I have library(rminer) installed yet wondering why mmertric is still not there and unable to use the function.
Anyone has come across this?
#probability of each landmass category
flagdmodel <- naiveBayes(landmass ~ ., data=trainfdata)
#flagdmodel
#predictionmodel
flagprediction <- predict(flagdmodel, testfdata)
mmetric(testfdata$landmass, flagprediction, c("ACC","PRECISION","TPR","F1"))
+ mmetric(testfdata$landmass, flagprediction, c("ACC","PRECISION","TPR","F1"))
mmetric()
Error in mmetric() : could not find function "mmetric"
mmetric()
Error in mmetric() : could not find function "mmetric"
Question: why can't R find functions for the package I installed with RStudio's install tool?
Answer:
When you want to use functions or other objects in packages that aren't in R, you need to do two things:
install.packages("rminer")
library(rminer)
RStudio can do the first step for you with the install tool, but you still need to do the second one. The first step installs the needed directories and files on your computer. The second step loads them into your current R environment.
In RStudio, you can use the packages tab to check both steps. Installed packages will be in the list in that tab. Loaded packages will have a checkmark to the left of the package name.
It may be easier to find, though if you just run the following in your console:
"package:rminer" %in% search()
If the output is TRUE you're good to go. If it's FALSE you need to run library(rminer)
Related
I am trying to run through a mixed stock analysis in RStudio based on the walkthrough provided by Bolker (https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=043730a02b148396ebd54b2f62e8f6364714b1b2), using the 'mixstock' package and the example 'lahanas98raw' dataset within. However, I am receiving a warning that the 'calc.RL.0' function cannot be found whilst trying to carry out Raftery and Lewis convergence diagnostics (p.14 of Bolker's walkthrough). I am wondering whether one of the packages has not installed properly (i.e., either 'mixstock' or 'coda'), or whether there is another package I can use to run this diagnostic instead.
When I initially tried to install the 'mixstock' package, the following warning came up:
'Warning in install.packages : package ‘mixstock’ is not available for this version of R.'
I tried installing the version of R (2.14.1) described as the 'current version of R' on page 2 of Bolker's walkthrough, but when I then tried to switch to this version of R in RStudio via the options menu, it says that this version of R is not compatible with RStudio. To work around this, I downloaded the 'mixstock' package (version 0.9.5.1) from the CRAN archive (https://cran.r-project.org/src/contrib/Archive/mixstock/) and uploaded it to RStudio this way instead.
This seemed to have worked, as I successfully ran through a significant amount of the code, but a new error arose when I tried to run Raftery and Lewis convergence diagnostics (p.14). When I try to run calc.RL.0(mydata), I receive the following error:
'Error in calc.RL.0(mydata) : could not find function "calc.RL.0"'
However, when I click on 'mixstock' in the package window, everything seems fine and the 'calc.RL.0' function appears, alongside several other 'calc' functions (e.g. 'calc.GR', 'calc.mult.GR', 'calc.mult.RL') that can all be found and run fine. The 'calc.RL.0' function relies on the 'raftery.diag' function within the 'coda' package, so I have also made sure that is installed and called. I have tried a bunch of other methods but nothing seems to be working.
Here is some of my code leading up to the warning message:
## Calculate confidence intervals - i.e., bootstrapping - and plot them
mydata.umlboot = genboot(mydata,"uml")
confint(mydata.umlboot)
plot(mydata.umlboot, ylim=c(0,1))
## Carry out Markov Chain Monte Carlo (MCMC) estimations and plot them
mydata.mcmc = tmcmc(mydata)
mydata.mcmc
confint(mydata.mcmc)
plot(mydata.mcmc, ylim=c(0,1))
## Check that the Markov chains have converged = run Raftery and Lewis diagnostics
library(mixstock)
library(coda)
calc.RL.0(mydata)
'Error in calc.RL.0(mydata) : could not find function "calc.RL.0"'
Could this be something to do with the way the 'mixstock' package was initially installed, or is it likely to be another issue? Is there another way to run Raftery and Lewis diagnostics and still get the outputs I need (diagnostics for the last chain evaluated; the history of how long each suggested chain was)? Any help would be much appreciated - thanks in advance!
The most reliable way to install mixstock, if you have development tools installed on your computer (compilers etc.), is remotes::install_github("bbolker/mixstock") (I don't think I've changed anything/fixed any bugs since the archived version, but if I did the changes would be reflected on GitHub.)
It looks like I forgot to export that function, so
mixstock:::calc.RL.0(mydata)
should work (this is something I can/should fix). Note that the Gelman-Rubin diagnostic (calc.GR(), which is properly exported) is more reliable than Raftery-Lewis anyway ...
As my code evolves from version to version, I'm aware that there are some packages for which I've found better/more appropriate packages for the task at hand or whose purpose was limited to a section of code which I've now phased out.
Is there any easy way to tell which of the loaded packages are actually used in a given script? My header is beginning to get cluttered.
Update 2020-04-13
I've now updated the referenced function to use the abstract syntax tree (AST) instead of using regular expressions as before. This is a much more robust way of approaching the problem (it's still not completely ironclad). This is available from version 0.2.0 of funchir, now on CRAN.
