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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28844?offset=1280</link>
	<atom:link href="https://bioinformaticsonline.com/related/28844?offset=1280" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</guid>
	<pubDate>Tue, 22 Jan 2019 06:26:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data</link>
	<title><![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies&#039; sequencing data]]></title>
	<description><![CDATA[<ul>
<li>Run a pipeline processing fast5s to a consensus in a single command.</li>
<li>Recommended fixed "standard" and "fast" pipelines.</li>
<li>Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.</li>
<li>Seemless distribution of tasks over local or distributed compute.</li>
<li>Highly configurable.</li>
<li>Open source (Mozilla Public License 2.0).</li>
</ul>
<p>Documentation can be found at&nbsp;<a href="https://nanoporetech.github.io/katuali/">https://nanoporetech.github.io/katuali/</a>.</p><p>Address of the bookmark: <a href="https://github.com/nanoporetech/katuali" rel="nofollow">https://github.com/nanoporetech/katuali</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21021/ra-bioinformatics-at-iiser</guid>
  <pubDate>Fri, 06 Feb 2015 04:05:49 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at IISER]]></title>
  <description><![CDATA[
<p>Advertisement: Research Position in Computational Biology in the group of Shree P. Pandey Positions available in the area of NGS data analysis, bioinformatics, plant genomics Project Description: Projects involves high throughput analysis of data mostly generated by massively parallel sequencing (RNA-Seq and small-RNA-Seq), microarrays and related platforms.</p>

<p>We are looking for highly motivated and bright individuals interested in high-throughput cutting-edge data analyses methods in genomics (computational positions). Available positions:</p>

<p>Applications are invited from suitable candidates in both, the Max Planck India Partner Program and the CRP Wheat Program for openings at the levels:</p>

<p>Minimum qualification Salary scale (per month)</p>

<p>Project assistant Bachelor’s / Master’s Rs. 10000 / Rs. 14000</p>

<p>Project fellow (junior data analyst) Masters + research experience Rs. 16000</p>

<p>Research fellow (senior data analyst) Masters + adequate research experience/desirable skill sets Rs. 22000</p>

<p>Research Associated PhD (&lt;1yr) / &gt; 1 yr experience Rs. 28000 / Rs. 32000</p>

<p>Condition to satisfactory performance, availability of funds and requirements of the project, the positions could be available upto a period of ~2 years (or funding of the project).</p>

<p>Essential qualification: MSc/BTech/MTech/PhD (or other suitable qualification) in discipline related to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance PostGraduate Diploma in Bioinformatics’. Proficiency in one of the programming languages or statistics (proficient in R for example) is compulsory.</p>

<p>Desirable qualification:</p>

<p>1. Programming experiences in at least one low level language such as C/C++ and one scripting language such as Perl/Python/PHP and knowledge of SQL/MySQL.</p>

<p>2. Substantial experience in the linux or other unix environments.</p>

<p>Application process: Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by email to Shree P. Pandey, Department of Biological Sciences, IISER-Kolkata, Mohanpur Campus, West Bengal within 2 weeks (Feb 19th 2015). E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in Brief description of the group: We are an interdisciplinary group focusing on small-RNA (miRNA, siRNA) mediated regulation of signaling and defense. Project equally involve bioinformatics and systems biology (specially microarrays and next-generation sequencing (NGS) data analysis and its use), along with plant molecular biology, genetic engineering, field biology, and analytical plant chemistry for understanding response of plants to biotic stresses. For more details visit: http://www.iiserkol.ac.in/~sppandey/</p>

