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8
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1 # wrapper
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0
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2 wrapper <- function(table, columns, options) {
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3
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4 # initialize output list
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5 l <- list()
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6
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7 # loop through all columns
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7
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8 m <- list()
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0
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9 for (key in names(columns)) {
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10 # load column data
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11 column <- as.numeric(columns[key])
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12 column_data <- sapply( table[column], as.numeric )
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13
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7
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14 # collect vectors in list
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15 m <- append(m, list(column_data))
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16 }
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17
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18 # get min/max boundaries
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12
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19 min_value <- min(unlist(m))
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7
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20 max_value <- max(unlist(m))
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8
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21
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9
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22 # check if single bin is enough
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23 if (min_value == max_value) {
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24 l <- append(l, max_value)
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10
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25 for (key in seq(m)) {
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26 l <- append(l, 1.0)
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27 }
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9
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28 return (l)
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29 }
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30
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8
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31 # identify increment
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12
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32 increment <- (max_value - min_value) / 10
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8
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33
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7
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34 # fix range and bins
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8
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35 bin_seq = seq(min_value, max_value, by=increment)
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9
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36
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7
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37 # add as first column
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38 l <- append(l, list(bin_seq[2: length(bin_seq)]))
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39
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40 # loop through all columns
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41 for (key in seq(m)) {
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42 # load column data
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43 column_data <- m[[key]]
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44
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0
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45 # create hist data
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7
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46 hist_data <- hist(column_data, breaks=bin_seq, plot=FALSE)
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0
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47
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48 # normalize densities
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5
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49 count_sum <- sum(hist_data$counts)
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50 if (count_sum > 0) {
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7
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51 hist_data$counts = hist_data$counts / count_sum
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5
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52 }
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0
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53
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54 # collect vectors in list
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55 l <- append(l, list(hist_data$counts))
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56 }
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57
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58
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59 # return
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60 return (l)
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61 }
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