<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/8848? https://bioinformaticsonline.com/news/view/22133/r-320-is-released Sat, 18 Apr 2015 05:06:44 -0500 https://bioinformaticsonline.com/news/view/22133/r-320-is-released <![CDATA[R 3.2.0 is released]]> R 3.2.0 (codename “Full of Ingredients”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below.

Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

NEW FEATURES

  • anyNA() gains a recursive argument.
  • When x is missing and names is not false (including the default value), Sys.getenv(x, names) returns an object of class "Dlist" and hence prints tidily.
  • (Windows.) shell() no longer consults the environment variable SHELL: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). R_SHELL, the preferred way to select a non-default shell, can be used instead.
  • Some unusual arguments to embedFonts() can now be specified as character vectors, and the defaults have been changed accordingly.
  • Functions in the Summary group duplicate less. (PR#15798)
  • (Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)
  • read.fwf() and read.DIF() gain a fileEncoding argument, for convenience.
  • Graphics devices can add attributes to their description in .Device and .Devices. Several of those included with R use a "filepath" attribute.
  • pmatch() uses hashing in more cases and so is faster at the expense of using more memory. (PR#15697)
  • pairs() gains new arguments to select sets of variables to be plotted against each other.
  • file.info(, extra_cols = FALSE) allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.
  • New function dir.exists() in package base to test efficiently whether one or more paths exist and are directories.
  • dput() and friends gain new controls hexNumeric and digits17 which output double and complex quantities as, respectively, binary fractions (exactly, see sprintf("%a")) and as decimals with up to 17 significant digits.
  • save(), saveRDS() and serialize() now support ascii = NA which writes ASCII files using sprintf("%a") for double/complex quantities. This is read-compatible with ascii = TRUE but avoids binary->decimal->binary conversions with potential loss of precision. Unfortunately the Windows C runtime’s lack of C99 compliance means that the format cannot be read correctly there in R before 3.1.2.
  • The default for formatC(decimal.mark =) has been changed to be getOption("OutDec"); this makes it more consistent with format() and suitable for use in print methods, e.g. those for classes "density", "ecdf", "stepfun" and "summary.lm".

    getOption("OutDec") is now consulted by the print method for class "kmeans", by cut(), dendrogram(), plot.ts() and quantile() when constructing labels and for the report fromlegend(trace = TRUE).

    (In part, wish of PR#15819.)

  • printNum() and hence format() and formatC() give a warning if big.mark and decimal.mark are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).
  • merge() can create a result which uses long vectors on 64-bit platforms.
  • dget() gains a new argument keep.source which defaults to FALSE for speed (dput() and dget() are most often used for data objects where this can make dget() many times faster).
  • Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.
  • A number of macros have been added in the new ‘share/Rd’ directory for use in package overview help pages, and promptPackage() now makes use of them.
  • tools::parse_Rd() gains a new permissive argument which converts unrecognized macros into text. This is used by utils:::format.bibentry to allow LaTeX markup to be ignored.
  • options(OutDec =) can now specify a multi-byte character, e.g., options(OutDec = "u00b7") in a UTF-8 locale.
  • is.recursive(x) is no longer true when x is an external pointer, a weak reference or byte code; the first enables all.equal(x, x) when x .
  • ls() (aka objects()) and as.list.environment() gain a new argument sorted.
  • The "source" attribute (which has not been added to functions by R since before R version 2.14.0) is no longer treated as special.
  • Function returnValue() has been added to give on.exit() code access to a function’s return value for debugging purposes.
  • crossprod(x, y) allows more matrix coercions when x or y are vectors, now equalling t(x) %*% y in these cases (also reported by Radford Neal). Similarly, tcrossprod(x,y) and %*% work in more cases with vector arguments.
  • Utility function dynGet() useful for detecting cycles, aka infinite recursions.
  • The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
  • apply(m, 2, identity) is now the same as the matrix m when it has named row names.
  • A new function debuggingState() has been added, allowing to temporarily turn off debugging.
  • example() gets a new optional argument run.donttest and tools::Rd2ex() a corresponding commentDonttest, with a default such that example(..) in help examples will run donttest code only if used interactively (a change in behaviour).
  • rbind.data.frame() gains an optional argument make.row.names, for potential speedup.
  • New function extSoftVersion() to report on the versions of third-party software in use in this session. Currently reports versions of zlib, bzlib, the liblzma from xz, PCRE, ICU, TRE and the iconv implementation.

