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Data visualization and analytics tools are crucial for businesses and researchers alike. Power BI, Spotfire, and R Shiny have emerged as significant players in the market.
This article aims to compare these data visualization tools for businesses across various parameters, helping you make informed decisions based on your specific dashboard needs.
This article compares R Shiny, Power BI and Spotfire, focusing on aspects like ease of use, customization, functionality, cost and performance.
R Shiny: High flexibility and customization, ideal for advanced analytics, requires R programming skills.
PowerBI: User-friendly for non-technical users, has good scalability and performance, and integrates well with other Microsoft products.
Spotfire: Offers robust analytics capabilities, excellent for handling complex data sets, and higher learning curve.
Choosing the right tool is based on your project’s needs, user skills, and organizational infrastructure.
Data Visualization Business Tools – The Overview
R Shiny
Shiny, an R package, is renowned for its ability to create interactive web applications directly from R. One of its core features is the seamless integration of powerful data visualization libraries such as ggplot2 and plotly, which enable users to construct sophisticated and dynamic visual representations of data.
The framework is designed to be accessible, allowing even those without web development experience to produce professional-looking applications. Shiny applications are inherently reactive; they automatically update outputs when inputs change, without requiring a page refresh. This reactivity is a cornerstone of Shiny’s interactive nature.
Shiny also adopts a modular approach to application development, enabling users to craft complex UIs using its core UI functions. These functions allow for the creation of engaging and visually appealing user interfaces without the necessity for direct HTML or CSS coding, simplifying the development process while offering extensive customization through custom server logic. You can find our open-source and fully customizable Shiny templates on our website.
Below is a list of key features that make Shiny a standout choice for data analysis:
PowerBI stands out in business intelligence for its robust integration with other Microsoft products and its user-friendly interface. One of the key capabilities of PowerBI is its Advanced AI features, which allow users to leverage artificial intelligence within their dataflows, enhancing the analytical power at their disposal.
The service is designed to accommodate a range of business scenarios, from small-scale reporting to enterprise-level data analysis. PowerBI’s ability to perform asynchronous refresh operations ensures that data is up-to-date without impacting system performance, a critical aspect for businesses that rely on real-time data insights.
PowerBI’s integration capabilities are further highlighted by its seamless connectivity with various data sources, both on-premises and in the cloud. This flexibility is crucial for organizations that manage diverse data ecosystems. PowerBI allows users to embed R/Python code, so this might be a neat benefit for someone with programming experience.
Spotfire stands out as an enterprise-level data visualization tool due to its analytical depth and flexibility. Users can delve into complex data analysis with a platform designed to handle vast datasets and sophisticated algorithms. Spotfire’s flexibility is evident in its ability to integrate with various data sources and its support for numerous data formats.
Its powerful in-memory processing enhances its analytical capabilities, which allows for real-time data exploration and discovery. This is particularly beneficial for organizations that require immediate insights from their data. The tool’s flexibility extends to its visualization options, which include a wide range of charts, graphs, and interactive dashboards.
Spotfire’s extensibility is another key advantage. Users can extend the platform’s functionality with custom applications and integrations, making it a versatile choice for businesses with specific analytical needs. Below is a list highlighting some of Spotfire’s flexible features:
Integration with R, Python, and MATLAB for advanced analytics
Customizable dashboards and interactive visualizations
Support for real-time and historical data analysis
Robust options for predictive and big data analytics
R Shiny vs. Power BI vs. Spotfire – Which Data Visualization Tool is Best for Your Business Needs?
The table you’re about to see showcases a comparative analysis between our three tools of choice:
R Shiny
PowerBI
Spotfire
Flexibility and Customization
R Shiny excels in customization, allowing users to create highly tailored and interactive dashboards.
This allows for extensive customization at potentially lower costs, especially if the in-house team has R programming skills.
PowerBI offers a balance between ease of use and customization.
It provides a user-friendly interface for creating custom reports and dashboards. Customizations might incur additional costs if there is a need for advanced analytics features or third-party integrations.
Spotfire offers a good balance of flexibility and customization with its advanced data visualization capabilities.
Customization costs can be high, particularly for complex data models and advanced analytics features.
Advanced Analytics and Visualization
R Shiny, being based on R, is powerful for statistical analysis and advanced analytics. It allows for customized analytics solutions, making it versatile for specific needs.
PowerBI has improved its analytics features over time and is a strong choice for businesses requiring analytics and visualization, especially when integrated with Microsoft tools.
