I am trying to use the stepacross function in the vegan package of R.
When I do, it throws an error and fails to run the code
Error in stepacross(distance.dat, path = "extended") :
object 'C_stepacross' not found
Anybody know the cause of this or what to do to fix it?
I'm using R (64-bit) 4.0.2 and vegan 2.5-6 (old version use is intentional).
It worked a few weeks ago, and I have made no changes since.
The C_stepacross object in question shows up in the stepacross() function code:
getAnywhere(stepacross)
function (dis, path = "shortest", toolong = 1, trace = TRUE,
...)
{
path <- match.arg(path, c("shortest", "extended"))
if (!inherits(dis, "dist"))
dis <- as.dist(dis)
oldatt <- attributes(dis)
n <- attr(dis, "Size")
if (path == "shortest")
dis <- .C(dykstrapath, dist = as.double(dis), n = as.integer(n),
as.double(toolong), as.integer(trace), out = double(length(dis)),
NAOK = TRUE)$out
else dis <- .C(C_stepacross, dis = as.double(dis), as.integer(n),
as.double(toolong), as.integer(trace), NAOK = TRUE)$dis
attributes(dis) <- oldatt
attr(dis, "method") <- paste(attr(dis, "method"),
path)
dis
}
You don't provide a reproducible example, but I can reproduce this problem if I don't use vegan::stepacross, but a different copy of the function. Check your workspace – it probably has a copy of this function. The C function is registred for use in vegan functions, but not for functions in other namespaces. This example will reproduce your problem:
library(vegan)
data(dune)
d <- vegdist(dune)
stepacross <- vegan::stepacross
environment(stepacross) <- environment() ## set to working environment
dd <- stepacross(d, "ext")
## Error in stepacross(d, "ext") : object 'C_stepacross' not found
dd <- vegan::stepacross(d, "ext") ### this will be OK
rm(stepacross) ## removes the local copy
dd <- stepacross(d, "ext") ## this will be OK: vegan copy was untouched
If getAnyewhere finds first a vegan version of stepacross, the last line of its output will be
<environment: namespace:vegan>
In your example this line was missing suggesting that your copy of stepacross was not in namespace:vegan. Moreover, getAnywhere should give package:vegan as the first place where this function was found.
Is it possible to get the year that an installed R package is released using some R code? I can get the version, but then have to look it up on the internet, when this version was released.
Background: I am working for the Swiss Federal Statistical Office and a small group is trying to get a better R environment (we are working for example with the dplyr version 0.7.4 from 2017... and it is not possible to install a newer version...).
Cheers
Renger
You can use versions package to get a timestamp of package version. The package pulls the published versions of the package from the MRAN snapshot server.
versions::installed.versions("dplyr")
# [1] "1.0.7"
versions::available.versions("dplyr")
# $dplyr
# version date available
# 1 1.0.7 2021-06-18 TRUE
# 2 1.0.6 2021-05-05 TRUE
# 3 1.0.5 2021-03-05 TRUE
# ...
Package age
So if you want to answer the specific question about the package age you can do the following:
how_old <- function(pkg, lib = .libPaths()[1], return_age = FALSE) {
pkg_ver <- versions::installed.versions(pkgs = pkg, lib = lib)
av_vers <- versions::available.versions(pkgs = pkg)
pkg_dte <- subset.data.frame(
x = as.data.frame(unname(av_vers)),
subset = version == pkg_ver,
select = date,
drop = TRUE
)
pkg_dte <- as.Date(pkg_dte)
if (return_age) {
return(epocakir::dob2age(dob = pkg_dte))
} else {
return(pkg_dte)
}
}
how_old("dplyr", return_age = TRUE)
Results
[1] "1123200s (~1.86 weeks)"
Package creation
Or if you want to find out when package was installed locally.
when_created <- function(pkg, lib = .libPaths()[1]) {
# Package will always have DESCRIPTION file so that's a safe bet
desc_file <- system.file("DESCRIPTION", package = pkg, lib.loc = lib)
info <- fs::file_info(desc_file)
info$birth_time
}
when_created("dplyr")
Results
# [1] "2021-06-25 08:47:21 BST"
As #Jonathan recommended, if the package has a citation, then you can call the year in the citation.
citation("dplyr")$year
An alternative is to get the date from a list of available versions of a package.
devtools::install_github("https://github.com/cran/versions")
library(versions)
x <- versions::available.versions(c("dplyr", "ggplot2"))
version_year <-
function(x,
package.name = "",
version = "") {
pckg <- x[[package.name]]
row <- which(pckg$version == version)
return(pckg$date[row])
}
version_year(x, "ggplot2", version = "2.0.0")
#[1] "2015-12-18"
As a last resort, you can find out when a package was created from its DESCRIPTION:
packageDescription(pkg)$Packaged
In fact, citation falls back to this very field if no other date was given (either as Date/Publication or via an explicit CITATION file).
