Calculation - Sum - Table(s)
This page contains the Standard R code for Calculation - Sum, and can be used to compute the sum of all elements in a single input or the elementwise sum for multiple inputs.
Example
Consider the following three input tables, each consisting of a single column of data.
When the feature is run with all three inputs selected, the result is the following output table.
By default the sum is still computed for the row with missing data (Jan-19) and Jul-19 is excluded from the output because it only appears in one input. These settings can be changed in the Object Inspector of the output, refer to the Inputs below.
Options
The output showing the results of the calculation has the following options available in the Object Inspector.
Input The tables to be used in the calculation.
Calculate for inputs with incomplete data A combo box with three options,
- Yes (show warning) Any missing values in any of the inputs will be ignored in the calculation. If a missing value is present in the input then a warning that missing values being removed from the calculation will be presented to the user.
- Yes The calculation will have the same behaviour as above, except the warning about missing values being removed from the calculation will not be given.
- No Missing values are not removed before calculation and will propagate as missing values in the output.
Automatically match elements Only shown when there are multiple inputs to Input. This controls how and whether matching is done between the labels of the inputs. The default, "Yes - hide unmatched", will look for matching labels in the rows and columns of the inputs before proceeding with the calculation, and any rows/columns that are not contained in all the inputs will not be included in the output. See the Example. For a full description of the matching algorithm, see the Technical Details. "Yes - show unmatched" will also perform matching, but any unmatched rows (columns) will appear in the output as rows (columns) of all missing values. Selecting "No" for this option will cause any labels in the data to be ignored and not perform any matching. Selecting "Custom" will bring up two additional controls that allow for specifying the matching behavior for rows and columns separately.
Match rows Only shown if Automatically match elements is set to "Custom". Specifies the matching behavior when comparing row labels of the inputs. "Yes - show unmatched" and "Yes - hide unmatched" look for exact matches in the row labels in the inputs. "Fuzzy - show unmatched" and "Fuzzy - hide unmatched" perform fuzzy matching so that labels that differ only by a single character are considered to be a match.
Match columns Only shown if Automatically match elements is set to "Custom". The options are the same as Match rows, but control the matching between columns.
Rows to exclude Here you can type in row labels that should be excluded from the calculation.
Columns to exclude As above, but for columns.
Technical Details
When there are multiple inputs, inputs that contain only a single row (column) may be recycled to a matrix/table with the same number of rows (columns) as the other inputs. For example, if the supplied inputs are a table with three rows and two columns and another table with two rows and a single column, the single column will be expanded by rows into a table with three rows and two columns with each row identical to the original column.
When Automatically match elements is set to Yes - show unmatched or Yes - hide unmatched, both exact matches and fuzzy matches (as described above) are considered, and the order of elements may be permuted so that the names match. It also may transpose an input if, for example, the column names of one input match the row names of another input.
When Calculate for inputs with incomplete data is checked and the input to the calculation consists entirely of missing values, then the returned output value is set to missing data. Prior to 5th October, 2021 the returned output value was different in this situation. For example, consider the input into the calculation with the three values.
| A | B | C |
|---|---|---|
| NaN | NaN | NaN |
Then applying Sum to this input would return the value of zero before 5th October, 2021. After this date, the Sum function returns the output value of NaN in this situation.