Spotfire offers advanced analytics and visualization features. It provides robust predictive modelling and data mining capabilities.
Usability for Non-technical Users
R Shiny may have a steeper learning curve, but its versatility becomes apparent once mastered, catering to the needs of both technical and non-technical users.
PowerBI is known for its ease of use, particularly for business users who may not have extensive technical backgrounds.
Spotfire provides a user-friendly interface, making it accessible to both technical and non-technical users.
Scalability, Performance, and Development Speed
R Shiny is highly scalable and can be optimized for performance. However, development may take longer due to the need for R programming expertise.
PowerBI is scalable, especially in Microsoft-heavy environments, and offers good performance. Its development speed is decent, particularly for those familiar with the Microsoft ecosystem.
Spotfire is scalable and performs well in various business sizes. Its development speed is relatively fast due to its intuitive interface.
Cost-Effectiveness
R Shiny, being open-source, is often the most cost-effective option in the long run, especially for organizations with skilled R programmers.
PowerBI offers competitive pricing and can be cost-effective, especially for organizations already invested in Microsoft technologies.
Spotfire can be expensive, especially for small to mid-sized organizations, which might impact long-term cost-effectiveness.
Cost of Maintenance
Maintenance of Shiny apps requires regular updates to the R environment and packages. Due to its open-source nature, it might need more hands-on maintenance, especially for custom-built applications.
The cost of maintenance can vary depending on the complexity of the app and the need for specialized R programming expertise.
PowerBI, being a Microsoft product, typically has a more streamlined update and maintenance process. However, the cost of maintenance could be higher due to licensing fees and the need for ongoing subscriptions for premium features.
Spotfire offers robust support and regular updates as part of its enterprise-grade solution. The cost of maintenance is generally higher due to its positioning as a premium product, but it offers strong support and integration capabilities.
R Shiny vs. PowerBI vs. Spotfire – Addressing the Limitations
No data dashboarding tool for business is perfect, and you must be aware of the limitations before pulling the trigger. Here are a couple of things you should be aware of:
Shiny:
While Shiny can handle large datasets, optimizing performance for these scenarios requires advanced R coding and server management skills.
While it’s possible to create aesthetically pleasing apps, achieving a high level of design polish may demand additional time and expertise in UI/UX design.
Shiny is a powerful tool, but it requires users to be proficient in R.
PowerBI:
PowerBI’s data modeling capabilities, while robust, might be limited in handling highly complex statistical analyses, which are better suited to specialized analytics tools.
Customization in PowerBI, though user-friendly, can be limited for specific or advanced requirements, potentially requiring additional tools or workarounds.
Dependency on Microsoft’s ecosystem could pose challenges in integration with certain non-Microsoft technologies or platforms.
Spotfire:
Despite its powerful analytics capabilities, Spotfire might not be the best choice for projects where simple data visualization is required, due to its complexity and cost.
The learning curve for effectively utilizing Spotfire’s advanced features can be steep, particularly for users without a background in data analytics.
Spotfire’s licensing and infrastructure costs can be significant, making it less accessible for smaller organizations or projects with limited budgets.
Finding the Best Data Visualization Tool for Your Organization
When assessing the best value for your organization, it’s crucial to look beyond the sticker price of data visualization tools. Consider the total cost of ownership (TCO), which includes not only the initial licensing fees but also the long-term costs associated with training, maintenance, and upgrades. A tool that seems inexpensive at first might require significant investment in these areas over time.
PowerBI and Spotfire offer different licensing models that cater to various organizational sizes and needs, and Shiny on the other hand is free to use. To determine which tool offers the best value, organizations should compare the features and support against their specific requirements. Here’s a simplified comparison:
Shiny: Free and open-source; ideal for R users and custom development. Offers subscription tiers for deploying applications to shinyapps.io.
PowerBI: Subscription tiers; integrates well with other Microsoft products.
Spotfire: Enterprise-level pricing; offers deep analytical capabilities.
In summary, Shiny, PowerBI, and Spotfire each offer unique strengths that cater to different business intelligence needs.
Shiny excels with its customizability and integration with R, making it ideal for statisticians and data scientists. PowerBI stands out for its user-friendly interface and deep integration with other Microsoft products, which is great for organizations entrenched in the Microsoft ecosystem. Spotfire, with its powerful analytical capabilities and real-time data exploration, is well-suited for enterprises requiring advanced analytics.
Ultimately, the choice between these tools should be guided by the specific requirements of the project, the technical proficiency of the users, and the existing infrastructure of the organization. By carefully considering these factors, businesses can leverage the right tool to transform their data into actionable insights and drive informed decision-making.
