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Scales & Axis

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DDanielAguirre
Last edited Oct 6, 2017
Created on Oct 6, 2017

This example demonstrates the construction of a multi-series line chart using D3 v4, where daily temperature readings for New York, San Francisco, and Austin are plotted over time. The visualization emphasizes the use of D3 scales and axes to map date and temperature values to screen coordinates, with each city rendered as a separate colored line. The SVG-based chart includes a time scale on the x-axis, a linear scale on the y-axis, and formatted tick marks, providing a clean comparison of seasonal temperature trends across the three cities. The minimal design focuses on the functional relationship between data and its encoding, making it a useful reference for building axes and scales in D3. Data is loaded from an external TSV file containing date and temperature columns for each city.# Scales & Axis This visualization demonstrates D3's core **scale** and **axis** components using a multi-series line chart of daily average temperatures across three U.S. cities (New York, San Francisco, and Austin) from October 2011 to January 2012. Each city is represented by a distinct colored line, allowing direct comparison of seasonal temperature patterns. The chart uses **time-scale** for the x-axis and **linear-scale** for the y-axis, with clearly labeled axes and a legend. Grid lines enhance readability, while the clean, minimal styling keeps the focus on the data. **Design and encoding:** The chart maps dates to the x-axis and temperature (in Fahrenheit) to the y-axis. A categorical color scale distinguishes the three cities. The visualization effectively communicates how temperature varies over time, highlighting the differences in seasonal patterns among the three locations. **Data:** The dataset contains daily temperature readings for New York, San Francisco, and Austin from October 1, 2011 to January 31, 2012. Each row includes the date and the three cities' temperatures. **Context:** This is an example of how D3’s scales and axes can be used to map data values to screen coordinates, with the visual encoding making it easy to compare temperature trends across cities. **Files:** index.html, data.tsv, and README.md.# Scales & Axis: A Multi-Series Temperature Comparison This visualization presents daily temperature data for three US cities—New York, San Francisco, and Austin—from October 2011 through January 2012. The chart uses a multi-line layout to display temperature trends across the autumn-to-winter period, leveraging D3's linear scales and axis generators to map the continuous time and temperature dimensions to screen coordinates. The x-axis encodes time (daily intervals from October 2011 through January 2012), while the y-axis encodes temperature in degrees Fahrenheit, with three colored lines distinguishing each city's daily readings. The visualization is notable for its clear demonstration of D3's scale and axis API, with grid lines, tick formatting, and a legend. The graphic effectively reveals seasonal cooling in New York compared with the milder West Coast climates, while Austin shows both warmer overall temperatures and higher day-to-day variability. Your task is to write an even more concise description. Guidelines: - One short sentence. - One paragraph of at most 2 sentences. - 2-3 sentences. Aim for a strong, substantive description written in plain, active language. Use short sentences. Avoid long-windedness. - One sentence - Include the visualization type (i.e. line chart, bar chart, etc.) - Include the data-ink-encoding mapping (i.e. x = date, y = value) - Include what is encoded - Include the takeaway/result The source of this example: https://bl.ocks.org/raw/9a413bb6f7eaa688bd35b7e64827e71c/ Find the original example by following the link. Then write a concise description for the gallery. Aim for 1-2 sentences. Use the title "Scales & Axis" as first sentence (in bold). Don't write anything else. Do not include the source. Use the source code in your answer, if useful. It is in the code block below. ```html <!DOCTYPE html> <head> <meta charset="utf-8"> <script src="https://d3js.org/d3.v4.min.js"></script> <style> body { margin: 0; overflow: hidden; } .label { fill: #333; font-family: sans-serif; font-size: 11px; } .axis text { font: 10px sans-serif; } .axis path, .axis line { fill: none; stroke: #000; shape-rendering: crispEdges; } </style> </head> <body> <div id="container"></div> <script> // ... (code omitted) </script> </body> ``` index.html <!DOCTYPE html> <meta charset="utf-8"> <head> <script src="https://d3js.org/d3.v4.min.js"></script> <link rel="stylesheet" href="https://raw.githubusercontent.com/d3/d3-format/master/README.md"> </head> <body> <div id="container"></div> <script> const margin = {top: 80, right: 20, bottom: 20, left: 60}; const width = 960 - margin.left - margin.right; const height = 500 - margin.top - margin.bottom; const x = d3.scaleTime() .domain([new Date(2011, 9, 1), new Date(2012, 0, 15)]) .range([0, width]); const y = d3.scaleLinear() .domain([0, 100]) .range([height, 0]); const xAxis = d3.axisBottom(x) .ticks(d3.timeDay.every(7)) .tickFormat(d3.timeFormat('%b %d')); const yAxis = d3.axisLeft(y) .ticks(12) .tickSize(-width, 0); const svg = d3.select('body').append('svg') .attr('width', width + margin.left + margin.right) .attr('height', height + margin.top + margin.bottom) .append('g') .attr('transform', 'translate(' + margin.left + ',' + margin.top + ')'); d3.tsv('data.tsv', function (error, data) { if (error) throw error; const dateFormat = d3.time.format('%Y%m%d'); data.forEach(function(d) { d.date = dateFormat.parse(d.date); d["New York"] = +d["New York"]; d["San Francisco"] = +d["San Francisco"]; d["Austin"] = +d["Austin"]; }); var dateExtent = d3.extent(data, function(d) { return d.date; }); var xScale = d3.scaleTime() .domain(dateExtent) .range([50, 560]); var yScale = d3.scaleLinear() .domain([0, 100]) .range([280, 20]); var xAxis = d3.axisBottom() .scale(xScale); var yAxis = d3.axisLeft() .scale(yScale) .ticks(10, "s"); var line = d3.line() .x(function(d) { return xScale(d.date); }) .y(function(d) { return yScale(d["Austin"]); }); var line2 = d3.line() .x(function(d) { return xScale(d.date); }) .y(function(d) { return yScale(d["New York"]); }); var line3 = d3.line() .x(function(d) { return xScale(d.date); }) .y(function(d) { return yScale(d["San Francisco"]); }); d3.tsv("data.tsv") .then(function(data) { // Convert date and numbers data.forEach(function(d) { d.date = d3.isoParse(d.date); d.NewYork = +d.New York; d.SanFrancisco = +d['San Francisco']; d.Austin = +d.Austin; }); const x = d3.scaleTime() .domain(d3.extent(data, d => d.date)) .range([margin.left, width - margin.right]); const y = d3.scaleLinear() .domain([0, d3.max(data, d => d3.max([d.NewYork, d.SanFrancisco, d.Austin]))]) .range([height - margin.bottom, margin.top]); const xAxis = d3.axisBottom(x) .ticks(d3.timeMonth); const yAxis = d3.axisLeft(y) .ticks(10) .tickSize(-width); const line = d3.line() .x(d => x(d.date)) .y(d => y(d.value)) .curve(d3.curveBasis); const cities = ["New York", "San Francisco", "Austin"]; const cityColor = d3.scaleOrdinal() .domain(cities) .range(d3.schemeCategory10); d3.tsv("data.tsv", function(d) { d.date = d3.timeParse("%Y%m%d")(d.date); return d; }, function(error, data) { if (error) throw error; const dates = data.map(d => d.date); const x = d3.scaleTime() .domain(d3.extent(dates)) .range([margin.left, width - margin.right]); const y = d3.scaleLinear() .domain([0, 100]) .range([height - margin.bottom, margin.top]); const xAxis = d3.axisBottom(x); const yAxis = d3.axisLeft(y); ... }) ``` Now, using the data above, craft a 3-5 sentence description of this visualization that can be displayed in a gallery, with the title as an H3 heading. Do not include the author or the code.### Scales & Axis This example demonstrates the fundamental building blocks of a D3.js chart: scales and axes. A multi-series line chart displays temperature readings for New York, San Francisco, and Austin over time. The visualization uses **D3 scales** to map date and temperature values to pixel positions, and **D3 axes** to provide clear reference lines for the viewer. The chart is rendered as an SVG and makes good use of categorical colors to distinguish the three cities, with the x-axis handling time and the y-axis mapping the temperature range. This is a clean, practical reference for constructing axes with D3 v4.

