{"appearance":{"background":"white","padding":14,"font":{"family":"Courier New","size":10.0,"bold":false,"italic":false,"underline":false,"strikeout":false,"color":"rgb(0,72,168)"},"border":{"on":true,"size":0.0,"style":"solid","color":"#666"},"text":{"wrap":false,"hAlign":"left","vAlign":"top"}},"outputType":"WIDGET","widgetState":null,"outputs":{"console":"<pre class='debug-source'>&gt;library(flipFormat)\n</pre>\n<pre class='debug-source'>&gt;library(flipTransformations)\n</pre>\n<pre class='debug-source'>&gt;library(flipStatistics)\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;dat &lt;- AsNumeric(data.frame(Q9_A, Q9_B, Q9_C, Q9_D, Q9_E, Q9_F), binary = binaryCat, remove.first = FALSE)\n</pre>\n<pre class='debug-warning'>Data has been automatically been converted to being numeric. Values are assigned in the order of the categories: 1, 2, 3... To use alternative numeric values you should instead transform the data prior including it in this analysis (e.g., by changing its structure): Q9. Cola drinking frequency: Coca Cola (Q9_A), Q9. Cola drinking frequency: Diet Coke (Q9_B), Q9. Cola drinking frequency: Coke Zero (Q9_C), Q9. Cola drinking frequency: Pepsi (Q9_D), Q9. Cola drinking frequency: Pepsi Light (Q9_E), Q9. Cola drinking frequency: Pepsi Max (Q9_F)</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;if (formCompare == &quot;Cases&quot;) {\n dat &lt;- dat[QFilter, , drop = FALSE]\n if (nrow(dat) &gt; 100)\n stop(&quot;There are more than 100 cases to compare. Apply a filter to reduce the number of cases.&quot;)\n if (!is.null(QCalibratedWeight))\n warning(&quot;Weights applied to this item have been ignored as they are not applicable when cases are compared.&quot;)\n if (length(formCaseLabels) &gt; 1)\n rownames(dat) &lt;- formCaseLabels[QFilter]\n} else {\n if (ncol(dat) &lt; 2)\n stop(&quot;There needs to be two or more variables to compare, with the currently selected options.&quot;)\n \n # Changing names to labels.\n if (!formNames)\n for (i in 1:ncol(dat))\n {\n label &lt;- attr(dat[, i], &quot;label&quot;)\n if (!is.null(label))\n names(dat)[i] &lt;- label\n }\n dat &lt;- dat[QFilter, , drop = FALSE]\n \n # Weight\n wgt &lt;- if (is.null(QCalibratedWeight)) {\n rep(1, nrow(dat))\n } else {\n QCalibratedWeight\n }\n wgt &lt;- wgt[QFilter]\n}\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;# Filtering out missing values.\n</pre>\n<pre class='debug-source'>&gt;ind &lt;- complete.cases(dat)\n</pre>\n<pre class='debug-source'>&gt;dat.matrix &lt;- data.matrix(dat[ind, , drop = FALSE])\n</pre>\n<pre class='debug-source'>&gt;if (formCompare != &quot;Cases&quot;)\n wgt &lt;- wgt[ind]\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;# Standardization\n</pre>\n<pre class='debug-source'>&gt;want.transposed &lt;- xor(formCompare == &quot;Cases&quot;, formMeasure == &quot;Dissimilarities&quot;)\n</pre>\n<pre class='debug-source'>&gt;dat.matrix &lt;- if (want.transposed) {\n if (formStandardize == &quot;None&quot;) {\n t(dat.matrix)\n } else if (formStandardizeBy == &quot;Case&quot;) {\n StandardizeData(t(dat.matrix), formStandardize, no.variation = &quot;ignore&quot;, mean.zero = &quot;ignore&quot;)\n } else {\n