Stata vs. R vs. SPSS for Data Analysis

As you do research with larger amounts of data, it becomes necessary to graduate from doing your data analysis in Excel and find a more powerful software. It can seem like a really daunting task, especially if you have never attempted to analyze big data before. There are a number of data analysis software systems out there, but it is not always clear which one will work best for your research. The nature of your research data, your technological expertise, and your own personal preferences are all going to play a role in which software will work best for you. In this post I will explain the pros and cons of Stata, R, and SPSS with regards to quantitative data analysis and provide links to additional resources. Every data analysis software I talk about in this post is available for University of Illinois students, faculty, and staff through the Scholarly Commons computers and you can schedule a consultation with CITL if you have specific questions.

Short video loop of a kid sitting at a computer and putting on sun glasses

Rock your research with the right tools!


STATA

Stata logo. Blue block lettering spelling out Stata.

Among researchers, Stata is often credited as the most user-friendly data analysis software. Stata is popular in the social sciences, particularly economics and political science. It is a complete, integrated statistical software package, meaning it can accomplish pretty much any statistical task you need it to, including visualizations. It has both a point-and-click user interface and a command line function with easy-to-learn command syntax. Furthermore, it has a system for version-control in place, so you can save syntax from certain jobs into a “do-file” to refer to later. Stata is not free to have on your personal computer. Unlike an open-source program, you cannot program your own functions into Stata, so you are limited to the functions it already supports. Finally, its functions are limited to numeric or categorical data, it cannot analyze spatial data and certain other types.

 

Pros

Cons

User friendly and easy to learn An individual license can cost
between $125 and $425 annually
Version control Limited to certain types of data
Many free online resources for learning You cannot program new
functions into Stata

Additional resources:


R logo. Blue capital letter R wrapped with a gray oval.

R and its graphical user interface companion R Studio are incredibly popular software for a number of reasons. The first and probably most important is that it is a free open-source software that is compatible with any operating system. As such, there is a strong and loyal community of users who share their work and advice online. It has the same features as Stata such as a point-and-click user interface, a command line, savable files, and strong data analysis and visualization capabilities. It also has some capabilities Stata does not because users with more technical expertise can program new functions with R to use it for different types of data and projects. The problem a lot of people run into with R is that it is not easy to learn. The programming language it operates on is not intuitive and it is prone to errors. Despite this steep learning curve, there is an abundance of free online resources for learning R.

Pros

Cons

Free open-source software Steep learning curve
Strong online user community Can be slow
Programmable with more functions
for data analysis

Additional Resources:

  • Introduction to R Library Guide: Find valuable overviews and tutorials on this guide published by the University of Illinois Library.
  • Quick-R by DataCamp: This website offers tutorials and examples of syntax for a whole host of data analysis functions in R. Everything from installing the package to advanced data visualizations.
  • Learn R on Code Academy: A free self-paced online class for learning to use R for data science and beyond.
  • Nabble forum: A forum where individuals can ask specific questions about using R and get answers from the user community.

SPSS

SPSS logo. Red background with white block lettering spelling SPSS.

SPSS is an IBM product that is used for quantitative data analysis. It does not have a command line feature but rather has a user interface that is entirely point-and-click and somewhat resembles Microsoft Excel. Although it looks a lot like Excel, it can handle larger data sets faster and with more ease. One of the main complaints about SPSS is that it is prohibitively expensive to use, with individual packages ranging from $1,290 to $8,540 a year. To make up for how expensive it is, it is incredibly easy to learn. As a non-technical person I learned how to use it in under an hour by following an online tutorial from the University of Illinois Library. However, my take on this software is that unless you really need a more powerful tool just stick to Excel. They are too similar to justify seeking out this specialized software.

Pros

Cons

Quick and easy to learn By far the most expensive
Can handle large amounts of data Limited functionality
Great user interface Very similar to Excel

Additional Resources:

Gif of Kermit the frog dancing and flailing his arms with the words "Yay Statistics" in block letters above

Thanks for reading! Let us know in the comments if you have any thoughts or questions about any of these data analysis software programs. We love hearing from our readers!

 

Exploring Data Visualization #16

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.

A shaded area chart in Excel

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.

