Learn and Understand D3.js for Data Visualization

Explore scalable vector graphics (svg) and how d3.js uses svg to render charts and graphs, keeping vector images crisp when resizing for the web.

Create your first inline SVG in an HTML document for data visualization with D3, set width and height to 500, and inspect the SVG in real time with developer tools.

Learn to create basic SVG shapes, rectangles, circles, and lines, and configure width, height, fill, stroke, and stroke-width while using the coordinate system to position them.

Learn how the svg path element enables drawing complex shapes with the D attribute and commands like M and L. D3 handles the heavy lifting while you learn the basics.

Add D3 via a cdn, wire up index.html and app.js, set utf-8, and compare unminified and minified D3 version 6 for development and production.

Explore how D3.js is structured as a modular library of packages, learn where to find feature-specific documentation via API references, and understand installing and using individual packages.

Discover how d3.js selections compare to native browser selectors, learn to use d3.select for single elements or selectAll for multiple nodes, and experiment with changing CSS properties.

Learn how to append elements in d3.js by chaining selections, appending a paragraph to the body, and understanding how d3 returns the new selection for further actions.

Learn how transformation methods modify a D3 selection, using attribute and text to change elements, append new nodes, and chain methods while returning updated selections.

Learn how to toggle classes with D3's class method without removing existing ones. Use the style method for inline CSS and understand how D3 returns new versus existing selections.

Discover how D3 joins data to elements, binding a five-item data array (10, 20, 30, 40, 50) to five list items with selectAll.

Explore the enter selection in d3.js, learn how to handle mismatched data-to-element ratios by creating new elements with join, and update text to display data in the document.

Explore the d3 exit selection—the opposite of the enter selection—that removes excess elements when data is fewer or more, by adding items and using join to remove them.

bind data to elements in d3 and display values using a callback with the data parameter d, then simplify with es6 arrow functions to color, attributes, and animations.

Explore how to customize D3.js join behavior by passing enter, update, and exit functions to control appending, updating, and removing elements with animations.

Explore the update pattern in d3.js, its deprecation, and how it manages update, enter, and exit selections; learn from outdated examples and adopt the join method.

Learn how to make JSON requests with d3-fetch, loading data from JSON or CSV files or APIs, and compare promises with async/await for clearer code.

Explore requesting and parsing csv data with d3.csv using await, see how csv files are parsed into columns array and row objects, and compare csv with json in d3-fetch workflow.

Begin drawing graphs with D3 to create a scatter plot that reveals the relationship between two data points, such as humidity and apparent temperature, using the provided data.json.

Draw the chart container by calculating dimensions, insert an SVG into the chart element with width and height using d3.select, and store the SVG reference for rendering.

Learn to add margins to a chart with an inner svg container moved by transform translate, so shapes stay visible inside the g element.

Discover how accessor functions map data properties to svg attributes in d3, using enter selections to draw circles bound to humidity on the x-axis and apparent temperature on the y-axis.

Explore how D3 scales, as functions, transform data by mapping input domain to output range, enabling flexible positioning, sizing, and color in charts.

Create and apply a d3 linear scale by setting its domain and range to transform data, using a CodePen demonstration to visualize the process.

Apply d3.js scales to position humidity on the x-axis and temperature on the y-axis in a scatter plot, using d3.scaleLinear, d3.extent, and accessor functions within the container bounds.

Explore refining d3 scales by chaining nice, rangeRound, and clamp to control domain and range, round decimals when appropriate, and prevent out-of-bounds values in a scatter plot.

Add an axis to a scatterplot using the D3 axis library, including axis bottom, ticks, and labels, and position it with a group transform for clear data reference.

Refine the x axis in D3.js by adding a humidity label and increasing the label font size with css, then apply shape-rendering: geometric precision for crisp lines.

Draw the y-axis for a scatter plot using the d3.axisLeft function with a y scale to represent temperature, including a labeled axis group.

Reverse the y-scale output in D3 to map higher temperatures to the top of the scatter plot, and use a data-temp attribute to debug circle positions.