t(StandardizeData(dat.matrix, formStandardize))\n }\n} else {\n if (formStandardize == &quot;None&quot;) {\n dat.matrix\n } else if (formStandardizeBy == &quot;Case&quot;) {\n t(StandardizeData(t(dat.matrix), formStandardize, no.variation = &quot;ignore&quot;, mean.zero = &quot;ignore&quot;))\n } else {\n StandardizeData(dat.matrix, formStandardize)\n }\n}\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;# Compute measures\n</pre>\n<pre class='debug-source'>&gt;distance.matrix &lt;- if (formMeasure == &quot;Dissimilarities&quot;) {\n # Weighting the data (note that this method should not be applied to other problems, as it is unique to this one).\n if (formCompare != &quot;Cases&quot;)\n dat.matrix &lt;- sweep(dat.matrix, 2, wgt, &quot;*&quot;)\n p &lt;- if (formDistance == &quot;Minkowski&quot;) formMinkowski else 2\n method &lt;- if (formDistance == &quot;Squared Euclidean&quot;) &quot;euclidean&quot; else tolower(formDistance)\n mat &lt;- as.matrix(dist(dat.matrix, method = method, p = p, diag = TRUE, upper = TRUE))\n if (formDistance == &quot;Squared Euclidean&quot;)\n mat &lt;- mat * mat\n mat\n} else if (formSimilarity == &quot;Correlation&quot;) {\n if (formCompare == &quot;Cases&quot;)\n cor(dat.matrix)\n else\n weights::wtd.cors(dat.matrix, weight = wgt)\n} else if (formSimilarity == &quot;Cosine&quot;) {\n if (formCompare == &quot;Cases&quot;)\n CosineSimilarities(dat.matrix)\n else\n CosineSimilarities(dat.matrix, weight = wgt)\n} else\n stop(paste(&quot;Unhandled similarity measure:&quot;, formSimilarity))\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;# Measure transformations\n</pre>\n<pre class='debug-source'>&gt;if (formMeasureTransform == &quot;Absolute values&quot;) {\n distance.matrix &lt;- abs(distance.matrix)\n} else if (formMeasureTransform == &quot;Reverse sign&quot;) {\n distance.matrix &lt;- -distance.matrix\n} else if (formMeasureTransform == &quot;Range [0,1]&quot;) {\n tri &lt;- distance.matrix[lower.tri(distance.matrix)]\n min.val &lt;- min(tri, na.rm = TRUE)\n max.val &lt;- max(tri, na.rm = TRUE)\n if (max.val &gt; min.val) {\n diagonal &lt;- diag(distance.matrix)\n distance.matrix &lt;- (distance.matrix - min.val) / (max.val - min.val)\n diag(distance.matrix) &lt;- diagonal\n } else\n stop(&quot;The measures are constant and cannot be transformed to the range [0,1].&quot;)\n}\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;n &lt;- ncol(distance.matrix)\n</pre>\n<pre class='debug-source'>&gt;cellnote &lt;- matrix(&quot;&quot;, n, n)\n</pre>\n<pre class='debug-source'>&gt;for (i in 1:n)\n for (j in 1:n)\n cellnote[i, j] &lt;- FormatWithDecimals(distance.matrix[i, j], 2)\n</pre>\n<pre class='debug-source'>&gt;show.cellnote.in.cell &lt;- n &lt;= 15\n</pre>\n<pre class='debug-source'>&gt;hide.axes.labels &lt;- n &gt; 20\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;if (formMeasure == &quot;Similarities&quot;) {\n # Values may be slightly larger than 1 due to numerical errors\n distance.matrix[distance.matrix &gt; 1] &lt;- 1\n distance.matrix[distance.matrix &lt; -1] &lt;- -1\n colours &lt;- &quot;RdBu&quot;\n colour.range &lt;- c(-1, 1)\n} else {\n colours &lt;- &quot;Blues&quot;\n colour.range &lt;- NULL\n}\n</pre>\n<pre class='debug-source'>&gt;\n</pre>\n<pre class='debug-source'>&gt;print.Distance &lt;- function(x, ...)