Scary Research to Share in the Dark: A Halloween-Themed Roundup

If you’re anything like us here in the Scholarly Commons, the day you’ve been waiting for is finally here. It’s time to put on a costume, eat too much candy, and celebrate all things spooky. That’s right, folks. It’s Halloween and we couldn’t be happier!

Man in all black with a jack o' lantern mask dancing in front of a green screen cemetery

If you’ve been keeping up with our Twitter (@ScholCommons) this month, you’ve noticed we’ve been sharing some ghoulish graphs and other scary scholarship. To keep the holiday spirit(s) high, I wanted to use this week’s blog post to gather up all our favorites.

First up, check out the most haunted cities in the US on The Next Web, which includes some graphs but also a heat map of the most haunted areas in the country. Which region do you think has the most ghosts?

If you’re more interested in what’s happening on across the pond, we’ve got you covered. Click on this project to see just how scary ArcGIS story maps can be.

https://twitter.com/ScholCommons/status/1187058855282462721

And while ghosts may be cool, we all know the best Halloween characters are all witches. Check out this fascinating project from The University of Edinburgh that explores real, historic witch hunts in Scotland.

The next project we want to show you might be one of the scariest. I was absolutely horrified to find out that Illinois’ most popular Halloween candy is Jolly Ranchers. If you’re expecting trick-or-treaters tonight, please think of the children and reconsider your candy offerings.

Now that we’ve share the most macabre maps around, let’s shift our focus to the future. Nathan Yau uses data to predict when your death will occur. And if this isn’t enough to terrify you, try his tool to predict how you’ll die.

Finally, if you’re looking for some cooking help from an AI or a Great Old One, check out this neural network dubbed “Cooking with Cthulhu.”

Do you have any favorite Halloween-themed research projects? If so, please share it with us here or on Twitter. And if you’re interested in doing your own deadly digital scholarship, feel free to reach out to the Scholarly Commons to learn how to get started or get help on your current work. Remember, in the words everyone’s favorite two-faced mayor…

A clip of the Mayor from Nightmare Before Christmas saying There's only 365 days left until next Halloween

Exploring Data Visualization #15

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.

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

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.

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

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.

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.

The interface of FastCharts, showing a line chart of global temperature anomalies from 1850 to 2017.

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

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.

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 13th, 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

 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.

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 7th. 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

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.

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:

A piece of paper that reads "Welcome to Room 220" at the top with sticky notes stuck to the page underneath.

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 2nd 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.

Exploring Data Visualization #9

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.

Map of election districts colored red or blue based on predicted 2018 midterm election outcome

This map breaks down likely outcomes of the 2018 Midterm elections by district.

 

Seniors at Montgomery Blair High School in Silver Spring, Maryland created the ORACLE of Blair 2018 House Election Forecast, a website that hosts visualizations that predict outcomes for the 2018 Midterm Elections. In addition to breakdowns of voting outcome by state and district, the students compiled descriptions of how the district has voted historically and what are important stances for current candidates. How well do these predictions match up with the results from Tuesday?

A chart showing price changes for 15 items from 1998 to 2018

This chart shows price changes over the last 20 years. It gives the impression that these price changes are always steady, but that isn’t the case for all products.

Lisa Rost at Datawrapper created a chart—building on the work of Olivier Ballou—that shows the change in the price of goods using the Consumer Price Index. She provides detailed coverage of how her chart is put together, as well as making clear what is missing from both hers and Ballou’s chart based on what products are chosen to show on the graph. This behind-the-scenes information provides useful advise for how to read and design charts that are clear and informative.

An image showing a scale of scientific visualizations from figurative on the left to abstract on the right.

There are a lot of ways to make scientific research accessible through data visualization.

Visualization isn’t just charts and graphs—it’s all manner of visual objects that contribute information to a piece. Jen Christiansen, the Senior Graphics Editor at Scientific American, knows this well, and her blog post “Visualizing Science: Illustration and Beyond” on Scientific American covers some key elements of what it takes to make engaging and clear scientific graphics and visualizations. She shares lessons learned at all levels of creating visualizations, as well as covering a few ways to visualize uncertainty and the unknown.

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