +That line also results in a large amount of memory being allocated and deallocated, which is somewhat suprising: just looking at the code, that line *appears* to just modify the data in-place, but that's not actually what's happening internally. + +In the flame graph, you'll see that some of the flame graph blocks have the label `$`, which means that those samples were spent in the `$` function for indexing into an object (in R, the expression `x$y` is equivalent to `` `$`(x, "y")``). + +Because `$` is a *generic* function, it calls the corresponding *method* for the object, in this case `$.data.frame`. This function in turn calls `[[`, which calls `[[.data.frame`. (Zoom in to see this more clearly.) + +Other flame graph cells have the label `$<-`. The usual syntax for calling this function is `x$y <- z`; this is equivalent to `` `$<-`(x, "y", z)``. (Assignment with indexing, as in `x$y[i] <- z` is actually a bit [more complicated](http://adv-r.had.co.nz/Functions.html#replacement-functions), and it turns out that this is the cause of the excessive memory allocation and deallocation.) + +Finally, many of the flame graph cells contain the entire expression from line 6. This can mean one of two things: + +1. R is currently evaluating the expression but is not inside another function call. +1. R is in another function, but that function does not show up on the stack. (A number of R's internal functions do not show up in the profiling data. See [more](#why-do-some-function-calls-not-show-in-the-profiler) about this in the FAQ.) + +This profiling data tells us that much of the time is spent in `$` and `$<-`. Maybe avoiding these functions entirely will speed things up. To do that, instead of operating on data frame columns, we can operate on temporary vectors. As it turns out, writing a function that takes a vector as input and returns a vector as output is not only convenient; it provides a natural way of creating temporary variables so that we can avoid calling `$` and `$<-` in a loop. + +```{r ex2_csum} +profvis({ + csum <- function(x) { + if (length(x) < 2) return(x) + + sum <- x[1] + for (i in seq(2, length(x))) { + sum[i] <- sum[i-1] + x[i] + } + sum + } + x <- runif(5e5) + x$sum <- csum(x) +}) +``` + +Using this `csum` function, it takes about 100 ms for 10x as much data, which is about 250x as fast as before. + +It may appear that no functions are called from line 7, but that's not quite true: that line also calls `[`, `[<-`, `-`, and `+`. + +* The `[` and `[<-` functions don't appear in the flame graph. They are internal R functions which contain C code to handle indexing into atomic vectors, and are not dispatched to methods. (Contrast this with the first version of the code, where `$` was dispatched to `$.data.frame`). +* The `-` and `+` functions can show up in a flame graph, but they are very fast so the sampling profiler may or may not happen to take a sample when they're on the call stack. + +The code panel shows that there is still a large amount of memory being allocated in the `csum` function. In the flame graph. you probably have noticed the gray blocks labeled ``. These represent times where R is doing *garbage collection* -- that is, when it is freeing (deallocating) chunks of memory that were allocated but no longer needed. If R is spending a lot of time freeing memory, that suggests that R is also spending a lot of time allocating memory. This is another common source of slowness in R code. + +> **Note:** Before R 3.4, growing a vector in a loop was expensive -- each time the vector grew by one, R would copy the vector to a new block of memory, with the new item at the end. However, as of R 3.4, R tries to put vectors in a memory location with room to grow, and if there exists is only one reference to the vector, it can grow in place, except in the uncommon case where there's no more room to grow. (If there are two references to the vector, then R must make a copy.) This means that growing a vector is a fast operation, as long as there's only one reference to it. +> +> Although growing a vector is now fast, growing a **data frame** is still an expensive operation and results in copying all of the data. + + + +### Example 3 - Profiling a Shiny Application + +In addition to R code, you can also profile [Shiny](http://shiny.rstudio.com) applications. To do this, simply execute