31:T4ff,random_numbers = { const div = html`

`; const button = div.querySelector('button'); button.onclick = () => { //arguments const mysnippet = new ocpu.Snippet($("#snippetInput").val()); //disable button $("button").attr("disabled", "disabled"); //clean screen $("#snippetOutput").text(""); // use the public base server ocpu.seturl("//cloud.opencpu.org/ocpu/library/base/R"); //perform the request const req = ocpu.call( "identity", { x: mysnippet }, function(session) { session.getConsole(function(outtxt) { $("#snippetOutput").text(outtxt); }); } ); //if R returns an error, put the error message req.fail(function() { const err = "Server error: " + req.responseText; $("#snippetOutput").text(err); }); req.always(function() { $("button").removeAttr("disabled"); }); }; return div; }32:T95d,{ const div = html`

# Create and initialise the necessary variables and objects
xArray = list()
sumArray = list()
tableArray = list()
vrange = 0

# Run the command to generate the random numbers 10k times and store the result into an array
i <- 1

while (i < 10001) {
  
  trial = sample(
    c(5, 6, 7, 8, 10, 13, 14, 16, 18, 19),
    size = 6,
    replace = TRUE,
    prob = c(0.07, 0.07, 0.07, 0.07, 0.07, 0.13, 0.07, 0.13, 0.20, 0.13)
  )
  
  xArray[[i]] <- trial
  
  i <- i + 1
  
}

# Walk all over the array to summarise the results
i <- 1

while (i < 10001) {
  
  sumArray[i] = do.call(sum, xArray[i])
  
  x = sumArray[i]

if ( x >= 10 & x < 20 ) {
    vrange = 10
  } else if ( x >= 20 & x < 30 ) {
    vrange = 20
  } else if ( x >= 30 & x < 40 ) {
    vrange = 30
  } else if ( x >= 40 & x < 50 ) {
    vrange = 40
  } else if ( x >= 50 & x < 60 ) {
    vrange = 50
  } else if ( x >= 60 & x < 70 ) {
    vrange = 60
  } else if ( x >= 70 & x < 80) {
    vrange = 70
  } else if ( x >= 80 & x < 90 ) {
    vrange = 80
  } else if ( x >= 90 & x < 100 ) {
    vrange = 90
  } else if ( x >= 100 & x < 110) {
    vrange = 100
  } else if ( x >= 110 & x < 120) {
    vrange = 110
  }

tableArray[i] = vrange
  
  i <- i + 1
  
}

# Build and plot a table view of the summarised results
df10k = do.call(rbind.data.frame, tableArray)
table(df10k)

`; const button = div.querySelector('button'); button.onclick = () => { //arguments const mysnippet2 = new ocpu.Snippet($("#snippet2Input").val()); //disable button $("button").attr("disabled", "disabled"); // use the public base server ocpu.seturl("//cloud.opencpu.org/ocpu/library/base/R"); //perform the request const req = ocpu.call( "identity", { x: mysnippet2 }, function(session) { session.getConsole(function(outtxt) { $("#snippet2Output").text(outtxt); }); } ); //if R returns an error, put the error message req.fail(function() { const err = "Server error: " + req.responseText; $("#snippet2Output").text(err); }); req.always(function() { $("button").removeAttr("disabled"); }); }; return div; }33:[] 2f:{"title":"Monte Carlo Simulation Study","cells":[{"id":0,"value":"md`# Monte Carlo Simulation Study\n\n### (Random Weighted Number Generator in R)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":244,"value":"slide`Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. A Monte Carlo simulator helps one visualize most or all of the potential outcomes to have a better idea regarding the risk of a decision.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"md`This notebook is a study of the Monte Carlo simulation.\nWe'll use some delivery metrics from a software development team to explore this technique.\n\nWe'll also use some R code to organise and present the data in this notebook. [R](https://www.r-project.org/) is a programming language and software environment for statistics which is widely used in data science.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"md`**The challenge: Forecast the Throughput of a specific team for the next 6 sprints using the historical data from the last 15 sprints as an input. In other words, how many work items this team will be able to deliver in the next 6 sprints?**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":106,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":50,"value":"md`Let's assume that the historical data for this team is the distribution below:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":65,"value":"table(data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":94,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"md`The summary of the above table is:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"table(summary_data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":114,"value":"md`We can now generate some random numbers using the past delivery data as an input to simulate what would be the Throughput for this team for the next 6 sprints.\n\n*Click the button \"Call R\" below, wait a few seconds and check the last line of the result.*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":118,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":121,"value":"md`Now we can run the same code 10,000 times and then summarise the results to see what is the more likely result we are going to have. We will group the results of this simulation in a group of tens. For example, 60 means that the sum of the simulation of the next 6 sprints resulted in a number between 60 and 69 work items.\n\n*Click the button \"Call R\" below, wait a few seconds and check the last 2 lines of the result.*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":127,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":154,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":147,"value":"md`You should have as a result (bottom 3 lines) a table like this (*the values may be slightly different*):\n