<p>Advertisement:</p>

<p>www.iiserkol.ac.in/images/iiserk/advertisements/advertisement_7_spp_feb_2015.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</guid>
	<pubDate>Sun, 02 Feb 2020 13:41:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</link>
	<title><![CDATA[3D de novo assembly (3D DNA) pipeline]]></title>
	<description><![CDATA[<p>For a detailed description of the pipeline and how it integrates with other tools designed by the Aiden Lab see&nbsp;<a href="http://aidenlab.org/assembly/manual_180322.pdf">Genome Assembly Cookbook</a>&nbsp;on&nbsp;<a href="http://aidenlab.org/assembly">http://aidenlab.org/assembly</a>.</p>
<p>For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in&nbsp;<a href="http://science.sciencemag.org/content/356/6333/92">(Dudchenko et al.,&nbsp;<em>Science</em>, 2017)</a>&nbsp;see the&nbsp;<a href="https://github.com/theaidenlab/3d-dna/tree/745779bdf64db6e55bddb70c24e9b58825938c33">original commit</a>.</p>
<p>For the detailed description of the merge section see&nbsp;<a href="https://github.com/theaidenlab/AGWG-merge">https://github.com/theaidenlab/AGWG-merge</a>.</p><p>Address of the bookmark: <a href="https://github.com/theaidenlab/3d-dna" rel="nofollow">https://github.com/theaidenlab/3d-dna</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21065/ra-bioinformatics-at-north-eastern-hill-university</guid>
  <pubDate>Sat, 07 Feb 2015 06:06:05 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA Bioinformatics at North Eastern Hill University]]></title>
  <description><![CDATA[
<p>Bioinformatics Infrastructure Facility, Department of RDAP, NEHU vacancy of Research Associate</p>

<p>Name of the Post: Research Associate<br />No. of the Post: 01 One<br />Age Limit: Max. 35 years<br />Salary: Rs. 22000/- per month plus HRA</p>

<p>Required Job Profile:<br />Candidate must possess M.Sc. in bioinformatics or biotechnology from recognized university or institute.<br />Desired Job Profile;<br />Candidate having Ph.D. or pursuing Ph.D. in the related subject or equivalent published work in reputed peer reviewed journals or advance PG dipoma in bioinformatics course.</p>

<p>How to apply:<br />Eligible and interested candidates should need to send the bio-data and bring all related documents in original and set of attested copies of the same in the time of interview.</p>

<p>Last date: 16.02.2015<br />Refer to http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf</p>