the `runApp()` command inside of `profvis`. For instance, we can run one of shiny's built-in examples using the `runExample` command (which is a wrapper for `runApp`). + +```{r ex3,eval=FALSE, echo=FALSE} +#This code block is what we run, it must be manually run each time +library(shiny) +p<-profvis({ + runExample(example = "06_tabsets", display.mode = "normal") +}) +saveRDS(p, "shinyapp.rds") + +``` + + +```{r ex3_tabsets,eval=FALSE} +library(shiny) +profvis({ + runExample(example = "06_tabsets", display.mode = "normal") +}) +``` + + +Your Shiny application will launch, and after interacting and closing the app a profile will be generated. + +![](profvis-shiny-demo.gif) + +```{r ex3_readRDS,echo=FALSE} +# This block loads the data from the first block and shows the correct output +# for the readers. +readRDS("shinyapp.rds") + +``` + +The profile for Shiny applications automatically hides functions that Shiny runs behind the scenes. More information is available in the [FAQ](http://rstudio.github.io/profvis/faq.html#why-does-the-flame-graph-hide-some-function-calls-for-shiny-apps). The profile automatically colors outputs in blue. In this example, you can identify each time `output$plot` was called to re-create the plot. + +Profiling a Shiny application is particularly helpful for understanding reactive dependencies. For more information, see this [video](https://rstudio.com/resources/webinars/how-to-profile-and-optimize-code-using-profvis-a-new-package-for-exploring-profiling-data/). diff --git a/vignettes/articles/faq.Rmd b/vignettes/articles/faq.Rmd new file mode 100644 index 00000000..d540e073 --- /dev/null +++ b/vignettes/articles/faq.Rmd @@ -0,0 +1,515 @@ +--- +title: "Frequently asked questions" +--- + +```{r echo = FALSE} +library(profvis) +library(knitr) + +knitr::opts_chunk$set(collapse = TRUE, comment = "#>") + +# Make output a little less tall by default +registerS3method("knit_print", "htmlwidget", function(x, ...) { + # Get the chunk height + height <- knitr::opts_current$get("height") + if (length(height) > 0 && height != FALSE) + x$height <- height + else + x$height <- "450px" + + htmlwidgets:::knit_print.htmlwidget(x, ...) +}) +``` + + +## Why do some function calls not show in the profiler? + +As noted earlier, some of R's built-in functions don't show in the profvis flame graph. These include functions like `<-`, `[`, and `$`. Although these functions can occupy a lot of time, they don't show on the call stack. (In one of the examples above, `$` does show on the call stack, but this is because it was dispatched to `$.data.frame`, as opposed to R's internal C code, which is used for indexing into lists.) + +In some cases the side-effects of these functions can be seen in the flamegraph. As we saw in the example above, using these functions in a loop led to many memory allocations, which led to garbage collections, or `` blocks in the flame graph. + + +## How do I share a profvis visualization? + +Right now the easiest way to do this is to run `profvis` in RStudio, and publish to [RPubs](http://rpubs.com/). + +Once the profile shows up in the RStudio IDE, click on the **Publish** button to send it to RPubs. You can see an example [here](http://rpubs.com/wch/178493). If you don't already have an account on RPubs, you'll need to set one up. + +You can also click on the save (disk) icon. This will save the profvis visualization to an .Rprofvis file. This file can be opened by RStudio, or if you rename it to have an .html extension, it can be opened in a web browser. + +![Publishing to RPubs](profvis-rpubs.png) + +Another way to publish a profvis visualization is to save the HTML output file using `htmlwidgets::saveWidget`, and put that on any web hosting service: + +```{r eval=FALSE} +p <- profvis({ + # Interesting code here +}) + +htmlwidgets::saveWidget(p, "profile.html") +``` + +It's also possible to put a profvis visualization in a knitr document. At this time, some CSS workarounds are needed needed for them to display properly. You can look at the source of [this website](https://github.com/rstudio/profvis/tree/gh-pages/) to see the workarounds. + + +## What does `` mean? + +It's not uncommon for R code to contain *anonymous* functions -- that is, functions that aren't named. These show up as `` in the profiling data collected from `Rprof`. + +In the code below there