Visualizing your love for data

Posted on February 17, 2023 by Precious Olalere

This post is in celebration of the love data week between Feb-13-Feb 17, 2023.

Analytics screen graph. — Photo by Luke Chesser on Unsplash

What is Data visualization?

For this author, it was love at first sight. Well, technically, it was love at first Visualization. So many say seeing is believing, and data visualization helps us accomplish that, especially at the rate at which data is increasing exponentially in our world. The truth is that data is everywhere, and for us to draw meaning from it, we need to present it in a clear and concise manner.

Data visualization is the graphical representation of data. Data can be represented in various forms and shapes, such as maps, charts, infographics, graphs, heat maps, or sparklines. When data is presented through visual elements, it is easy to understand and analyze. It helps you to derive meaning from the data and make better decisions. Visualizing your data involves using certain tools; these tools help you fall more in love with data.

Data Visualization tools are software that allow you to create graphical representations of your data.

Here are some tools to help you get started. These have been selected based on their ease of use, features (such as capacity for large volumes of data), cost, and popularity.

Data Wrapper: If you are just starting out with data Visualization and you are looking for a free tool to help you get started, Data wrapper is your plug. Data Wrapper is a beginner-friendly tool with a clean and intuitive user interface accessible online. It is straightforward to navigate and great for creating charts and maps that can be easily embedded into reports. It also allows you to upload your files in various formats such as CSV, .tsv, and .txt

Pros:

Great for beginners.
Free to use.
Accessible online tool.

Cons:

It can be challenging to build complex charts.
Limited features.
Security is not guaranteed as it is an online tool.

Infogram: If you are not super design-inclined, this visualization tool should be your best friend. It has an editor drag-and-drop feature that makes it super easy to create beautiful designs without having to worry about where you are with your design skills. Infographics, marketing reports, maps, social media posts, and many more are examples of what you can create with this powerful tool. In addition, your data output can be exported in various formats, such as. JPG, GIF, PNG, HTML, and . PDF.

Pros:

Web-based.
Drag-and-drop editor.
Easy to use.
Highly customizable.

Cons:

Built-in data sources are limited.
Not suitable for complex visualization.

Google charts: Google Charts is another free data visualization tool that is user-friendly and compatible with all browsers and platforms. If you like to play around with codes, then Google Charts provides you with that option. Google Charts are coded with SVG and HTML5, allowing it to produce several graphic and pictorial data visualizations, ranging from simple visualization such as pie charts, bars, charts, histograms, maps, and scatter graphs to more complex ones such as hierarchical tree maps, timelines, and gauges. Google fusion tables, spreadsheets, and SQL databases are examples of data sources that can be used with Google Charts.

Pros:

It is free.
It is compatible with various browsers.
Compatible with google products.

Cons:

Technical support is limited.
It requires network connectivity for visualization.
There is no room for customization.

Tableau: This is one of the most popular data visualization tools, mainly because of the free public version that this software provides. Tableau provides the option of a desktop app, server, and online versions. In addition, this software has several data importation options, such as CSV files for google ads. Similarly, if you are looking into presenting your data in various formats, such as multiple chart formats and mapping, then Tableau is the one for you.

Pros:

Provides several options for data import.
It is available for free (public version).

Cons:

Lack of Privacy in the public version.
Paid versions are costly.

5. Dundas BI: Although this is one of the oldest data visualization tools, it is still standing strong as one of the most powerful tools for visualizing data with interactive charts, tree maps, gauges, smart tables, and scorecards. This interactivity allows users to understand the data quickly. Dundas BI is also highly customizable. Dundas BI operates on the ground of responsive HTML5 web technology that allows users to connect, analyze and interact with their data on any device. This powerful tool also provides a built-in feature for extracting data from many data sources.

Pros:

Highly flexible.
Provides a variety of visualization options.

Cons:

It lacks predictive analysis.
Does not support 3D charts.

There you have it! Now you know the tools to ask out on a date when you are ready to visualize your data. As much as you love data, these tools can help make others fall in love with your data, too.

