Get Object methods R - r

Given an arbitrary R object, how can I obtain all the methods associated with the object?

The closest I can think of is methods (if S3 object/function, List all available methods for an S3 generic function, or all methods for a class.), or showMethods (if S4).
e.g.:
> A <- matrix(runif(10))
> B <- methods(class=class(A))
> B
[1] anyDuplicated.matrix as.data.frame.matrix as.raster.matrix*
[4] boxplot.matrix determinant.matrix duplicated.matrix
[7] edit.matrix* head.matrix isSymmetric.matrix
[10] relist.matrix* subset.matrix summary.matrix
[13] tail.matrix unique.matrix
Non-visible functions are asterisked
> attr(B,'info')
visible from
anyDuplicated.matrix TRUE package:base
as.data.frame.matrix TRUE package:base
as.raster.matrix FALSE registered S3method
boxplot.matrix TRUE package:graphics
determinant.matrix TRUE package:base
duplicated.matrix TRUE package:base
edit.matrix FALSE registered S3method
head.matrix TRUE package:utils
isSymmetric.matrix TRUE package:base
relist.matrix FALSE registered S3method
subset.matrix TRUE package:base
summary.matrix TRUE package:base
tail.matrix TRUE package:utils
unique.matrix TRUE package:base
Or for a function:
> methods(summary)
[1] summary.aov summary.aovlist summary.aspell*
[4] summary.connection summary.data.frame summary.Date
[7] summary.default summary.ecdf* summary.factor
[10] summary.glm summary.infl summary.lm
[13] summary.loess* summary.manova summary.matrix
[16] summary.mlm summary.nls* summary.packageStatus*
[19] summary.PDF_Dictionary* summary.PDF_Stream* summary.POSIXct
[22] summary.POSIXlt summary.ppr* summary.prcomp*
[25] summary.princomp* summary.srcfile summary.srcref
[28] summary.stepfun summary.stl* summary.table
[31] summary.tukeysmooth*
Non-visible functions are asterisked
?Methods may also prove a useful read.

The class of an R object is recovered with class. Objects do not have methods associated with them in typical R parlance. The class of an object determines what function-methods will be applied to it. In order to determine what functions have methods associated with a given class you would need to test all available functions to see whether there was a class-specific method. Even then generic functions would attempt to use a "default" method in most instances.
Some methods associated with a generic S3 function are displayed with methods. The methods of an S4 function are recovered with showMethods. So, for what most people would call "objects", your question does not make sense, but if it happened that you were including functions under the general term "objects" (which is technically fair) then I have answered.
showMethods(classes="data.frame")
methods(class="data.frame")
Then there are a group of methods that might be called "implicit" although their R name is "groupGeneric"
?groupGeneric
methods("Math") # These are "add-on" methods to the primitive Math functions
[1] Math.data.frame Math.Date Math.dates* Math.difftime Math.factor
[6] Math.mChoice Math.polynomial* Math.POSIXt Math.ratetable* Math.Surv*
[11] Math.times*
Non-visible functions are asterisked
?"+"
methods("Ops") # The binary operators such as "+", "-", "/"
[1] Ops.data.frame Ops.Date Ops.dates* Ops.difftime Ops.factor
[6] Ops.findFn Ops.mChoice Ops.numeric_version Ops.ordered Ops.polynomial*
[11] Ops.POSIXt Ops.raster* Ops.ratetable* Ops.Surv* Ops.times*
[16] Ops.ts* Ops.unit* Ops.yearmon* Ops.yearqtr* Ops.zoo*
Non-visible functions are asterisked
And even then you have not really display the members of the Math or the Ops family, but you would have seen them at the help page for ?groupGeneric. You do not see Ops.numeric. A somewhat lower level view is provided by:
.Primitive("+")
# function (e1, e2) .Primitive("+")
These will throw an error if offered the wrong class argument.

Some packages define functions that are not methods but which are nevertheless intended for use with a particular class. For example, library(igraph) defines the function radius(_), which is intended for use on objects in the igraph class. Since such functions are not methods, methods(_) and showMethods(_) will not reveal them.
In such cases, lsf.str(_) can be very helpful. For example:
lsf.str("package:igraph")
includes the line:
radius : function (graph, mode = c("all", "out", "in", "total"))

