I realize there are generic functions like plot, predict for a list of packages. I am wondering how could I get the R script of these generic functions for a specific package, like the lme4::predict. I tried lme4::predict, but it comes with error:
> lme4::predict
Error: 'predict' is not an exported object from 'namespace:lme4'
Since you state that my suggestion above was helpful I will tell you my process. I used my own co-authored package called pacman. This package was developed because we had a hard time remembering all the obscurely named functions to get information on and work with add on packages.
I used this to figure out what you wanted:
library(pacman)
p_funs(lme4, all=TRUE)
I set all = TRUE as predict is a method for a specific class (like print, summary and plot). Generally, these methods are not exported so p_funs won't return them unless you set all = TRUE. Then I scrolled down to the p section and found only a single predict method: predict.merMod
Next I realized it wasn't exported so :: won't show me the stuff and extra colon power is needed, hence: lme4:::predict.merMod
As pointed out by David and rawr above, some functions can be suborn little snippets (methods etc.), thus methods and getAnywhere are helpful.
Here is an example of that:
library(tm)
dissimilarity #The good stuff is hid
methods(dissimilarity) #I want the good stuff
getAnywhere("dissimilarity.DocumentTermMatrix")
Small end note
And of course you don't need pacman to look at functions for packages, it's what I use and helpful but it just wraps base R things. Use THE SOURCE to figure out exactly what.
Related
I am learning R package SimInf to simulate data-driven stochastic epidemiological models. As I was reading the documentation I came across an unrecognized funcion Nn when defining a function for epicurves. Specifically, this line:
j <- sample(seq_len(Nn(model)), 1)
Values of model are integers. My guess is that Nn selects non-negative values, however my R does not recognize this function. From documentation it does not look like they pre-defined Nn either. Can someone please tell if they know what "Nn" is for? Thank you.
A way to go is always taking the package-name and triple-":" it, such that you can find nearly all functions inside the package. Maybe you are familiar with namespacing a function via packageName::functionFrompackageTocall. The packageName::: shows (nearly) all functions defined in this package. If you do this in R-Studio with SimInf:: and SimInf:::, you will see that the latter gives much more functions. But you can only find the functions SimInf:::Nd and SimInf:::Nc, not the Nn-function. Hence you will have to go to the github-sources of the package, in this case https://github.com/stewid/SimInf .Then search for Nn the whole repository. You will see that it seems like it is always an int, but this doesn't help you since you want to get ii as a function, not as a variable. Scrolling further down in the search-results, you will find the NEWS.md-file which mentions The 'Nn' function to determine the number of nodes in a model has been replaced with the S4 method 'n_nodes'. in the https://github.com/stewid/SimInf/blob/fd7eb4a29b82a4a97f64b528bb0e78e5474aa8a5/NEWS.md file under SimInf 8.0.0 (2020-09-13). Hence having a current version of SimInf installed, it shouldn't use the method Nn anymore. If you use it in your code, replace it by n_nodes. If you find it in current package code, you can email the package-maintainer that you found a bug in his code.
TLDR: Nn is an outdated version of n_nodes
When making your own package for R, one often wants to make use of functions from a different package.
Maybe it's a plotting library like ggplot2, dplyr, or some niche function.
However, when making a function that depends on functions in other packages, what is the appropriate way to call them? In particular, I am looking for examples of when to use
myFunction <- function(x) {
example_package::function(x)
}
or
require(example_package)
myFunction <- function(x) {
function(x)
}
When should I use one over the other?
If you're actually creating an R package (as opposed to a script to source, R Project, or other method), you should NEVER use library() or require(). This is not an alternative to using package::function(). You are essentially choosing between package::function() and function(), which as highlighted by #Bernhard, explicitly calling the package ensures consistency if there are conflicting names in two or more packages.
Rather than require(package), you need to worry about properly defining your DESCRIPTION and NAMESPACE files. There's many posts about that on SO and elsewhere, so won't go into details, see here for example.
Using package::function() can help with above if you are using roxygen2 to generate your package documentation (it will automatically generate a proper NAMESPACE file.
The douple-colon variant :: has a clear advantage in the rare situations, when the same function name is used by two packages. There is a function psych::alpha to calculate Cronbach's alpha as a measure of internal consistency and a function scales::alpha to modify color transparency. There are not that many examples but then again, there are examples. dplyr even masks functions from the stats and base package! (And the tidyverse is continuing to produce more and more entries in our namespaces. Should you use dyplr you do not know, if the base function you use today will be masked by a future version of dplyr thus leading to an unexpected runtime problem of your package in the future.)
All of that is no problem if you use the :: variant. All of that is not a problem if in your package the last package opened is the one you mean.
The require (or library) variant leads to overall shorter code and it is obvious, at what time and place in the code the problem of a not-available package will lead to an error and thus become visible.
In general, both work well and you are free to choose, which of these admittedly small differences appears more important to you.
Q. How does one write an R package function that references an R package dataset in a way that is simple/friendly/efficient for an R user. For example, how does one handle afunction() that calls adataset from a package?
What I think may not be simple/friendly/efficient:
User is required to run
data(adataset) before running
afunction(...)
or else receiving an Error: ... object 'adataset' not found. I have noticed some packages have built-in datasets that can be called anytime the package is loaded, for example, iris, which one can call without bringing it to the Global Environment.
Possible options which I have entertained:
Write data(NamedDataSet) directly into the function. Is this a bad idea. I thought perhaps it could be, looking at memory and given my limiting understanding of function environments.
Code the structure of the dataset directly into the function. I think this works depending on the size of the data but it makes me wonder about how to go about proper documentation in the package.
Change Nothing. Given a large enough dataset, maybe it does not make sense to implement a way different from reading it before calling the function.
Any comments are appreciated.
You might find these resources about writing R data packages useful:
the "External Data" section of R Packages
Creating an R data package
Creating an R data package (overview)
In particular, take note of the DESCRIPTION file and usage of the line LazyData: true. This is how datasets are made available without having to use data(), as in the iris example that you mention.
I need to see the code for the summary function which is used in mirt package to see the factor loading matrix.
I have tried edit(summary) which in return giving me this
function (object, ...)
standardGeneric("summary")
which is not very helpful for me. How can I see the code for summary?
summary is a generic function. So you need to see the class-specific method for whatever type of object you're trying to summary-ize. E.g., see summary.lm (for lm objects). You don't specify the object class you're working with, so it's impossible to say what specific summary function you need to look at.
UPDATE: These are s4 generics, which is a bit more complicated than summary.lm, which is s3. Some quick googling reveals that you can see the relevant s4 methods on the package's Github page. The contents of summary will depend on what specific class you're looking at (and there appear to be four classes in the package):
Exploratory
Mixed
Confirmatory
MultipleGroup
This question and answer will also be helpful for addressing these kinds of questions in general in the future.
I'm using the randomForest package (v 4.6-7) in R (v 2.15.3) and can easily use the function randomForest to create a model. However, when I try to predict on my test set, the predict.randomForest function cannot be found. I've also tried plotting with plot.randomForest only to get the same error, "could not find function."
I've already tried reinstalling the package (figuring maybe it was out of date) and made sure the spelling is absolutely correct. I cannot figure out what's causing this error, any ideas?
It appears that the functions of interest are not exported from the package.
If you use ls(package:randomForest) you'll get a list of the exported functions.
If you want to see all the functions available the use: ls(getNamespace("randomForest"), all.names=TRUE). Thanks #Joshua.
You'll see the functions you want there.
In order to refer to one of them explicitly, use: randomForest:::predict.randomForest()
or just make a object which inherits the class 'randomForest' and call predict() on it directly.