\n\nThe result in this table shows the sum of the Throughput of the next 6 sprints for each one of the 10,000 simulations.\n\nIf we plot the result from the Monte Carlo simulation, we have something like this:\n

\n\nNow we are ready to explore some different ways to read this information. Let's check some scenarios!\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":199,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"md`**Scenario 1 - You present this graph to a program manager and you are trying to articulate what is the main outcome you've got from this graph. Your speech could be...**\n\n\"*After running the Monte Carlo simulation using the team's historical delivery performance, we can forecast the systems' behaviour for the next 6 sprints. This graph shows some forecasted scenarios with different confidence levels. For example, it's unlikely (just 20.55% of confidence level) that the team will deliver more than 80 work items in the next 6 sprints. It's much more likely (79.07% of confidence level) that the team will deliver at least 60 work items in the next 6 sprints.*\"\n\n\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"md`**Scenario 2 - Your client is running an extremely important initiative, such as a mandatory compliance piece of work enforced by a regulator. The portfolio lead needs to provide a highly assertive forecast to the executive team, which means he or she needs to have a high confidence level. How many work items should the team commit to deliver in the next quarter (6 sprints)?**\n\n\"*Based on the past performance of this team, with 94.59% of confidence level, we can deliver between 50 and 59 work items. If you need an even higher confidence level, we can say that we forecast we can deliver between 40 an 49 work items with 99.23% of confidence level.*\"\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":216,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"md`**Scenario 3 - You have a prioritised backlog with 70 work items and you want to know what is the likelihood that your team can deliver all the 70 work items in the next 3 months.**\n\n\"*After running the Monte Carlo simulation based on the team's past performance, the likelihood of delivering the 70 prioritised work items is around 50.30%, in other words, it's not very likely that your team will deliver the entire prioritised backlog.*\"\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":124,"value":"md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":231,"value":"md`There are many other ways to interpret the output from the Monte Carlo simulation. You just need to find what is the best way to articulate your speech and develop a transparent conversation with your stakeholder.\n\nGood luck!`","pinned":false,"hidden":false,"mode":"ojs"},{"id":157,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"data = await d3.csv(\n \"https://gist.githubusercontent.com/gabovailati/34ed248686250cc83d17388cd1f31820/raw/e2e7d3368f477cfabd9383c0996ee6ea24b497e3/throughput.csv\",\n function(d) {\n return {\n sprint: +d.sprint,\n throughput: +d.throughput\n };\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"summary_data = await d3.csv(\n \"https://gist.githubusercontent.com/gabovailati/a68b615a66e09d96676bf0c91f7d5947/raw/470101bba0b82119d47090773868089d483c2343/summary-throughput.csv\",\n function(d) {\n return {\n throughput: +d.throughput,\n occurrences: +d.occurrences,\n percentage: +d.percentage\n };\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3,"value":"import { $, ocpu } from '@bryangingechen/hello-r-on-opencpu'","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"import { table } from '@tmcw/tables/2'","pinned":false,"hidden":false,"mode":"ojs"},{"id":248,"value":"slide_style","pinned":true,"hidden":false,"mode":"ojs"},{"id":247,"value":"import {slide, slide_style} from \"@mbostock/slide\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":252,"value":"id = 'UA-147646415-4'","pinned":false,"hidden":false,"mode":"ojs"},{"id":255,"value":"pageAnalytics = {\n await require(`https://www.googletagmanager.com/gtag/js?id=${id}`).catch(\n () => {}\n );\n const dataLayer = (window.dataLayer = window.dataLayer || []);\n const location = html``;\n gtag('js', new Date());\n gtag('config', id, {\n page_path: location.pathname,\n page_location: location.href\n });\n return md`^{[Google Analytics](https://www.google.com/analytics/terms/).}`;\n function gtag() {\n dataLayer.push(arguments);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}