    A similar function grSoftVersion() in package grDevices reports on third-party graphics software.

    Function tcltk::tclVersion() reports the Tcl/Tk version.

  • Calling callGeneric() without arguments now works with primitive generics to some extent.
  • vapply(x, FUN, FUN.VALUE) is more efficient notably for large length(FUN.VALUE); as extension of PR#16061.
  • as.table() now allows tables with one or more dimensions of length 0 (such as as.table(integer())).
  • names(x) now clears the names of call and ... objects.
  • library() will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.
  • A new plot() method for class "raster" has been added.
  • New check_packages_in_dir_changes() function in package tools for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.
  • Speed-up from Peter Haverty for ls() and methods:::.requirePackage() speeding up package loading. (PR#16133)
  • New get0() function, combining exists() and get() in one call, for efficiency.
  • match.call() gains an envir argument for specifying the environment from which to retrieve the ... in the call, if any; this environment was wrong (or at least undesirable) when thedefinition argument was a function.
  • topenv() has been made .Internal() for speedup, based on Peter Haverty’s proposal in PR#16140.
  • getOption() no longer calls options() in the main case.
  • Optional use of libcurl (version 7.28.0 from Oct 2012 or later) for Internet access:
    • capabilities("libcurl") reports if this is available.
    • libcurlVersion() reports the version in use, and other details of the "libcurl" build including which URL schemes it supports.
    • curlGetHeaders() retrieves the headers for http://, https://, ftp:// and ftps:// URLs: analysis of these headers can provide insights into the ‘existence’ of a URL (it might for example be permanently redirected) and is so used in R CMD check --as-cran.
    • download.file() has a new optional method "libcurl" which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.
    • url() has a new method "libcurl" which handles more URL schemes and follows redirections. The default method is controlled by a new option url.method, which applies also to the opening of URLs via file() (which happens implicitly in functions such as read.table.)
    • When file() or url() is invoked with a https:// or ftps:// URL which the current method cannot handle, it switches to a suitable method if one is available.
  • (Windows.) The DLLs ‘internet.dll’ and ‘internet2.dll’ have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an Rsession.

    The Windows internet functions are still selected by flag –internet2 or setInternet2(). This can be overridden for an url() connection via its new method argument.

    download.file() has new method "wininet", selected as the default by –internet2 or setInternet2().

  • parent.env<- can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.
  • New function isLoadedNamespace() for readability and speed.
  • names(env) now returns all the object names of an environment env, equivalently to ls(env, all.names = TRUE, sorted = FALSE) and also to the names of the corresponding list,names(as.list(env, all.names = TRUE)). Note that although names() returns a character vector, the names have no particular ordering.
  • The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.
  • New function trimws() for removing leading/trailing whitespace.
  • cbind() and rbind() now consider S4 inheritance during S3 dispatch and also obey deparse.level.
  • cbind() and rbind() will delegate recursively to methods::cbind2 (methods::rbind2) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).
  • (Windows.) download.file(quiet = FALSE) now uses text rather than Windows progress bars in non-interactive use.
  • New function hsearch_db() in package utils for building and retrieving the help search database used by help.search(), along with functions for inspecting the concepts and keywords in the help search database.
  • New function .getNamespaceInfo(), a no-check version of getNamespaceInfo() mostly for internal speedups.
  • The help search system now takes keyword entries in Rd files which are not standard keywords (as given in ‘KEYWORDS’ in the R documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.
  • New lengths() function for getting the lengths of all elements in a list.
  • New function toTitleCase() in package tools, tailored to package titles.
  • The matrix methods of cbind() and rbind() allow matrices as inputs which have 2^31 or more elements. (For cbind(), wish of PR#16198.)
  • The default method of image() has an explicit check for a numeric or logical matrix (which was always required).
  • URLencode() will not by default encode further URLs which appear to be already encoded.
  • BIC(mod) and BIC(mod, mod2) now give non-NA numbers for arima() fitted models, as nobs(mod) now gives the number of “used” observations for such models. This fixes PR#16198, quite differently than proposed there.
  • The print() methods for "htest", "pairwise.htest" and "power.htest" objects now have a digits argument defaulting to (a function of) getOption("digits"), and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.
  • Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.
  • The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.
  • unzip() will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to PR#16243).
  • methods() reports S4 in addition to S3 methods; output is simplified when the class argument is used. .S3methods() and methods::.S4methods() report S3 and S4 methods separately.
  • Higher order functions such as the apply functions and Reduce() now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves PR#16093.