Related

How to pass variable to a method which calls a macro in Julia

I'm trying to call this macro from within a method where the parameters to the macro are passed in to the method. It works fine when I call it directly but there is something about the macro expansion which is preventing the variables lat and lon from being correctly used in the macro.
The macro I'm calling is #select here: https://github.com/Alexander-Barth/NCDatasets.jl/blob/4e35e843a53cdcff7f7ef66ebc3ceab1ee1e860b/src/select.jl#L54-L168
and here is the function where the lat and lon variables are not being expanded correctly
function data_for_lat_lon(ds, region, lat_lon_pair)
println("have latlon piar ", lat_lon_pair)
lat = lat_lon_pair[1]
lon = lat_lon_pair[2]
data = []
if(ArchGDAL.contains(region[1], ArchGDAL.createpoint(lon, lat)))
println(lat, " ", lon)
#the below call fails when called in this way
single_lat_lon = NCDatasets.#select(ds, latitude==$lat && longitude==$lon)
for (varname, var) in single_lat_lon
if varname in ["latitude", "longitude", "time"]
continue
end
push!(var_names, varname)
push!(data, Array[single_lat_lon[varname]][1][:])
end
return reduce(hcat, data)'
end
end
This is the error & stack trace I get when calling it:
MethodError: no method matching (::NCDatasets.var"#154#155")(::Float64)
The applicable method may be too new: running in world age 32645, while current world is 32646.
Closest candidates are:
(::NCDatasets.var"#154#155")(::Any) at none:0 (method too new to be called from this world context.)
Stacktrace:
[1] _broadcast_getindex_evalf
# .\broadcast.jl:670 [inlined]
[2] _broadcast_getindex
# .\broadcast.jl:643 [inlined]
[3] getindex
# .\broadcast.jl:597 [inlined]
[4] copy
# .\broadcast.jl:899 [inlined]
[5] materialize
# .\broadcast.jl:860 [inlined]
[6] findall(testf::NCDatasets.var"#154#155", A::Vector{Float64})
# Base .\array.jl:2311
[7] macro expansion
# C:\Users\scott\.julia\packages\NCDatasets\sLdiM\src\select.jl:242 [inlined]
[8] data_for_lat_lon(ds::NCDatasets.MFDataset{DeferDataset, 1, String, NCDatasets.DeferAttributes, NCDatasets.DeferDimensions, NCDatasets.DeferGroups}, region::DataFrameRow{DataFrame, DataFrames.Index}, lat_lon_pair::Tuple{Float64, Float64})
# Main .\In[46]:8
[9] top-level scope
# .\In[50]:3
[10] eval
# .\boot.jl:368 [inlined]
[11] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
# Base .\loading.jl:1428
I'm fairly confident this has something to do with the concept of "hygiene" and variable expansion with a macro but I'm new enough to julia to not understand what needs to be done in my calling function to resolve this. I have reveiwed this question but am not sure it applies to this case: How to pass variable value to a macro in julia?
Thanks!
Turns out the issue was unrelated and was a known issue in julia: https://discourse.julialang.org/t/how-to-bypass-the-world-age-problem/7012

How to use `hasmethod` (or any other way) to find if `hasmethod(fn, Tuple{Type1, Type2, **Any Type**})`