I am having difficulty with creating multiple heatmaps with the ComplexHeatmap package. When I run a script that contains code exactly lifted from the documentation (https://bioconductor.org/packages/release/bioc/vignettes/ComplexHeatmap/inst/doc/s3.a_list_of_heatmaps.html)...
library(ComplexHeatmap)
mat1 = matrix(rnorm(80, 2), 8, 10)
mat1 = rbind(mat1, matrix(rnorm(40, -2), 4, 10))
rownames(mat1) = paste0("R", 1:12)
colnames(mat1) = paste0("C", 1:10)
mat2 = matrix(rnorm(60, 2), 6, 10)
mat2 = rbind(mat2, matrix(rnorm(60, -2), 6, 10))
rownames(mat2) = paste0("R", 1:12)
colnames(mat2) = paste0("C", 1:10)
ht1 = Heatmap(mat1, name = "ht1")
ht2 = Heatmap(mat2, name = "ht2")
class(ht1)
class(ht2)
ht1 + ht2
... I get the error message:
Error in ht1 + ht2 : non-numeric argument to binary operator
Execution halted
I am running R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch" on Mac OS X 10.12.2 with ComplexHeatmap version 1.12.0. Thank you for any help!
I figured it out. The problem was that the "methods" package needs to be attached. If you run the above code directly in R (which I was NOT doing), it works as-is (becuase R apparently loads the methods package by default), but if you have the script in a file and run it via Rscript (which is what I WAS doing), you get the indicated error. However, If you add
library(methods)
to the top of the script, it works via Rscript.
hi i am getting this error message while installing DMwR package from RGUI-3.3.1.
Error in read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) :
cannot open the connection
In addition: Warning messages:
1: In unzip(zipname, exdir = dest) : error 1 in extracting from zip file
2: In read.dcf(file.path(pkgname, "DESCRIPTION"), c("Package", "Type")) :
cannot open compressed file 'bitops/DESCRIPTION', probable reason 'No such file or directory'
Approach 1:
The error being reported is inability to open a connection. In Windows that is often a firewall problem and is in the Windows R FAQ. The usual first attempt should be to run internet2.dll. From a console session you can use:
setInternet2(TRUE)
NEWS for R version 3.3.1 Patched (2016-09-13 r71247)
(Windows only) Function
setInternet2()
has no effect and will be removed in due
course. The choice between methods
"internal"
and
"wininet"
is now made by the
method
arguments of
url()
and
download.file()
and their defaults can be set
via
options. The out-of-the-box default remains
"wininet"
(as it has been since
R
3.2.2)
You are using version 3.3.1, this is why it is not working anymore.
Approach 2
The error is suggesting that the package requires another package bitops that is not available. That package is not in any of the dependencies but perhaps one of the dependencies requires it in turn(In this case, it is: ROCR).
Try installing:
install.packages("bitops",repos="https://cran.r-project.org/bin/windows/contrib/3.3/bitops_1.0-6.zip",dependencies=TRUE,type="source")
The package DMwR contains packages abind, zoo, xts, quantmod and ROCR as imports. So, additionally to installing 5 packages you must install DMwR package, Install these packages manually.
Install packages in following sequence:
install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")
library("DMwR")
Approach 3:
chooseCRANmirror()
Select CRAN mirror from popup list. Then install packages:
install.packages("bitops")
install.packages("DMwR")
Package ‘DMwR’ was removed from the CRAN repository.
Formerly available versions can be obtained from the archive.
https://CRAN.R-project.org/package=DMwR
You can use the function as written in CRAN package. Copy the following code in a new RScript, run it and save it for future use if you want. Once you run this function, you should be able to use the way you have been trying to use it.
# ===================================================
# Creating a SMOTE training sample for classification problems
#
# If called with learner=NULL (the default) is does not
# learn any model, simply returning the SMOTEd data set
#
# NOTE: It does not handle NAs!
#
# Examples:
# ms <- SMOTE(Species ~ .,iris,'setosa',perc.under=400,perc.over=300,
# learner='svm',gamma=0.001,cost=100)
# newds <- SMOTE(Species ~ .,iris,'setosa',perc.under=300,k=3,perc.over=400)
#
# L. Torgo, Feb 2010
# ---------------------------------------------------
SMOTE <- function(form,data,
perc.over=200,k=5,
perc.under=200,
learner=NULL,...
)