is a function, `make_adder`, that returns a function. We'll invoke the returned function in two ways. + +First, we'll run `make_adder(1)(10)`. The call `make_adder(1)` returns a function, which is invoked immediately (without being saved in a variable), and shows up as `` in the flame graph. + +Next, we'll call `make_adder(2)` but this time, we'll save the result in a variable, `adder2`. Then we'll call `adder2(10)`. When we do it this way, the profiler records that the function label is `adder2`. + +```{r, height="400px"} +profvis({ + make_adder <- function(n) { + function(x) { + pause(0.25) # Wait for a moment so that this shows in the profiler + x + n + } + } + + # Called with no intermediate variable, it shows as "" + make_adder(1)(10) + + # With the function saved in a variable, it shows as "adder2" + adder2 <- make_adder(2) + adder2(10) +}) +``` + +Similarly, in versions of R before 3.3.0, functions that are accessed with `::` or `$` will also appear as ``. The form `package::function()` is a common way to explicitly use a namespace to find a function. The form `x$fun()` is a common way to call functions that are contained in a list, environment, reference class, or R6 object. As of R 3.3.0, these will display as `package::function`, or `x$fun`. + +Those are equivalent to `` `::`(package, function)`` and `` `$`(x, "fun")``, respectively. These calls return anonymous functions, and so R's internal profiling code labels these as ``. If you want labels in the profiler to have a different label, you can assign the value to a temporary variable (like `adder2` above), and then invoke that. + +Finally, if a function is passed to `lapply`, it will be show up as `FUN` in the flame graph. If we inspect the source code for `lapply`, it's clear why: when a function is passed to `lapply`, the name used for the function inside of `lapply` is `FUN`. + +```{r} +lapply +``` + + +## What does `cmpfun` mean? + +The first time we run `profvis` on a function in a clean 3.4.0 or greater R session, we'll see `compiler:::tryCmpfun`. For example, + +```{r eval=FALSE, echo=FALSE} +# This code block is what we actually run. It must be run manually each time +# this is updated. +# The next block is what we show to the readers. +p <- profvis::profvis({ + data <- data.frame(value = runif(5e4)) + + csum <- function(x) { + if (length(x) < 2) return(x) + + sum <- x[1] + for (i in seq(2, length(x))) { + sum[i] <- sum[i-1] + x[i] + } + sum + } + data$sum <- csum(data$value) +}) +p$x$message$split <- "v" + +saveRDS(p, "cmpfun-profile1.rds") + +p <- profvis::profvis({ + data <- data.frame(value = runif(5e4)) + + csum <- function(x) { + if (length(x) < 2) return(x) + + sum <- x[1] + for (i in seq(2, length(x))) { + sum[i] <- sum[i-1] + x[i] + } + sum + } + data$sum <- csum(data$value) +}) + +p$x$message$split <- "v" + +saveRDS(p, "cmpfun-profile2.rds") +``` +```{r eval = FALSE} +profvis({ + data <- data.frame(value = runif(5e4)) + + csum <- function(x) { + if (length(x) < 2) return(x) + + sum <- x[1] + for (i in seq(2, length(x))) { + sum[i] <- sum[i-1] + x[i] + } + sum + } + data$sum <- csum(data$value) +}) +``` +```{r echo=FALSE} +readRDS("cmpfun-profile1.rds") +``` + +As of R 3.4.0, R attempts to compile functions when they are first ran to byte code. On subsequent function calls, instead of reinterpreting the body of the function, R executes the saved and compiled byte code. Typically, this results in faster execution times on later function calls. For example, let's profile `csum` a second time in the same R session: + +```{r echo=FALSE} +readRDS("cmpfun-profile2.rds") +``` + +Now the flame graph shows that the function is no longer being compiled. And after compiling, `csum` is about 40 ms faster. + +## How do I get code from an R package to show in the code panel? + +In typical use, only code written by the user is shown in the code panel. (This is code for which *source references* are available.) Yellow blocks in the flame graph have corresponding lines of code in the code panel, and when moused over, the line of code will be highlighted. White blocks in the flame graph don't have corresponding lines in the code panel. In most cases, the calls represented by the white blocks are to functions that are in base R and other packages. + +Profvis can also show code that's inside an R package. To do this, source refs