\n{\n rhtmlHeatmap::Heatmap(x, Rowv = NULL, Colv = NULL,\n cellnote = cellnote, colors = colours,\n show_cellnote_in_cell = show.cellnote.in.cell,\n xaxis_location = &quot;bottom&quot;, yaxis_location = &quot;left&quot;,\n lower_triangle = TRUE, cexRow = 0.79,\n xaxis_hidden = hide.axes.labels,\n yaxis_hidden = hide.axes.labels,\n color_range = colour.range)\n}\n</pre>\n<pre class='debug-source'>&gt;class(distance.matrix) &lt;- &quot;Distance&quot;\n</pre>\n<pre class='debug-source'>&gt;distance.matrix\n</pre>\r\n<div class=\"debug-summarystatistics\">\r\n<table>\r\n<tr><th>Total time:</th><td>2.18s</td></tr>\r\n<tr><th>Time on R server:</th><td title=\"rApacheServe 1.79s (pre 0.00s, post 0.00s) httpget_code() setup for eval 0.01s session$eval 1.77s (pre 0.00s, post 0.19s) unexplained 0.02s apparmor forking (pre 0.03s, post 0.00s)\">1.79s</td></tr>\r\n<tr><th>Time evaluating code:</th><td>1.54s</td></tr>\r\n<tr><th>Bytes sent:</th><td>11,769</td></tr>\r\n<tr><th>Bytes received:</th><td>19,325</td></tr>\r\n</table>\r\n</div>","warning":"Data has been automatically been converted to being numeric. 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<- AsNumeric(data.frame(Q9_A, Q9_B, Q9_C, Q9_D, Q9_E, Q9_F), binary = binaryCat, remove.first = FALSE)\n\nif (formCompare == \"Cases\") {\n dat <- dat[QFilter, , drop = FALSE]\n if (nrow(dat) > 100)\n stop(\"There are more than 100 cases to compare. Apply a filter to reduce the number of cases.\")\n if (!is.null(QCalibratedWeight))\n warning(\"Weights applied to this item have been ignored as they are not applicable when cases are compared.\")\n if (length(formCaseLabels) > 1)\n rownames(dat) <- formCaseLabels[QFilter]\n} else {\n if (ncol(dat) < 2)\n stop(\"There needs to be two or more variables to compare, with the currently selected options.\")\n \n # Changing names to labels.\n if (!formNames)\n for (i in 1:ncol(dat))\n {\n label <- attr(dat[, i], \"label\")\n if (!is.null(label))\n names(dat)[i] <- label\n }\n dat <- dat[QFilter, , drop = FALSE]\n \n # Weight\n wgt <- if (is.null(QCalibratedWeight)) {\n rep(1, nrow(dat))\n } else {\n QCalibratedWeight\n }\n wgt <- wgt[QFilter]\n}\n\n# Filtering out missing values.\nind <- complete.cases(dat)\ndat.matrix <- data.matrix(dat[ind, , drop = FALSE])\nif (formCompare != \"Cases\")\n wgt <- wgt[ind]\n\n# Standardization\nwant.transposed <- xor(formCompare == \"Cases\", formMeasure == \"Dissimilarities\")\ndat.matrix <- if (want.transposed) {\n if (formStandardize == \"None\") {\n t(dat.matrix)\n } else if (formStandardizeBy == \"Case\") {\n StandardizeData(t(dat.matrix), formStandardize, no.variation = \"ignore\", mean.zero = \"ignore\")\n } else {\n t(StandardizeData(dat.matrix, formStandardize))\n }\n} else {\n if (formStandardize == \"None\") {\n dat.matrix\n } else if (formStandardizeBy == \"Case\") {\n t(StandardizeData(t(dat.matrix), formStandardize, no.variation = \"ignore\", mean.zero = \"ignore\"))\n } else {\n StandardizeData(dat.matrix, formStandardize)\n }\n}\n\n# Compute measures\ndistance.matrix <- if (formMeasure == \"Dissimilarities\") {\n # Weighting the data (note that this method should not be applied to other problems, as it is unique to this one).