Distribution charts in data visualization are graphical representations of data that help to understand how data points are distributed or spread out. They provide a visual way to explore the central tendency, variation, and shape of a dataset. Common types of distribution charts include:
- Histograms: Histograms are used to represent the distribution of continuous data. They divide the data into intervals and display the frequency of data points within each interval as bars, which helps to see the frequency of values within different ranges.
- Box plots: Box plots provide a summary of the distribution of continuous data. They display the median, quartiles, and potential outliers in a dataset. Box plots help visualize the spread and skewness of the data.
- Violin plots: Violin plots combine a box plot with a kernel density estimation. They show both the summary statistics of the data (similar to a box plot) and the probability density of the data at different values. Violin plots can help visualize multimodal distributions.
- Line charts: Line charts are primarily used to display trends and changes over time. They connect data points with lines, making them ideal for showing how a single variable changes or progresses.
- Scatter plots: Scatter plots are used to visualize the relationship between two continuous variables. Each point on the plot represents a data pair, with one variable on the x-axis and the other on the y-axis. They help identify patterns, correlations, and outliers.
These different distribution charts are selected based on the type of data and the specific insights one wants to gain from the visualization. Some common uses of distribution charts include:
- Understanding data spread
- Identifying patterns and trends
- Comparing distributions
- Detecting outliers
- Assessing normality
- Statistical inference
- Quality control
- Risk assessment
- Market research
- Geospatial analysis
List of recommended resources #
For a broad overview
A Complete Guide to Histograms
This data tutorial guide to histograms by Chartip provides a broad overview of histograms, a type of chart that plots the distribution of values.
This data visualization catalog groups the various types of charts and graphs used for presenting distribution of data and provides a brief overview of each chart.
For in depth understanding #
Data Visualization: A Practical Introduction
This accessible primer by Kieran Healy explains how to create effective graphics from data. It explains what makes some graphs succeed while others fail and how to think about data visualization in an honest and effective way.
Graphical Methods for Data Analysis
This book by J. M. Chambers provides various old and new graphical methods like scatter plots for analyzing data.
Case study #
An HCI survey on elderly users in India
This paper by Pradipta Biswas and Pat Langdon uses a histogram to present the various age groups in India and studies the human-computer interaction (HCI) among its users.
This paper uses a scatter plot of the number of exporters, average exporter size, and concentration against both income and income per capita, using country-year level data averaged for the period 2006-2008 to present new data on the micro structure of the export sector for 45 countries and studies how exporter behavior varies with country size and stage of development.
References #
Data Visualization Resources: Types of Charts and Graphs for Data Viz