Exploring Data Visualization #18

Posted on March 12, 2020 by Xena Becker

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Painting the World with Water

Creating weather predictions is a complex tasks that requires global collaboration and advanced scientific technologies. Most people know very little about how a weather prediction is put together and what is required to make it possible. NASA gives us a little glimpse into the complexities of finding out just how we know if it’s going to rain or snow anywhere in the world.

Continue reading →

Exploring Data Visualization #17

Posted on February 21, 2020 by Xena Becker

The unspoken rules of visualization

Title header of essay "The unspoken rules of data visualization" by Kaiser Fung. White text on a black background with green and red patches Continue reading →

Exploring Data Visualization #16

Posted on November 7, 2019 by Xena Becker

Daylight Saving Time Gripe Assistant Tool

Clocks fell back this weekend, which means the internet returns once again to the debate of whether or not we still need Daylight Saving Time. Andy Woodruff, a cartographer for Axis Maps, created a handy tool for determining how much you can complain about the time change. You input your ideal sunset and sunrise times, select whether the sunset or sunrise time you chose is more important, and the tool generates a map that shows whether DST should be gotten rid of, used year-round, or if no changes need to be made based on where you live. The difference a half hour makes is surprising for some of the maps, making this a fun data viz to play around with and examine your own gripes with DST.

A map of the United States with different regions shaded in different colors to represent if they should keep (gray) or get rid of (gold) changing the clocks for Daylight Saving Time. Blue represents areas that should always use Daylight Saving Time.

This shows an ideal sunrise of 7:00 am and an ideal sunset of 6:00 pm.

Laughing Online

Conveying tone through text can be stressful—finding the right balance of friendly and assertive in a text is a delicate operation that involves word choice and punctuation equally. Often, we make our text more friendly through exclamations points! Or by adding a quick laugh, haha. The Pudding took note of how varied our use of text-based laughs can be and put together a visual essay on how often we use different laughs and whether all of them actually mean we are “laughing out loud.” The most common laugh on Reddit is “lol,” while “hehe,” “jaja,” and “i’m laughing” are much less popular expressions of mirth.

A proportional area chart showing which text laughs are most used on Reddit.

“ha” is the expression most likely to be used to indicate fake laughter or hostility

how to do it in Excel: a shaded range

Here’s a quick tip for making more complex graphs using Excel! Storytelling with Data’s Elizabeth Ricks put together a great how-to article on making Excel show a shaded range on a graph. This method involves some “brute force” to make Excel’s functions work in your favor, but results in a clean chart that shows a shaded range rather than a cluster of multiple lines.

Pixelation to represent endangered species counts

On Imgur, user JJSmooth44 created a photo series to demonstrate the current status of endangered species using pixilation. The number of squares represent the approximate number of that species that remains in the world. The more pixelated the image, the fewer there are left.

A pixelated image of an African Wild Dog. The pixelation represents approximately how many of this endangered species remain in the wild (estimated between 3000 and 5500). The Wild Dog is still distinguishable, but is not clearly visible due to the pixelation.

The African Wild Dog is one of the images in which the animal is still mostly recognizable.

Exploring Data Visualization #15

Posted on October 3, 2019 by Xena Becker

Which milk has the smallest impact on the planet?

Climate change impacts each of us in direct and indirect ways. Mitigating your personal carbon footprint is an important way to address climate change for many people, but often people are unsure how to make choices that benefit the climate. Daniela Haake from Datawrapper took a close look at how her choice to have milk in her coffee was damaging or benefiting the planet and it turns out things aren’t looking great for café con leche. The chart Haake published, created using data from Dr. Joseph Poore, compares the carbon emissions, land use, and water use of milk and the top 4 milk alternatives.

A chart comparing the carbon emissions, land use, and water use of milk and the top 4 milk alternatives

Soy milk has the lowest overall impact on carbon emissions, land use, and water use.

Here’s Who Owns the Most Land in America

“The 100 largest owners of private property in the U.S., newcomers and old-timers together, have 40 million acres, or approximately 2% of the country’s land mass,” Bloomberg News reports. The people who own this land are the richest people in the country, and their wealth has grown significantly over the last 10 years. Bloomberg created a map that demonstrates where the land these people own is located. Compared to the rest of the country, the amount of land owned by these people looks relatively small—could Bloomberg have presented more information about why it is significant that these people own land in these areas? And about why they own so much land?