Refine the scatter plot by reducing x-axis ticks, using tick values for precise control, and formatting humidity labels as percentages to finalize the D3.js visualization.

Learn to draw scatter plots in D3 by retrieving data, setting dimensions, and drawing the image; recreate scales, position shapes, draw an axis, and add optional animations.

Explore the d3 scale library, distinguish continuous and discreet data, and learn to pick appropriate scales while navigating the documentation.

Map a continuous input domain to a continuous output range with the linear scale, transforming data into a new dataset inside and outside the domain.

Learn to draw heat maps with color-graded squares in a reusable d3.js function, using a starter project with multiple heat maps and a data.json of US income.

Draw a 5 by 20 heat map in d3.js by creating 100 rectangles in a group, binding data with join, and positioning each cell with index-based x and y coordinates.

Color a heat map with a linear d3 scale by mapping income to colors using a domain and range. Explore scale comparisons and data sorting for readability.

Transform continuous data into discreet color buckets with the quantize scale. Map values to white, pink, and red using three equal intervals.

Learn how the quantile scale in d3.js maps a continuous data domain to discrete color buckets, using white, pink, and red across 100 or 300 incomes.

Use the threshold scale to transform continuous data into discrete buckets by providing domain thresholds. Map these buckets to the colors white, pink, and red for a heat map.

Explore the quantize, quantile, and threshold scales in D3.js, compare how each transforms continuous data into discrete classes, and learn how manual thresholds shape heat map results.

Explore D3 color scales and color schemes—categorical, diverging, and sequential—using the D3 scale chromatic package; learn to interpolate colors, inspect schemes, and apply three-color red schemes to heat maps.

Explore the long scale in d3.js data visualization by applying a log transformation to the domain, aligning data distribution and improving readability for money, time, and distance data.

Learn how to apply the scaleLog() scale in D3.js to visualize continuous data, compare linear vs log scales, and display circles, labels, and an axis for readable charts.

Explore how to choose and apply different chart scales in D3.js, distinguishing continuous and discrete data to narrow suitable scale options and experiment with various scales.

Explore how to enhance data visualizations by adding animations and interactivity to charts, highlight regions, and trigger actions from events like clicks and hovers.

Explore how histograms reveal data distributions by showing frequency across value ranges with D3. Apply this to weather data, switch between metrics, and view the mean and median.

Prepare a d3.js histogram by loading data.json, setting up the svg container and margins, and building an x-axis scale with d3.extent and a humidity accessor.

Show how histograms use buckets to display frequency, not a single value, and how to format data with d3's bin function into buckets for visualization.

Learn to create bins with D3.bin to format humidity data into a histogram, using domain, value, and threshold to generate bin groups and the X0 and X1 ranges.

Finish the histogram by adjusting bar y coordinates and heights, add an x and y axis, and label each bar to improve readability, using d3.js scales and accessors.

Leverages D3.js to update a histogram in response to a dropdown change, refreshing bars, axes, and labels for humidity, temperature, dew point, wind speed, cloud cover, and ozone.

Update the chart to reflect the user's metric selection by moving scales and bars into the histogram function and using metric-aware accessors and groups for labels and x access.

Learn to animate svg charts with D3 transition, animating colors, coordinates, sizes, and other attributes; chain and synchronize multiple animations for bars, labels, and axes, with interruptions and strong performance.

Identify and fix animation issues in D3 charts by understanding how shapes start at zero coordinates, and use join with update, enter, and exit selections to control transitions.

Learn to fix d3.js transitions by setting initial bar coordinates with an enter selection and join function, animate bars rising over three seconds, and handle metric switches.

Learn how to fix the D3 exit transition by overriding the join function, animating bars as they leave, and coordinating update and exit transitions before removal.

Learn to tweak d3.js histogram visuals by coloring bars for added or removed data and synchronizing label animations with bar transitions using enter, update, and exit functions.

Learn how to add a mean line to a histogram in d3.js, calculating the average with d3.mean and drawing a red, animated line that moves as metrics change.

Add interactive tooltips to your D3.js scatter plot by creating an html tooltip and showing it on hover, displaying date, humidity, and temperature values.