Code
const UNCHECK_NAMES = ["SUM", "NET", "TOTAL"];
const MULTI_QUESTIONTYPES = ["Text - Multi",
"Pick One - Multi", "Pick Any - Compact",
"Pick Any - Grid", "Number - Grid"];
const ALLOWED_R_CLASSES = ["NULL", "numeric", "integer", "logical", "factor", "matrix", "array", "data.frame", "table"];
function getInputNames(input, dim = 0){
var input_names;
var listbox_names = {};
let input_type = input.type;
if (input_type === "R Output") {
try {
var output_class = input.outputClasses;
if (output_class.includes("array") || output_class.includes("matrix")) {
var dimnames = input.data.getAttribute([], "dimnames");
if (dim < dimnames.length && dimnames[dim] != null)
input_names = dimnames[dim];
else
input_names = [];
} else if (output_class.includes("data.frame")) {
if (dim === 1)
input_names = input.data.getAttribute([], "names");
else {
let row_names = input.data.getAttribute([], "row.names");
input_names = typeof(row_names[0]) === "string" ? row_names : [];
}
} else {
input_names = dim === 0 ? input.data.getAttribute([], "names") : [];
}
}catch(e) {
input_names = [];
}
listbox_names["names"] = input_names;
listbox_names["initial"] = filterSingleNames(input_names);
} else {
let primary_type = input.primary.variableSetStructure;
let has_multi_or_grid = primary_type.endsWith("Multi") || primary_type.endsWith("Grid");
let has_columns = !!input.secondary || has_multi_or_grid || input.cellStatistics.length > 1;
listbox_names = {names: ["foo"], initial: has_columns ? ["bar"] : []};//getTableDimNames(input, dim);
}
// DS-3147: replace newline chars/any whitespace with single space
if (listbox_names["names"].length > 0) {
Object.keys(listbox_names).map(key => {
listbox_names[key] = listbox_names[key].map(str => typeof(str) === "string" ? str.replace(/\s+/g, " ") : str);
});
}
return listbox_names;
}
function getTableDimNames(table, dim)
{
let has_primary = table.primary != null;
let table_output_names = {"names": [], "initial": []};
if (has_primary)
{
let table_output = table.calculateOutput();
let is_crosstab_or_multi_or_raw = table.secondary.type === "Question"
|| MULTI_QUESTIONTYPES.includes(table.primary.questionType)
|| table.secondary === "RAW DATA";
if (table.primary.isBanner && table.secondary === "SUMMARY")
is_crosstab_or_multi_or_raw = false;
if (dim === 0)
{
let row_names = table_output.rowLabels;
let row_spans = table_output.rowSpans;
let row_indices = table_output.rowIndices(include_nets_sums = false);
if (row_spans.length > 1)
{
table_output_names = flattenSpanNames(row_names, row_spans);
} else
{
let initial = !!row_indices ? row_names.filter((name, i) => row_indices.includes(i)) : filterSingleNames(row_names);
table_output_names = {"names": row_names, "initial": initial};
}
}
if (dim === 1)
{
let n_columns = table_output.numberColumns;
let col_spans = n_columns < 2 ? [] : table_output.columnSpans;
let col_indices = table_output.columnIndices(include_nets_sums = false);
let col_names = [];
if (col_spans.length > 1)
{
col_names = table_output.columnLabels;
table_output_names = flattenSpanNames(col_names, col_spans);
} else
{
col_names = is_crosstab_or_multi_or_raw ? table_output.columnLabels : table_output.statistics;
let initial = !!col_indices ? col_names.filter((name, i) => col_indices.includes(i)) : filterSingleNames(col_names);
table_output_names = {"names": col_names, "initial": initial};
}
}
}
return table_output_names;
}
function filterSingleNames(names)
{
return names.filter(n => !UNCHECK_NAMES.includes(n));
}
function flattenSpanNames(labels, span_names)
{
let span_length = span_names.length;
let span_labels = labels;
let unselect_labels = span_names.filter(span => UNCHECK_NAMES.includes(span["label"]));
let unselect_span_indices = [];
if(unselect_labels.length > 0)
{
unselect_span_indices = unselect_labels.map(unselect => unselect["indices"]);
unselect_span_indices = [].concat.apply([], unselect_span_indices);
unselect_span_indices = uniq(unselect_span_indices);
}
let unselected_base_indices = labels.map((l, i) => UNCHECK_NAMES.includes(l) ? i : "").filter(Number);
let unselected_indices = [].concat.apply([], [unselect_span_indices, unselected_base_indices]);
unselected_indices = uniq(unselected_indices)
labels.forEach((item, i) => {
for (j = 0; j < span_length; j++)
{
let curr_span = span_names[j];
if (curr_span["indices"].includes(i))
{
span_labels[i] = span_names[j]["label"] + " - " + span_labels[i];
}
}
});
let initial_values = span_labels.filter((label, i) => !unselected_indices.includes(i));
return {"names": span_labels, "initial": initial_values};
}
function recursiveGetItemByGuid(group_item, guid) {
var cur_sub_items = group_item.subItems;
for (var j = 0; j < cur_sub_items.length; j++)