<p>Summary <br />Employer Address:	Dr.B.K. Mishra Coordinator BIF, RDAP Department, North Eastern Hill University, Tura Campus, Tura, Meghalaya<br />Email:	drbkm1972@yahoo.co.in;birendramishra14@gmail.com<br />URL:	http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf<br />Phone:	03651-223107<br />Required Skills:	not mentioned / required for this post<br />Required Experience:	not mentioned / required for this job post<br />Required Education:	M.Sc. in bioinformatics or biotechnology from recognized university or institute.<br />Job Location:	Tura, Meghalaya, India</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41998/wgddetector-a-pipeline-for-detecting-whole-genome-duplication-events-using-the-genome-or-transcriptome-annotations</guid>
	<pubDate>Thu, 23 Jul 2020 05:52:56 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41998/wgddetector-a-pipeline-for-detecting-whole-genome-duplication-events-using-the-genome-or-transcriptome-annotations</link>
	<title><![CDATA[WGDdetector: a pipeline for detecting whole genome duplication events using the genome or transcriptome annotations]]></title>
	<description><![CDATA[<p><span>WGDdetector pipeline that integrates all analyses including gene family constructing, dS estimating and phasing, and outputting the dS values of each paralogs pairs processed with only one command. We further chose four species (</span><em>Arabidopsis thaliana</em><span>,<span>&nbsp;</span></span><em>Juglans regia</em><span>,<span>&nbsp;</span></span><em>Populus trichocarpa</em><span><span>&nbsp;</span>and<span>&nbsp;</span></span><em>Xenopus laevis</em><span>) representing herb, wood and animal, to test its practicability. Our final results showed a high degree of accuracy with the previous studies using both genome and transcriptome data.</span></p>
<p><span>More at <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2670-3">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2670-3</a></span></p><p>Address of the bookmark: <a href="https://github.com/yongzhiyang2012/wgddetector" rel="nofollow">https://github.com/yongzhiyang2012/wgddetector</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/21241/pacman</guid>
	<pubDate>Mon, 16 Feb 2015 12:15:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/21241/pacman</link>
	<title><![CDATA[Pacman]]></title>
	<description><![CDATA[<p><span>The pacman package is an R package management tool that combines the functionality of base library related functions into intuitively named functions. This package is ideally added to .Rprofile to increase workflow by reducing time recalling obscurely named functions, reducing code and integrating functionality of base functions to simultaneously perform multiple actions.<br /><br />Function names in the pacman package follow the format of p_xxx where &lsquo;xxx&rsquo; is the task the function performs. For instance the p_load function allows the user to load one or more packages as a more generic substitute for the library or require functions and if the package isn&rsquo;t available locally it will install it for you.<br /><br /></span></p><p><strong>Installation</strong></p><p><span>To download the development version of pacman:</span></p><p><span>Download the </span><a href="https://github.com/trinker/pacman/zipball/master">zip ball</a><span> or </span><a href="https://github.com/trinker/pacman/tarball/master">tar ball</a><span>, decompress and run </span><code>R CMD INSTALL</code><span> on it, or use th</span><span>e </span><strong>devtools</strong><span> package to install the development version:</span></p><pre title="">## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")
</pre><p>Note: Windows users need <a href="http://www.murdoch-sutherland.com/Rtools/">Rtools</a> and <a href="http://CRAN.R-project.org/package=devtools">devtools</a> to install this way.</p><p>More at https://github.com/trinker/pacman</p><p>&nbsp;</p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42941/csa-a-high-throughput-chromosome-scale-assembly-pipeline-for-vertebrate-genomes</guid>
	<pubDate>Wed, 10 Mar 2021 06:13:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42941/csa-a-high-throughput-chromosome-scale-assembly-pipeline-for-vertebrate-genomes</link>
	<title><![CDATA[CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes]]></title>
	<description><![CDATA[<p>The pipeline can use information from scaffolded assemblies (for example from HiC or 10X Genomics), or even from diverged (~65-100 Mya) reference genomes for ordering the contigs and thus support the assembly process. This typically results in improved contig N50 when compared to current state of the art methods.</p>
<p><img src="https://github.com/HMPNK/CSA2.6/raw/master/Fig1.png" alt="image" style="border: 0px;"></p>
<p>For smaller vertebrate genomes (~1 Gbp) chromosome scale assemblies can be achieved within 12h on high-end Desktop computers (Intel i7, 12 CPU threads, 128 GB RAM). Larger mammalian genomes (~3Gbp) can be processed within 15-18 h on server equipment (Xeon, 96 CPU threads, 1TB RAM).</p><p>Address of the bookmark: <a href="https://github.com/HMPNK/CSA2.6" rel="nofollow">https://github.com/HMPNK/CSA2.6</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/21435/ra-walk-in-interview-nbfgr-lucknow</guid>
  <pubDate>Tue, 24 Feb 2015 08:23:48 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA WALK-IN-INTERVIEW @ NBFGR, Lucknow]]></title>
  <description><![CDATA[
<p>F.No. 1(122)/2015-Admn. (CABin Project)<br />Research Associate/Young Professional/SRF Zoology job vacancies in National Bureau of Fish Genetic Resources (NBFGR)<br />Post Name: Research Associate (Computer Science/ Applications)                <br />Qualification: Ph.D. In Computer Science/Computer Applications or equivalent. Or Post-Graduation in Computer Science/ Computer Applications with 1st Division or 60% marks or equivalent overall grade point average with at least two years of research experience. Desirable: 1. Expertise and experience of working/ handling High Performance Computing (H PC) and genomic resource data. 2. Expertise on database management, data mining technologies/ softwares/tools. 3. Published Research papers	<br />No.of Post: 1<br />Pay Scale: Consolidated Rs.24,000/- p.m. + HRA (as admissible) for Ph.D. holders and consolidated `23,000/- + HRA (as admissible) for Master degree holder.	<br />Age:40 years</p>

<p>Young Professional II (Computer Science/Applications)	<br />Master degree in Computer Science/Computer Applications/B.Tech (Computer Science) or equivalent. <br />Desirable: 1. Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics/Data mining tools. 2. Experience in handling HPC, programming languages and database management packages.	<br />A consolidated salary of Rs.25,000/- per month.	<br />21 to 45 year</p>

<p>Young Professional II (Biotechnology/ Bioinformatics)	<br />Master degree in Bioinformatics/ Biotechnology/ B. Tech(Biotech) or equivalent. Desirable: 1. Knowledge of Computational Genomics/Proteomics/Bioinformatics. 2. Expertise in NGS data analysis and knowledge of allied software and tools.	<br />A consolidated salary of Rs.25,000/- per month.	</p>