for the package code must be available. There are two general ways to do this: you can install the package with source refs, or you can use `devtools::load_all()` to load a package from sources on disk. + + +### Installing with source refs + +There are many ways to install a package with source refs. Here are some examples of installing ggplot2: + +* From CRAN: + ```{r eval=FALSE} + ## First, restart R ## + install.packages("ggplot2", type="source", INSTALL_opts="--with-keep.source") + ``` + +* From an RStudio package project on local disk: Go to **Build** -> **Configure Build Tools** -> **Build Tools** -> **Build and Reload -- R CMD INSTALL additional options**, and add `--with-keep.source`. Then run **Build** -> **Build and Reload**. + +* From sources on disk with devtools: + ```{r eval=FALSE} + ## First, restart R ## + # Assuming sources are in a subdirectory ggplot2/ + devtools::install("ggplot2", keep_source = TRUE) + ``` + +* From sources on disk using the command line: + ```{sh eval=FALSE} + R CMD INSTALL --with-keep.source ggplot2/ + ``` + +* From sources on Github: + ```{r eval=FALSE} + ## First, restart R ## + remotes::install_github("hadley/ggplot2", INSTALL_opts="--with-keep.source") + ``` + + +### Loading packages with source refs (without installing) + +* Instead of installing an in-development package, you can simply load it from source using devtools. + ```{r eval=FALSE} + # Assuming sources are in a subdirectory ggplot2/ + devtools::load_all("ggplot2") + ``` + +Once a package is loaded or installed with source refs, profvis visualizations will display source code for that package. For example, the visualization below has yellow blocks for both user code and for code in ggplot2, and it contains ggplot2 code in the code panel: + +```{r echo=FALSE, eval=FALSE} +# This code block is what we actually run. It must be run manually each time +# this is updated. +# The next block is what we show to the readers. +# This also generates the livedemo.html file. + +install.packages("ggplot2", type="source", INSTALL_opts="--with-keep.source") +library(ggplot2) + +p <- profvis({ + g <- ggplot(diamonds, aes(carat, price)) + geom_point(size = 1, alpha = 0.2) + print(g) +}) + +saveRDS(p, "ggplot2.rds") +p$x$message$split <- "v" +htmlwidgets::saveWidget(p, "livedemo.html") +``` + +```{r eval=FALSE} +library(ggplot2) +profvis({ + g <- ggplot(diamonds, aes(carat, price)) + geom_point(size = 1, alpha = 0.2) + print(g) +}) +``` + +```{r echo=FALSE, height="800px"} +# This block loads the data from the first block and shows the correct output +# for the readers. +readRDS("ggplot2.rds") +``` + + +## Can I profile code without calling `profvis()`? + +Yes. There are two ways to do it. + +If you are in RStudio, you can select Profile->Start Profiling, run your code, and then Profile->Stop Profiling. When you stop the profiling, the profvis viewer will come up. + +Another way is to start and stop the R profiler manually, then have profvis read in the recorded profiling data. To profile your code, run: + +```{r eval=FALSE} +# Start profiler +Rprof("data.Rprof", interval = 0.01, line.profiling = TRUE, + gc.profiling = TRUE, memory.profiling = TRUE) + +## Run your code here + +# Stop profiler +Rprof(NULL) +``` + +Then you can load the data into profvis: + +```{r eval=FALSE} +profvis(prof_input = "data.Rprof") +``` + +This technique can also be used to profile just one section of your code. + + +## Why does the flame graph hide some function calls for Shiny apps? + +When profiling [Shiny](http://shiny.rstudio.com/) applications, the profvis flame graph will hide many function calls by default. They're hidden because they aren't particularly informative for optimizing code, and they add visual complexity. This feature requires Shiny 0.13.0 or greater. + +If you want to see these hidden blocks, uncheck **Options** -> **Hide internal function calls**: + +```{r echo=FALSE, eval=FALSE} +# This code block is what we actually run. It must be run manually each time +# this is updated. +# The next block is what we show to the readers. +library(shiny) +p <- profvis({ + runExample("06_tabsets", display.mode = "normal") +}) +p$x$message$split <- "v" + +saveRDS(p, "shinyapp.rds") +``` + +```{r eval=FALSE} +library(shiny) +profvis({ + # After this app has started, interact with it a bit, then quit + runExample("06_tabsets", display.mode = "normal") +}) +``` + +```{r echo=FALSE, height="700px"} +# This block loads the data from the first block and shows the correct output +# for the readers. +readRDS("shinyapp.rds") +``` + +To make the hiding work, Shiny has special functions called `..stacktraceon..