I want to use the hasmethod function to find if an object t::T supports t[!, something] syntax.
The key is something can be of many types and I don't want to check them all, I just want a way to express that hasmethod(getindex, Tuple{T, typeof{!}, S}) regardless of what S is.
How do I do that?
I think the way to get the list of methods is:
methods(getindex, Tuple{Any, typeof(!), Any})
The only problem is that if the second argument allowed by getindex is a supertype of typeof(!) it will be also listed. I do not think it can be avoided though, as you cannot rule out that such getindex definition actually allows ! to be passed as a first argument.
For instance if no packages are loaded the following is the result of the call above:
julia> methods(getindex, Tuple{Any, typeof(!), Any})
# 10 methods for generic function "getindex":
[1] getindex(md::Markdown.MD, args...) in Markdown at C:\cygwin\home\Administrator\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.2\Markdown\src\parse\parse.jl:24
[2] getindex(r::Distributed.Future, args...) in Distributed at C:\cygwin\home\Administrator\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.2\Distributed\src\remotecall.jl:624
[3] getindex(::Type{Any}, vals...) in Base at array.jl:357
[4] getindex(::Type{T}, x, y) where T in Base at array.jl:353
[5] getindex(::Type{T}, vals...) where T in Base at array.jl:344
[6] getindex(A::SparseArrays.SparseMatrixCSC, i, ::Colon) in SparseArrays at C:\cygwin\home\Administrator\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.2\SparseArrays\src\sparsematrix.jl:1879
[7] getindex(A::AbstractArray, I...) in Base at abstractarray.jl:979
[8] getindex(t::AbstractDict, k1, k2, ks...) in Base at abstractdict.jl:476
[9] getindex(itr::Base.SkipMissing, I...) in Base at missing.jl:232
[10] getindex(r::Distributed.RemoteChannel, args...) in Distributed at C:\cygwin\home\Administrator\buildbot\worker\package_win64\build\usr\share\julia\stdlib\v1.2\Distributed\src\remotecall.jl:626
All of the cases are the ones where getindex does not put restrictions on the second argument so you cannot rule out that they could possibly allow ! as a valid value.
But if e.g. you load DataFrames.jl and restrict the first argument to AbstractDataFrame you get:
julia> methods(getindex, Tuple{AbstractDataFrame, typeof(!), Any})
# 5 methods for generic function "getindex":
[1] getindex(df::DataFrame, ::typeof(!), col_ind::Symbol) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:367
[2] getindex(df::DataFrame, ::typeof(!), col_ind::Union{Signed, Unsigned}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:358
[3] getindex(df::DataFrame, row_ind::typeof(!), col_inds::Union{Colon, Regex, AbstractArray{T,1} where T, All, Between, InvertedIndex}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:405
[4] getindex(sdf::SubDataFrame, ::typeof(!), colind::Union{Signed, Symbol, Unsigned}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\subdataframe\subdataframe.jl:127
[5] getindex(df::SubDataFrame, row_ind::typeof(!), col_inds::Union{Colon, Regex, AbstractArray{T,1} where T, All, Between, InvertedIndex}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\subdataframe\subdataframe.jl:137
which is now more informative, because in DataFrames.jl we try to be careful not to leave indexing arguments free (i.e. allow them to be Any and only internally check what is valid).
Additionally you can use methodswith to check which methods accept ! explicitly and filter only getindex instances. Here is the result after loading DataFrames.jl:
julia> filter(x -> x.name == :getindex, methodswith(typeof(!)))
[1] getindex(df::DataFrame, ::typeof(!), col_ind::Symbol) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:367
[2] getindex(df::DataFrame, ::typeof(!), col_ind::Union{Signed, Unsigned}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:358
[3] getindex(df::DataFrame, row_ind::typeof(!), col_inds::Union{Colon, Regex, AbstractArray{T,1} where T, All, Between, InvertedIndex}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\dataframe\dataframe.jl:405
[4] getindex(sdf::SubDataFrame, ::typeof(!), colind::Union{Signed, Symbol, Unsigned}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\subdataframe\subdataframe.jl:127
[5] getindex(df::SubDataFrame, row_ind::typeof(!), col_inds::Union{Colon, Regex, AbstractArray{T,1} where T, All, Between, InvertedIndex}) in DataFrames at D:\AppData\.julia\packages\DataFrames\yH0f6\src\subdataframe\subdataframe.jl:137
Finally note that x[!, y] syntax can also mean a view (if preceded by e.g. #view) or a setindex! operation (if it appears on LHS of assignment) so you might wan to check also these functions if they accept ! (and in DataFrames.jl they actually do).
You can do that in theory, if you get the syntax right:
julia> hasmethod(getindex, Tuple{Vector{Int}, typeof(!), Any})
true
This should work since Tuples are covariant.
But its returning true is obviously nonsense:
julia> getindex([1], !, 1)
ERROR: ArgumentError: invalid index: ! of type typeof(!)
Stacktrace:
[1] to_index(::Function) at ./indices.jl:270
[2] to_index(::Array{Int64,1}, ::Function) at ./indices.jl:247
[3] to_indices(::Array{Int64,1}, ::Tuple{Base.OneTo{Int64}}, ::Tuple{typeof(!),Int64}) at ./indices.jl:298
[4] to_indices at ./indices.jl:294 [inlined]
[5] getindex(::Array{Int64,1}, ::Function, ::Int64) at ./abstractarray.jl:981
[6] top-level scope at REPL[26]:1
The reason is that it delegates to other methods internally.
I think it's better to constrain T to some abstract type(s) for which this kind of indexing is known to work. Or, as a last resort, use try.