Business Intelligence: R Shiny vs Power BI vs Spotfire
Data visualization and analytic tools play a vital role in businesses and research. Among many tools available, Power BI, Spotfire, and R Shiny have emerged as significant contenders. This comprehensive comparison touches upon the core aspects of these tools, helping businesses make informed decisions based on their specific needs.
Contacting the Comparison
The comparison aims to help your organization choose the best tool for your specific dashboard needs. The focus is on ease-of-use, cost, performance, functionality, and customization of R Shiny, Power BI, and Spotfire.
R Shiny
Incorporating R Shiny into business allows for flexibility and customization. It is ideal for advanced analytics but requires R programming skills, making it a versatile option for data visualization needs, irrespective of the technical proficiency of the user.
Power BI
Power BI offers a user-friendly interface for non-technical users. Couples with good scalability, it delivers sound performance and integrates seamlessly with other Microsoft products, offering a solid platform for data visualization and analysis in a Microsoft-heavy environment.
Spotfire
Spotfire provides robust analytic capabilities and exhibits excellent prowess in handling complex data sets. However, it has a steep learning curve, making it more suitable for those with advanced data analysis requirements.
Long-Term Implications and Future Developments
In the long run, an organization’s choice of these data visualization tools will impact project execution, analytics, and ultimately decision-making. These tools are likely to experience continued growth and improvements, delivering a more robust collection of capabilities to handle increasingly complex data-sets.
Looking Ahead
Consider user skills, organizational infrastructure, and project requirements while selecting a data visualization tool. Future developments in these tools are likely to include new features for handling larger datasets, improved performance and scalability, and more seamless integration with other core business technologies.
Actionable Advice
Given the long-term implications of tool selection, businesses should invest time to evaluate several factors: the project’s specific dashboard needs; skill levels of users; and the organization’s existing infrastructure. While Power BI is user-friendly and has good scalability, Spotfire could be ideal for handling complex data sets. On the other hand, R Shiny is excellent for advanced analytics needs. Choose the tool that caters to most of your organization’s needs.
If the in-house team has R programming skills, R Shiny might be more cost-effective in the long run. Spotfire, despite being expensive, could be worth the cost for complex data models. On the other hand, PowerBI might provide an optimal balance between cost and features, especially for organizations already using other Microsoft products.
Lastly, look beyond the sticker price – consider total cost of ownership to find the best value for your organization.
In summary, all of these tools offer powerful capabilities for business intelligence and data visualization. Your choice should primarily be guided by your business’s specific needs, rather than trying to adapt your business to fit the tool. By carefully considering your specific requirements and understanding the strengths and weaknesses of each platform, you can leverage the best tool for your data visualization needs.
Seven Important Generative AI and Machine Learning Concepts Visually Explained in one-minute Data Animations
Understanding Generative AI and Machine Learning Through Visual Animations
The initial text does not provide an overview of the seven important generative AI and machine learning concepts visually explained in one-minute data animations. However, visual animation as an educational tool for complex subjects like AI and machine learning could significantly improve comprehension and retention of such concepts. This article will deliberate the potential long-term implications of such an approach, consider predicted future developments, and provide advice on how these insights can be leveraged.
Long-term Implications
Presenting multi-layered AI and machine learning concepts in a visually engaging manner holds the promise of wider accessibility and comprehension, bridging gaps between subject matter experts and novices or laypeople. The ability to understand these technologies means that more people can contribute to their evolution, strengthening the potential for innovation and progress.
Potential Future Developments
As visual learning becomes more prevalent, developments could extend beyond the purely academic sphere. We could see more usage of animated explanations within technology, finance, and other industries reliant on data-heavy technical concepts. Additionally, with the permeation of AR and VR technologies, immersive data animations could be the next frontier.
Actionable Advice
Given the potential benefits and developments detailed above, here are a few recommendations regarding to ensure the effective use of data visualizations:
Embrace Visual Learning: Whether you’re a researcher, student, teacher, or industry professional, explore and adopt visual learning techniques for complex subjects like AI and machine learning.
Invest in development: Businesses and educational institutions should consider investing in the development of visual learning tools that can simplify complicated concepts.
Collaborate cross-industry: Collaboration between the technology, education and animation sectors is essential to create high-quality, accurate and effective visual learning tools.
Stay tuned to new trends: Keep abreast with evolving trends in visual learning technology, such as AR and VR, to remain at the forefront of innovation.
