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<br/>User info:
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<center> <titleinbody> Data examples </titleinbody> </center>
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<center><b>Contents:</b></center>
&bull;&nbsp;<a href="#flins_data">flins data</a><br/>
&bull;&nbsp;<a href="#Color/shape_data">Color/shape data</a><br/>
&bull;&nbsp;<a href="#Program_timing">Program timing</a><br/>
</div>
<p/>
<a id="flins_data"/><h1>flins data</h1>
<p/> The <a href="data/flins.csv">flins.csv</a> file is some sample data
obtained from <a href="https://support.spatialkey.com/spatialkey-sample-csv-data">https://support.spatialkey.com/spatialkey-sample-csv-data</a>.
<p/>Vertical-tabular format is good for a quick look at CSV data layout &mdash; seeing what columns you have to work with:
<p/>
<div class="pokipanel">
<pre>
$ head -n 2 data/flins.csv | mlr --icsv --oxtab cat
policyID 119736
statecode FL
county CLAY COUNTY
eq_site_limit 498960
hu_site_limit 498960
fl_site_limit 498960
fr_site_limit 498960
tiv_2011 498960
tiv_2012 792148.9
eq_site_deductible 0
hu_site_deductible 9979.2
fl_site_deductible 0
fr_site_deductible 0
point_latitude 30.102261
point_longitude -81.711777
line Residential
construction Masonry
point_granularity 1
</pre>
</div>
<p/>
<p/> A few simple queries:
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint count-distinct -f county | head
county count
CLAY COUNTY 363
SUWANNEE COUNTY 154
NASSAU COUNTY 135
COLUMBIA COUNTY 125
ST JOHNS COUNTY 657
BAKER COUNTY 70
BRADFORD COUNTY 31
HAMILTON COUNTY 35
UNION COUNTY 15
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint count-distinct -f construction,line
construction line count
Masonry Residential 9257
Wood Residential 21581
Reinforced Concrete Commercial 1299
Reinforced Masonry Commercial 4225
Steel Frame Commercial 272
</pre>
</div>
<p/>
<p/> Categorization of total insured value:
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint stats1 -a min,mean,max -f tiv_2012
tiv_2012_min tiv_2012_mean tiv_2012_max
73.370000 2571004.097342 1701000000.000000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint stats1 -a min,mean,max -f tiv_2012 -g construction,line
construction line tiv_2012_min tiv_2012_mean tiv_2012_max
Masonry Residential 261168.070000 1041986.129217 3234970.920000
Wood Residential 73.370000 113493.017049 649046.120000
Reinforced Concrete Commercial 6416016.010000 20212428.681840 60570000.000000
Reinforced Masonry Commercial 1287817.340000 4621372.981117 16650000.000000
Steel Frame Commercial 29790000.000000 133492500.000000 1701000000.000000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --oxtab stats1 -a p0,p10,p50,p90,p95,p99,p100 -f hu_site_deductible
hu_site_deductible_p0 0.000000
hu_site_deductible_p10 0.000000
hu_site_deductible_p50 0.000000
hu_site_deductible_p90 76.500000
hu_site_deductible_p95 6829.200000
hu_site_deductible_p99 126270.000000
hu_site_deductible_p100 7380000.000000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint stats1 -a p95,p99,p100 -f hu_site_deductible -g county then sort -f county | head
county hu_site_deductible_p95 hu_site_deductible_p99 hu_site_deductible_p100
ALACHUA COUNTY 30630.600000 107312.400000 1641375.000000
BAKER COUNTY 0.000000 0.000000 0.000000
BAY COUNTY 26131.500000 181912.500000 630000.000000
