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377 lines
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377 lines
12 KiB
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content="John Kerl, Kerl, Miller, miller, mlr, OLAP, data analysis software, regression, correlation, variance, data tools, " />
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<title> About Miller </title>
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The background image is from a screenshot of a Google search for "data analysis
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tools", lightened and sepia-toned. Over this was placed a Mac Terminal app with
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very light-grey font and translucent background, in which a few statistical
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Miller commands were run with pretty-print-tabular output format.
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<body background="pix/sepia-overlay.jpg">
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<table width="100%">
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<tr>
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<td width="15%">
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<!--
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-->
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<div class="pokinav">
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<center><titleinbody>Miller</titleinbody></center>
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<!-- PAGE LIST GENERATED FROM template.html BY poki -->
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<br/><b>Overview:</b>
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<br/>• <a href="index.html"><b>About Miller</b></a>
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<br/>• <a href="10-min.html">Miller in 10 minutes</a>
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<br/>• <a href="file-formats.html">File formats</a>
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<br/>• <a href="feature-comparison.html">Miller features in the context of the Unix toolkit</a>
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<br/>• <a href="record-heterogeneity.html">Record-heterogeneity</a>
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<br/>• <a href="internationalization.html">Internationalization</a>
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<br/><b>Using Miller:</b>
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<br/>• <a href="faq.html">FAQ</a>
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<br/>• <a href="cookbook.html">Cookbook part 1</a>
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<br/>• <a href="cookbook2.html">Cookbook part 2</a>
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<br/>• <a href="cookbook3.html">Cookbook part 3</a>
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<br/>• <a href="data-examples.html">Data-diving examples</a>
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<br/>• <a href="manpage.html">Manpage</a>
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<br/>• <a href="reference.html">Reference</a>
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<br/>• <a href="reference-verbs.html">Reference: Verbs</a>
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<br/>• <a href="reference-dsl.html">Reference: DSL</a>
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<br/>• <a href="release-docs.html">Documents by release</a>
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<br/>• <a href="build.html">Installation, portability, dependencies, and testing</a>
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<br/><b>Background:</b>
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<br/>• <a href="why.html">Why?</a>
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<br/>• <a href="whyc.html">Why C?</a>
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<br/>• <a href="etymology.html">Why call it Miller?</a>
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<br/>• <a href="originality.html">How original is Miller?</a>
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<br/>• <a href="performance.html">Performance</a>
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<br/><b>Repository:</b>
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<br/>• <a href="to-do.html">Things to do</a>
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<br/>• <a href="contact.html">Contact information</a>
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<br/>• <a href="https://github.com/johnkerl/miller">GitHub repo</a>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/>
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</div>
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independent scroll of the nav and body panels. In particular the nav
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stays on-screen as you scroll the body.
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However, two problems:
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(1) In Firefox & Chrome both I get janky end-of-body scrolls: there is
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more content but I can't scroll down to it unless I repeatedly retry the
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scrolldown. Which is weird.
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(2) Worse, only the first page renders in PDF (again, Firefox & Chrome).
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For now I'm disabling this separate-scroll feature. A frontender, I am
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not ... maybe someday I'll find a config which gets *all* the features
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I want; for now, it's a tradeoff.
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<div>
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<center> <titleinbody> About Miller </titleinbody> </center>
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<p/>
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<!-- BODY COPIED FROM content-for-index.html BY poki -->
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<p/>Miller is like awk, sed, cut, join, and sort for <b>name-indexed data such as
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CSV, TSV, and tabular JSON</b>. You get to work with your data using named
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fields, without needing to count positional column indices.
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<p/>This is something the Unix toolkit always could have done, and arguably
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always should have done. It operates on key-value-pair data while the familiar
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Unix tools operate on integer-indexed fields: if the natural data structure for
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the latter is the array, then Miller’s natural data structure is the
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insertion-ordered hash map. This encompasses a <b>variety of data formats</b>,
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including but not limited to the familiar CSV, TSV, and JSON. (Miller can handle
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<b>positionally-indexed data</b> as a special case.)
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<p/> Features:
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<ul>
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<li/> Miller is <b>multi-purpose</b>: it’s useful for <b>data
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cleaning</b>, <b>data reduction</b>, <b>statistical reporting</b>,
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<b>devops</b>, <b>system administration</b>, <b>log-file processing</b>,
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<b>format conversion</b>, and <b>database-query post-processing</b>.
