| Title: | Graphical User Interface for Rprof() |
|---|---|
| Description: | Show graphically the results of profiling R functions by tracking their execution time. |
| Authors: | Fernando de Villar<[email protected]> and Angel Rubio <[email protected]> |
| Maintainer: | Fernando de Villar<[email protected]> |
| License: | GPL |
| Version: | 2.0.1 |
| Built: | 2025-02-13 03:56:43 UTC |
| Source: | https://github.com/cran/GUIProfiler |
Show graphically the results of profiling R functions by tracking their execution time.
| Package: | GUIProfiler |
| Type: | Package |
| Version: | 2.0.1 |
| Date: | 2015-08-23 |
| License: | GPL |
This package mimics the behavior of the Matlab profiler: after the code in a file is executed, it is generated an HTML report. This HTML report includes information on the time spent on each of the lines of the profiled code and hyperlinks to jump across the included functions. The graphical interface makes it easy to identify which are the specific lines that may slow down the code.
Fernando de Villar and Angel Rubio
Maintainer: Fernando de Villar <[email protected]>
RRprofStart, RRprofStop, RRprofReport, Rprof
temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()
Generate the report based on the output of the R profiler.
RRprofReport(file.name = "RRprof.out", notepad.path = "C:/Program Files/Notepad++/notepad++.exe",reportname = "my_report")RRprofReport(file.name = "RRprof.out", notepad.path = "C:/Program Files/Notepad++/notepad++.exe",reportname = "my_report")
file.name |
Name of a file produced by RRprofStart() |
notepad.path |
Path where notepad++.exe is |
reportname |
Name of the html file to be generated |
This function generates a profiling report as a html file in the working directory.
The report consists of two main groups of tables: a summary of the called functions with the time spent for each of them and a second group of tables with the time spent on each line of code for each function. Furthermore, it includes a graph of the different hierarchical relationships between the called functions generated using functions in the package 'proftools'.
In the RStudio environment, this report is shown in the viewer pane. In addition, the markers panel indicates the lines of code where more time was spent to ease the navigation across the source code by simply clicking on them.
When it is opened in a browser, a convenient implemented feature is that the line numbers of the functions are clickable. If Notepad++ is installed, once a line number is clicked, the corresponding file is opened with the cursor on the selected line.
It is advisable to open the Report using Internet Explorer, because other browsers can block the clickable line numbers feature
Fernando de Villar and Angel Rubio
RRprofStop, RRprofStart, Rprof
temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()
Rprof() is activated and started
RRprofStart(filename = "RRprof.out", interval = 0.02, numfiles = 100L, bufsize = 10000L)RRprofStart(filename = "RRprof.out", interval = 0.02, numfiles = 100L, bufsize = 10000L)
filename |
The file to be used for recording the profiling results. |
interval |
real: time interval between samples. |
numfiles |
integers: line profiling memory |
bufsize |
allocation |
The profiler interrupts R asynchronously, and it cannot allocate memory to store results as it runs. This affects line profiling, which needs to store an unknown number of file pathnames. The numfiles and bufsize arguments control the size of pre-allocated buffers to hold these results: the former counts the maximum number of paths, the latter counts the numbers of bytes in them. If the profiler runs out of space it will skip recording the line information for new files, and issue a warning when Rprof(NULL) is called to finish profiling.
The timing interval cannot be too small, for the time spent in each profiling step is added to the interval. What is feasible is machine-dependent, but 10ms seemed as small as advisable on a 1GHz machine.
Fernando de Villar and Angel Rubio
RRprofStop, RRprofReport, Rprof
temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()
Once the R expressions to be profiled have finished, the profile is stopped.
RRprofStop()RRprofStop()
Fernando de Villar and Angel Rubio
RRprofStart, RRprofReport, Rprof
temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()temp<-tempdir() # Definition of two functions normal.solve <- function(A,b) { Output <- solve(crossprod(A), t(A)%*%b) } chol.solve <- function(A,b) { L <- chol(crossprod(A)) Output1 <- backsolve(L, t(A)%*%b, transpose=TRUE) Output2 <- backsolve(L, Output1) } compareMethods <- function() { library(MASS) # Call the functions source(paste(temp,"/normal.solve.R",sep="")) source(paste(temp,"/chol.solve.R",sep="")) # Solving a big system of equations nrows <- 1000 ncols <- 500 A <- matrix(rnorm(nrows*ncols),nrows,ncols) b <- rnorm(nrows) # Testing different possibilities Sol1 <- qr.solve(A,b) # Using QR factorization Sol2 <- coefficients(lm.fit(A,b)) # lm.fit, based on QR but with some overhead Sol3 <- ginv(A) %*% b # Using the pseudoinverse based on SVD Sol4 <- normal.solve(A,b) # Using a function based on the normal equations. Sol5 <- chol.solve(A,b) # Using a function based on the Choleski factorization. } # Dump these functions to three different files dump("normal.solve",file=paste(temp,"/normal.solve.R",sep="")) dump("chol.solve",file=paste(temp,"/chol.solve.R",sep="")) dump("compareMethods",file=paste(temp,"/compareMethods.R",sep="")) source(paste(temp,"/compareMethods.R",sep="")) # Profile the code RRprofStart() compareMethods() RRprofStop() # Uncomment to open the report #RRprofReport()