` and `..stacktraceoff..`. Profvis goes up the stack, and when it sees a `..stacktraceoff..`, it will hide all function calls until it sees a corresponding `..stacktraceon..`. If there are nukltiple `..stacktraceoff..` calls in the stack, it requires an equal number of `..stacktraceon..` calls before it starts displaying function calls again. + + +## Can I profile just part of a Shiny application? + +Sometimes it it useful to profile just part of a Shiny application, instead of the whole thing from start to finish. + +If you are in RStudio, you can start your application, then select Profile->Start Profiling, interact with your application, and then select Profile->Stop Profiling. When you stop the profiling, the profvis viewer will come up. + +Profivs also provides a [Shiny Module](http://shiny.rstudio.com/articles/modules.html) to initiate the profiling, and provides a UI to start, stop, view, and download profvis sessions. This is done with `profvis::profvis_server` and `profvis::profvis_ui`. + +For example, here's a small app that uses the module: + +```{r eval=FALSE} +library(shiny) +library(ggplot2) +library(profvis) +shinyApp( + fluidPage( + plotOutput("plot"), + actionButton("new", "New plot"), + profvis_ui("profiler") + ), + function(input, output, session) { + callModule(profvis_server, "profiler") + + output$plot <- renderPlot({ + input$new + ggplot(diamonds, aes(carat, price)) + geom_point() + }) + } +) +``` + +In the server function, `callModule(profvis_server, "profiler")` sets up the profvis session, and in the UI `profvis_ui("profiler")` sets up a basic interface to start, stop, view, and download profvis sessions. + +You can create your own `profvis_server` and `profvis_ui` functions by calling `Rprof()` to start and stop profiling (as described in [this answer](#can-i-profile-code-without-using-profvis)), and trigger it with an `actionButton`. For example, you could put this in your UI: + +```{r eval=FALSE} + radioButtons("profile", "Profiling", c("off", "on")) +``` + +And put this in your server function: + +```{r eval=FALSE} + observe({ + if (identical(input$profile, "off")) { + Rprof(NULL) + } else if (identical(input$profile, "on")){ + Rprof(strftime(Sys.time(), "%Y-%m-%d-%H-%M-%S.Rprof"), + interval = 0.01, line.profiling = TRUE, + gc.profiling = TRUE, memory.profiling = TRUE) + } + }) +``` + + +It will add radio buttons to turn profiling on and off. Turn it on, then interact with your app, then turn it off. There will be a file with a name corresponding to the start time. You can view the profiler output with profvis, with something like this: + +```{r eval=FALSE} + profvis(prof_input = "2018-08-07-12-22-35.Rprof") +``` + + +## Can I profile a Shiny application running on a remote server? + +Yes. One option is to include the Profvis Shiny Module desribed in the [previous question](#-can-i-profile-just-part-of-a-shiny-application?). + +You can also set it up manually. The main idea is to start and stop profiling (as described in [this answer](#can-i-profile-code-without-using-profvis)). At the top of your app.R or server.R, you can add the following: + +```{r eval=FALSE} +Rprof(strftime(Sys.time(), "%Y-%m-%d-%H-%M-%S.Rprof"), + interval = 0.01, line.profiling = TRUE, + gc.profiling = TRUE, memory.profiling = TRUE) + +onStop(function() { + Rprof(NULL) +}) +``` + +This will start profiling when the app starts, and stop when it exits. + +## Why does `Sys.sleep()` not show up in profiler data? + +The R profiler doesn't provide any data when R makes a system call. If, for example, you call `Sys.sleep(5)`, the R process will pause for 5 seconds, but you probably won't see any instances of `Sys.sleep` in the profvis visualization -- it won't even take any horizontal space. For these examples, we've used the `pause` function instead, which is part of the profvis package. It's similar to `Sys.sleep`, except that it does show up in the profiling data. For example: + +```{r, height="350px"} +profvis({ + # Does not show in the flame graph + Sys.sleep(0.25) + + # Does show in the flame graph + pause(0.25) +}) +``` + +Calls to external programs and libraries also may not show up in the profiling data. If you call functions from a package to fetch data from external sources, keep in mind that time spent in those functions may not show in the profiler. + + +## Why is the call stack reversed for some of my code? + +One of the unusual features of R as a programming language is that it has *lazy evaluation* of function arguments. If you pass an expression to a function, that expression won't be evaluated until it's actually used somewhere in that function. + +The result of this is that sometimes the stack can look like it's in the wrong order. In this example below, we call `times_10` and `times_10_lazy`. They both call `times_5()` and `times_2()`, but the "regular" version uses an intermediate variable `y`, while the lazy version nests the calls, with `times_2(times_5(x))`. + +```{r} +profvis({ + times_5 <- function(x) { + pause(0.5) + x * 5 + } + + times_2 <- function(x) { + pause(0.2) + x * 2 + } + + times_10 <- function(x) { + y <- times_5(x) + times_2(y) + } + + times_10_lazy <- function(x) { + times_2(times_5(x)) + } + + times_10(10) + times_10_lazy(10) +}) +``` + +In most programming languages, the flame graph would look the same for both: the `times_10` (or `times_10_lazy`) block would be on the bottom, with `times_5` and `times_2` side-by-side on the next level up on the stack. + +With lazy evaluation, when the `times_10_lazy` function calls `times_2(times_5(x))`, the `times_2` function receives a *promise* with the unevaluated expression `times_5(x)`, and evaluates it only when it reaches line 9, `x * 2` (the expression gets evaluated in the correct context, so there's no naming collision of the `x` variable). + +It's not only the call stack that has a surprising order with `times_10_lazy` -- the temporal order the simulated work we're doing in the function (represented by the `pause` blocks) is different. The `times_2` and `times_5` functions pause for 0.2 and 0.5 seconds, respectively. Those pauses occur in opposite order in `times_10` and `times_10_lazy`. + +Keep in mind that lazy evaluation may result in counterintuitive results in the flame graph. If you want to avoid some of the possible confusion from lazy evaluation, you can use intermediate variables to force the evaluation of arguments at specific locations in your code, as we did in `times_10`. + + +## Why does profvis tell me the the wrong line is taking time? + +In some cases, multi-line expressions will report that the first line of the expression is the one that takes all the time. In the example below, there are two `for` loops: one with curly braces, and one without. In the loop with curly braces, it reports that line 3, containing the `pause` is the one that takes all the time. In the loop without curly braces, it reports that line 6, containing `for`, is the one that takes all the time, even though the time is really spent on line 7, with the `pause`. + +```{r, height="350px"} +profvis({ + for (i in 1:3) { + pause(0.1) + } + + for (i in 1:3) + pause(0.1) + +}) +``` + +For code that contains multi-line expressions like these, using curly braces will allow the profiler to identify the correct line where code is running. + + +## How do I interpret memory profiling information? + +The memory profiling information can be somewhat tricky to interpret, for two reasons. The first reason is that, compared to call stack information, memory usage information is collected with different temporal characteristics: call stack information is recorded instantaneously at each sample, while memory information is recorded between each sample. + +The second reason that is that memory deallocations happen somewhat randomly, and may happen long after the point where the memory was no longer needed. The deallocations occur in garbage collection (``) events. + +For these reasons, it might look like a particular line of code (or function call in the flame graph) is responsible for memory allocation or deallocation, when in reality the memory use is due to a previous line of code. + +If a section of code results in a large amount of allocation and deallocation, it means that it's "churning" through memory and using a large amonut of temporary memory storage. This can be seen in Example 1 above. In these cases, it may be possible to optimize