Learn to use d3.js mouse events to detect hover over dots, display a tooltip with dot's data, and change the dot color and size using on('mouse enter') and mouse leave.

Format tooltip data in a d3.js dot chart by creating formatters for humidity, temperature, and date, converting seconds to milliseconds, and applying two-decimal precision for numbers.

Explore how Voronoi diagrams improve tooltips on scatter plots by partitioning the plane to identify the nearest point, using the D3 Delaunay library to generate the diagram.

Create a Voronoi diagram using the D3 Delaunay library, generating partitions for each data point and enabling interactive tool tips on hover.

Fix the voronoi diagram hover in D3 by drawing an enlarged dot on hover and removing it on mouse leave, with pointer-events none to prevent flicker and keep the tooltip.

Learn to draw additional charts in D3, extending the core functionality covered earlier. Jump between chart types as new steps and code are explained, avoiding repetition.

Draw a line chart using D3 to show data over time, with dots connected by a continuous line, using a csv file of dates and closing prices.

Build a d3.js line chart by using time scales for the x axis, parsing dates with d3.timeParse, and adopting a utc scale.

Use the d3 line generator to draw a line chart by calculating coordinates with x and y accessors and binding data to a path.

Add a left y-axis with a dollar-formatted tick label and a bottom x-axis for the time scale, then render both axes below the chart to improve readability.

Create an interactive d3.js line-chart tooltip with a dot and a price/date display, using hover and touch events and an invisible overlay to trigger the tooltip anywhere on the chart.

learn how to position tooltips on a d3.js line chart by using a bisector and a custom bisector, with invert, x and y scales, and data lookup.

Build a custom D3 bisector for dates with an accessor to parse strings, choose the left option, and use the index to update tooltip and circle as the mouse moves.

Finish the tooltip by displaying the date and price above the dot, positioning with the y-scale and x-scale accessors, and formatting the date with D3.timeformat on mouse leave.

Draw a pie chart with d3.js to visualize population by age group from a csv with name and value, compute slice sizes with d3.pie, and render arcs in radians.

draw arcs for a pie chart using d3.arc and the pie function to format data, set the radius from the container, and center the arc group for accurate rendering.

Explore how ordinal scales transform discrete categories into colors for scalable d3.js charts, assigning a unique color to each pie arc and handling additional data like age groups.

Construct ordinal color scale for age groups in a D3 visualization by building a domain of age groups, generating colors with quantize and interpolate spectral, then applying colors to arcs.

Learn to add labels to a d3 pie chart by centering text on each arc, showing age group and population, and using d3 join and arc.centroid for precise positioning.

Adjust the labels by moving age group above population, hide small slices, and switch to a donut with an inner radius using arc labels for clearer centroid placement.

Draw a stacked bar chart with d3.js by loading a csv of state populations, stacking age groups across states, and converting string data to numbers with autotype.

Format data with D3's stack function to prepare 52 states by 9 age groups into 468 bars, and attach age group keys to each state.

Learn to create y and band scales in d3.js for a population bar chart across 52 states, using linear and band scales, domain and range, max calculations, and padding concepts.

Learn to draw stacked bars with d3.js by looping through age-group data, joining with groups, and applying a nine-color ordinal color scale to distinguish age groups.

Draw the axes for the chart using D3, placing states on the x-axis and population on the y-axis, with x-axis bottom and y-axis left and tick formatting to show millions.

Apply finishing touches to a stacked bar chart in d3.js by adjusting band scale spacing, using inner and outer padding, and sorting by total population after summing age groups.

Sketching helps you quickly decide a suitable visualization for your data, improving readability and collaboration; a rough bar graph or scatter plot from a quick pen-and-paper sketch guides code decisions.

Learn to build responsive SVG charts using the viewBox attribute to define the coordinate system and aspect ratio, avoiding manual resize logic.

Explore how D3.js enables flexible data visualizations, with extensive examples on the official site and Observable notebooks, plus open-source code you can reuse; leverage Kaggle for real-world data to practice.