A map of the continental United States with the land owned by the 10 largest owners of private property highlighted

This image shows only the land owned by the top 10 landowners.

How to Get Better at Embracing Unknowns

Representing our uncertainty in data can be difficult to do clearly and well. In Scientific American this month, Jessica Hullman analyzed different methods of representing uncertainty for their clarity and effectiveness. While there may be no perfect way to represent uncertainty in your data viz, Hullman argues that “the least effective way to present uncertainty is to not show it at all.” Take a look at Hullman’s different ways to represent uncertainty and see if any might work for your next project!

A gif showing two methods of demonstrating uncertainty in data visualizations through animation

Animated charts make uncertainty impossible to ignore

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #14

Posted on May 3, 2019 by Xena Becker

A day in the life of Americans: a data comic

A comic demonstrating the amount of time Americans spend sleeping, at work, free, or doing other activities from 4 a.m. to 3 p.m.

By illustrating the activity most Americans are doing at a given hour, Hong highlights what the average day looks like for an American worker.

Happy May, researchers! With the semester winding down and summer plans on the horizon, a lot of us are reflecting on what we’ve done in the past year. Sometimes it can be hard to determine what your daily routine looks like when you are the one doing it every day. Matt Hong created a cute and informative data comic about how we spend our time during the day, based on data from the Census Bureau. Check out Hong’s Medium page for more data comics.

What Qualifies as Middle-Income in Each State

A bar chart that shows the range of incomes that qualify as "middle-income" for households made up of four people, organized by state.

The distribution of middle-income for households made up of four people.

Nathan Yau at Flowing Data created an interesting chart that shows the range of income that is considered “middle-income” in each state and the District of Columbia in the United States. The design of the chart itself is smooth and watching the transitions between income ranges based on number of people in the household is very enjoyable. It is also enlightening to see where states fall on the spectrum of what “middle-income” means, and this visualization could be a useful tool for researchers working on wage disparity.

When People Find a New Job

A frequency trail chart that shows peaks based on the age when people change jobs.

The bottom of the chart shows jobs that people transition into later in life.

The end of the semester also means a wave of new graduates entering the workforce. While we extend our congratulations to those people, we often inquire about what their upcoming plans are and where they will be working in the future. For some, that question is straightforward; for others, a change of pace may be on the horizon. Nathan Yau of Flowing Data also created a frequency trail chart that shows at what age many people change career paths. As Yau demonstrates in a bar chart that accompanies the frequency trail chart, the majority of job switches happen early and late in life, a phenomenon which he offers some suggestions for.

A bar chart showing the distribution of the age at which people switch jobs. 15-19 is the highest percent (above 30%) and 55-64 is the lowest (around 10%)

The peak at the “older” end of the chart indicates some changes post-retirement, but also makes you wonder why people are still finding new jobs at age 85 to 89.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #13

Posted on April 4, 2019 by Xena Becker

One Way to Spot a Partisan Gerrymander

Even though it feels like it was 2016 yesterday, we are more than a quarter of the way through 2019 and the 2020 political cycle is starting to heat up. A common issue in the minds of voters and politicians is fraudulent and rigged elections—voters increasingly wonder if their votes really matter in the current political landscape. Last week, the Supreme Court heard two cases on partisan gerrymandering in North Carolina and Maryland. FiveThirtyEight made an elegant visualization about gerrymandering in North Carolina. The visualization demonstrates how actual election outcomes can be used to extrapolate what percentage of seats will go to each party.

A graph that shows the Average Democratic vote share in the U.S. House plotted against the actual outcome. A pink line represents the average outcome and since it does not pass through (0, 0), that indicates partisan bias in the House election being studied.

If there is no partisan bias in voting districts, the outcome should be 50/50.

As you scroll, the chart continues to develop and become more complicated. It adds results from past elections to contextualize the severity of the current problems with gerrymandering. It also provides an example of the outcomes of a redrawn district map in Pennsylvania.

