Data-diving examples¶
flins data¶
The flins.csv file is some sample data obtained from https://support.spatialkey.com/spatialkey-sample-csv-data.
Vertical-tabular format is good for a quick look at CSV data layout – seeing what columns you have to work with:
$ head -n 2 data/flins.csv | mlr --icsv --oxtab cat
county Seminole
tiv_2011 22890.55
tiv_2012 20848.71
line Residential
A few simple queries:
$ mlr --from data/flins.csv --icsv --opprint count-distinct -f county | head
county count
Seminole 1
Miami Dade 2
Palm Beach 1
Highlands 2
Duval 1
St. Johns 1
$ mlr --from data/flins.csv --icsv --opprint count-distinct -f construction,line
Categorization of total insured value:
$ mlr --from data/flins.csv --icsv --opprint stats1 -a min,mean,max -f tiv_2012
tiv_2012_min tiv_2012_mean tiv_2012_max
19757.91 1.0615314637499999e+06 2.78555163e+06
$ mlr --from data/flins.csv --icsv --opprint stats1 -a min,mean,max -f tiv_2012 -g construction,line
$ mlr --from data/flins.csv --icsv --oxtab stats1 -a p0,p10,p50,p90,p95,p99,p100 -f hu_site_deductible
$ mlr --from data/flins.csv --icsv --opprint stats1 -a p95,p99,p100 -f hu_site_deductible -g county then sort -f county | head
county
Duval
Highlands
Miami Dade
Palm Beach
Seminole
St. Johns
$ mlr --from data/flins.csv --icsv --oxtab stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012
tiv_2011_tiv_2012_corr 0.9353629581411828
tiv_2011_tiv_2012_ols_m 1.0890905877734807
tiv_2011_tiv_2012_ols_b 103095.52335638746
tiv_2011_tiv_2012_ols_n 8
tiv_2011_tiv_2012_r2 0.8749038634626236
$ 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
Seminole - - - 1 -
Miami Dade 1 0.9306426512386247 -2311.1543275160047 2 0.9999999999999999
Palm Beach - - - 1 -
Highlands 0.9999999999999997 1.055692910750992 -4529.7939388307705 2 0.9999999999999992
Duval - - - 1 -
St. Johns - - - 1 -
Color/shape data¶
The colored-shapes.dkvp file is some sample data produced by the mkdat2 script. The idea is:
Produce some data with known distributions and correlations, and verify that Miller recovers those properties empirically.
Each record is labeled with one of a few colors and one of a few shapes.
The
flagfield is 0 or 1, with probability dependent on colorThe
ufield is plain uniform on the unit interval.The
vfield is the same, except tightly correlated withufor red circles.The
wfield is autocorrelated for each color/shape pair.The
xfield is boring Gaussian with mean 5 and standard deviation about 1.2, with no dependence on color or shape.
Peek at the data:
$ wc -l data/colored-shapes.dkvp
10078 data/colored-shapes.dkvp
$ 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
Look at uncategorized stats (using creach for spacing).
Here it looks reasonable that u is unit-uniform; something’s up with v but we can’t yet see what:
$ mlr --oxtab stats1 -a min,mean,max -f flag,u,v data/colored-shapes.dkvp | creach 3
flag_min 0
flag_mean 0.39888866838658465
flag_max 1
u_min 4.3912454007477564e-05
u_mean 0.4983263438118866
u_max 0.9999687954968421
v_min -0.09270905318501277
v_mean 0.49778696527477023
v_max 1.0724998185026013
The histogram shows the different distribution of 0/1 flags:
$ 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
-0.010000000000000002 0.09000000000000002 6058 0 36
0.09000000000000002 0.19000000000000003 0 1062 988
0.19000000000000003 0.29000000000000004 0 985 1003
0.29000000000000004 0.39000000000000007 0 1024 1014
0.39000000000000007 0.4900000000000001 0 1002 991
0.4900000000000001 0.5900000000000002 0 989 1041
0.5900000000000002 0.6900000000000002 0 1001 1016
0.6900000000000002 0.7900000000000001 0 972 962
0.7900000000000001 0.8900000000000002 0 1035 1070
0.8900000000000002 0.9900000000000002 0 995 993
0.9900000000000002 1.0900000000000003 4020 1013 939
1.0900000000000003 1.1900000000000002 0 0 25
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:
$ 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 0.5843537414965987 1 4.3912454007477564e-05 0.517717155039078 0.9999687954968421 0.0014886830387470518 0.49105642841387653 0.9995761761685742
green 0 0.20919747520288548 1 0.00048750676198217047 0.5048610622924616 0.9999361779701204 0.0005012669003675585 0.49908475928072205 0.9996764373885353
orange 0 0.5214521452145214 1 0.00123537823160913 0.49053241689014415 0.9988853487546249 0.0024486660337188493 0.4877637745987629 0.998475130432018
purple 0 0.09019264448336252 1 0.0002655214518428872 0.4940049543793683 0.9996465731736793 0.0003641137096487279 0.497050699948439 0.9999751864255598
red 0 0.3031674208144796 1 0.0006711367180041172 0.49255964831571375 0.9998822102016469 -0.09270905318501277 0.4965350959465078 1.0724998185026013
yellow 0 0.8924274593064402 1 0.001300228762057487 0.49712912165196765 0.99992313390574 0.0007109695568577878 0.510626599360317 0.9999189897724752
$ 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 0.3998456194519491 1 4.3912454007477564e-05 0.49855450951394115 0.99992313390574 -0.09270905318501277 0.49552415740048406 1.0724998185026013
square 0 0.39611178614823817 1 0.0001881939925673093 0.499385458061097 0.9999687954968421 8.930277299445954e-05 0.49653825501903986 0.9999751864255598
triangle 0 0.4015421115065243 1 0.000881025170573424 0.4968585405884252 0.9996614910922645 0.000716883409890845 0.501049532862137 0.9999946837499262
Look at bivariate stats by color and shape. In particular, u,v pairwise correlation for red circles pops out:
$ mlr --opprint --right stats2 -a corr -f u,v,w,x data/colored-shapes.dkvp
u_v_corr w_x_corr
0.13341803768384553 -0.011319938208638764
$ 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 0.9807984157534667 -0.018565046320623148
orange square 0.17685846147882145 -0.07104374629148885
green circle 0.05764430126828069 0.011795210176784067
red square 0.055744791559722166 -0.0006802175149145207
yellow triangle 0.04457267106380469 0.02460476240108526
yellow square 0.04379171794446621 -0.04462267239937856
purple circle 0.03587354791796681 0.13411247530136805
blue square 0.03241156493114544 -0.05350791240143263
blue triangle 0.015356295190464324 -0.0006084778850362686
orange circle 0.01051866723398945 -0.1627949723421722
red triangle 0.00809781003735548 0.012485753551391776
purple triangle 0.005155038421780437 -0.04505792148014131
purple square -0.02568020549187632 0.05769444883779078
green square -0.025775985300150128 -0.003265248022084335
orange triangle -0.030456930370361554 -0.131870019629393
yellow circle -0.06477338560056926 0.07369474300245252
blue circle -0.1023476302678634 -0.030529007506883508
green triangle -0.10901830007460846 -0.0484881707807228