<p>Senior Research Fellow	<br />1. Bachelors degree with Zoology, Fisheries and 2. Master's degree in Fishery science/ Zoology with Fisheries/ Biotechnology/ Life Sciences with specialization in Fisheries/ Molecular Biology. 3. 1 st Division or 60% marks or equivalent overall grade point average. <br />Desirable: Work experience in Fisheries, molecular research techniques, bioinformatics and Computer skills. NET qualified <br />Note: The project involves extensive exploration tours and sampling from water bodies all over India	<br />Rs.16,000/- p.m. for 1st &amp; 2nd year and `18,000/- p.m. for 3rd and subsequent years +HRA (as per rules)	35 years for male and 40 years for female candidate</p>

<p>How to apply</p>

<p>A walk-in-interview will be held on 04th March, 2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow. Eligible and desirous candidates fulfilling all the requirements may appear for the interview with duly filled in application giving full details of academic records and experience(s) along with attested photocopy as well as original copy of the relevant documents and a passport size photograph on the attached proforma.</p>

<p>http://www.nbfgr.res.in/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</guid>
	<pubDate>Fri, 17 Sep 2021 01:57:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43384/lncpipea-nextflow-based-pipeline-for-comprehensive-analyses-of-long-non-coding-rnas-from-rna-seq-datasets</link>
	<title><![CDATA[LncPipe:A Nextflow-based pipeline for comprehensive analyses of long non-coding RNAs from RNA-seq datasets]]></title>
	<description><![CDATA[<p><span>The pipeline was developed based on a popular workflow framework&nbsp;</span><a href="https://github.com/nextflow-io/nextflow">Nextflow</a><span>, composed of four core procedures including reads alignment, assembly, identification and quantification. It contains various unique features such as well-designed lncRNAs annotation strategy, optimized calculating efficiency, diversified classification and interactive analysis report.&nbsp;</span><a href="https://github.com/likelet/LncPipe">LncPipe</a><span>&nbsp;allows users additional control in interuppting the pipeline, resetting parameters from command line, modifying main script directly and resume analysis from previous checkpoint.</span></p>
<p>Ref&nbsp;https://www.lncrnablog.com/lncpipe-a-nextflow-based-pipeline-for-identification-and-analysis-of-long-non-coding-rnas-from-rna-seq-data/</p>
<p><img src="https://ars.els-cdn.com/content/image/1-s2.0-S1673852718301176-gr1.jpg" alt="image" style="border: 0px;"></p><p>Address of the bookmark: <a href="https://github.com/likelet/LncPipe" rel="nofollow">https://github.com/likelet/LncPipe</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</guid>
	<pubDate>Tue, 24 Feb 2015 20:15:08 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/21443/a-guide-for-complete-r-beginners-getting-data-into-r</link>
	<title><![CDATA[A guide for complete R beginners :- Getting data into R]]></title>
	<description><![CDATA[<p>For a beginner this can be is the hardest part, it is also the most important to get right.</p><p>It is possible to create a vector by typing data directly into R using the combine function &lsquo;c&rsquo;</p><blockquote><p><strong>x </strong></p></blockquote><p>same as</p><blockquote><p><strong>x </strong></p></blockquote><p>creates the vector x with the numbers between 1 and 5.</p><p>You can see what is in an object at any time by typing its name;</p><blockquote><p><strong>x</strong></p></blockquote><p>will produce the output<strong> &lsquo;[1] 1 2 3 4 5&prime;</strong></p><p>Note that names need to be quoted</p><blockquote><p><strong>daysofweek </strong><strong>&larr; c(&lsquo;Monday&rsquo;, &lsquo;Tuesday&rsquo;, &lsquo;Wednesday&rsquo;, &lsquo;Thursday&rsquo;, &lsquo;Friday&rsquo;);</strong></p></blockquote><p>Usually however you want to input from a file. We have touched on the &lsquo;read.table&rsquo; function already.</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Now <strong>mydata</strong> is a data frame with multiple vectors</p><p>each vector can be identified by the default syntax</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$V1 mydata$V2 mydata$V3 </strong></p></blockquote><p>By default the function assumes certain things from the file</p><ul>
<li>The file is a plain text file (there are function to read excel files: <em>not covered here</em>)</li>
<li>columns are separated by any number of tabs or spaces</li>
<li>there is the same number of data points in each column</li>
