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HTML 

              
                <!-- html structure
.viz, wrapping container
    header, introducing the project
    section, one for each visualization
    footer, closing the project referencing the source data
-->
<div class="viz">
    <header>
        <h1>D3 Array</h1>
        <p>
            The module provides convenience methods when working with arrays.
            <br/>
            Methods like <code>d3.min()</code> and <code>d3.median()</code> rapidly provide key statistics.
            <br/>
            Methods like <code>d3.bin()</code> aggregate the data points according to their values.
        </p>

    </header>

    <!-- in the first visualization structure a table to display a few statistics -->
    <section class="viz__statistics">
        <h2>Statistics</h2>
        <!-- in a two-column table specify a statistic and corresponding value
        ! add the values through array methods provided by the d3 library
        -->
        <table>
            <thead>
                <tr>
                    <th>Key</th>
                    <th>Value</th>
                </tr>
            </thead>
            <tbody>
                <tr>
                    <td>min</td>
                    <td id="min"></td>
                </tr>
                <tr>
                    <td>q1</td>
                    <td id="q1"></td>
                </tr>
                <tr>
                    <td>mean</td>
                    <td id="mean"></td>
                </tr>
                <tr>
                    <td>median</td>
                    <td id="median"></td>
                </tr>
                <tr>
                    <td>q3</td>
                    <td id="q3"></td>
                </tr>
                <tr>
                    <td>max</td>
                    <td id="max"></td>
                </tr>
            </tbody>
        </table>
    </section>

    <!-- in the second visualization draw a box plot to visualize the computed statistics -->
    <section class="viz__box-plot">
        <h2>Box Plot</h2>
    </section>

    <!-- in the third visualization draw a histogram to illustrate the .bin() method -->
    <section class="viz__histogram">
        <h2>Histogram</h2>
    </section>

    <footer>
        <p><strong>Note</strong>: the statistics and visualization in this project relate to the real interest rate in Australia, as measured on the <a href="https://data.worldbank.org/indicator/FR.INR.RINR?locations=AU">World Bank Open Data</a>.</p>
    </footer>
</div>
!

CSS 

              
                @import url("https://fonts.googleapis.com/css?family=Muli:400,900&display=swap");

* {
  box-sizing: border-box;
  padding: 0;
  margin: 0;
}
body {
  min-height: 100vh;
  background: hsl(250, 23%, 90%);
  font-family: "Muli", sans-serif;
}
/* cap the width of the .viz container */
.viz {
  max-width: 800px;
  margin: 1rem auto;
  /* display the content in a grid with the boxplot and histogram on the side */
  display: grid;
  grid-template-columns: 1fr 2fr;
  grid-template-areas: "header boxplot" "statistics histogram" "footer footer";
  background: hsl(250, 23%, 98%);
  color: hsl(227, 50%, 10%);
  box-shadow: 0 2px 15px -2px hsla(230, 48%, 21%, 0.2);
  align-items: baseline;
}
/* on smaller viewports remove the grid display in favor of the default top down layout */
@media (max-width: 800px) {
  .viz {
    display: block;
    margin: initial;
  }
}
/* increase the white space for every direct children (the header, section and footer elements) */
.viz > * {
  padding: 1.25rem;
}

/* highlight the header with a lighter background */
.viz header {
  grid-area: header;
  line-height: 2;
  background: hsl(250, 23%, 100%);
  position: relative;
}
/* flip the color and background value for the code element */
.viz header code {
  background: hsl(227, 50%, 10%);
  color: #fff;
  padding: 0.2rem 0.5rem;
  font-family: "Fira Code", monospace;
  font-size: 0.75rem;
}
/* with a pseudo element draw a triangle in the top left corner, as to describe the header as the starting point of the project */
.viz header:before {
  position: absolute;
  content: "";
  width: 20px;
  height: 20px;
  top: 0;
  left: 0;
  background: hsl(152, 70%, 60%);
  clip-path: polygon(0% 0%, 100% 0%, 0% 100%);
}

.viz section {
  line-height: 2;
}
/* separate the sections' headings from connected visualization */
.viz section h2 {
  margin-bottom: 1rem;
}
.viz__boxplot {
  grid-area: boxplot;
}
.viz__statistics {
  grid-area: statistics;
}
.viz__histogram {
  grid-area: histogram;
}
.viz footer {
  grid-area: footer;
}
/* style the table to cover the available width */
.viz section table {
  width: 100%;
  text-align: center;
  line-height: 2;
  border-collapse: collapse;
}
/* use the theme colors for the table's heading */
.viz section table thead {
  background: hsl(227, 50%, 10%);
  border-bottom: 3px solid hsl(152, 70%, 60%);
  color: #fff;
}
.viz section table tbody {
  background: hsl(152, 70%, 60%, 0.15);
  text-transform: capitalize;
}

