PBI-DataVizzle

let
    P1 = "myParameter1",
    P2 = "myParameter2",
    #"Header" = Binary.ToText(Text.ToBinary(#"UserName"&":" & #"Password")),
    #"BaseURL" = "https://yourwebpage.com/api/3",
#"DaysLogic" = 
        if
            #"JIRA Days" = null
        then
            ""

PBI-DataVizzle

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "transform": [
    {"filter": "indexof(selectedDepartments, datum['Department']) >=0"},
    {"fold": ["Budgeted $", "Paid $"]},
    {
      "joinaggregate": [
        {"op": "min", "field": "value", "as": "min"},
        {"op": "max", "field": "value", "as": "max"}
      ],

PBI-DataVizzle

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "title": {
    "text": "A Peek Inside The Mind",
    "subtitle": ["of Michael Scott"],
    "orient": "top",
    "align": "center",
    "anchor": "middle",
    "fontSize": 22,
    "subtitleFontSize": 16

PBI-DataVizzle

5x5 Waffle = 
VAR _max = 500
VAR _actual = 120
VAR _waffles = ROUND((_actual/_max) * 50, 0)
VAR _gray = "#7A7979"
VAR _red = "#FF0000"
VAR _header = "data:image/svg+xml;utf8,<svg width=""200"" height=""200"" viewBox=""0 0 200 200"" fill=""none"" xmlns=""http://www.w3.org/2000/svg"">
<rect width=""200"" height=""200"" fill=""white""/>"

VAR _footer = "</svg>"

PBI-DataVizzle

Pie Card = 

// set the actual value
VAR _actual = 120

// set the target
VAR _max = 150

// determine the pct
VAR _pct = _actual/_max

PBI-DataVizzle

bullet = 
// set a percentage for underachieving score
VAR _bad = .5 // set a percentage for sat score
VAR _sat = .75 // target will be 100%
VAR _target = 1 // max width set at 500 in case actual is over 100%
VAR _max_width = 500 // set actual percentage
VAR _actual = 1.2 // figure out ratio for underperforming area
VAR b_ratio = _bad * _max_width // figure out ratio for sat area
VAR s_ratio = _sat * _max_width // figure out ratio for target line
VAR t_ratio = _target * _max_width // figure out ratio for actual and ensure it is not larger than default width of svg

PBI-DataVizzle

ONS = 
{
    ("W07000081", "Aberafan Maesteg" ),
    ("W07000082", "Alyn And Deeside" ),
    ("W07000083", "Bangor Aberconwy" ),
    ("W07000084", "Blaenau Gwent And Rhymney" ),
    ("W07000085", "Brecon, Radnor And Cwm Tawe" ),
    ("W07000086", "Bridgend" ),
    ("W07000087", "Caerfyrddin" ),
    ("W07000088", "Caerphilly" ),

PBI-DataVizzle

// white wine dataset used from http://archive.ics.uci.edu/dataset/186/wine+quality

{
  "data": {
    "name": "dataset"
  },
  "params": [
    {
      "name": "grid",
      "select": "interval",

PBI-DataVizzle

let 
  fnErlangC = 

  (
    number_of_calls as number, 
    period_of_minutes as number, 
    average_handling_time as number, 
    required_service_level as number, 
    target_answer_time as number, 
    maximum_occupancy as number, 

PBI-DataVizzle

/* 
Function: fnHoltWinters
Description: Trying to replicate HoltWinters quantitative smoothing with Power Query.
Author: Chris Aragao

Change Log:

11.13.2023 - Initial release. Initial code was based off of this tutorial: https://www.exceldemy.com/holt-winters-exponential-smoothing-in-excel/.
It was aimed at solely duplicating the output and did so.