Chris Aragao cbaragao
cbaragao
/ haversine.pfx
Created
December 3, 2025 21:58
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| // Function to calculate the distance between two geographic coordinates | |
| GetDistance(lat1:Number, lon1:Number, lat2:Number, lon2:Number):Number = With( | |
| { | |
| // Convert latitude and longitude differences from degrees to radians | |
| dLat: (lat2 - lat1) * Pi() / 180.0, | |
| dLon: (lon2 - lon1) * Pi() / 180.0, | |
| // Convert original latitudes to radians |
cbaragao
/ fnDynamicSelectList.pq
Created
October 31, 2025 16:43
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| (l as list, selections as list)=> | |
| let | |
| Source = | |
| List.Accumulate( | |
| selections, | |
| {}, | |
| (state,current)=> | |
| if | |
| Text.Contains(current,":") | |
| then |
cbaragao
/ fnSelectListByRange.pq
Created
October 30, 2025 12:04
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| (l as list, range as list)=> | |
| let | |
| Start_Position = List.PositionOf(l, range{0}), | |
| End_Position = List.PositionOf(l, range{1}), | |
| Result= List.Range(l,Start_Position,(End_Position-Start_Position)+1) | |
| in | |
| Result |
cbaragao
/ BarbellPlot.json
Created
October 27, 2025 18:47
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| //SPEC | |
| { | |
| "data": { | |
| "name": "dataset" | |
| }, | |
| "title": "Sales Variance by Product", | |
| "transform": [ | |
| { | |
| "calculate": "17000000", | |
| "as": "target" |
cbaragao
/ ClevelandPlot.json
Created
October 27, 2025 18:46
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| //SPEC | |
| { | |
| "data": { | |
| "name": "dataset" | |
| }, | |
| "title": "Sales Variance by Product", | |
| "transform": [ | |
| { | |
| //"calculate": "datum['Total Sales'] *.80","as": "target" | |
| "calculate": "17000000", |
cbaragao
/ HorizontalDivergingwithLabels.json
Created
October 27, 2025 18:45
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| //SPEC | |
| { | |
| "data": { | |
| "name": "dataset" | |
| }, | |
| "title": "Sales Variance by Product", | |
| "transform": [ | |
| { | |
| //"calculate": "datum['Total Sales'] *.80","as": "target" | |
| "calculate": "17000000", |
cbaragao
/ VerticalBarChartWithLabels.json
Created
October 27, 2025 18:44
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| { | |
| "data": { | |
| "name": "dataset" | |
| }, | |
| "title":"Total Sales by Segment - Vertical Bar Chart", | |
| "encoding": { | |
| "x": { | |
| "field": "Segment", | |
| "type": "nominal", | |
| "sort":"-y", |
cbaragao
/ BarChartWithTarget.json
Created
October 27, 2025 18:43
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| // SPECS | |
| { | |
| "data": { | |
| "name": "dataset" | |
| }, | |
| "title":"Sales Variance by Product", | |
| "transform": [ | |
| { | |
| //"calculate": "datum['Total Sales'] *.80","as": "target" |
cbaragao
/ fnRandomNumber.pq
Last active
October 10, 2025 13:01
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| (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
/ fnRPredictWithLogitModel.pq
Last active
October 8, 2025 21:50
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| (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] |
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