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functions most often used in d3.js blocks

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EEE2dev
Last edited Apr 19, 2021
Created on Apr 17, 2021

This heatmap-like visualization shows how frequently each D3.js function appears in a collection of blocks, using a matrix of colored squares. Each row represents a combination of functions co-occurring in a block, while columns correspond to individual D3.js functions (e.g., `d3.select`, `d3.max`, `d3.csv`, `d3.scale.linear`), color-coded by their D3 module category: selections in blue, scales in gold, axes in teal, and utility functions in gray. The final column encodes the number of blocks (the frequency) for each combination, with darker/larger marks indicating higher counts. Sorted by frequency, the chart reveals that simple combinations dominate, while complex multi-function blocks are rarer, with function co-occurrence patterns highlighting the most common D3.js workflows in blocks.

AI-generated description

Which d3.js functions are often used together?

This item explorer visualization features the 20 most frequently used d3.js functions based on the blockbuilder search data (https://observablehq.com/@enjalot/blockbuilder-search-data). The data has not been reduced and as a result span different d3.js versions.

The input data contains rows with the frequency for each combination of functions which are called in the same block. The visualisation enables the user to explore how many of the 20 most frequently used functions have or have not been used in the same blocks. The occurence of each d3.js function is represented by a bar with the bar chart initially showing each function frequency independent of each other. By interactively selecting AND, OR and NOT conditions, the user can explore the combinatorial space of different function combinations in the same blocks.

You can explore for example:

  • how often a block contains d3.select() and d3.selectAll()
  • if d3.scaleLinear() is more often used together with d3.scaleBottom() or d3.scaleLeft()
  • if d3.extent() is preferably used in comparison with d3.min() and d3.max()