BRADFORD COUNTY 3355.200000 8163.000000 8163.000000
BREVARD COUNTY 5360.400000 78975.000000 1973461.500000
BROWARD COUNTY 0.000000 148500.000000 3258900.000000
CALHOUN COUNTY 0.000000 33339.600000 33339.600000
CHARLOTTE COUNTY 5400.000000 52650.000000 250994.700000
CITRUS COUNTY 1332.900000 79974.900000 483785.100000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --oxtab stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012
tiv_2011_tiv_2012_corr 0.973050
tiv_2011_tiv_2012_ols_m 0.983558
tiv_2011_tiv_2012_ols_b 433854.642897
tiv_2011_tiv_2012_ols_n 36634
tiv_2011_tiv_2012_r2 0.946826
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ cat data/flins.csv | mlr --icsv --opprint stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012 -g county
county tiv_2011_tiv_2012_corr tiv_2011_tiv_2012_ols_m tiv_2011_tiv_2012_ols_b tiv_2011_tiv_2012_ols_n tiv_2011_tiv_2012_r2
CLAY COUNTY 0.962716 1.090115 46450.531268 363 0.926822
SUWANNEE COUNTY 0.989208 1.074658 36253.003174 154 0.978533
NASSAU COUNTY 0.973135 1.296321 -45369.242673 135 0.946993
COLUMBIA COUNTY 0.999492 0.931447 117183.548383 125 0.998985
ST JOHNS COUNTY 0.966170 1.230056 -596.623856 657 0.933485
BAKER COUNTY 0.963515 0.942771 29063.065747 70 0.928360
BRADFORD COUNTY 0.999766 0.849029 69544.341944 31 0.999533
HAMILTON COUNTY 0.987026 1.224952 1045.052170 35 0.974220
UNION COUNTY 0.997745 1.432575 -56.125738 15 0.995495
MADISON COUNTY 0.985213 1.512114 -84278.028498 81 0.970645
LAFAYETTE COUNTY 0.967499 1.134289 9904.860798 68 0.936055
FLAGLER COUNTY 0.984854 1.007922 95340.508354 204 0.969937
DUVAL COUNTY 0.978815 1.245630 -60831.675023 1894 0.958079
LAKE COUNTY 0.999727 1.293864 -107695.848518 206 0.999455
VOLUSIA COUNTY 0.994636 1.202247 -36277.755477 1367 0.989300
PUTNAM COUNTY 0.961167 1.176294 6405.060826 268 0.923841
MARION COUNTY 0.975774 1.175642 20434.945602 1138 0.952136
SUMTER COUNTY 0.989760 1.372395 -62648.989750 158 0.979625
LEON COUNTY 0.978644 1.259681 -90816.033261 246 0.957743
FRANKLIN COUNTY 0.989430 1.048513 36026.508852 37 0.978972
LIBERTY COUNTY 0.995175 1.369834 -79755.544362 36 0.990373
GADSDEN COUNTY 0.997898 1.180585 7335.013009 196 0.995801
WAKULLA COUNTY 0.978267 1.192350 44607.922080 85 0.957006
JEFFERSON COUNTY 0.976543 0.976066 74884.170791 57 0.953637
TAYLOR COUNTY 0.981770 1.386188 -56856.945239 113 0.963873
BAY COUNTY 0.975404 1.004452 373000.300167 403 0.951412
WALTON COUNTY 0.985855 1.319583 -83273.091503 288 0.971909
JACKSON COUNTY 0.991195 1.171538 8128.438198 208 0.982468
CALHOUN COUNTY 0.967974 1.274077 -739.602262 68 0.936973
HOLMES COUNTY 0.997366 1.159384 42610.647058 40 0.994738
WASHINGTON COUNTY 0.982582 1.213413 -13125.214494 116 0.965468
GULF COUNTY 0.990367 1.135626 26094.474571 72 0.980826
ESCAMBIA COUNTY 0.986666 1.195336 46106.277408 494 0.973509
SANTA ROSA COUNTY 0.972696 1.013849 30496.045069 856 0.946138
OKALOOSA COUNTY 0.970781 1.462083 -116127.032201 1115 0.942416
ALACHUA COUNTY 0.982825 1.142748 52671.269211 973 0.965945
GILCHRIST COUNTY 0.977467 1.375740 -15309.425813 39 0.955442
LEVY COUNTY 0.956302 1.200506 265.391211 126 0.914513
DIXIE COUNTY 0.995780 1.640150 -98273.767115 40 0.991578
SEMINOLE COUNTY 0.985925 0.880108 427892.123991 1100 0.972048
ORANGE COUNTY 0.990658 0.872027 1298970.668186 1811 0.981403
BREVARD COUNTY 0.978015 1.271225 -19295.177646 872 0.956513
