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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/>
</div>
<p/>
<p/>
<button style="font-weight:bold;color:maroon;border:0" onclick="expand_all();" href="javascript:;">Expand all sections</button>
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<a id="flins_data"/><h1>flins data</h1>
<button style="font-weight:bold;color:maroon;border:0" padding=0 onclick="toggle_by_name('section_toggle_flins');" href="javascript:;">Toggle section visibility</button>
<div id="section_toggle_flins" style="display: block">
<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>
$ mlr --from data/flins.csv --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>
$ mlr --from data/flins.csv --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>
$ mlr --from data/flins.csv --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>
$ mlr --from data/flins.csv --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 133492500.000000 1701000000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ mlr --from data/flins.csv --icsv --oxtab stats1 -a p0,p10,p50,p90,p95,p99,p100 -f hu_site_deductible
hu_site_deductible_p0 0
hu_site_deductible_p10 0
hu_site_deductible_p50 0
hu_site_deductible_p90 76.500000
hu_site_deductible_p95 6829.200000
hu_site_deductible_p99 126270
hu_site_deductible_p100 7380000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ mlr --from data/flins.csv --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
BAKER COUNTY 0 0 0
BAY COUNTY 26131.500000 181912.500000 630000
BRADFORD COUNTY 3355.200000 8163 8163
BREVARD COUNTY 5360.400000 78975 1973461.500000
BROWARD COUNTY 0 148500 3258900
CALHOUN COUNTY 0 33339.600000 33339.600000
CHARLOTTE COUNTY 5400 52650 250994.700000
CITRUS COUNTY 1332.900000 79974.900000 483785.100000
</pre>
</div>
<p/>
<p/>
<div class="pokipanel">
<pre>
$ mlr --from data/flins.csv --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>
$ mlr --from data/flins.csv --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 - - - 1 -
Orlando - - - 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 - - - 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/>
</div>
<a id="Color/shape_data"/><h1>Color/shape data</h1>
<button style="font-weight:bold;color:maroon;border:0" padding=0 onclick="toggle_by_name('section_toggle_color_shape');" href="javascript:;">Toggle section visibility</button>
<div id="section_toggle_color_shape" style="display: block">
<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
10078 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
yellow triangle 1 11 0.6321695890307647 0.9887207810889004 0.4364983936735774 5.7981881667050565
red square 1 15 0.21966833570651523 0.001257332190235938 0.7927778364718627 2.944117399716207
red circle 1 16 0.20901671281497636 0.29005231936593445 0.13810280912907674 5.065034003400998
red square 0 48 0.9562743938458542 0.7467203085342884 0.7755423050923582 7.117831369597269
purple triangle 0 51 0.4355354501763202 0.8591292672156728 0.8122903963006748 5.753094629505863
red square 0 64 0.2015510269821953 0.9531098083420033 0.7719912015786777 5.612050466474166
</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&rsquo;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
flag_mean 0.398889
flag_max 1
u_min 0.000044
u_mean 0.498326
u_max 0.999969
v_min -0.092709
v_mean 0.497787
v_max 1.072500
</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 6058 0 36
0.000000 0.100000 0 1062 988
0.100000 0.200000 0 985 1003
0.200000 0.300000 0 1024 1014
0.300000 0.400000 0 1002 991
0.400000 0.500000 0 989 1041
0.500000 0.600000 0 1001 1016
0.600000 0.700000 0 972 962
0.700000 0.800000 0 1035 1070
0.800000 0.900000 0 995 993
0.900000 1.000000 4020 1013 939
1.000000 1.100000 0 0 25
</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 0.584354 1 0.000044 0.517717 0.999969 0.001489 0.491056 0.999576
green 0 0.209197 1 0.000488 0.504861 0.999936 0.000501 0.499085 0.999676
orange 0 0.521452 1 0.001235 0.490532 0.998885 0.002449 0.487764 0.998475
purple 0 0.090193 1 0.000266 0.494005 0.999647 0.000364 0.497051 0.999975
red 0 0.303167 1 0.000671 0.492560 0.999882 -0.092709 0.496535 1.072500
yellow 0 0.892427 1 0.001300 0.497129 0.999923 0.000711 0.510627 0.999919
</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 0.399846 1 0.000044 0.498555 0.999923 -0.092709 0.495524 1.072500
square 0 0.396112 1 0.000188 0.499385 0.999969 0.000089 0.496538 0.999975
triangle 0 0.401542 1 0.000881 0.496859 0.999661 0.000717 0.501050 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
0.133418 -0.011320
</pre>
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<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 0.980798 -0.018565
orange square 0.176858 -0.071044
green circle 0.057644 0.011795
red square 0.055745 -0.000680
yellow triangle 0.044573 0.024605
yellow square 0.043792 -0.044623
purple circle 0.035874 0.134112
blue square 0.032412 -0.053508
blue triangle 0.015356 -0.000608
orange circle 0.010519 -0.162795
red triangle 0.008098 0.012486
purple triangle 0.005155 -0.045058
purple square -0.025680 0.057694
green square -0.025776 -0.003265
orange triangle -0.030457 -0.131870
yellow circle -0.064773 0.073695
blue circle -0.102348 -0.030529
green triangle -0.109018 -0.048488
</pre>
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