More at http://cran.rstudio.com/

Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/

]]> Neel https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r Fri, 27 Mar 2020 12:11:21 -0500 https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r <![CDATA[New Machine Learning Packages in R]]> Machine Learning

autokeras v1.0.1: Implements an interface to AutoKeras, an open source software library for automated machine learning. See README for an example.

MTPS v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in Xing et al. (2019). See the vignette to get started.

quanteda.textmodels v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the Laver et al. (2003) wordscores model, the Perry & Benoit’s (2017) class affinity scaling model, and the Slapin & Proksch (2008) wordfish model. See the vignette to get started.

SeqDetect v1.0.7: Implements the automaton model found in Krleža, Vrdoljak & Brčić (2019) to detect and process sequences. See the vignette for examples and theory.

studyStrap v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See Kishida, et al. (2019) for background and the vignette for how to use the package.

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio Mon, 25 Apr 2016 16:22:28 -0500 https://bioinformaticsonline.com/bookmarks/view/27070/venn-diagrams-on-r-studio <![CDATA[Venn Diagrams on R Studio]]> First step: Install & load “VennDiagram” package. 
# install.packages('VennDiagram')
library(VennDiagram)

Second step: Load data

Add filepath if “catdoge.csv” is not in working-directory.

d <- read.csv("catdoge.csv")

Address of the bookmark: http://rstudio-pubs-static.s3.amazonaws.com/13301_6641d73cfac741a59c0a851feb99e98b.html

]]> Jitendra Prajapati https://bioinformaticsonline.com/blog/view/32358/list-of-goi-approved-peer-reviewed-bioinformatics-and-computational-biology-journals Tue, 25 Apr 2017 05:03:27 -0500 https://bioinformaticsonline.com/blog/view/32358/list-of-goi-approved-peer-reviewed-bioinformatics-and-computational-biology-journals <![CDATA[List of GOI approved peer reviewed bioinformatics and computational biology journals]]> Unfortunately, we now live in a world where the integrity of peer-reviewed journals is being threatened by the rise of the academic version of fake news – something many call “predatory publishing".  Mostly in academic publishing world, "predatory open access publishing" is an exploitative open-access publishing business model that involves charging publication fees to authors without providing the editorial and publishing services associated with legitimate journals (open access or not).

Nearly 20% of the such journals have a flashy impact factor and quick publication time, which are quick give-aways. Interestingly, under contact address, some journal websites do not even provide any address to contact. All of this has led to the emergence of a new and dark market of deceptive publishers that exploit the concept of open access and provide channels for “scientific journal” publication with little or no peer review. For a fee, they will publish almost anything – even if the study was fatally flawed. And these journals provide a forum that can be used as a channel to publish fraudulent “advocacy research.” You can find list of certain such publishers at "Beall's List" http://beallslist.weebly.com/

Keeping all these in mind, Government of India (GOI) decided to approved certain bioinformatics and computational biology journals for your research publication.