{
if (cur_sub_items[j].type == "ReportGroup") {
var res = recursiveGetItemByGuid(cur_sub_items[j], guid);
if (res != null)
return(res)
}
else if (cur_sub_items[j].guid == guid)
return(cur_sub_items[j]);
}
return null;
}
function removeErroredSelections(item)
{
return item.type !== "Table" || item.primary != null;
}
function uniq(a) {
var seen = {};
return a.filter(function(item) {
return seen.hasOwnProperty(item) ? false : (seen[item] = true);
});
}
function addListBoxAfterProcessingNames(all_listbox_names, dim, guid)
{
if (all_listbox_names.length === 1)
{
addListBox(all_listbox_names[0], dim, guid);
} else
{
let keys = Object.keys(all_listbox_names[0]);
let final_listbox_names = {};
keys.forEach(key => {
let names = all_listbox_names.map(names => names[key]);
names = [].concat.apply([], names);
final_listbox_names[key] = uniq(names);
})
addListBox(final_listbox_names, dim,guid);
}
}
let user_inputs = form.dropBox({name: "formInputs",
label: "Input",
duplicates: true,
types: ["Table", "RItem: " + ALLOWED_R_CLASSES.join(", ")],
multi:true,
prompt: "Input data such as a table or R vector or matrix"});
user_inputs = user_inputs.getValues();
form.comboBox({name: 'formRemoveMissing',
alternatives: ['Yes (show warning)', 'No', 'Yes'],
label: 'Calculate for inputs with incomplete data',
prompt: 'If set to \'Yes\', any missing values are removed from the data before the calculation occurs. ' +
'If set to \'No\', inputs with any missing values will be assigned a missing value. ' +
'Inputs whose values are entirely missing, will always be assigned a missing value ' +
'regardless of this setting.',
default_value: 'Yes (show warning)'});
let row_names = [];
let col_names = [];
var inputs = [];
if (user_inputs.length !== 0)
{
inputs = user_inputs.map(input => recursiveGetItemByGuid(project.report, input.guid));
inputs = inputs.filter(removeErroredSelections);
}
if (inputs.length > 0)
{
let inputs = user_inputs.map(input => recursiveGetItemByGuid(project.report, input.guid));
row_names = inputs.map(input => getInputNames(input, 0));
col_names = inputs.map(input => getInputNames(input, 1));
}
row_names = row_names.filter(item => item["names"].length > 0);
col_names = col_names.filter(item => item["names"].length > 0);
let add_row_listbox = row_names.length > 0;
let add_col_listbox = col_names.length > 0;
let add_matching_control = row_names.length > 1 || col_names.length > 1;
if (add_matching_control)
{
var automatic_choice = form.comboBox({label: "Automatically match elements",
name: "formMatchElements",
alternatives : ["Yes - hide unmatched",
"Yes - show unmatched",
"No",
"Custom"],
default_value: "Yes - hide unmatched",
prompt: "Automatically determine elements to match based off the input row and column labels"});
automatic_choice = automatic_choice.getValue();
if (automatic_choice === "Custom")
{
let has_both_row_names = row_names.every(item => item["names"].length > 0);
let has_both_col_names = col_names.every(item => item["names"].length > 0);
form.comboBox({name: "formMatchRows",
label: "Match rows",
alternatives: ["Yes - hide unmatched", "Yes - show unmatched", "Fuzzy - hide unmatched", "Fuzzy - show unmatched", "No"],
default_value: has_both_row_names ? "Yes - hide unmatched" : "No"});
form.comboBox({name: "formMatchColumns",
label: "Match columns",
alternatives: ["Yes - hide unmatched", "Yes - show unmatched", "Fuzzy - hide unmatched", "Fuzzy - show unmatched", "No"],
default_value: has_both_col_names ? "Yes - hide unmatched" : "No"});
}
}
form.textBox({name: "formIncludeRows", label: "Rows to exclude", prompt: "Select the row labels to be excluded in the output table.", default_value: "NET; SUM", required: false});
form.textBox({name: "formIncludeColumns", label: "Columns to exclude", prompt: "Select the columns labels to be excluded in the output table.", default_value: "NET; SUM", required: false});
form.setHeading("Sum");
library(verbs)
match.elements <- get0("formMatchElements", ifnotfound = c(rows = "No", columns = "No"))
if (length(match.elements) == 1L && match.elements == "Custom")
match.elements <- c(rows = formMatchRows, columns = formMatchColumns)
removal.choices <- list(formIncludeRows, formIncludeColumns)
categories.to.remove <- ParseCategoriesToRemove(removal.choices, formInputs)
remove.rows <- categories.to.remove[[1L]]
remove.columns <- categories.to.remove[[2L]]
remove.missing <- startsWith(formRemoveMissing, "Yes")
warn <- if (endsWith(formRemoveMissing, "(show warning)")) TRUE else "MuffleMissingValueWarning"
sum.output <- Sum(QInputs(formInputs),
remove.missing = remove.missing,
remove.rows = remove.rows,
remove.columns = remove.columns,
match.elements = match.elements,
warn = warn)