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<li/> You can use Miller to snarf and munge <b>log-file data</b>, including
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selecting out relevant substreams, then produce CSV format and load that into
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all-in-memory/data-frame utilities for further statistical and/or graphical
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processing.
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<li/> Miller complements <b>data-analysis tools</b> such as <b>R</b>,
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<b>pandas</b>, etc.: you can use Miller to <b>clean</b> and <b>prepare</b> your
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data. While you can do <b>basic statistics</b> entirely in Miller, its
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streaming-data feature and single-pass algorithms enable you to <b>reduce very
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large data sets</b>.
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<li/> Miller complements SQL <b>databases</b>: you can slice, dice, and
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reformat data on the client side on its way into or out of a database. You can
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also reap some of the benefits of databases for quick, setup-free one-off tasks
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when you just need to query some data in disk files in a hurry.
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<li/> Miller also goes beyond the classic Unix tools by stepping fully into our
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modern, <b>no-SQL</b> world: its essential record-heterogeneity property allows
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Miller to operate on data where records with different schema (field names) are
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interleaved.
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<li/> Miller is <b>streaming</b>: most operations need only a single record in
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memory at a time, rather than ingesting all input before producing any output.
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For those operations which require deeper retention (<tt>sort</tt>,
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<tt>tac</tt>, <tt>stats1</tt>), Miller retains only as much data as needed.
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This means that whenever functionally possible, you can operate on files which
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are larger than your system’s available RAM, and you can use Miller in
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<b>tail -f</b> contexts.
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<li/> Miller is <b>pipe-friendly</b> and interoperates with the Unix toolkit
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<li/> Miller’s I/O formats include <b>tabular pretty-printing</b>,
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<b>positionally indexed</b> (Unix-toolkit style), CSV, JSON, and others
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<li/> Miller does <b>conversion</b> between formats
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<li/> Miller’s <b>processing is format-aware</b>: e.g. CSV <tt>sort</tt>
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and <tt>tac</tt> keep header lines first
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<li/> Miller has high-throughput <b>performance</b> on par with the Unix toolkit
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<li/> Not unlike <a href="http://stedolan.github.io/jq/">jq</a> (for JSON),
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Miller is written in portable, modern C, with <b>zero runtime dependencies</b>.
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You can download or compile a single binary, <tt>scp</tt> it to a faraway
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machine, and expect it to work.
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</ul>
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<p>Releases and release notes:
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<a href="https://github.com/johnkerl/miller/releases">https://github.com/johnkerl/miller/releases</a>.
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<p/> Examples:
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<div class="pokipanel">
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<pre>
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# Column select
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% mlr --csv cut -f hostname,uptime mydata.csv
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Add new columns as function of other columns
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% mlr --nidx put '$sum = $7 < 0.0 ? 3.5 : $7 + 2.1*$8' *.dat
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Row filter
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% mlr --csv filter '$status != "down" && $upsec >= 10000' *.csv
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Apply column labels and pretty-print
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% grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Join multiple data sources on key columns
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% mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Multiple formats including JSON
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% mlr --json put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*.json
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Aggregate per-column statistics
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% mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Linear regression
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% mlr stats2 -a linreg-pca -f u,v -g shape data/*
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Aggregate custom per-column statistics
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% mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}' data/*
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Iterate over data using DSL expressions
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% mlr --from estimates.tbl put '
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for (k,v in $*) {
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if (is_numeric(v) && k =~ "^[t-z].*$") {
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$sum += v; $count += 1
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}
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}
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$mean = $sum / $count # no assignment if count unset
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'
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Run DSL expressions from a script file
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% mlr --from infile.dat put -f analyze.mlr
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Split/reduce output to multiple filenames
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% mlr --from infile.dat put 'tee > "./taps/data-".$a."-".$b, $*'
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Compressed I/O
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% mlr --from infile.dat put 'tee | "gzip > ./taps/data-".$a."-".$b.".gz", $*'
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Interoperate with other data-processing tools using standard pipes
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% mlr --from infile.dat put -q '@v=$*; dump | "jq .[]"'
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</pre>
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</div>
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<div class="pokipanel">
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<pre>
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# Tap/trace
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% mlr --from infile.dat put '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'
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</pre>
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</div>
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</div>
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</td>
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</table>
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</html>
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