Following are the list of GOI validated and peer reviewed bioinformatics and computational biology journals:

NOTE:Each journal details are in following order Tittle\nSource\nSubject.
Point to remember: The list of journals are NOT sorted in any ascending or descending order.

If I missed any other GOI validated bioinformatics journal, then please report me in comment section.

Open Bioinformatics Journal
Scopus
Computer Science; Engineering; Medicine

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
WoS
BIOLOGY & BIOCHEMISTRY

Advances and Applications in Bioinformatics and Chemistry
Scopus
Biochemistry, Genetics and Molecular Biology Chemistry; Computer Science

Advances in Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science; Engineering

Applied Bioinformatics
Scopus
Agricultural and Biological Sciences; Computer Science

BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

Bioinformatics and Biology Insights
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science; Mathematics

BMC BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

BRIEFINGS IN BIOINFORMATICS
WoS & Scopus
COMPUTER SCIENCE

Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference
Scopus
Medicine

Current Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

Current Protocols in Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology

JOURNAL OF COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS
ICI
BIOLOGICAL SCIENCE

Journal of integrative bioinformatics
Scopus
Medicine

Journal of Proteomics and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science

Mathematical Biology and Bioinformatics
Scopus
Engineering; Mathematics

Trends in Bioinfprmatics
Scopus
Computer Science

Eurasip Journal on Bioinformatics and Systems Biology
Scopus
General; Computer Science; Mathematics; Medicine

Evolutionary Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

Genomics, Proteomics and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Mathematics

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Mathematics

IEEE-ACM Transactions on Computational Biology and Bioinformatics
WoS
COMPUTER SCIENCE

International Journal of Bioinformatics Research and Application
Scopus
Biochemistry, Genetics and Molecular Biology; Medicine, Health

International Journal o f Data M ining and Bioinformatics
WoS & Scopus
COMPUTER SCIENCE

IPSJ Transactions on Bioinformatics
Scopus
Biochemistry, Genetics and Molecular Biology;Computer Science

Journal of Bioinformatics and Computational Biology
WoS & Scopus
COMPUTER SCIENCE

Journal of Clinical Bioinformatics
Scopus
Medicine

PLoS Computational Biology
WoS & Scopus
BIOLOGY & BIOCHEMISTRY

Reviews in Computational Chemistry
WoS & Scopus
CHEMISTRY

RSC Theoretical and Computational Chemistry Series
Scopus
Chemistry; Computer Science

Annual Reports in Computational Chemistry
Scopus
Chemistry; Mathematics

Computational and Structural Biotechnology Journal
Scopus
Biochemistry, Genetics and Molecular Biology; Computer Science

Computational and Theoretical Chemistry
WoS & Scopus
CHEMISTRY

COMPUTATIONAL BIOLOGY AND CHEMISTRY
WoS & Scopus
COMPUTER SCIENCE

COMPUTATIONAL CHEMISTRY
WoS
CHEMISTRY

Journal of Theoretical and Computational Chemistry
Scopus
Chemistry; Computer Science

Theoretical and Computational Chemistry
Scopus
Chemistry

Wiley Interdisciplinary Reviews: Computational Molecular Science
Scopus
Biochemistry, Genetics and Molecular Biology;Chemistry; Computer Science; Materials Science; Mathematics

Wiley Interdisciplinary Reviews- Computational Molecular Science
WoS
CHEMISTRY

Interdisciplinary sciences, computational life sciences
Scopus
Medicine

Interdisciplinary Sciences-Computational Life Science
WoS
Biology and Biochemistry

International Journal of Computational Biology and Drug Design
Scopus
Computer Science; Pharmacology, Toxicology and Pharmaceutics

]]> Jit https://bioinformaticsonline.com/news/view/44640/new-blast-core-nucleotide-database-core-nt Tue, 13 Aug 2024 07:12:53 -0500 https://bioinformaticsonline.com/news/view/44640/new-blast-core-nucleotide-database-core-nt <![CDATA[New BLAST Core Nucleotide Database (core_nt)]]> The Core Nucleotide Database (core_nt) is now the default nucleotide BLAST database. Core_nt is also available on the command line. You get faster searches & more focused results.

