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

Takes a table (typically from a CSV data set) as input and reports a summary of the table grouped by the field in the specified column number. The remaining three inputs are used only if the field values are numbers, in which case they can be grouped into buckets (e.g., decades, centuries, etc.). Those three inputs specify the smallest and largest values of interest and, most importantly, the width of a bucket (10 for decades, 100 for centuries). If the field isn't numeric, leave these three inputs empty or set them to zero. In that case, each string value of the field is its own bucket, and they appear sorted alphabetically. The block reports a new table with three columns. The first column contains the bucket name or smallest number. The second column contains a nonnegative integer that says how many records in the input table fall into this bucket. The third column is a subtable containing the actual records from the original table that fall into the bucket. If your buckets aren't of constant width, or you want to group by some function of more than one field, load the "Frequency Distribution Analysis" library instead.

ca:gràfica de la taula _ agrupada pel camp: _ des de: _ fins: _ interval: _ 11ceilingceiling10add missing entries to a sorted list. Used for histograms

pt:os itens de _ com a chave _ entre _ e _ com passo _ completados por _ ca:gràfica omplint _ amb clau: _ des de: _ fins: _ interval: _ amb _ 11

pt:desenha gráfico de barras de _ em (x: _ , y: _ ) com largura _ e altura _ ca:dibuixa gràfic _ a x: _ y: _ amplada: _ alçada: _ -200-100400200If smallest value < 0, the x axis isn't at the bottom. Note that sort is by > not by <. Not clear this can happen in a histogram!max y1ratio1label delta y5010steplength2190draw x axis at y=00draw y axis902012i15Ready to draw bars.2data90flat line ends

