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Data Visualization


Different disciplines have different standards and approaches when it comes to visualization of data. As you do your literature search and discover research in your topic of interest, you may notice different kinds of graphics used to describe data. In many cases, the most effective data visualization approaches can be determined when you think about what kinds of data that you have, and how much information that you want to share. The table below outlines some common graphing or visualization strategies depending on the type of data you may be working with.

If you need help choosing the appropriate visualization type for your data, here are some resources to help:

Visualization Types

Data Types Graphic Types
Quantitative Data


  • Histograms allow a reader to view the frequency distribution for a single quantitative variable


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Histogram plot depicting the body mass of Penguins on Biscoe Island. Adelie penguins show in light blue, and Gentoo penguins shown in dark blue

Source: Palmer Penguins Data Set


Two Variables 

Scatter Plots

  • Scatter plots can be used to view the relationship between two measurements. Useful when working with up to hundreds of observations.


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Scatterplot showing in-state college tuition vs early and mid career salary

Source: College Tuition Data


Line Plots

  • Line plots allow you to see the trend or linear relationship between two measurements (e.g. change over time)


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Line plot for US incarceration trends in urban areas over the years

Source: Incarceration Trends Dataset


Qualitative Data

Word Frequency

  • Word clouds use size to represent the frequency of different words in a particular text.


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Blue wordcloud with words from this research guide

Source: Text scraped from this research guide



  • Depiction of events that occur over time


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Timeline showing segments of pre-1900s American wars over 5 years in length

Source: War Data


Pie Charts

  • Pie charts are best used for depicting the proportion of qualitative responses relative to a whole Caution: If there are too many response types, or if multiple different responses appear in similar proportions, it can be difficult to visualize differences between responses.


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Pie chart depicting the distribution of conflict groups for pre-1900s American wars with about half being Native American, a fourth colonial, and the rest split between Latin American, internal, and European

Source: War Data


Mind Maps

  • Depiction of an arrangement of ideas or logical flow


  • Depiction of the similarity or difference between a variety of qualitative categories.
Mix of Quantitative and Qualitative Data

Bar and Column Charts

  • These plots are frequently used to compare measurements collected between different groups. Caution: Bar and column plots may not show the spread or range of data which could significantly impact how the data can be interpreted.


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Bar chart of 2020 average US energy costs for gas, solar, and wind

Source: Berkeley Lab Energy Data



  • A very common visualization method that depicts basic information on the spread of a quantitative measurement for different categories.


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Boxplot of Biscoe Island penguin mass distribution for the Adelie and Gentoo species

Source: Palmer Penguins Data Set


Dot Plots

  • Useful visualization that depicts every measurement grouped by their respective categories. Useful when you have relatively fewer measurements in each category.

Violin Plots

  • Visualization depicting the frequency of a quantitative measurement separated into categories. Useful when you have many measurements in each category.


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Vionlin plot of Biscoe Island penguin mass distribution for the Adelie and Gentoo species

Source: Palmer Penguins Data Set


Complex Data Visualizations

Three Variables


  • Visualization depicting the quantitative measurement intensity between two related qualitative categories. Can be paired with cladograms to view and organize categorical relationships.

Density Plots

  • Visualization depicting the measurement intensity between three quantitative variables on a two-dimensional plot. Most useful when working with thousands or tens of thousands of measurements.

Many Variables

Principal Component Analysis

  • A qualitative depiction of the primary factors impacting similarity between observations with many simultaneously measured variables in a two dimensional plot. Useful when working with dozens, hundreds or thousands of quantitative measurements across multiple variables.