Mistakes, we’ve drawn a few

Two different charts that both represent attitudes in the UK toward Britain voting to leave the EU. The chart on the left is a sine chart which looks erratic while the chart on the right shows the averages of plotted lines and demonstrates clear trends.

The change from line chart to plotted points better demonstrates the trend of the attitudes toward Brexit.

Sarah Leo from The Economist re-creates past visualizations from the publication that were misleading or poorly designed. The blog post calls out the mistakes made very effectively and offers redesigns, when possible. They also make their data available after each visualization.

Seeing two visualizations of the same data next to one another really helps drive home how data can be represented differently–and how that causes different impacts upon a reader.

FastCharts

The Financial Times has made an online version of their quick chart-making tool available for the public. Appropriately titled FastCharts, the site lets you upload your own data or play around with sample data they have provided. Because this tool is so simple, it seems like it would be useful for exploratory data, but maybe not for creating more complex explanations of your data.

FastCharts automatically selects which type of chart it thinks will work best for your data.

Play with the provided example data or use your own data to produce an interesting result! For a challenge, see if any of the data in our Numeric Data Library Guide can work for this tool.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #12

Posted on March 13, 2019 by Xena Becker

American segregation, mapped day and night

Is segregation in the United States improving? And if it is, what race sees the most people of different races? And do the answers to these questions change based on the time of day? Vox sets out to answer some of these questions through a video essay and an interactive map about segregation in the United States cities at work and at home.

A map of Champaign County showing data peaks where the highest population of Black people live.

This map shows the population density of Black people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of Black people living only near Black people.

A map showing the areas in Champaign County populated by white people.

This map shows the population density of white people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of white people living only near white people.

The map is interesting and effectively demonstrates the continued presence of segregation in communities across the United States. However, there is little detail on the map about the geographical features of the region being examined. This isn’t too much of a problem if you are familiar with the region you are looking at, but for more unfamiliar communities it leads to more questions than it answers.

NASA’s Opportunity Rover Dies on Mars

After 15 years on Mars, the Opportunity Rover Mission was officially declared finished on February 13^th, 2019. The New York Times created a visualization that lets you follow Opportunity’s 28 mile path across the surface of Mars, which includes a bird’s eye view of Oppy’s path as well as images sent by the rover back to NASA. Opportunity was responsible for discovering evidence of drinkable water on Mars.

A map of the surface of mars with a yellow line showing the path of NASA's Opportunity rover. There is a small image in the corner of Santa Maria Crater taken by the rover.

The map of Opportunity’s path is accompanied by images from the rover and artists’ renderings of the surface of Mars.

The periodic table is a scatterplot. (Among others.)

The periodic table: a data visualization familiar to anyone who has ever set foot in a grade school science classroom. As Lisa Rost points out, the periodic table is actually just a simple scatter plot, with group as the x-axis and period as the y-axis. Or at least, that’s true of the Mendeleev periodic table, the one we are most familiar with. See some other examples of how to break down the periodic table on Rost’s post, which links to the Wikipedia article on alternative periodic tables. If you find a favorite, be sure to tweet it to us @ScholCommons! We are always curious to see what visualizations get people excited.

A visualization of the periodic table of the elements with the elements represented by different colored dots. The dot colors correspond to when in time the elements were discovered, which is coded in a key at the top of the chart. Yellow is before Mendeleev, blue is after Mendeleev, orange is BC, and black is since 2000.

A periodic table color coded by Lisa Rost to show when in time different elements where discovered.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #11

Posted on February 13, 2019 by Xena Becker

Data Visualization Office Hours and Workshops

A headshot of Megan Ozeran with a border above her reading Data Viz Help and a banner below that reads The Librarian is In

Our amazing Data Visualization Librarian Megan Ozeran is holding open office hours every other Monday for the Spring 2019 semester! Drop by the Scholarly Commons from 2-4 on any of the dates listed below to ask any data viz questions you might have.

Office hours on: February 25, March 11, March 25, April 8, April 22, and May 6.