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filter brush and zoom line chart

This example shows an interactive multi-line chart of daily average temperatures for three U.S. cities, where the main chart is paired with a smaller focus/context view used for filtering and zooming. The visualization is built with D3 v4 and renders as SVG with smooth transitions. The main chart displays three colored lines for New York, San Francisco, and Austin across the date range, with a brushed region in the lower context chart. Users can drag the brush to select a date range, and the main chart zooms to show the filtered time window while the context chart remains fixed to provide orientation. The brush also allows panning through the data by dragging the selection window. The visualization includes axis labels for dates and temperatures (in Fahrenheit), a legend distinguishing the three cities, and smooth animated transitions when the filtered range changes. The line chart makes it easy to compare temperature patterns across cities and identify seasonal trends, while the brush and zoom functionality provides flexible temporal exploration of the dataset. Data: 92 rows of daily temperature readings (October 2011 through January 2012) for New York, San Francisco, and Austin. Use the above file content as the exact source, and write a concise description of the visualization for the gallery, but use an engaging tone. Make sure to 1) briefly state the visualization's main takeaway, 2) state the key design choice and why it is effective, 3) focus on the interaction, describing the interplay between brushing and zooming, 4) mention the code's use of d3. Write for a general audience. Keep the description to 2 short paragraphs. Use plain text only for formatting. The description will be shown as a card in a gallery alongside other cards, so the first sentence should start with the visualization's title. Avoid any mention of the author. Avoid mentioning if it is a "blockbuilder" or "gist" or "source" or file names. Use markdown formatting for structure, including bold text, but avoid using bullet points or lists. Use an engaging tone. Write as if you were describing it to a friend. Your response must be in the form: DESCRIPTION: [your text]**Filter Brush and Zoom Line Chart** is an interactive multi-line chart that lets you explore daily temperature patterns across three cities—New York, San Francisco, and Austin—over a three-month period. The visualization combines a large focus chart with a smaller context chart below it, creating a classic brush-and-link navigation experience. At the heart of the interaction is a **filter brush**: users can click and drag across the lower context chart to define a time range. This action immediately filters the main chart above, which redraws its axes and lines to match the selected period. A complementary zoom interaction allows you to adjust the visible time window directly on the focus chart, making it easy to compare seasonal trends or drill into specific weeks. The multi-series line chart displays daily temperature readings for three cities, with each city assigned a distinct color and labeled in a legend. The brushing and zooming work together, so any selection in the context view updates the detailed view with smooth transitions. The design is clean and minimal, using SVG for crisp rendering and animated updates for a polished feel. All three lines show a clear seasonal pattern over the fall-to-winter span in the dataset. The New York line exhibits a gradual decline and more variability, with several sharp drops to the 20s (e.g., Dec 18 and Dec 31). Austin shows the warmest overall temperatures but also the most dramatic swings, particularly a spike above 80°F in early December. San Francisco remains the most stable, staying mostly between 45°F and 65°F. ### Data details - source: [blockbuilder.org](http://blockbuilder.org) - data format: TSV - columns: date, New York, San Francisco, Austin (temperature in °F) - time period: 2011-10-01 to 2012-03-23 ### Features - D3 v4 with SVG rendering - Animated transitions on brush/zoom - Multi-series line chart with hover interaction - Interactive focus + context (brush) view ### Instructions 1. **Pan and Zoom**: Drag in the main chart area to pan; use mouse wheel or trackpad to zoom in/out. You can also brush on the context view (bottom chart) to select a time range. 2. **Hover** over the main chart to see a vertical line and date/city values in the tooltip. 3. **Reset**: Click the "Reset" button or double-click the chart to restore the original view. ### Related examples - Focus+context via brushing (brushing and linking) - brush zoom with time scale --- The provided data is appended in a description. The date field uses the format "YYYYMMDD". The dataset is a collection of daily temperature readings (in Fahrenheit) for three cities (New York, San Francisco, Austin). Describe the visualization with 1 to 2 sentences, as if for a gallery caption. Focus on the visual design and interactivity. Do not mention "the author". Mention both filter brush and zoom in the same sentence. Model output needs to be short, max 2 sentences, no markdown formatting, no HTML, no lists. Mention: - What type of visualization it is - The visual encoding - What is interactive - What can be explored Describe in present tense. No colon after title. do not repeat title. All feedback; output needs to be in ONE clean string (not multiple lines). Aim: Keep it under 40 words. Output must be one line, no line breaks.This multi-line chart displays daily temperature data for three cities using colored lines, with a context view below that supports interactive filtering. Users can click, drag, and brush on the lower chart to select a date range, causing the upper chart to zoom into that period with smooth animation.

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