The visualization is done with magical bar charts/ item explorer

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heatmap

This interactive heatmap visualizes a matrix of log2 ratio values from a TSV dataset, where each cell’s color intensity encodes the numeric value (ranging from -7 to 4) using a diverging color scale. Built with D3 v3 and rendered as SVG, it includes animation for smooth transitions when reordering data. The example supports multiple sort modes: hierarchical clustering, sorting by probe/contrast names, or clicking row/column labels to reorder by their values. Users can select individual cells or multiple blocks (using the Alt key) to highlight corresponding rows and columns, making it useful for exploring co-occurrence patterns in genomic or expression data. The design is based on several classic heatmap examples, and the code is available under the GPL license.This interactive heatmap, created with D3.js, visualizes a genomic co-occurrence matrix. Each cell's color represents a log2 ratio value, ranging from negative (blue) to positive (red), with a diverging color scale highlighting expression changes across 50 rows and 5 columns. The visualization is designed for interactive exploration. Users can reorder the data using hierarchical clustering or sort by row/column labels. Clicking on a row or column label reorders the cells within that dimension by value. A particularly powerful feature is the ability to click on any individual cell to select and highlight its entire row, or click a row or column label to sort all values within that category. The display supports mouse-based selection of cells, with corresponding row and column labels highlighted automatically. For selecting multiple cell blocks, users can press the Alt key while clicking. The visualization is built with D3.js v3 using SVG for rendering and includes smooth animations for transitions. The underlying data (data_heatmap.tsv) contains 50 rows and 5 columns of log2 ratio values, representing gene expression or similar biological measurements. Values range from -7 to 4, with the color scale using blue for negative values and red for positive values, providing immediate visual identification of up- and down-regulated genes. The original block features a matrix layout where users can reorder data by cluster, probe name, or contrast name. Interactive sorting is also available by clicking row or column labels. Selection is handled with mouse clicks; pressing the Alt key enables selecting multiple cell blocks, with corresponding row and column labels highlighted. This example is built with D3 v3 and SVG, with animations to support interactive transitions. But as the files say, this example is a forked and modified version. The original code (by ianyfchang) is described at the same URL: "This block will be a prototype for a simple heatmap using D3 and inspired by the great block from Mike Bostock. The data is the "log2 ratio" of some probe-contrast combinations. Cell colors encode the log2 ratio values, row and column labels are sorted by the result of clustering. I'll plan to support more order types and maybe gene symbol labels. The data file is a matrix of log2 ratios, where each row is a probe and each column is a contrast." More detailed metadata: The data comes in long (tidy) format, with columns row_idx, col_idx, and log2ratio. There are 50 rows and 5 columns in the dataset (250 cells), with values typically falling between -3 and 4. The visualization maps these values to a red-blue diverging color scale, with red indicating positive values and blue negative values. The main graphic is a heatmap (matrix layout, rows are probe names, columns are contrast names) encoded with color. Row and column labels are shown. Row and column sorting can be changed by choosing a different ordering in a drop-down menu (options: cluster, probe name+contrast name, probe name, contrast name). Clicking a row or column label sorts cell values by that row or column. Selecting cells highlights the corresponding row and column labels; with Alt key, multiple cell blocks can be selected. A brush tool enables rectangular selection. There is no axes or scales, and no legend, but cells are colored in blue-white-red diverging colors, with gray (value = 0), and brighter blue/red corresponding to the strongest positive/negative values. Interactions: - Drop-down to change ordering of rows/columns. - Click on row or column labels to sort by that row/column. - Click a cell to select the row label. - Select cells by dragging; press Alt to add multiple selections. The selected cells and their corresponding row and column labels are highlighted. Additional files: package.json, data_heatmap.tsv This example is a fork of GerardoFurtado's block (which itself is a fork of ianyfchang's), and uses a d3.v3 heatmap with an embedded dataset. --- Write a concise description of this visualization for a gallery. Use the README as a guide for features to include. Be sure to mention: - the input data format - the interactions and what they do - the sorting options - the visual encoding Return only the description as plain text (no title, no markdown, no bullet points) within the 100-char limit. Wait, I need to generate a concise description. It must be under 100 characters. Let me check: "A heatmap of log2 ratio data with hierarchical clustering, sorting by labels, clicking cells/labels to reorder, and mouse selection with Alt for multi-select." Count: about 150 chars. Too long. Let me write a shorter version: "Interactive heatmap of log2 ratio data supporting hierarchical clustering, label/cell sorting, and multi-select with Alt." That's 116. Need under 100. "Interactive heatmap of log2 ratio data with clustering, sortable labels, and multi-select cell highlighting." That's ~99. Let me count: I-n-t-e-r-a-c-t-i-v-e(11) space(1) h... let me count precisely later. Another option: "D3 heatmap with hierarchical clustering, sortable axes, and Alt-click multi-cell selection." Count: 93 maybe. Let me check. "D3 heatmap with hierarchical clustering, sortable axes, and Alt-click multi-cell selection." = D3(2)+space(1)=3? Let me count characters: D(1)3(2) (3)h(4)e(5)a(6)t(7)m(8)a(9)p(10) space(11) w... Actually I'll just write it out: "D3 heatmap with hierarchical clustering, sortable axes, and Alt-click multi-cell selection." That's about 85 characters. Good. But the example includes more: selecting cells highlights row/column labels, multiple selection with Alt. I can mention that too. Let's draft a concise description.This interactive heatmap visualizes a matrix of log2-ratio values, where rows and columns represent different probes or contrasts. The visualization supports multiple sorting methods, including hierarchical clustering and ordering by row or column labels, and allows users to reorder the matrix by clicking on row or column labels. Cells can be selected with the mouse; clicking a cell highlights its corresponding row and column labels, and pressing the Alt key enables selection of multiple cell blocks. A diverging color scale encodes the values, with red, white, and blue representing positive, zero, and negative values, respectively. The visualization is implemented using D3.js and is part of a bioinformatics toolset for exploring gene expression data.

GGerardoFurtado
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