In summary, the use of visual animations to explain complex AI and machine learning concepts holds immense promise. Harnessing this tool could lead to greater comprehension, innovation, and progress in these critical fields.
Learn about the importance of color in data visualization. Understand its power in conveying insights effectively and engaging your audience.
Understanding the Role of Color in Data Visualization
Color in data visualization is a remarkable tool that significantly enhances the comprehension of complex datasets. This fundamental feature plays crucial roles in conveying data insights effectively and engaging with the audience.
The Long-term Implications of Color in Data Visualization
The use of color in data visualizations has potent long-term implications in various sectors including; business, education, healthcare, and technology among other domains. By simplifying the understanding of complex data sets, color enhances decision-making processes, problem-solving, and strategy formulation that is data-driven.
In Education, clearer visual representation leads to better understanding and information retention. In Business, it promotes strategic planning and marketing as trends and patterns are detected more easily. Technology and healthcare services are also becoming more reliant on data. Hence, the need for effective visualization tools for easier interpretation and decision making.
Effective Color Schemes Enhance Accessibility
Another critical long-term effect is the improved accessibility of data. When colors are utilized effectively, they can enhance the readability of the visual representation for individuals with certain visual impairments. Thus, promoting inclusivity in data readability.
Potential Future Developments
As the demand for data interpretation continues to grow, there will be more advancements in the field of data visualization. In the future, we anticipate a deeper research and development into dynamic color schemes that adapt to certain parameters and variables. This will enable more personalized data experiences.
Another likely development is the creation of automated color selection algorithms that can choose the best colors for each dataset based on several factors such as the type of data, the intended audience, and the specific message the data conveys. This will eliminate the need for manual color selection, thereby making data visualization more efficient.
Actionable Advice
As color plays an instrumental role in data visualization, it is essential to follow some practices and techniques:
Choose a color scheme that fits your data: Always select colors that better represent your data and its variables. For example, if illustrating data about the environment, greens and blues may be more effective.
Ensure readability: Make sure your colors are visible and can be distinguished from each other. Contrasting colors often work best for this purpose.
Consider colorblind-friendly palettes: Not everyone sees colors the same way. Therefore, it’s important to consider colorblind-friendly palettes to ensure inclusivity and accessibility.
Keep it simple: Avoid using too many colors as it can create confusion. Stick to a pallet of 2-10 colors. Remember, the aim is to improve comprehension, not to create a piece of art.
In conclusion, the power of color in data visualization should not be underestimated. When used properly, it can greatly enrich the way data is perceived and interpreted, hence leading to better decision-making processes.
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Last year, R Consortium talked to John Blischak and Tim Hoolihan of the Cleveland R User Group about their regular structured and casual virtual meetups during the pandemic. Recently, Alec Wong, another co-organizer of the Cleveland R User Group, updated the R Consortium about how the group provides a networking platform for a small but vibrant local R community. Alec shared details of a recent event from the group regarding the use of R for analyzing baseball data. He also discussed some tools for keeping the group inclusive and improving communication among group members.
Please share about your background and involvement with the RUGS group.
I completed my Bachelor of Science degree in Fisheries and Wildlife from the University of Nebraska-Lincoln in 2013, and my Master of Science degree in Statistical Ecology from Cornell University in late 2018. During my graduate program, I gained extensive experience using R, which is the de facto language of the ecological sciences. I discovered a passion for the language, as it is extremely intuitive and pleasant to work with.
After completing my program in 2018, I moved to Cleveland and immediately began attending the Cleveland R User Group in 2019, and have been a consistent member ever since. I eagerly look forward to each of our events.
After completing my graduate program, I started working at Progressive Insurance. Working for a large organization like Progressive provides me with many diverse opportunities to make use of my extensive experience with R. I was happy to find a vibrant R community within the company, which allowed me to connect with other R users, share knowledge, and I enthusiastically offer one-on-one assistance to analysts from all over Progressive.
Starting in 2022, I accepted the role of co-organizer of the Cleveland R User Group. As a co-organizer, I help with various tasks related to organizing events, such as the one we held last September. I am passionate about fostering the growth of these communities and helping to attract more individuals who enjoy using R.
Our group events are currently being held in a hybrid format. When we manage to find space, we will meet in person, such as when we met to view the 2023 posit::conf in October–several members visited in person and watched and discussed videos from the conference. Most of our meetups continue to be virtual, including our Saturday morning coffee meetups, but we are actively searching for a more permanent physical space to accommodate our regular meetups.