<li>there is no header row (labels for the columns)</li>
<li>there is no column with names for the rows** [I&rsquo;ll explain].</li>
</ul><p><span style="text-decoration: underline;">If any of these are false, we need to tell that to the function</span></p><p>If it has a header column</p><blockquote><p><strong>mydata <em>header=T also works</em></strong></p></blockquote><p>Note that there is a comma between different parts of the functions arguments</p><p>If there is one less column in the header row, then R assumes that the 1<sup>st</sup> column of data after the header are the row names</p><p>Now the vectors (columns) are identified by their name</p><p>#if any of these are typed it will print to screen</p><blockquote><p><strong>mydata$A mydata$B mydata$C </strong></p></blockquote><p># Summary about the whole data frame</p><blockquote><p><strong>summary(mydata)</strong></p></blockquote><p># Summary information of column A</p><blockquote><p><strong>summary(mydata$A) </strong></p></blockquote><p>We can shortcut having to type the data frame each time by attaching it</p><blockquote><p><strong>attach(mydata)</strong></p></blockquote><p># summary of column B as &lsquo;mydata&rsquo; is attached</p><blockquote><p><strong>summary(B)</strong></p></blockquote><p><span style="text-decoration: underline;">Two other important options for </span><em><span style="text-decoration: underline;">read.table</span></em></p><p>If is is separated only by tabs and has a header</p><blockquote><p><strong>mydata </strong></p></blockquote><p>Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.</p><p>If you know that the file has uneven columns</p><blockquote><p><strong>mydata </strong></p></blockquote><p>This causes R to fill empty spaces in a columns with &lsquo;NA&rsquo; .</p><p>The last two examples will still work with our file and give the same result as with only headers=T</p><p><span style="text-decoration: underline;">Graphs</span></p><p>to get an idea of what R is capable of type</p><blockquote><p><strong>demo(graphics)</strong></p></blockquote><p>steps through the examples, and the code is printed to the screen</p><p>We will work with simpler examples that have immediate use to biologists.</p><p>Remember to get more information about the options to a function type &lsquo;?function&rsquo;</p><p><span style="text-decoration: underline;">Histogram of A</span><span style="text-decoration: underline;"></span></p><blockquote><p><strong>hist(mydata$A)</strong></p></blockquote><p>If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).</p><blockquote><p><strong>boxplot(mydata)</strong></p></blockquote><p>We can get rid of the need to type the data frame each time by using the <strong>attach</strong> function</p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>boxplot(mydata$A, mydata$B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>same as</p><blockquote><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Scatter plot</span></p><p># if not already done so</p><blockquote><p><strong>attach(mydata) </strong></p><p><strong>plot(A,B) # or plot(mydata$A, mydata$B)</strong></p></blockquote><p><strong><span style="text-decoration: underline;">SAVING an image</span></strong></p><p>Windows users (Rgui) RIGHT click on image and select which you want.</p><p><span style="text-decoration: underline;">These instructions work for everyone.</span></p><p>You need to create a new device of the type of file you need, then send the data to that device</p><p>to save as a png file (easy to load into the likes of powerpoint, also great for web applications.</p><blockquote><p><strong>png(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p>or to save as a pdf</p><blockquote><p><strong>pdf(&lsquo;filename&rsquo;) </strong></p><p><strong>boxplot(A, B, name=c(&ldquo;Value A&rdquo;, &ldquo;Value B&rdquo;) , ylab=&ldquo;Count of Something&rdquo;)</strong></p></blockquote><p><span style="text-decoration: underline;">Note</span></p><ul>
<li>Nothing will appear on screen, the output is going to the file</li>
<li>Also it may not be saved immediately but will once the device (or R) is turned quit.</li>
</ul><p>To quit R type</p><p><strong>q() # </strong>If you save your session, next time you start R, you will have your data preloaded.</p><p>Or if you want to remain in R</p><blockquote><pre><strong>dev.off() #</strong>turns of the png (or pdf etc) device, thus forces the data to save</pre></blockquote>]]></description>
	<dc:creator>Archana Malhotra</dc:creator>
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