/* style the anchor link with no decoration and a border matching the project's palette */
.viz footer a {
  text-decoration: none;
  display: inline-block;
  color: inherit;
  border-bottom: 2px solid hsl(227, 50%, 10%);
}
/* increase the size of the text in the visualizations */
.viz section svg text {
  font-size: 1.2rem;
}
!

JS 

              
                // dataset describing the real interest rate in Australia between 1961 and 2018
// https://data.worldbank.org/indicator/FR.INR.RINR?locations=AU
const data = [2.20192877675305, 5.58540353096757, 3.27426206402178, 1.73117500574349, 2.25949294588104, 2.62157170803393, 0.485511769939369, 3.25964521959553, 1.06997722082935, 1.68627392761528, 1.96233657787761, 0.783337878323126, -1.50603605860954, -6.01827571279595, -5.33931397533848, -3.59758101116202, -1.3850598652733, 1.05174177237822, 0.452274918324396, -0.0340965425476336, 2.10701235642412, 1.41659149691651, 2.05091846004363, 3.37176137088193, 7.44387087635544, 8.02521004999169, 7.50523525122644, 6.40745933698599, 6.6035464089397, 9.66734259720985, 10.0726572327522, 8.94800409569146, 8.44723104852863, 7.98204363248875, 8.05222422170466, 6.84880884746632, 5.86109478223985, 5.3423337284395, 6.18759255059486, 5.00774385062176, 2.12270228152934, 3.40477128529011, 3.39329282720625, 3.61907954781133, 3.33455981692418, 2.39802213383888, 3.03222849228063, 4.19302925472188, 0.970400198200039, 6.04147227701786, 1.39733435535196, 5.00773264232463, 6.34314890121023, 4.44149027991943, 6.32118230974146, 5.9470353697114, 1.47390222942331, 3.37286926136901];


/* FIRST VISUALIZATION
using functions from the D3 array module estimate key figures behind the dataset
! while most of the functions work with a general array, d3.quantile requires a **sorted** array
for this create a shallow copy of the original data, to avoid mutating the original array
*/
// d3.ascending() is another convenience method to rapidly sort two integers
const sortedData = [...data].sort((a, b) => d3.ascending(a, b));

// statistics
const min = d3.min(data);
const q1 = d3.quantile(sortedData, 0.25);
const mean = d3.mean(data);
const median = d3.median(data);
const q3 = d3.quantile(sortedData, 0.75);
const max = d3.max(data);

// create a formatting function to show only three digits after the decimal point
const format = d3.format('.3f');

// populate the table with the computed statistics
const vizStatistics = d3
  .select('.viz__statistics');

vizStatistics
  .select('#min')
  .text(`${format(min)}%`);

vizStatistics
  .select('#q1')
  .text(`${format(q1)}%`);

vizStatistics
  .select('#mean')
  .text(`${format(mean)}%`);

vizStatistics
  .select('#median')
  .text(`${format(median)}%`);
vizStatistics
  .select('#q3')
  .text(`${format(q3)}%`);

vizStatistics
  .select('#max')
  .text(`${format(max)}%`);


/* SECOND AND THIRD VISUALIZATION
specify the measures shared by the box plot and the histogram
*/
const margin = {
  top: 40,
  right: 40,
  bottom: 40,
  left: 40,
};

const width = 500 + (margin.left + margin.right);
const height = 200 + (margin.top + margin.bottom);

// base the horizontal scale on the minimum and maximum data points
// ! the vertical scale is defined for the histogram once the bins are created, since it's based on the bins' sizes
const xScale = d3
  .scaleLinear()
  .domain(d3.extent(data))
  .range([0, width])
  .nice();


/* SECOND VISUALIZATION
build a box plot from the computed statistics
*/
const vizBoxPlot = d3
  .select('.viz__box-plot');

// describe the size of the box plot to be at most half the height of the visualization
const boxHeight = 120;

const svgBoxPlot = vizBoxPlot
  .append('svg')
  .attr('viewBox', `0 0 ${width + (margin.left + margin.right)} ${height + (margin.top + margin.bottom)}`);