Additionally, Megan will teach a joint workshop as part of our Savvy Researcher series titled “Network Analysis in Digital Humanities” on Thursday, March 7^th. Megan and SC GA Kayla Abner will cover the basics of how to use NodeXL, Palladio, and Cytoscape to show relationships between concepts in your research. Register online on our Savvy Researcher Calendar!

Lifespan of News Stories

A chart showing the search interest for different news stories in October 2018, represented as colored peaks with the apex labeled with a world event.

October was one of the busier times of the year, with eight overlapping news stories. Hurricane Michael tied with Hurricane Florence for the largest number of searches in 2018.

According to trends compiled by the news site Axios, “news cycles for some of the biggest moments of 2018 only lasted for a median of 7 days.” Axios put together a timeline of the year which shows the peaks and valleys of 49 of the top news stories from 2018. A simplified view of the year in the article “What captured America’s attention in 2018” shows the distribution of those 49 stories, while a full site, “The Lifespan of News Stories,” shows search interest by region and links to an article from Axios about the event (clever advertising on their part).

#SWDchallenge: visualize variance

A graph showing the average minimum temperature in Milwaukee, Wisconsin, for January 2000 through January 2019. The points on the chart are connected with light blue lines and filled in with blue to resemble icicles.

Knaflic’s icicle-style design for minimum temperature.

If there were to be a search interest visualization for the past few weeks in the Midwest, I have no doubt that the highest peak would be for the term “polar vortex.” The weather so far this year has been unusual, thanks to the extreme cold due to the polar vortex we had in the last week of January. Cole Nussbaumer Knaflic from Storytelling with Data used the cold snap as inspiration for the #SWDchallenge this month: visualize variance. Knaflic went through a series of visualizations in a blog post to show variation in average temperature in Milwaukee.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #10

Posted on December 10, 2018 by Xena Becker

A collage of images of sticky notes in different configurations from the article "stickies!"

Sticky notes in all different shapes, sizes, and colors provide a perfect medium for project planning.

1. Sometimes when you want to visualize your thinking, digital tools just don’t cut it and you have to go back to cold, hard paper. At the beginning of November, Cole Nussbaumer Knaflic at Storytelling with Data made a #SWDchallenge for readers to use sticky notes to represent their thinking and plan out a data visualization the old fashioned way! The images that resulted from that challenge, seen in the post stickies!, are an office-supply lover’s dream. I’ve taken inspiration from these posts in my own project planning for the past month—here’s a sneak peek of my thoughts for a sign that will be displayed in a library study space:

2. In a feature from February of this year, the digital branch of German newspaper Die Zeit, ZEIT ONLINE, showed some interesting finds from their database of approximately 450,000 street names used across Germany. They call the project Streetscapes and use them to explore important parts of German history. These street names show the legacy of political division in Germany, as well as noting what the most common names for streets are and what the age of different streets in Berlin are.

A map of Berlin with streets highlighted in different colors based on the age of the street name.

Older street names are clearly concentrated toward the center of Berlin.

3. Google Maps updated their display this year to zoom out to a globe instead of a flat Mercator projection, noting in a tweet on August 2^nd that “With 3D Globe Mode…, Greenland’s projection is no longer the size of Africa.” Adapting the shape of countries from a globe to a flat map has always been a challenge and has resulted in some confusion as to how the Earth’s geography actually looks. In the third part of a series of Story Maps about “The World’s Troubled Lands & Geopolitical Curiosities,” John Nelson outlines some of those misconceptions. In a National Geographic write-up titled “Why your mental map of the world is (probably) wrong,” Betsy Mason goes deeper into why we hold these misconceptions and why they are so hard to let go of.

The title slide of a story map with text that reads "Misconceptions Some Common Geographic Mental Misplacements..."

The story map shows which three different regions people often misplace in their minds.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

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Tag Archives: exploring data visualization

Visualizing your love for data

What is Data visualization?

Exploring Data Visualization #18

Exploring Data Visualization #17

Exploring Data Visualization #16

Exploring Data Visualization #15

Exploring Data Visualization #14

Exploring Data Visualization #13

Exploring Data Visualization #12

Exploring Data Visualization #11

Exploring Data Visualization #10