I am only one of several co-organizers of the Cleveland R user group. The other co-organizers include Tim Hoolihan from Centric Consulting, John Blischak who operates his consulting firm JDB Software Consulting, LLC, and Jim Hester, currently a Senior Software Engineer at Netflix. Their contributions are invaluable and the community benefits tremendously from their efforts.
Can you share what the R community is like in Cleveland?
I believe interest in R has been fairly steady over time in Cleveland since 2019. We have a handful of members who attend regularly, and typically each meeting one or two new attendees will introduce themselves.
I would venture to say that R continues to be used frequently in academic settings in Cleveland, though I am unfamiliar with the standards at local universities. At least two of our members belong to local universities and they use R in their curricula.
As for industry usage, many local companies, including Progressive use R. At Progressive, we have a small, but solid R community; although it is not as large as the Python community, I believe that the R community is more vibrant. This seems characteristic of R communities in varying contexts, as far as I’ve seen. Another Cleveland company, the Cleveland Guardians baseball team, makes use of R for data science. In September 2023 we were fortunate to invite one of their principal data scientists to speak to us about their methods and analyses. (More details below.)
Typically, our attendance is local to the greater Cleveland area, but with virtual meetups, we’ve been able to host speakers and attendees from across the country; this was a silver lining of the pandemic. We also hold regular Saturday morning coffee and informal chat sessions, and it’s great to see fresh faces from outside Cleveland joining in.
On September 27th, 2023, we invited Keith Woolner, principal data scientist at the Cleveland Guardians baseball team, to give a presentation to our group. This was our first in-person meetup after the pandemic, and Progressive generously sponsored our event, affording us a large presentation space, food, and A/V support. We entertained a mixed audience from the public as well as Progressive employees.
Keith spoke to us about “How Major League Baseball Teams Use R to Analyze Baseball Data.” In an engaging session, he showcased several statistical methods used in sports analytics, the code used to produce these analyses, and visualizations of the data and statistical methods. Of particular interest to me was his analysis using a generalized additive model (GAM) to evaluate the relative performance of catchers’ ability to “frame” a catch; in other words, their ability to convince the umpire a strike occurred. The presentation held some relevance for everyone, whether they were interested in Cleveland baseball, statistics, or R, making it a terrific option for our first in-person presentation since January 2020. His presentation drove a lot of engagement both during and after the session.
Any techniques you recommend using for planning for or during the event? (Github, zoom, other) Can these techniques be used to make your group more inclusive to people that are unable to attend physical events in the future?
One of our co-organizers, John Blischak, has created a slick website using GitHub Pages to showcase our group and used GitHub issue templates to create a process for speakers to submit talks. Additionally, the Cleveland R User group has posted recordings of our meetups to YouTube since 2017, increasing our visibility and accessibility. Many people at Progressive could not attend our September meetup and asked for the recording of our September 2023 meetup as soon as it was available.
Recently, we have also created a Discord server, a platform similar to Slack. This was suggested by one of our members, Ken Wong, and it has been a great addition to our community. We have been growing the server organically since October of last year by marketing it to attendees who visit our events, particularly on the Saturday morning meetups. This has opened up an additional space for us to collaborate and share content asynchronously. Ken has done an excellent job of organizing the server and has added some automated processes that post from R blogs, journal articles, and tweets from high-profile R users. Overall, we are pleased with our progress and look forward to continuing to improve our initiatives.
How do I Join?
R Consortium’s R User Group and Small Conference Support Program (RUGS) provides grants to help R groups organize, share information, and support each other worldwide. We have given grants over the past four years, encompassing over 68,000 members in 33 countries. We would like to include you! Cash grants and meetup.com accounts are awarded based on the intended use of the funds and the amount of money available to distribute.
Cleveland R User Group: Embracing Hybrid Models and R Analytics in Baseball
The Cleveland R User Group, co-organized by Alec Wong, has been actively navigating the shifting dynamics of community involvement during the pandemic, with regular virtual meetups and post-pandemic hybrid models. A recently spotlighted event discussed the use of R for analyzing baseball data. This article explores the key details of the event, the use of R in both academic and industrial settings within Cleveland, and how the group is heightening inclusivity and communication methods.
Use of R in Cleveland
According to Wong, the usage and interest of R in Cleveland has remained steady since 2019. While it’s particularly prevalent in academic environments, the programming language is also utilized by several companies, including Progressive Insurance where Wong works. Additionally, the Cleveland Guardians baseball team uses R for data science applications.