I've just got around to writing a quick-and-dirty function to handle this which I call stale_package_check, and I've added it to my package (funchir).
e.g., if we save the following script as test.R:
library(data.table)
library(iotools)
DT = data.table(a = 1:3)
Then (from the directory with that script) run funchir::stale_package_check('test.R'), we'll get:
Functions matched from package data.table: data.table
**No exported functions matched from iotools**
Have you considered using packrat?
packrat::clean() would remove unused packages, for example.
I've written a command-line script to accomplish this task. You can find it in this Github gist. I'm sure there are edge cases that it misses, but it works pretty well, on both R scripts and Rmd files.
My approach always is to close my R script or IDE (i.e. RStudio) and then start it again.
After this I run my function without loading any dependecies/packages beforehand.
This should result in various warning and error messages telling you which functions couldn't be found and executed. This again will give you hints on what packages are necessary to load beforehand and which one you can leave out.
In package ISLR, there is a data set called Default.
I want to use that data set, but the ISLR package is not installed on my machine.
data(Default)
# Warning message:
# In data(Default) : data set ‘Default’ not found
library(ISLR)
# Error in library(ISLR) : there is no package called ‘ISLR’
Since I'll probably never use it again, I don't want to install the package. I thought about reading it from the web, but it's not in the linked web page from the package description.
In general, is there a way to load a data set from a package without installing the package?
You can do this from within R:
download.file("http://cran.r-project.org/src/contrib/ISLR_1.0.tar.gz",
dest="ISLR.tar.gz")
untar("ISLR.tar.gz",files="ISLR/data/Default.rda")
L <- load("ISLR/data/Default.rda")
summary(Default)
If you want to keep a copy of the data file:
file.copy("ISLR/data/Default.rda",".")
Clean up:
unlink(c("ISLR.tar.gz","ISLR"),recursive=TRUE)
I'm not sure you can get around having to download the tarball -- in principle you might be able to run untar() directly on a network connection, but I don't think the underlying machinery can actually extract a file without downloading the whole tarball to somewhere on your machine first.
You said, "Since I'll probably never use it again, I don't want to install the package." If the fact that you'll never use it again is your main concern, then perhaps this solution is not quite what you want, but it is probably the simplest solution:
Install the package with install.packages().
Extract and save the dataset that you want.
Uninstall the package with remove.packages().
So the final result is what you want in three simple steps, though the process does involve installing the package, which you hoped to avoid. But you end up without the package in your system that you don't want, so the end result is the same as what you want.
Based on the answer to this question: Elegant way to check for missing packages and install them?
I'm using the following code to make sure that all packages are installed when I upgrade R, or set up other users:
list.of.packages <- c("RODBC", "reshape2", "plyr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
I've placed it in my .First function in my .Rprofile, but when I start up R it gives the following error and continues starting up:
Error in match(x, table, nomatch = 0L) :
could not find function "installed.packages"
If I run the code after I get a prompt it works fine. Any ideas why?
Thanks!
It appears from reading ?Startup that:
Next, if a function .First is found on the search path, it is executed
as .First(). Finally, function .First.sys() in the base package is
run. This calls require to attach the default packages specified by
options("defaultPackages").
Now, installed.packages is in the utils package, which is typically one of the default packages. So it's not available at the time .First is called.
Perhaps try replacing installed.packages with utils::installed.packages?
As Josh notes below my eyes skimmed over the piece that addresses this issue directly, namely:
Note that when the site and user profile files are sourced only the
base package is loaded, so objects in other packages need to be
referred to by e.g. utils::dump.frames or after explicitly loading the
package concerned.
I have built an R package but I do not want my users to have to install it before using it.
Is there a way to load a package without having to install it?
For example, if I have a package mypackage.tar.gz, is there something like
library("mypackage.tar.gz")
?
I'll join in "the chorus" of suggesting you should really install the package.
That having been said, you can take a look at Hadley's devtools package, which will let you load packages into the workspace without dumping in your global workspace.
The package will have to be untar'd/unzipped and follow the standard R package structure.
In order for this to work, though, your users would have to have the devtools package installed, so ... I'm not sure that this is any type of win for you.
If you only need the code to be loaded without it being installed, take the raw R script and source it:
source(myScript.R)
If you have different functions, you can create an R script that just loads all the necessary source files. What I sometimes do when developing, is name all my functions F_some_function.R and my classes Class_some_function.R. This allows me to source a main file containing following code :
funcdir <- "C:/Some/Path"
files <- dir(funcdir)
srcfiles <- c(grep("^Class_",dir(funcdir),value=T),
grep("^F_",dir(funcdir),value=T)
)
for( i in paste(funcdir,srcfiles,sep="/")) source(i)
If you present them with the tarred file, they can untar themselves using untar() before sourcing the main file.
But honestly, please use a package. You load everything in the global environment (or in a specified environment if you use local=T), but you lose all functionality of a package. Installing a package is no hassle, and removing neither.
If it's a matter of writing rights on the C drive (which is the only possible reason not to use a package that I met in my carreer), one can easily set another library location. R 2.12 actually does this by itself on Windows. See ?.libPaths()