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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: _ 1

110

add 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 y5010step2190draw x axis at y=00draw y axis902012i15Ready to draw bars.2data90

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

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

pt:a aplicação a _ de _ ca:canalitza _ $arrowRight _

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