POKI_PUT_TOC_HERE
flins data
The flins.csv file is some sample data
obtained from https://support.spatialkey.com/spatialkey-sample-csv-data.
Note: please use "mlr --csv --rs lf" for for native Un*x (linefeed-terminated) CSV files.
(You can also have MLR_CSV_DEFAULT_RS=lf in your shell environment, e.g.
export MLR_CSV_DEFAULT_RS=lf or setenv MLR_CSV_DEFAULT_RS lf depending on
which shell you use.)
Vertical-tabular format is good for a quick look at CSV data layout — seeing what columns you have to work with:
POKI_RUN_COMMAND{{head -n 2 data/flins.csv | mlr --icsv --oxtab cat}}HERE
A few simple queries:
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint count-distinct -f county | head}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint count-distinct -f construction,line}}HERE
Categorization of total insured value:
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint stats1 -a min,mean,max -f tiv_2012}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint stats1 -a min,mean,max -f tiv_2012 -g construction,line}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --oxtab stats1 -a p0,p10,p50,p90,p95,p99,p100 -f hu_site_deductible}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint stats1 -a p95,p99,p100 -f hu_site_deductible -g county then sort -f county | head}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --oxtab stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012}}HERE
POKI_RUN_COMMAND{{mlr --from data/flins.csv --icsv --opprint stats2 -a corr,linreg-ols,r2 -f tiv_2011,tiv_2012 -g county}}HERE
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 flag field is 0 or 1, with probability dependent on color
- The u field is plain uniform on the unit interval.
- The v field is the same, except tightly correlated with u for red circles.
- The w field is autocorrelated for each color/shape pair.
- The x field is boring Gaussian with mean 5 and standard deviation about 1.2, with no dependence on color or shape.
Peek at the data:
POKI_RUN_COMMAND{{wc -l data/colored-shapes.dkvp}}HERE
POKI_RUN_COMMAND{{head -n 6 data/colored-shapes.dkvp | mlr --opprint cat}}HERE
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:
POKI_RUN_COMMAND{{mlr --oxtab stats1 -a min,mean,max -f flag,u,v data/colored-shapes.dkvp | creach 3}}HERE
The histogram shows the different distribution of 0/1 flags:
POKI_RUN_COMMAND{{mlr --opprint histogram -f flag,u,v --lo -0.1 --hi 1.1 --nbins 12 data/colored-shapes.dkvp}}HERE
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:
POKI_RUN_COMMAND{{mlr --opprint stats1 -a min,mean,max -f flag,u,v -g color then sort -f color data/colored-shapes.dkvp}}HERE
POKI_RUN_COMMAND{{mlr --opprint stats1 -a min,mean,max -f flag,u,v -g shape then sort -f shape data/colored-shapes.dkvp}}HERE
Look at bivariate stats by color and shape. In particular, u,v pairwise correlation for red circles pops out:
POKI_RUN_COMMAND{{mlr --opprint --right stats2 -a corr -f u,v,w,x data/colored-shapes.dkvp}}HERE
POKI_RUN_COMMAND{{mlr --opprint --right stats2 -a corr -f u,v,w,x -g color,shape then sort -nr u_v_corr data/colored-shapes.dkvp}}HERE