INDIAN RIVER COUNTY 0.985673 1.284620 -116579.613922 380 0.971550
MIAMI DADE COUNTY 0.987833 1.293106 -237168.505282 4315 0.975815
BROWARD COUNTY 0.983847 1.187689 81931.896276 3193 0.967954
MONROE COUNTY 0.982555 1.013142 455469.576218 152 0.965414
PALM BEACH COUNTY 0.982591 1.247594 -77252.429421 2791 0.965485
MARTIN COUNTY 0.975896 1.032873 8668.746202 109 0.952374
HENDRY COUNTY 0.971645 0.969699 208613.031856 74 0.944093
PASCO COUNTY 0.986556 1.288225 -152936.104164 790 0.973294
GLADES COUNTY 0.983518 0.982993 125666.627729 22 0.967308
HILLSBOROUGH COUNTY 0.985446 1.211620 214512.927989 1166 0.971103
HERNANDO COUNTY 0.974068 0.759748 701096.129434 120 0.948809
PINELLAS COUNTY 0.987215 1.154797 38609.763660 1774 0.974593
POLK COUNTY 0.979963 1.094848 153371.308143 1629 0.960327
North Fort Myers - nan nan 1 -
Orlando - nan nan 1 -
HIGHLANDS COUNTY 0.993054 1.528760 -300198.361569 369 0.986157
HARDEE COUNTY 0.977999 1.323440 -98513.434797 81 0.956482
MANATEE COUNTY 0.967526 1.068496 137190.708238 518 0.936106
OSCEOLA COUNTY - nan nan 1 -
LEE COUNTY 0.978945 1.252722 -16843.109269 678 0.958334
CHARLOTTE COUNTY 0.979024 1.013211 178461.328878 414 0.958488
COLLIER COUNTY 0.958031 1.169759 110270.385201 787 0.917824
SARASOTA COUNTY 0.984781 1.292514 -109939.723017 417 0.969793
DESOTO COUNTY 0.980130 1.286205 -9987.042982 108 0.960654
CITRUS COUNTY 0.989943 0.965940 138635.818880 384 0.979986
</pre>
</div>
<p/>
<a id="Color/shape_data"/><h1>Color/shape data</h1>
<p/> The <a href="data/colored-shapes.dkvp">colored-shapes.dkvp</a> file is some sample data produced by the
<a href="https://github.com/johnkerl/miller/blob/master/doc/datagen/mkdat2">mkdat2</a> script. The idea is
<ul>
<li> Produce some data with known distributions and correlations, and verify that Miller recovers those properties empirically.
<li> Each record is labeled with one of a few colors and one of a few shapes.
<li> The <tt>flag</tt> field is 0 or 1, with probability dependent on color
<li> The <tt>u</tt> field is plain uniform on the unit interval.
<li> The <tt>v</tt> field is the same, except tightly correlated with <tt>u</tt> for red circles.
<li> The <tt>w</tt> field is autocorrelated for each color/shape pair.
<li> The <tt>x</tt> field is boring Gaussian with mean 5 and standard deviation about 1.2, with no dependence on color or shape.
</ul>
<p/> Peek at the data:
<p/>
<div class="pokipanel">
<pre>
$ wc -l data/colored-shapes.dkvp
100000 data/colored-shapes.dkvp
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ head -n 6 data/colored-shapes.dkvp | mlr --opprint cat
color shape flag i u v w x
green circle 1 1 0.3333681345949695 0.21376251997142037 0.500764608334147 5.335248996137616
red square 0 2 0.9698237361677843 0.49622821725976396 0.8040548802590068 4.860269966304398
yellow circle 1 3 0.9879523154172577 0.8487346180375205 0.6423100990686053 5.277708912462331
red circle 0 4 0.4622496635675988 0.5046956653096718 0.13094461569540355 5.370828092344023
blue circle 1 5 0.9992738066015238 0.08847422779789516 0.020449531719886935 4.712152492660485
purple triangle 0 6 0.983871885308542 0.5390716976565088 0.8211678541134113 3.873918555931081
</pre>
</div>
<p/>
<p/> Look at uncategorized stats (using <a href="https://github.com/johnkerl/scripts/blob/master/fundam/creach"><tt>creach</tt></a> for spacing).