ca:ordena taula _ pel camp _ en ordre _ ascending descending

ca:agrupa taula _ pel camp _ per intervals de _

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Year,Number.All,Number.Opioid.Any,Number.Opioid.Prescription,Number.Opioid.Synthetic,Number.Opioid.Heroin,Number.Opioid.Cocaine,Rate.All.Total,Rate.All.Sex.Female,Rate.All.Sex.Male,Rate.All.Race.White,Rate.All.Race.Black,Rate.All.Race.Asian or Pacific Islander,Rate.All.Race.Hispanic,Rate.All.Race.American Indian or Alaska Native,Rate.Opioid.Any.Total,Rate.Opioid.Any.Sex.Female,Rate.Opioid.Any.Sex.Male,Rate.Opioid.Any.Race.White,Rate.Opioid.Any.Race.Black,Rate.Opioid.Any.Race.Asian or Pacific Islander,Rate.Opioid.Any.Race.Hispanic,Rate.Opioid.Any.Race.American Indian or Alaska Native,Rate.Opioid.Prescription.Total,Rate.Opioid.Prescription.Sex.Female,Rate.Opioid.Prescription.Sex.Male,Rate.Opioid.Prescription.Race.White,Rate.Opioid.Prescription.Race.Black,Rate.Opioid.Prescription.Race.Hispanic,Rate.Opioid.Prescription.Race.American Indian or Alaska Native,Rate.Opioid.Synthetic.Total,Rate.Opioid.Synthetic.Sex.Female,Rate.Opioid.Synthetic.Sex.Male,Rate.Opioid.Synthetic.Race.White,Rate.Opioid.Synthetic.Race.Black,Rate.Opioid.Synthetic.Race.Hispanic,Rate.Opioid.Heroin.Total,Rate.Opioid.Heroin.Sex.Female,Rate.Opioid.Heroin.Sex.Male,Rate.Opioid.Heroin.Race.White,Rate.Opioid.Heroin.Race.Black,Rate.Opioid.Heroin.Race.Hispanic,Rate.Opioid.Cocaine.Total,Rate.Opioid.Cocaine.Sex.Female,Rate.Opioid.Cocaine.Sex.Male,Rate.Opioid.Cocaine.Race.White,Rate.Opioid.Cocaine.Race.Black,Rate.Opioid.Cocaine.Race.Hispanic,Rate.Opioid.Cocaine.Race.American Indian or Alaska Native1999,16849,8050,3442,730,1960,3822,6.1,3.9,8.2,6.2,7.5,1.2,5.4,6.0,2.9,1.4,4.3,2.8,3.5,0.3,3.5,2.9,1.2,0.7,1.7,1.3,0.8,1.6,1.3,0.3,0.2,0.3,0.3,0.1,0.1,0.7,0.2,1.2,0.7,0.8,1.1,1.4,0.6,2.1,1.0,3.7,1.7,0.92000,17415,8407,3785,782,1842,3544,6.2,4.1,8.3,6.6,7.3,1.0,4.6,5.5,3.0,1.6,4.4,3.1,3.5,0.3,2.7,2.7,1.3,0.9,1.8,1.6,0.8,1.1,1.3,0.3,0.3,0.3,0.3,0.1,0.1,0.7,0.2,1.1,0.6,0.9,0.9,1.3,0.6,1.9,1.0,3.3,1.3,1.02001,19394,9496,4770,957,1779,3833,6.8,4.6,9.0,7.4,7.6,1.2,4.5,6.9,3.3,1.9,4.8,3.7,3.3,0.3,2.6,3.5,1.7,1.1,2.2,2.0,0.9,1.1,2.0,0.3,0.3,0.4,0.4,0.2,0.1,0.6,0.2,1.0,0.6,0.8,0.9,1.3,0.7,2.0,1.0,3.6,1.3,1.02002,23518,11920,6483,1295,2089,4599,8.2,5.8,10.6,9.2,8.2,1.4,5.4,8.5,4.1,2.6,5.7,4.7,3.6,0.5,3.2,4.1,2.3,1.6,2.9,2.8,1.2,1.4,2.6,0.4,0.4,0.5,0.6,0.2,0.2,0.7,0.2,1.2,0.7,0.9,1.0,1.6,0.8,2.4,1.3,4.0,1.5,1.12003,25785,12940,7461,1400,2080,5199,8.9,6.4,11.5,10.2,8.2,1.4,5.6,10.8,4.5,2.8,6.1,5.2,3.5,0.3,3.2,5.3,2.6,1.8,3.3,3.2,1.1,1.5,3.3,0.5,0.4,0.5,0.6,0.1,0.1,0.7,0.2,1.2,0.7,0.8,1.0,1.8,0.9,2.7,1.6,4.1,1.6,1.72004,27424,13756,8577,1664,1878,5443,9.4,6.9,11.8,11.0,8.3,1.5,5.2,12.5,4.7,3.1,6.3,5.7,3.2,0.4,2.9,6.2,2.9,2.1,3.7,3.7,1.4,1.4,4.2,0.6,0.5,0.6,0.7,0.2,0.2,0.6,0.2,1.1,0.7,0.6,0.7,1.9,1.0,2.8,1.7,4.3,1.4,1.62005,29813,14918,9612,1742,2009,6208,10.1,7.3,12.8,11.8,9.3,1.7,5.8,13.1,5.1,3.4,6.6,6.2,3.4,0.6,3.0,6.9,3.2,2.4,4.1,4.1,1.6,1.5,4.7,0.6,0.5,0.6,0.8,0.2,0.2,0.7,0.3,1.1,0.7,