\n if (formCompare != \"Cases\")\n dat.matrix <- sweep(dat.matrix, 2, wgt, \"*\")\n p <- if (formDistance == \"Minkowski\") formMinkowski else 2\n method <- if (formDistance == \"Squared Euclidean\") \"euclidean\" else tolower(formDistance)\n mat <- as.matrix(dist(dat.matrix, method = method, p = p, diag = TRUE, upper = TRUE))\n if (formDistance == \"Squared Euclidean\")\n mat <- mat * mat\n mat\n} else if (formSimilarity == \"Correlation\") {\n if (formCompare == \"Cases\")\n cor(dat.matrix)\n else\n weights::wtd.cors(dat.matrix, weight = wgt)\n} else if (formSimilarity == \"Cosine\") {\n if (formCompare == \"Cases\")\n CosineSimilarities(dat.matrix)\n else\n CosineSimilarities(dat.matrix, weight = wgt)\n} else\n stop(paste(\"Unhandled similarity measure:\", formSimilarity))\n\n# Measure transformations\nif (formMeasureTransform == \"Absolute values\") {\n distance.matrix <- abs(distance.matrix)\n} else if (formMeasureTransform == \"Reverse sign\") {\n distance.matrix <- -distance.matrix\n} else if (formMeasureTransform == \"Range [0,1]\") {\n tri <- distance.matrix[lower.tri(distance.matrix)]\n min.val <- min(tri, na.rm = TRUE)\n max.val <- max(tri, na.rm = TRUE)\n if (max.val > min.val) {\n diagonal <- diag(distance.matrix)\n distance.matrix <- (distance.matrix - min.val) / (max.val - min.val)\n diag(distance.matrix) <- diagonal\n } else\n stop(\"The measures are constant and cannot be transformed to the range [0,1].\")\n}\n\nn <- ncol(distance.matrix)\ncellnote <- matrix(\"\", n, n)\nfor (i in 1:n)\n for (j in 1:n)\n cellnote[i, j] <- FormatWithDecimals(distance.matrix[i, j], 2)\nshow.cellnote.in.cell <- n <= 15\nhide.axes.labels <- n > 20\n\nif (formMeasure == \"Similarities\") {\n # Values may be slightly larger than 1 due to numerical errors\n distance.matrix[distance.matrix > 1] <- 1\n distance.matrix[distance.matrix < -1] <- -1\n colours <- \"RdBu\"\n colour.range <- c(-1, 1)\n} else {\n colours <- \"Blues\"\n colour.range <- NULL\n}\n\nprint.Distance <- function(x, ...)\n{\n rhtmlHeatmap::Heatmap(x, Rowv = NULL, Colv = NULL,\n cellnote = cellnote, colors = colours,\n show_cellnote_in_cell = show.cellnote.in.cell,\n xaxis_location = \"bottom\", yaxis_location = \"left\",\n lower_triangle = TRUE, cexRow = 0.79,\n xaxis_hidden = hide.axes.labels,\n yaxis_hidden = hide.axes.labels,\n color_range = colour.range)\n}\nclass(distance.matrix) <- \"Distance\"\ndistance.matrix","lastSavedCode":"library(flipFormat)\nlibrary(flipTransformations)\nlibrary(flipStatistics)\n\ndat <- AsNumeric(data.frame(Q9_A, Q9_B, Q9_C, Q9_D, Q9_E, Q9_F), binary = binaryCat, remove.first = FALSE)\n\nif (formCompare == \"Cases\") {\n dat <- dat[QFilter, , drop = FALSE]\n if (nrow(dat) > 100)\n stop(\"There are more than 100 cases to compare. Apply a filter to reduce the number of cases.\")\n if (!is.null(QCalibratedWeight))\n warning(\"Weights applied to this item have been ignored as they are not applicable when cases are compared.