// translate the group to vertically center the box plot elements
const groupBoxPlot = svgBoxPlot
  .append('g')
  .attr('transform', `translate(${margin.left} ${margin.top + height / 2})`);

// draw a line considering the interquartile range (q1 - iqr*1.5, q3 + iqr *1.5)
// where the interquartile range is (q3 - q1)
const IQR = q3 - q1;
// the line is drawn from (q1 - IQR *1.5) to (q3 + IQR *1.5)
// cap the values to the minimum/maximum data points
const minBoxPlot = Math.max(q1 - IQR * 1.5, d3.min(data));
const maxBoxPlot = Math.min(q3 + IQR * 1.5, d3.max(data));

groupBoxPlot
  .append('path')
  .attr('fill', 'none')
  .attr('stroke', 'currentColor')
  .attr('stroke-width', '4')
  .attr('d', `M ${xScale(minBoxPlot)} 0 H ${xScale(maxBoxPlot)}`);

// draw a rectangle from q1 to q3
groupBoxPlot
  .append('rect')
  .attr('x', xScale(q1))
  .attr('width', xScale(q3) - xScale(q1))
  .attr('y', -boxHeight / 2)
  .attr('height', boxHeight)
  .attr('fill', 'hsl(227, 50%, 10%)');

// for the details of the boxplot, include a group to translate the elements at the desired coordinates
// this to include connected elements in the same group

// median: draw the line in the rectangle, a text element describing the purpose and a line connecting the two
const medianBoxPlot = groupBoxPlot
  .append('g')
  .attr('transform', `translate(${xScale(median)} ${-(boxHeight / 2)})`);

medianBoxPlot
  .append('path')
  .attr('fill', 'none')
  .attr('stroke', 'hsl(152, 70%, 60%)')
  .attr('stroke-width', 4)
  .attr('d', `M 0 0 v ${boxHeight}`);

medianBoxPlot
  .append('text')
  .text('Median')
  .attr('x', 0)
  .attr('y', -70)
  .attr('text-anchor', 'middle');

medianBoxPlot
  .append('path')
  .attr('d', 'M 0 -60 v 50')
  .attr('fill', 'none')
  .attr('stroke-linecap', 'round')
  .attr('stroke-width', 2)
  .attr('stroke', 'currentColor');

// q1 and q3: include text elements and lines connecting the text to the matching values
const q1BoxPlot = groupBoxPlot
  .append('g')
  .attr('transform', `translate(${xScale(q1)} 0)`);

q1BoxPlot
  .append('text')
  .text('q1')
  .attr('x', -45)
  .attr('y', -60)
  .attr('text-anchor', 'middle');

q1BoxPlot
  .append('path')
  .attr('d', 'M -45 -50 v 25 l 35 15')
  .attr('fill', 'none')
  .attr('stroke-linejoin', 'round')
  .attr('stroke-linecap', 'round')
  .attr('stroke-width', 2)
  .attr('stroke', 'currentColor');

const q3BoxPlot = groupBoxPlot
  .append('g')
  .attr('transform', `translate(${xScale(q3)} 0)`);

q3BoxPlot
  .append('text')
  .text('q3')
  .attr('x', 45)
  .attr('y', -60)
  .attr('text-anchor', 'middle');

q3BoxPlot
  .append('path')
  .attr('d', 'M 45 -50 v 25 l -35 15')
  .attr('fill', 'none')
  .attr('stroke-linejoin', 'round')
  .attr('stroke-linecap', 'round')
  .attr('stroke-width', 2)
  .attr('stroke', 'currentColor');

// interquartile range: include a text element and a path wrapping the matching region
const iqrBoxPlot = groupBoxPlot
  .append('g')
  .attr('transform', `translate(${xScale(minBoxPlot)} 0)`);

iqrBoxPlot
  .append('text')
  .text('Interquartile Range')
  .attr('x', (xScale(maxBoxPlot) - xScale(minBoxPlot)) / 2)
  .attr('y', (boxHeight / 2 + 60))
  .attr('text-anchor', 'middle');

iqrBoxPlot
  .append('path')
  // draw arcs to figuratively wrap the area from the points described by the interquartile range
  .attr('d', `M 0 ${boxHeight / 2 + 10} a 15 15 0 0 0 15 15 h ${(xScale(maxBoxPlot) - xScale(minBoxPlot)) / 2 - 25} a 10 10 0 0 1 10 10 a 10 10 0 0 1 10 -10 h ${(xScale(maxBoxPlot) - xScale(minBoxPlot)) / 2 - 25} a 15 15 0 0 0 15 -15`)
  .attr('fill', 'none')
  .attr('stroke-linecap', 'round')
  .attr('stroke-width', 2)
  .attr('stroke', 'currentColor');