# INPUTS:
# form a model formula
# data the original training set (with the unbalanced distribution)
# minCl the minority class label
# per.over/100 is the number of new cases (smoted cases) generated
# for each rare case. If perc.over < 100 a single case
# is generated uniquely for a randomly selected perc.over
# of the rare cases
# k is the number of neighbours to consider as the pool from where
# the new examples are generated
# perc.under/100 is the number of "normal" cases that are randomly
# selected for each smoted case
# learner the learning system to use.
# ... any learning parameters to pass to learner
{
# the column where the target variable is
tgt <- which(names(data) == as.character(form[[2]]))
minCl <- levels(data[,tgt])[which.min(table(data[,tgt]))]
# get the cases of the minority class
minExs <- which(data[,tgt] == minCl)
# generate synthetic cases from these minExs
if (tgt < ncol(data)) {
cols <- 1:ncol(data)
cols[c(tgt,ncol(data))] <- cols[c(ncol(data),tgt)]
data <- data[,cols]
}
newExs <- smote.exs(data[minExs,],ncol(data),perc.over,k)
if (tgt < ncol(data)) {
newExs <- newExs[,cols]
data <- data[,cols]
}
# get the undersample of the "majority class" examples
selMaj <- sample((1:NROW(data))[-minExs],
as.integer((perc.under/100)*nrow(newExs)),
replace=T)
# the final data set (the undersample+the rare cases+the smoted exs)
newdataset <- rbind(data[selMaj,],data[minExs,],newExs)
# learn a model if required
if (is.null(learner)) return(newdataset)
else do.call(learner,list(form,newdataset,...))
}
# ===================================================
# Obtain a set of smoted examples for a set of rare cases.
# L. Torgo, Feb 2010
# ---------------------------------------------------
smote.exs <- function(data,tgt,N,k)
# INPUTS:
# data are the rare cases (the minority "class" cases)
# tgt is the name of the target variable
# N is the percentage of over-sampling to carry out;
# and k is the number of nearest neighours to use for the generation
# OUTPUTS:
# The result of the function is a (N/100)*T set of generated
# examples with rare values on the target
{
nomatr <- c()
T <- matrix(nrow=dim(data)[1],ncol=dim(data)[2]-1)
for(col in seq.int(dim(T)[2]))
if (class(data[,col]) %in% c('factor','character')) {
T[,col] <- as.integer(data[,col])
nomatr <- c(nomatr,col)
} else T[,col] <- data[,col]
if (N < 100) { # only a percentage of the T cases will be SMOTEd
nT <- NROW(T)
idx <- sample(1:nT,as.integer((N/100)*nT))
T <- T[idx,]
N <- 100
}
p <- dim(T)[2]
nT <- dim(T)[1]
ranges <- apply(T,2,max)-apply(T,2,min)
nexs <- as.integer(N/100) # this is the number of artificial exs generated
# for each member of T
new <- matrix(nrow=nexs*nT,ncol=p) # the new cases
for(i in 1:nT) {
# the k NNs of case T[i,]
xd <- scale(T,T[i,],ranges)
for(a in nomatr) xd[,a] <- xd[,a]==0
dd <- drop(xd^2 %*% rep(1, ncol(xd)))
kNNs <- order(dd)[2:(k+1)]
for(n in 1:nexs) {
# select randomly one of the k NNs
neig <- sample(1:k,1)
ex <- vector(length=ncol(T))
# the attribute values of the generated case
difs <- T[kNNs[neig],]-T[i,]
new[(i-1)*nexs+n,] <- T[i,]+runif(1)*difs
for(a in nomatr)
new[(i-1)*nexs+n,a] <- c(T[kNNs[neig],a],T[i,a])[1+round(runif(1),0)]
}
}
newCases <- data.frame(new)
for(a in nomatr)
newCases[,a] <- factor(newCases[,a],levels=1:nlevels(data[,a]),labels=levels(data[,a]))
newCases[,tgt] <- factor(rep(data[1,tgt],nrow(newCases)),levels=levels(data[,tgt]))
colnames(newCases) <- colnames(data)
newCases
}
It has been removed from the CRAN library. There are instructions on how to retrieve it from the archive.
Either follow the link - https://packagemanager.rstudio.com/client/#/repos/2/packages/DMwR
OR copy-paste the three lines of code mentioned below:
install.packages("devtools")
devtools::install_version('DMwR', '0.4.1')
library("DMwR")
EDIT: this is the error I got while downloading the DMwR package in 2022, but looks like when the question was posted, the error happened because of another reason.
The reason is that the package 'DMwR' was built under R version 3.4.3 So the solution is actually explained in the marked answer in details. Hence, to be short:Just run the script below to get the problem solved!
install.packages('abind')
install.packages('zoo')
install.packages('xts')
install.packages('quantmod')
install.packages('ROCR')
install.packages("DMwR")
library("DMwR")
I'm trying to turn this function found here into an R package. I'm following the directions found here.
Here are the steps I take:
1) Load required library
library(devtools)
2) Go to a new location
setwd('C:\\myRpkgs\\')
3) Create skeleton
create('conveniencePkg')
4) Copy function to file and save in 'C:\\myRpkgs\\conveniencePkg\\R\\lsos.R'
5) Run document function
setwd("./conveniencePkg")
document()
6) Install package
setwd("..")
install("conveniencePkg")
7) Load library
library(conveniencePkg)
8) try to use lsos function
>lsos()
Error in is.na(obj.dim)[, 1] : subscript out of bounds
Result is the following error:
> traceback()
2: .ls.objects(..., order.by = "Size", decreasing = TRUE, head = TRUE,
n = n)
1: conveniencePkg::lsos()
The function runs fine if I put it into an R file and just use the source() function. Anything seem incorrect in the above steps?