the code so that it doesn't use as much temporary memory. + +If a section of code results in a large amount of allocation but does not have a large amount of deallocation, then it means the memory is not being released. This could be because the code genuinely requires that extra memory, but it could also be a sign of a memory leak. + + +## How do I split the panes vertically instead of horizontally? + +The profvis examples in this document have a vertical split, but by default, profvis visualizations have a horizontal split. To switch directions, you can check or uncheck **Options -> Split horizontally**. + +To change the split direction when the visualization opens, use `split="v"`: + +```{r eval=FALSE} +profvis({ + # Code here +}, split = "v") + + +# Also possible to control the split when calling print() +p <- profvis({ + # Code here +}) +print(p, split = "v") +``` + + +## How do I get source code to show with `Rscript`? + +If you run profvis from a script, the source code won't show in the source panel. This is because source refs are not recorded by default when R is run non-interactively. To make it work, use `options(keep.source=TRUE)`. For example: + + +``` +Rscript -e "options(keep.source=TRUE); p <- profvis::profvis({ profvis::pause(0.2) }); htmlwidgets::saveWidget(p, 'test.html')" +``` + +## What are some other resources for profiling R code? + +Base R comes with the `Rprof` function (it's what `profvis` calls to collect profiling data) as well as the `summaryRprof` function for getting a human-readable summary of the profiling data collected by `Rprof`. + +Luke Tierney and Riad Jarjour have authored the [proftools](https://cran.r-project.org/web/packages/proftools/index.html) package, which provides many more tools for summarizing profiling data, including hot paths, call summaries, and call graphs. 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If you want to be able to view code in a package, see [this FAQ](faq.html#how-do-i-get-code-from-an-r-package-to-show-in-the-code-panel). + +* **Opening and saving profiles:** Profiles can be saved for sharing or viewing in the future. Profile files have extension `.Rprofvis`. If you wish to share a profile for viewing in a web browser, you can simply rename the file to have an `.html` extension. + +* **Profiling Shiny applications:** Entire Shiny applications can be profiled by starting the profiler before launching the application. To capture just a part of a Shiny session, you can start and stop the profiler while the application is running. + +* **Publishing profiles:** Profiles can be shared on RPubs by clicking on the **Publish** button. 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It provides a interactive graphical interface for visualizing data from `Rprof()`, R's built-in tool for collecting profiling data. + +Most R users have had times where we've wanted our code to run faster. However, it's not always clear how to accomplish this. A common approach is to rely on intuition, and on wisdom from the broader R community about speeding up R code. One drawback to this is it can lead to a focus on optimizing things that actually take a small proportion of the overall running time. + +Suppose you make a loop run 5 times faster. That sounds like a huge improvement, but if that loop only takes 10% of the total time, it's still only a 8% speedup overall. Another drawback is that, although many of the commonly-held beliefs are true (for example, preallocating memory can speed things up), some are not (e.g., that `*apply` functions are inherently faster than `for` loops). This can lead us to spend time doing "optimizations" that don't really help. To make slow code faster, we need accurate information about what is making our code slow. + +## Getting started + +Below is an example of profvis in use. The code generates a random 50,000 row data set, draws a plot, then fits a linear model, and draws a line for the model. (The plot isn't displayed in this document, though.) If you copy and paste this code into your R console, it'll open a window with the same profvis interface that you see in this HTML document. + +```{r abline, fig.show="hide"} +library(profvis) + +profvis({ + df <- data.frame(x = rnorm(5e5), y = rnorm(5e5)) + + plot(y ~ x, data = df) + m <- lm(y ~ x, data = df) + abline(m, col = "red") +}) +``` + +On top is