cbaragao

//SPEC
{
  "data": {
    "name": "dataset"
  },
  "title": "Sales Variance by Product",
  "transform": [
    {
      "calculate": "17000000",
      "as": "target"

cbaragao

//SPEC
{
  "data": {
    "name": "dataset"
  },
  "title": "Sales Variance by Product",
  "transform": [
    {
      //"calculate": "datum['Total Sales'] *.80","as": "target"
      "calculate": "17000000",

cbaragao

//SPEC
{
  "data": {
    "name": "dataset"
  },
  "title": "Sales Variance by Product",
  "transform": [
    {
      //"calculate": "datum['Total Sales'] *.80","as": "target"
      "calculate": "17000000",

cbaragao

{
  "data": {
    "name": "dataset"
  },
  "title":"Total Sales by Segment - Vertical Bar Chart",
  "encoding": {
    "x": {
      "field": "Segment",
      "type": "nominal",
      "sort":"-y",

cbaragao

// SPECS

{
  "data": {
    "name": "dataset"
  },
  "title":"Sales Variance by Product",
  "transform": [
    {
      //"calculate": "datum['Total Sales'] *.80","as": "target"

cbaragao

(t as table, seed as number) =>
let
    // Step 1: Add an index column to the input table to preserve row order and enable alignment
    Source = Table.AddIndexColumn(t, "__Index", 0, 1, Int64.Type),

    // Step 2: Generate a reproducible list of random numbers using the provided seed
    // This ensures consistent output across refreshes or deployments
    Random_List = List.Random(Table.RowCount(t), seed),

    // Step 3: Convert the random list into a table and add an index column for joining

cbaragao

(df as table, independent as text, dependent as text)=>
let
    Source = 
        R.Execute(
            "df <- dataset " & 
            "#(lf)logit <- glm(" & dependent & " ~ " & independent & ", data = df, family = ""binomial"")" & 
            "#(lf)df$Predicted <- predict(logit, newdata = df, type = ""response"")" & 
            "#(lf)df$Prediction <- ifelse(df$Predicted > 0.5, 1, 0)" & 
            "#(lf)df", [dataset=df]),
    Return = Source{[Name="df"]}[Value]

cbaragao

(df as table, independent as text, dependent as text, family as text)=>
let
    Source = 
        R.Execute(
            "df <- dataset" & 
            "#(lf)logit <- glm(" & dependent & " ~ " & independent & ", data = df, family = """ & family &""") " & 
            "#(lf)summary_model <- summary(logit) " & 
            "#(lf)coef_df <- as.data.frame(summary_model$coefficients) " &
            "#(lf)coef_df$Variable <- rownames(coef_df) " & 
            "#(lf)coef_df <- coef_df[, c(""Variable"", ""Estimate"", ""Std. Error"", ""z value"", ""Pr(>|z|)"")] " &

cbaragao

let
    EWMA = Function.From(
        type function(alpha as number, values as list, index as number) as number,
        (params) =>
            let
                alpha = params{0},
                values = params{1},
                index = params{2},
                // Accumulate EWMA values up to the specified index
                resultList = List.Accumulate(

cbaragao

Cpl = 
/*
=============EXAMPLE DATASET===================
CallID	Duration_Minutes	Agent	Date
C1001	4.2	Agent_A	2024-01-15
C1002	3.5	Agent_B	2024-01-15
C1003	2.8	Agent_C	2024-01-15
C1004	4.6	Agent_A	2024-01-15
C1005	3.9	Agent_B	2024-01-16
C1006	3.2	Agent_C	2024-01-16