// outliers (if any): draw a circle, add a text element describing the point and a path connecting the two
// values outside of the interquartile range
const outliers = data.filter(dataPoint => dataPoint < minBoxPlot || dataPoint > maxBoxPlot);

// group to position the outliers
const outliersBoxPlot = groupBoxPlot
  .selectAll('g.outlier')
  .data(outliers)
  .enter()
  .append('g')
  .attr('class', 'outlier')
  .attr('transform', d => `translate(${xScale(d)} 0)`);

// circle, text and connecting line
outliersBoxPlot
  .append('circle')
  .attr('cx', 0)
  .attr('cy', 0)
  .attr('r', 3);

outliersBoxPlot
  .append('text')
  .text('Outlier')
  .attr('x', 0)
  .attr('y', -60)
  .attr('text-anchor', 'middle');

outliersBoxPlot
  .append('path')
  .attr('d', 'M 0 -50 v 40')
  .attr('fill', 'none')
  .attr('stroke-linecap', 'round')
  .attr('stroke-width', 2)
  .attr('stroke', 'currentColor');

/* THIRD VISUALIZATION
build a histogram by creating bins aggregating the data points
*/

// function creating the bins
// ! by default the function creates bins considering the minimum and maximum data points
// the domain function can be specified to have the bins match the scale
const bin = d3
  .bin()
  .domain(xScale.domain());

// bin the data in arrays
// each array describes the data points falling in the bin
// additionally, each array provides x0 and x1 values describing the start and end of the bin
const binData = bin(data);

// create the vertical scale based on the size of the largest bin
const yScale = d3
  .scaleLinear()
  .domain([0, d3.max(binData, ({ length }) => length)])
  .range([height, 0]);

const vizHistogram = d3
  .select('.viz__histogram');

const svgHistogram = vizHistogram
  .append('svg')
  .attr('viewBox', `0 0 ${width + (margin.left + margin.right)} ${height + (margin.top + margin.bottom)}`);

const groupHistogram = svgHistogram
  .append('g')
  .attr('transform', `translate(${margin.left} ${margin.top})`);

// in the nested group add one group for each bin
const bins = groupHistogram
  .selectAll('g.bin')
  .data(binData)
  .enter()
  .append('g')
  // translate the bin horizontally according to the value describing the beginning of the bin
  // vertically according to the size
  .attr('transform', ({ x0, length }) => `translate(${xScale(x0)} ${yScale(length)})`);

// for each bin add a rectangle describing the bin's size
bins
  .append('rect')
  .attr('x', 0)
  .attr('y', 0)
  .attr('fill', 'hsl(227, 50%, 10%)')
  // use the start and end values for the bin's width
  .attr('width', ({ x0, x1 }) => (xScale(x1) - xScale(x0)))
  // use the length for the bin's height
  .attr('height', ({ length }) => height - yScale(length));

// for each bin add a text element spelling out the bin's size
bins
  .append('text')
  // center the text in the matching bin
  .attr('x', ({ x0, x1 }) => (xScale(x1) - xScale(x0)) / 2)
  .attr('y', -5)
  .attr('text-anchor', 'middle')
  .text(({ length }) => length);

// include a line where the axis would be
groupHistogram
  .append('path')
  .attr('fill', 'none')
  .attr('stroke', 'hsl(152, 70%, 60%)')
  .attr('stroke-width', 4)
  .attr('d', `M 0 ${height} h ${width}`);

// include an axis to specify the bins' values
const xAxis = d3
  .axisBottom(xScale)
  .tickPadding(10)
  .tickFormat(d => `${d}%`);

groupHistogram
  .append('g')
  .attr('class', 'axis')
  .attr('transform', `translate(0 ${height})`)
  .call(xAxis);

// remove the tick lines and the path describing the axis
d3
  .select('.axis')
  .selectAll('line')
  .remove();
d3
  .select('.axis')
  .select('path')
  .remove();

// style the text elements to be larger and semitransparent
d3
  .select('.axis')
  .attr('opacity', 0.5);
!
999px

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