\")\n if (length(formCaseLabels) > 1)\n rownames(dat) <- formCaseLabels[QFilter]\n} else {\n if (ncol(dat) < 2)\n stop(\"There needs to be two or more variables to compare, with the currently selected options.\")\n \n # Changing names to labels.\n if (!formNames)\n for (i in 1:ncol(dat))\n {\n label <- attr(dat[, i], \"label\")\n if (!is.null(label))\n names(dat)[i] <- label\n }\n dat <- dat[QFilter, , drop = FALSE]\n \n # Weight\n wgt <- if (is.null(QCalibratedWeight)) {\n rep(1, nrow(dat))\n } else {\n QCalibratedWeight\n }\n wgt <- wgt[QFilter]\n}\n\n# Filtering out missing values.\nind <- complete.cases(dat)\ndat.matrix <- data.matrix(dat[ind, , drop = FALSE])\nif (formCompare != \"Cases\")\n wgt <- wgt[ind]\n\n# Standardization\nwant.transposed <- xor(formCompare == \"Cases\", formMeasure == \"Dissimilarities\")\ndat.matrix <- if (want.transposed) {\n if (formStandardize == \"None\") {\n t(dat.matrix)\n } else if (formStandardizeBy == \"Case\") {\n StandardizeData(t(dat.matrix), formStandardize, no.variation = \"ignore\", mean.zero = \"ignore\")\n } else {\n t(StandardizeData(dat.matrix, formStandardize))\n }\n} else {\n if (formStandardize == \"None\") {\n dat.matrix\n } else if (formStandardizeBy == \"Case\") {\n t(StandardizeData(t(dat.matrix), formStandardize, no.variation = \"ignore\", mean.zero = \"ignore\"))\n } else {\n StandardizeData(dat.matrix, formStandardize)\n }\n}\n\n# Compute measures\ndistance.matrix <- if (formMeasure == \"Dissimilarities\") {\n # Weighting the data (note that this method should not be applied to other problems, as it is unique to this one).\n if (formCompare != \"Cases\")\n dat.matrix <- sweep(dat.matrix, 2, wgt, \"*\")\n p <- if (formDistance == \"Minkowski\") formMinkowski else 2\n method <- if (formDistance == \"Squared Euclidean\") \"euclidean\" else tolower(formDistance)\n mat <- as.matrix(dist(dat.matrix, method = method, p = p, diag = TRUE, upper = TRUE))\n if (formDistance == \"Squared Euclidean\")\n mat <- mat * mat\n mat\n} else if (formSimilarity == \"Correlation\") {\n if (formCompare == \"Cases\")\n cor(dat.matrix)\n else\n weights::wtd.cors(dat.matrix, weight = wgt)\n} else if (formSimilarity == \"Cosine\") {\n if (formCompare == \"Cases\")\n CosineSimilarities(dat.matrix)\n else\n CosineSimilarities(dat.matrix, weight = wgt)\n} else\n stop(paste(\"Unhandled similarity measure:\", formSimilarity))\n\n# Measure transformations\nif (formMeasureTransform == \"Absolute values\") {\n distance.matrix <- abs(distance.matrix)\n} else if (formMeasureTransform == \"Reverse sign\") {\n distance.matrix <- -distance.matrix\n} else if (formMeasureTransform == \"Range [0,1]\") {\n tri <- distance.matrix[lower.tri(distance.matrix)]\n min.val <- min(tri, na.rm = TRUE)\n max.val <- max(tri, na.rm = TRUE)\n if (max.val > min.val) {\n diagonal <- diag(distance.matrix)\n distance.matrix <- (distance.matrix - min.val) / (max.val - min.val)\n diag(distance.matrix) <- diagonal\n } else\n stop(\"The measures are constant and cannot be transformed to the range [0,1].