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13
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1 # utilities
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2 boundary <- function(x, increment) {
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3 return (floor(x / increment) * increment)
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4 }
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5
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6 roundup <- function(x) {
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7 return (sign(x) * 10^ceiling(log10(abs(x))))
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8 }
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9
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8
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10 # wrapper
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0
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11 wrapper <- function(table, columns, options) {
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12
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13 # initialize output list
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14 l <- list()
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15
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16 # loop through all columns
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7
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17 m <- list()
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0
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18 for (key in names(columns)) {
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19 # load column data
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20 column <- as.numeric(columns[key])
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21 column_data <- sapply( table[column], as.numeric )
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22
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7
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23 # collect vectors in list
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24 m <- append(m, list(column_data))
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25 }
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26
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27 # get min/max boundaries
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12
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28 min_value <- min(unlist(m))
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7
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29 max_value <- max(unlist(m))
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8
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30
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13
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31 # identify increment
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32 increment <- roundup((max_value - min_value) / 10)
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33
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34 # fix min value
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35 min_value <- boundary(min_value, increment)
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36
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37 # fix max value
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38 max_value <- min_value + increment * 10
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39
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9
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40 # check if single bin is enough
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41 if (min_value == max_value) {
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42 l <- append(l, max_value)
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10
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43 for (key in seq(m)) {
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44 l <- append(l, 1.0)
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45 }
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9
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46 return (l)
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47 }
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48
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7
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49 # fix range and bins
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8
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50 bin_seq = seq(min_value, max_value, by=increment)
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13
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51
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7
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52 # add as first column
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53 l <- append(l, list(bin_seq[2: length(bin_seq)]))
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54
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55 # loop through all columns
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56 for (key in seq(m)) {
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57 # load column data
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58 column_data <- m[[key]]
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59
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0
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60 # create hist data
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7
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61 hist_data <- hist(column_data, breaks=bin_seq, plot=FALSE)
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0
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62
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63 # normalize densities
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5
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64 count_sum <- sum(hist_data$counts)
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65 if (count_sum > 0) {
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7
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66 hist_data$counts = hist_data$counts / count_sum
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5
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67 }
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0
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68
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69 # collect vectors in list
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70 l <- append(l, list(hist_data$counts))
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71 }
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72
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73
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74 # return
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75 return (l)
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76 }
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