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Related Concept Videos

Pie Chart01:04

Pie Chart

16.8K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
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Bar Graph01:07

Bar Graph

23.5K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.4K
Run Charts01:12

Run Charts

323
Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
323
Time-Series Graph00:54

Time-Series Graph

5.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Converting Basic D3 Charts into Reusable Style Templates.

Jonathan Harper, Maneesh Agrawala

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    |February 11, 2017
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    Summary
    This summary is machine-generated.

    This study introduces a method to create reusable style templates from D3 charts. These templates allow users to apply existing visual styles to new data, enhancing chart design and data visualization consistency.

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    Area of Science:

    • Computer Science
    • Data Visualization

    Background:

    • Data visualization charts are essential for data interpretation.
    • Reusing chart styles can improve consistency and efficiency in data presentation.
    • D3.js is a popular JavaScript library for creating dynamic and interactive data visualizations.

    Purpose of the Study:

    • To develop a technique for converting existing D3 charts into reusable style templates.
    • To enable the application of these style templates to new datasets, preserving the original visual style.
    • To facilitate the creation of visually consistent charts across different data sources.

    Main Methods:

    • Deconstructing input D3 charts to identify data, marks, and mappings.
    • Ranking the perceptual effectiveness of visual encodings (mappings).
    • Applying style templates to new data by matching data field importance to mapping effectiveness.

    Main Results:

    • A method for generating reusable D3 chart style templates.
    • Demonstration of applying templates to data tables and other D3 charts.
    • Successful application of diverse style templates to various datasets.

    Conclusions:

    • The proposed technique effectively converts D3 charts into reusable style templates.
    • Style templates can be applied to new data, maintaining visual consistency.
    • This approach simplifies the process of applying consistent visual styles to common chart types.