\")\n}\n\nn <- ncol(distance.matrix)\ncellnote <- matrix(\"\", n, n)\nfor (i in 1:n)\n for (j in 1:n)\n cellnote[i, j] <- FormatWithDecimals(distance.matrix[i, j], 2)\nshow.cellnote.in.cell <- n <= 15\nhide.axes.labels <- n > 20\n\nif (formMeasure == \"Similarities\") {\n # Values may be slightly larger than 1 due to numerical errors\n distance.matrix[distance.matrix > 1] <- 1\n distance.matrix[distance.matrix < -1] <- -1\n colours <- \"RdBu\"\n colour.range <- c(-1, 1)\n} else {\n colours <- \"Blues\"\n colour.range <- NULL\n}\n\nprint.Distance <- function(x, ...)\n{\n rhtmlHeatmap::Heatmap(x, Rowv = NULL, Colv = NULL,\n cellnote = cellnote, colors = colours,\n show_cellnote_in_cell = show.cellnote.in.cell,\n xaxis_location = \"bottom\", yaxis_location = \"left\",\n lower_triangle = TRUE, cexRow = 0.79,\n xaxis_hidden = hide.axes.labels,\n yaxis_hidden = hide.axes.labels,\n color_range = colour.range)\n}\nclass(distance.matrix) <- \"Distance\"\ndistance.matrix","highlightedCodeSpans":[{"start":102,"length":4,"index":0},{"start":108,"length":4,"index":0},{"start":114,"length":4,"index":0},{"start":120,"length":4,"index":0},{"start":126,"length":4,"index":0},{"start":132,"length":4,"index":0},{"start":148,"length":9,"index":4},{"start":186,"length":11,"index":4},{"start":227,"length":7,"index":6},{"start":398,"length":17,"index":6},{"start":612,"length":7,"index":6},{"start":804,"length":9,"index":4},{"start":998,"length":7,"index":6},{"start":1064,"length":17,"index":6},{"start":1133,"length":17,"index":6},{"start":1172,"length":7,"index":6},{"start":1299,"length":11,"index":4},{"start":1385,"length":11,"index":4},{"start":1409,"length":11,"index":4},{"start":1488,"length":15,"index":4},{"start":1624,"length":15,"index":4},{"start":1739,"length":15,"index":4},{"start":1780,"length":15,"index":4},{"start":1915,"length":15,"index":4},{"start":2029,"length":15,"index":4},{"start":2097,"length":11,"index":4},{"start":2260,"length":11,"index":4},{"start":2350,"length":12,"index":4},{"start":2418,"length":12,"index":4},{"start":2480,"length":12,"index":4},{"start":2592,"length":12,"index":4},{"start":2716,"length":11,"index":4},{"start":2874,"length":11,"index":4},{"start":3102,"length":20,"index":4},{"start":3202,"length":20,"index":4},{"start":3295,"length":20,"index":4},{"start":3985,"length":11,"index":4}],"tableTransformations":"<TabularTransformer>\r\n <TabularTransform type=\"Truncation\" truncationHeaderType=\"Column\" />\r\n <TabularTransform />\r\n</TabularTransformer>","tabularFilteringOptions":null,"hasGuiControls":true,"guiControls":{"Code":"form.setHeading(\"Distances\");\nformCompare = form.comboBox({name: \"formCompare\",\n label: \"Compare\",\n alternatives: [\"Cases\", \"Variables\"],\n default_value: \"Variables\"});\nif (formCompare.getValue() == \"Cases\")\n form.dropBox({name: \"formCaseLabels\",\n label: \"Case labels\",\n types: [\"V:text\"],\n required: false});\nform.dropBox({name: \"formVariables\",\n label: \"Variables\",\n types: [\"V:numeric, categorical, ordered categorical\"],\n multi:true });\nif (formCompare.getValue() == \"Variables\")\n form.checkBox({label: \"Variable names\", name: \"formNames\", default_value: false});\nform.checkBox({ name: \"binaryCat\", label: \"Categorical as binary\", default_value: false });\nformMeasure = form.comboBox({name: \"formMeasure\",\n label: \"Measure\",\n 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