How to distinguish package namespace environment from other environment objects

Is there any way to programmatically distinguish between package environments and non-package environment objects? For example, the objects x and y below are both environments, with the same class and attributes.
x <- as.environment(cars)
y <- getNamespace("graphics")
However judging from the print method there is a difference:
> print(x)
<environment: 0x1d38118>
> print(y)
<environment: namespace:graphics>
Now suppose I have an arbitrary object, how can I determine which of the two it is (without looking at the output of print)? I would like to know this to determine how to store the object on disk. In case of the former I need to store the list representation of the environment (and perhaps its parents), but for the latter I would just store the name and version of the package.
isNamespace ?
isNamespace(y)
# [1] TRUE
isNamespace(x)
# [1] FALSE
And, for future reference, apropos is often helpful when you've got a question like this.
apropos("namespace")
# [1] "..getNamespace" ".BaseNamespaceEnv" ".getNamespace"
# [4] ".methodsNamespace" "asNamespace" "assignInMyNamespace"
# [7] "assignInNamespace" "attachNamespace" "fixInNamespace"
# [10] "getFromNamespace" "getNamespace" "getNamespaceExports"
# [13] "getNamespaceImports" "getNamespaceInfo" "getNamespaceName"
# [16] "getNamespaceUsers" "getNamespaceVersion" "isBaseNamespace"
# [19] "isNamespace" "loadedNamespaces" "loadingNamespaceInfo"
# [22] "loadNamespace" "namespaceExport" "namespaceImport"
# [25] "namespaceImportClasses" "namespaceImportFrom" "namespaceImportMethods"
# [28] "packageHasNamespace" "parseNamespaceFile" "requireNamespace"
# [31] "setNamespaceInfo" "unloadNamespace"

How can I get a list of all methods defined on an S4 class in R?

Is there a way in R to get a list of all methods defined on an S4 class, given the name of that class?
Edit: I know that showMethods can show me all the methods, but I want to manipulate the list programmatically, so that's no good.
Maybe this would be useful:
mtext <- showMethods(class="SpatialPolygons", printTo =FALSE )
fvec <- gsub( "Function(\\:\\s|\\s\\\")(.+)(\\s\\(|\\\")(.+$)",
"\\2", mtext[grep("^Function", mtext)] )
fvec
[1] ".quad" "[" "addAttrToGeom"
[4] "area" "as.data.frame" "click"
[7] "coerce" "coordinates" "coordnames"
[10] "coordnames<-" "coords" "disaggregate"
[13] "extract" "fromJSON" "isDiagonal"
[16] "isTriangular" "isValidJSON" "jsType"
[19] "over" "overlay" "plot"
[22] "polygons" "polygons<-" "rasterize"
[25] "recenter" "spChFIDs" "spsample"
[28] "spTransform" "text" "toJSON"
The original version did not properly extract the quoted non S4 generics in mtext such as:
[60] "Function \"jsType\":"
[61] " <not an S4 generic function>"
Are you looking for showMethods()?
library(sp)
showMethods(class="SpatialPolygons")
Maybe something like
library(sp)
x=capture.output(showMethods(class="SpatialPolygons"))
unlist(lapply(strsplit(x[grep("Function: ",x,)]," "),function(x) x[2]))
Also stumbled upon it, how about
library(sp)
attr(methods(class="SpatialPolygons"), "info")$generic
# Alternatively:
# attr(.S4methods(class="SpatialPolygons"), "info")$generic
This will directly yield a vector of method names.

spChFIDs() on level 1 or higher map-files

Hopefully (one of) the last question on map-files.
Why is this not working, and how would I do that right?
load(url('http://gadm.org/data/rda/CUB_adm1.RData'))
CUB <- gadm
CUB <- spChFIDs(CUB, paste("CUB", rownames(CUB), sep = "_"))
Thank you very much!!!
seems to work with row.names()
load(url('http://gadm.org/data/rda/CUB_adm1.RData'))
CUB <- gadm
CUB <- spChFIDs(CUB, paste("CUB", row.names(CUB), sep = "_"))
The answer is apparent once one reads the help for ?row.names() and ?rownames().
The rownames() function only knows something about matrix-like objects, and CUB is not one of those, hence it doesn't have row names that rownames() can find:
> rownames(CUB)
NULL
row.names() is different, it is an S3 generic function and that means package authors can write methods for specific types of objects such that the row names of those objects can be extracted.
Here is a list of the methods available for row.names() in my current session, with the sp package loaded:
> methods(row.names)
[1] row.names.data.frame
[2] row.names.default
[3] row.names.SpatialGrid*
[4] row.names.SpatialGridDataFrame*
[5] row.names.SpatialLines*
[6] row.names.SpatialLinesDataFrame*
[7] row.names.SpatialPixels*
[8] row.names.SpatialPoints*
[9] row.names.SpatialPointsDataFrame*
[10] row.names.SpatialPolygons*
[11] row.names.SpatialPolygonsDataFrame*
Non-visible functions are asterisked
The class of the object CUB is:
> class(CUB)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
So what is happening is that the SpatialPolygonsDataFrame method of the row.names() function is being used and it knows where to find the required row names.

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