Here it looks reasonable that <tt>u</tt> is unit-uniform; something&rsquo;s up with <tt>v</tt> but we can't yet see what:
<p/>
<div class="pokipanel">
<pre>
$ mlr --oxtab stats1 -a min,mean,max -f flag,u,v data/colored-shapes.dkvp | creach 3
flag_min 0.000000
flag_mean 0.397960
flag_max 1.000000
u_min 0.000010
u_mean 0.501086
u_max 0.999983
v_min -0.096195
v_mean 0.500016
v_max 1.095540
</pre>
</div>
<p/>
<p/>The histogram shows the different distribution of 0/1 flags:
<p/>
<div class="pokipanel">
<pre>
$ mlr --opprint histogram -f flag,u,v --lo -0.1 --hi 1.1 --nbins 12 data/colored-shapes.dkvp
bin_lo bin_hi flag_count u_count v_count
-0.100000 0.000000 60204 0 287
0.000000 0.100000 0 10089 9613
0.100000 0.200000 0 9844 10022
0.200000 0.300000 0 9976 10115
0.300000 0.400000 0 9923 10027
0.400000 0.500000 0 9918 9988
0.500000 0.600000 0 10017 9934
0.600000 0.700000 0 9998 9968
0.700000 0.800000 0 10202 10124
0.800000 0.900000 0 10003 9970
0.900000 1.000000 39796 10030 9650
1.000000 1.100000 0 0 302
</pre>
</div>
<p/>
<p/> Look at univariate stats by color and shape. In particular,
color-dependent flag probabilities pop out, aligning with their original
Bernoulli probablities from the data-generator script:
<p/>
<div class="pokipanel">
<pre>
$ mlr --opprint stats1 -a min,mean,max -f flag,u,v -g color then sort -f color data/colored-shapes.dkvp
color flag_min flag_mean flag_max u_min u_mean u_max v_min v_mean v_max
blue 0.000000 0.599086 1.000000 0.000044 0.503581 0.999976 0.000057 0.496972 0.999870
green 0.000000 0.207102 1.000000 0.000010 0.504358 0.999983 0.000079 0.498956 0.999889
orange 0.000000 0.505079 1.000000 0.000464 0.496712 0.999574 0.000185 0.493220 0.999967
purple 0.000000 0.098957 1.000000 0.000037 0.498311 0.999693 0.000011 0.502892 0.999993
red 0.000000 0.297820 1.000000 0.000019 0.499904 0.999946 -0.096195 0.500635 1.095540
yellow 0.000000 0.898270 1.000000 0.000327 0.502877 0.999923 0.000138 0.501022 0.999983
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ mlr --opprint stats1 -a min,mean,max -f flag,u,v -g shape then sort -f shape data/colored-shapes.dkvp
shape flag_min flag_mean flag_max u_min u_mean u_max v_min v_mean v_max
circle 0.000000 0.398115 1.000000 0.000037 0.502212 0.999976 -0.096195 0.496996 1.095540
square 0.000000 0.397398 1.000000 0.000037 0.499935 0.999970 0.000088 0.500389 0.999975
triangle 0.000000 0.398542 1.000000 0.000010 0.501676 0.999983 0.000010 0.501804 0.999995
</pre>
</div>
<p/>
<p/> Look at bivariate stats by color and shape. In particular, <tt>u,v</tt> pairwise correlation for red circles pops out:
<p/>
<div class="pokipanel">
<pre>
$ mlr --opprint --right stats2 -a corr -f u,v,w,x data/colored-shapes.dkvp
u_v_corr w_x_corr
0.115078 0.000020
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ mlr --opprint --right stats2 -a corr -f u,v,w,x -g color,shape then sort -nr u_v_corr data/colored-shapes.dkvp
color shape u_v_corr w_x_corr
red circle 0.980343 0.004277
blue triangle 0.029301 0.017138
orange circle 0.021871 -0.040626
purple circle 0.021701 0.037687
green triangle 0.010633 -0.025945
red square 0.006553 -0.007587
orange square 0.005342 0.042003
yellow square 0.001905 -0.002849
orange triangle 0.000620 -0.013529
red triangle -0.000349 0.003179
yellow triangle -0.001735 0.032633
yellow circle -0.002083 -0.012606
purple triangle -0.004290 0.006341
green square -0.006983 0.001269
purple square -0.011754 -0.003372
blue circle -0.017771 0.014975
blue square -0.019537 -0.006952
green circle -0.034313 -0.049415
</pre>
</div>
<p/>
<a id="Program_timing"/><h1>Program timing</h1>
This admittedly artificial example demonstrates using Miller time and stats
functions to introspectly acquire some information about Miller&rsquo;s own
runtime. The <tt>delta</tt> function computes the difference between successive
timestamps.
<p/>
<div class="pokipanel">
<pre>
$ ruby -e '10000.times{|i|puts "i=#{i+1}"}' &gt; lines.txt
$ head -n 5 lines.txt
i=1
i=2
i=3
i=4
i=5
mlr --ofmt '%.9le' --opprint put '$t=systime()' then step -a delta -f t lines.txt | head -n 7
i t t_delta
1 1430603027.018016 1.430603027e+09
2 1430603027.018043 2.694129944e-05
3 1430603027.018048 5.006790161e-06
4 1430603027.018052 4.053115845e-06
5 1430603027.018055 2.861022949e-06
6 1430603027.018058 3.099441528e-06
mlr --ofmt '%.9le' --oxtab \
put '$t=systime()' then \
step -a delta -f t then \
filter '$i&gt;1' then \
stats1 -a min,mean,max -f t_delta \
lines.txt
t_delta_min 2.861022949e-06
t_delta_mean 4.077508505e-06
t_delta_max 5.388259888e-05
</pre>
</div>
<p/>
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