the code, and on the bottom is a flame graph. In the flame graph, the horizontal direction represents time in milliseconds, and the vertical direction represents the call stack. Looking at the bottom-most items on the stack, most of the time, about 2 seconds, is spent in `plot`, and then a much smaller amount of time is spent in `lm`, and almost no time at all is spent in `abline` -- it doesn't even show up on the flame graph. + +Traveling up the stack, `plot` called `plot.formula`, which called `do.call`, and so on. Going up a few more levels, we can see that `plot.default` called a number of functions: first `deparse`, and later, `plot.xy`. Similarly, `lm` calls a number of different functions. + +On top, we can see the amount of time spent on each line of code. This tells us, unsurprisingly, that most of the time is spent on the line with `plot`, and a little bit is spent on the line with `lm`. + +The code panel also shows memory allocation and deallocation. Interpreting this information can be a little tricky, because it does not necessarily reflect memory allocated and deallcated *at* that line of code. The sampling profiler records information about memory allocations that happen between the previous sample and the current one. This means that the allocation/deallocation values on that line may have actually occurred in a previous line of code. + +### Navigating profvis + +Profvis is interactive. You can try the following: + +* As you mouse over the flame graph, information about each block will show in the info box. +* Yellow flame graph blocks have corresponding lines of code on the left. (White blocks represent code where profvis doesn't have the source code -- for example, in base R and in R packages. But see [this FAQ](#how-do-i-get-code-from-an-r-package-to-show-in-the-code-panel) if you want package code to show up in the code panel.) If you mouse over a yellow block, the corresponding line of code will be highlighted. Note that the highlighted line of code is where the yellow function is called *from*, not the content of that function. +* If you mouse over a line of code, all flame graph blocks that were called from that line will be highlighted. +* Click on a block or line of code to lock the current highlighting. Click on the background, or again on that same item to unlock the highlighting. Click on another item to lock on that item. +* Use the mouse scroll wheel or trackpad's scroll gesture to zoom in or out in the x direction. +* Click and drag on the flame graph to pan up, down, left, right. +* Double-click on the background to zoom the x axis to its original extent. +* Double-click on a flamegraph block to zoom the x axis the width of that block. + +Each *block* in the flame graph represents a call to a function, or possibly multiple calls to the same function. The width of the block is proportional to the amount of time spent in that function. When a function calls another function, another block is added on top of it in the flame graph. + +The profiling data has some limitations: some internal R functions don't show up in the flame graph, and it offers no insight into code that's implemented in languages other than R (e.g. C, C++, or Fortran). + +### The data view + +In addition to the flame graph view, profvis provides a *data view*, which can be seen by clicking on the Data tab. It provides a top-down tabular view of the profile. Click the **code** column to expand the call stack under investigation and the following columns to reason about resource allocation: + +* Memory: Memory allocated or deallocated (for negative numbers) for a given call stack. This is represented in megabytes and aggregated over all the call stacks over the code in the given row. +* Time: Time spent in milliseconds. This field is also aggregated over all the call stacks executed over the code in the given row. + +## How profiling data is collected + +Profvis uses data collected by `Rprof()`, which is part of the base R distribution. At each time interval (`profvis()` uses a default interval of 10ms), the profiler stops the R interpreter, looks at the current function call stack, and records it to a file. Because it works by *sampling*, the result isn't deterministic. Each time you profile your code, the result will be slightly different.

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