0.8,0.8,2.1,1.1,3.1,1.9,4.9,1.7,2.32006,34425,17545,11589,2707,2088,7448,11.5,8.2,14.8,13.6,10.8,1.8,6.3,14.1,5.9,3.9,7.8,7.3,4.2,0.5,3.3,7.1,3.9,2.8,4.9,5.0,1.8,1.8,4.7,0.9,0.7,1.1,1.1,1.0,0.3,0.7,0.2,1.2,0.8,0.7,0.8,2.5,1.3,3.8,2.3,5.6,1.9,1.62007,36010,18516,12796,2213,2399,6512,11.9,8.8,14.9,14.5,9.7,1.8,5.9,14.2,6.1,4.3,8.0,7.8,3.6,0.5,3.2,7.8,4.2,3.2,5.3,5.6,1.8,1.9,5.2,0.7,0.7,0.8,1.0,0.2,0.2,0.8,0.3,1.3,0.9,0.8,0.8,2.2,1.1,3.2,2.0,4.7,1.6,2.12008,36450,19582,13149,2306,3041,5129,11.9,8.9,14.9,14.8,8.4,1.7,5.8,15.8,6.4,4.4,8.4,8.2,3.4,0.6,3.4,9.3,4.3,3.2,5.4,5.7,1.8,1.9,6.1,0.8,0.7,0.8,1.0,0.2,0.2,1.0,0.4,1.6,1.2,0.8,1.0,1.7,0.9,2.5,1.6,3.4,1.3,1.52009,37004,20422,13523,2946,3278,4350,11.9,9.1,14.8,15.0,8.3,1.9,5.8,17.7,6.6,4.6,8.7,8.6,3.6,0.6,3.2,11.3,4.4,3.3,5.5,5.8,1.9,1.7,7.8,1.0,0.9,1.0,1.2,0.3,0.3,1.1,0.4,1.8,1.3,0.9,1.0,1.4,0.7,2.1,1.3,3.2,1.1,1.82010,38329,21089,14583,3007,3036,4183,12.3,9.6,15.0,15.7,8.0,1.9,5.6,16.8,6.8,4.9,8.7,9.1,3.4,0.7,2.9,9.4,4.7,3.6,5.8,6.4,2.0,1.7,7.2,1.0,0.9,1.0,1.3,0.4,0.2,1.0,0.4,1.6,1.2,0.8,0.8,1.3,0.7,2.0,1.2,3.1,0.9,1.92011,41340,22784,15140,2666,4397,4681,13.2,10.2,16.1,16.9,8.5,2.3,6.1,18.2,7.3,5.2,9.4,9.7,3.8,0.8,3.3,10.3,4.9,3.8,5.9,6.6,2.0,1.8,7.1,0.8,0.8,0.9,1.2,0.3,0.3,1.4,0.6,2.3,1.8,1.0,1.0,1.5,0.8,2.2,1.4,3.1,1.1,1.62012,41502,23166,14240,2628,5925,4404,13.1,10.2,16.1,16.8,8.7,2.2,6.3,18.9,7.4,5.3,9.5,9.8,4.0,0.9,3.5,11.0,4.5,3.7,5.3,6.0,2.0,1.8,7.5,0.8,0.7,0.9,1.1,0.3,0.3,1.9,0.8,3.1,2.5,1.3,1.3,1.4,0.8,2.0,1.3,3.0,1.0,1.52013,43982,25052,14145,3105,8257,4944,13.8,10.6,17.0,17.6,9.7,2.3,6.7,18.7,7.9,5.6,10.2,10.5,4.7,0.8,3.8,10.6,4.4,3.7,5.1,5.9,2.2,1.9,7.1,1.0,0.9,1.1,1.3,0.5,0.3,2.7,1.2,4.2,3.4,2.0,1.6,1.6,0.9,2.3,1.5,3.4,1.1,1.32014,47055,28647,14838,5544,10574,5415,14.7,11.1,18.3,19.0,10.5,2.4,6.7,20.9,9.0,6.3,11.7,12.0,5.6,0.9,4.0,12.0,4.6,3.9,5.2,6.2,2.5,1.8,7.2,1.8,1.3,2.2,2.4,1.1,0.6,3.4,1.6,5.2,4.4,2.5,1.9,1.7,1.0,2.4,1.7,3.4,1.0,1.42015,52404,33091,15281,9580,12989,6784,16.3,11.8,20.8,21.1,12.2,2.7,7.7,21.2,10.4,7.1,13.7,13.9,6.6,1.1,4.6,12.1,4.7,4.0,5.4,6.4,2.6,1.8,7.0,3.1,1.9,4.2,4.2,2.1,0.9,4.1,2.0,6.3,5.4,3.1,2.3,2.1,1.2,3.1,2.2,4.0,1.3,1.62016,63632,42249,17087,19413,15469,10375,19.8,13.4,26.2,25.3,17.1,3.1,9.5,24.2,13.3,8.5,18.1,17.5,10.3,1.5,6.1,13.9,5.2,4.3,6.2,7.0,3.3,2.1,6.5,6.2,3.5,8.9,8.2,5.6,2.7,4.9,2.4,7.5,6.3,4.5,2.8,3.2,1.8,4.7,3.4,6.1,2.0,2.12017,70237,47600,17029,28466,15482,13942,21.7,14.4,29.1,27.5,20.6,3.5,10.6,25.7,14.9,9.4,20.4,19.4,12.9,1.6,6.8,15.7,5.2,4.2,6.1,6.9,3.5,2.2,7.2,9.0,5.0,13.0,11.9,9.0,3.7,4.9,2.5,7.3,6.1,4.9,2.9,4.3,2.5,6.2,4.6,8.3,2.5,2.52018,67367,46802,14975,31335,14996,14666,20.7,13.6,27.9,25.7,21.1,3.7,11.0,27.0,14.6,9.0,20.1,18.6,14.0,1.5,7.5,14.2,4.5,3.7,5.3,6.0,3.3,2.0,4.7,9.9,5.5,14.2,12.6,11.0,4.7,4.7,2.3,7.1,5.8,4.9,3.1,4.5,2.6,6.4,4.6,9.0,3.0,3.32019,70630,49860,14139,36359,14019,15883,21.6,13.7,29.6,25.9,24.5,3.9,12.7,30.0,15.5,9.3,21.7,19.0,17.1,1.7,8.8,17.7,4.2,3.4,5.1,5.5,3.5,2.0,5.4,11.4,6.3,16.6,13.8,14.2,6.3,4.4,2.2,6.6,5.2,5.0,3.1,4.9,2.7,7.1,4.6,10.7,3.4,3.7