Core_nt contains the same eukaryotic transcript and gene-related sequences as nt. The core_nt database is nt without most eukaryotic chromosome sequences. Most nucleotide BLAST searches with core_nt will be similar to the nt database. However, core_nt is better than nt for accomplishing your most common BLAST search goals, such as identifying gene-related sequences like transcript sequences and complete bacterial chromosomes. This is because, in recent years, nt has acquired more low-relevance, non-annotated, and non-gene content. 

Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2024/07/18/new-blast-core-nucleotide-database/

]]> LEGE https://bioinformaticsonline.com/news/view/120/user Wed, 10 Jul 2013 14:41:49 -0500 https://bioinformaticsonline.com/news/view/120/user <![CDATA[useR!]]> The R project actively supports two conference series, organized regularly by members from the R community: useR! - providing a forum to the R user community - and DSC - a platform for developers of statistical software.

Recently useR! conference have been organized University of Castilla-La Mancha, Albacete, Spain.

http://www.edii.uclm.es/~useR-2013//

 

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/2457/rdataminingcom-r-and-data-mining Thu, 15 Aug 2013 18:37:23 -0500 https://bioinformaticsonline.com/bookmarks/view/2457/rdataminingcom-r-and-data-mining <![CDATA[Rdatamining.com : R and Data Mining]]> This website presents examples, documents and resources on data mining with R.
Documents on using R for data mining are available to download for non-commercial personal use, including R Reference card for Data Mining, R and Data Mining: Examples and Case Studies and Time Series Analysis and Mining with R.

Address of the bookmark: http://www.rdatamining.com/

]]> Poonam Mahapatra https://bioinformaticsonline.com/bookmarks/view/11030/r-programming-and-jobs-website Sun, 25 May 2014 14:43:57 -0500 https://bioinformaticsonline.com/bookmarks/view/11030/r-programming-and-jobs-website <![CDATA[R programming and Jobs website]]> Welcome to the R Jobs section of ProgrammingR.com. If your organization has an R employment opportunity that you would like to have posted here, submit it via the contact page. Prospective employees: use the contact information provided in the position listing to apply or contact the hiring organization.

Address of the bookmark: http://www.programmingr.com/category/stype/r-job-listings/

]]> Pragati Singh https://bioinformaticsonline.com/news/view/19087/dcgor Sat, 08 Nov 2014 14:54:28 -0600 https://bioinformaticsonline.com/news/view/19087/dcgor <![CDATA[dcGOR]]> An R package for analysing ontologies and protein domain annotations has been published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1003929). The package is distributed as part of CRAN (http://cran.r-project.org/package=dcGOR), and also at GitHub for version control.

The dedicated website is available in http://supfam.org/dcGOR, from which several demos are also provided:

1. Analysing SCOP domains: http://supfam.org/dcGOR/demo-Fang.html

2. Analysing Pfam domains: http://supfam.org/dcGOR/demo-Basu.html

3. Analysing InterPro domains: http://supfam.org/dcGOR/demo-Customisation.html

 

]]> Martin Jones https://bioinformaticsonline.com/news/view/21312/r-for-microsoft-excel Wed, 18 Feb 2015 00:43:27 -0600 https://bioinformaticsonline.com/news/view/21312/r-for-microsoft-excel <![CDATA[R for Microsoft Excel]]>

If you currently use a spreadsheet like Microsoft Excel for data analysis, you might be interested in taking a look at this tutorial on how to transition from Excel to R by Tony Ojeda. The tutorial explains how to use R functions in place of Excel formulas, including tools like =AVERAGE and =VLOOKUP. For the most part, it uses modern R packages to keep the R code clear and concise.

You'll likely still be using Excel as a data source, though, so you'll also want to check out this guide to importing data from Excel to R from MilanoR.

Reference http://www.r-bloggers.com/an-r-tutorial-for-microsoft-excel-users/

]]> Jitendra Narayan