WI Fast Stats: a collection of web apps for the visualization and analysis of WI Fast Plants data
WWI F
AST S TATS : A COLLECTION OF WEB APPS FOR THEVISUALIZATION AND ANALYSIS OF
WI F
AST P LANTS DATA
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Yizhou Liu
Department of Computer ScienceUniversity of Wisconsin-Madison
Claudia Solís-Lemus ∗ Wisconsin Institute for Discoveryand Department of Plant PathologyUniversity of Wisconsin-MadisonDecember 8, 2020 A BSTRACT
WI Fast Stats is the first and only dedicated tool tailored to the WI Fast Plants educational objectives.WI Fast Stats is an integrated animated web page with a collection of R-developed web apps thatprovide Data Visualization and Data Analysis tools for WI Fast Plants data. WI Fast Stats is auser-friendly easy-to-use interface that will render Data Science accessible to K-16 teachers andstudents currently using WI Fast Plants lesson plans. Users do not need to have strong programmingor mathematical background to use WI Fast Stats as the web apps are simple to use, well documented,and freely available. K eywords WI Fast Plants · biology education · data science · middle school · high school · elementary school WI Fast Plants ( https://fastplants.org/ ) [1] were developed as a research tool at the University of Wisconsin-Madison and have been used by K-16 teachers around the world for nearly 30 years as an educational model-organism.As the name suggest, WI Fast Plants have a short time from planting to flowering (about 2 weeks), and thus, theseplants are used in classrooms to engage learners of all ages and grade levels into the investigation of plant life cycles,the growth of flowers, the role of environmental factors on plants, the energy transformation of plants, the genetics ofhybrids among many other topics.Thousands of students from elementary school to college level grow WI Fast Plants in class or at home, and collect datafrom their experiments, yet they do not have any user-friendly tool to visualize or analyze these data. Existing DataScience platforms such as CODAP [2] are either too complicated to use or expensive. Furthermore, existing interfacesare not tailored to WI Fast Plants data or educational objectives, and lack the flexibility to evolve alongside the lessonoptions provided by WI Fast Plants.
WI Fast Stats ( https://wi-fast-stats.wid.wisc.edu/ ) is an integrated animated web page which serves as amedium to a collection of R-developed web apps that provide Data Visualization and Data Analysis tools for WI FastPlants data. Each web app corresponds to a educational unit linked to a specific WI Fast Plants webinar and it servestwo main functions: 1) K-16 teachers attending the WI Fast Plants webinar will learn how to design Data Scienceexercises through the web app for their students and 2) students can use the web app independently to learn aboutvisualization and analysis via the publicly available sample datasets and educational materials.In this sense, WI Fast Stats is designed to be used by both K-16 teachers and by students from elementary school tocollege. A preliminary version of a web app has already been used in a 2020 WI Fast Plants webinar with over 300 ∗ Corresponding author: [email protected] a r X i v : . [ q - b i o . O T ] D ec PREPRINT - D
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8, 2020Figure 1: WI Fast Stats website home page: https://wi-fast-stats.wid.wisc.edu/ .high-school teachers across the U.S. in attendance. Based on the comments at the webinar and follow-up messages, theattendees were deeply impressed by the potential of the web app to revolutionize the manner in which they currentlyteach Data Science in the classroom.WI Fast Stats is a user-friendly easy-to-use interface that will render Data Science accessible to teachers and studentswithout strong programming or mathematical background. Because WI Fast Stats was also created and is maintained atthe University of Wisconsin-Madison, it is the perfect companion to the already widely successful educational tool ofWI Fast Plants. WI Fast Stats will evolve alongside WI Fast Plants by fulfilling the Data Science needs of the thousandsof teachers and students around the world who currently use WI Fast Plants to learn the complexities of plants and thebiological world.
WI Fast Stats ( https://wi-fast-stats.wid.wisc.edu/ ) is an integrated animated web page which hosts acollection of web applications and relevant webinars for WI Fast Plants. The web page contains six modules includingHome, About, Webapps, Webinars, Source Code, and FAQ (Figures 1 and 2). The Home page utilizes animate.css to construct the animation with three scrolling images retrieved from WI Fast Plants.
Font-awesome.css offers afantastic mode to show buttons representing the links of our web applications as well as the Github source code. Wealso built a timeline to store all the webinar-related information.The web page and accompanying web apps are all open-source with the code stored in the GitHub repository: https://github.com/crsl4/fast-stats . The web apps within WI Fast Stats provide an interactive and easy-to-use platform for data visualization and dataanalysis for data collected in accordance to the WI Fast Plants webinars and educational materials. The web apps arebased on the
R shiny package and contain three main modules: Data Summary, Data Visualization, and Data Analysiswhich are built with the
ShinyDashboard framework (Figures 3 and 4).2
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8, 2020Figure 2: WI Fast Stats website comprises different web apps each corresponding to a given WI Fast Plants webinar.The Data Upload section allows the users to upload their own collected data based on their fast plants experiments, orto utilize the already loaded sample dataset which illustrates the same educational outcomes intended in the webinarwithout having to run the experiments. In addition, this section also provides a summary button to show only first rowsof the dataset or the whole dataset.The Data Visualization section allows the users to create five plots: Mosaic plot, Scatter plot, Box plot, Violin plot,and Density plot. It utilizes the ggplot2 library to declaratively create graphs based on particular group variablesand quantity variables. Furthermore, the
Plotly library extends the features of the plot by adding
Lasso Select , autoscale , and data-toggle tooltip. Finally, color palettes, transparency, point size, and point shape are availableoptions to improve the overall appearance of the graph.The Data Analysis section (not present in web apps tailored to middle school students) allows the user to performstatistical tests like the chi-square test and the t test to compare the characteristics of the plants under differentenvironmental or experimental settings.Finally, the web apps maintain a validator system detecting any illegal actions done by users and providing meaningfulerror messages. The website and web apps are accompanied by a specialized Google user group ( wi-fast-stats ) forgeneral questions. WI Fast Stats will be continuously evolving to provide data visualization and data analysis capabilities for the ever-growing needs of the WI Fast Plants community.
Acknowledgements
This work was supported by the Department of Energy [DE-SC0021016 to CSL]. We thank Hedi Baxter Laufferand everybody at WI Fast Plants for inviting us to work with them on the creation of these Data Science educational3
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8, 2020Figure 3: WI Fast Stats web app corresponding to the WI Fast Plants webinar on the selection of polycot plants: https://wi-fast-stats.wid.wisc.edu/cotyledon/ .Figure 4: WI Fast Stats web app corresponding to the WI Fast Plants webinar on the effect of the ecosystem on theplants.: https://wi-fast-stats.wid.wisc.edu/ecosystem/ .4 PREPRINT - D
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8, 2020open-source tools. Finally, we acknowledge the work in [3] which helped us improve the scientific writing of thismanuscript.
References [1] Paul H Williams and Curtis B Hill. Rapid-cycling populations of brassica.
Science , 232(4756):1385–1389, 1986.[2] The Concord Consortium. Common online data analysis platform [computer software]. https://codap.concord.org/,2014.[3] Scott Hotaling. Simple rules for concise scientific writing.