Local and Remote Involvement
The Cleveland R User Group regularly holds meetups in hybrid format. While some members prefer to meet in person, the majority of the meetings take place virtually. The user group is actively searching for a permanent physical meeting space. This virtual trend paved the way to host speakers and attendees from across the country, extending the reach outside of Cleveland.
Event Spotlight: Using R to Analyze Baseball Data
The group recently held an event on September 27th, 2023, titled “How Major League Teams Use R to Analyze Baseball Data,” with Keith Woolner, the principal data scientist at the Cleveland Guardians baseball team. Keith illustrated several statistical methods used in sports analytics with R, including the use of a generalized additive model to evaluate the performance of catchers’ ability.
Greater Inclusivity and Improved Communication
The Cleveland R User Group is working on enhancing inclusivity and improving communication among its members by leveraging technologies like GitHub and Discord. John Blischak, a fellow co-organizer of the team, has developed a website using GitHub Pages, and the team has been posting recordings of their meetups on YouTube to improve accessibility. Recently, a Discord server was created to provide a platform for collaboration and content sharing among community members.
Actionable Advice
Encourage Hybrid Meetups: Companies and communities alike shouldn’t hesitate to continue embracing virtual platforms for increased accessibility and wider reach even post-pandemic.
Utilize Digital Tools for Inclusivity: By leveraging digital platforms like GitHub and Discord, communities like the Cleveland R User Group can streamline communications, improve visibility, and promote inclusivity.
Apply for Grants: For similar user groups or communities, it might be worth scrambling to the relevance of R Consortium’s R User Group and Small Conference Support Program (RUGS) that offers grants to help R groups organize.
Exploit the Power of R: With versatile use cases of R in different industries, it’s an opportunity for academia and businesses to keep exploring and harnessing the power of R for both simple and complex analytical tasks.
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Nadejda Sero, the founder of the R Ladies Cotonou chapter, shared with the R Consortium her experiences learning R, the challenges of running an R community in a developing country, and her plans for 2024. She also emphasized the importance of considering the realities of the local R community when organizing an R User Group (RUG).
Please share about your background and involvement with the RUGS group.
My name is Nadejda Sero, and I am a plant population and theoretical ecologist. I have a Bachelor of Science in Forestry and Natural Resources Management and a Master of Science in Biostatistics from the University of Abomey-Calavi (Benin, West Africa). I discovered R during my Master’s studies in 2015. From the first coding class, I found R exciting and fun. However, as assignments became more challenging, I grew somewhat frustrated due to my lack of prior experience with a programming language.
So, I jumped on Twitter (current X). I tweeted, “The most exciting thing I ever did is learning how to code in R!” The tweet caught the attention of members of the R Ladies global team. They asked if I was interested in spreading #rstats love with the women’s community in Benin. I was thrilled by the opportunity and thus began my journey with R-Ladies Global.
The early days were challenging due to the novelty of the experience. I did not know much about community building and social events organization. I started learning about the R-Ladies community and available resources. The most significant work was adjusting the resources/tools used by other chapters to fit my realities in Benin. My country, a small French-speaking developing African country, had poor internet access and few organizations focused on gender minorities. (We are doing slightly better now.) On top of that, I often needed to translate some materials into French for the chapter.
As I struggled to make headway, the R-Ladies team launched a mentoring program for organizers. I was fortunate enough to participate in the pilot mentorship. The program helped me understand how to identify, adjust, and use the most effective tools for R-Ladies Cotonou. I also gained confidence as an organizer and with community work. With my fantastic mentor’s help, I revived the local chapter of R-Ladies in Cotonou, Benin. I later joined her in the R-Ladies Global team to manage the mentoring program. You can read more about my mentoring experience on the R-Ladies Global blog.
Happy members of R-Ladies Cotonou sharing some pastries after the presentation. At our first official meetup, the attendees discovered and learned everything about R-Ladies Global and R-Ladies Cotonou.
I am grateful for the opportunity to have been a part of the R-Ladies community these last six years. I also discovered other fantastic groups like AfricaR. I am particularly proud of the journey with R-Ladies Cotonou. I am also thankful to the people who support us and contribute to keeping R-Ladies Cotonou alive.
Can you share what the R community is like in Benin?
R has been commonly used in academia and more moderately in the professional world over the past 2-3 years. For example, I worked with people from different areas of science. I worked in a laboratory where people came to us needing data analysts or biostatisticians. We always used R for such tasks, and many registered in R training sessions. The participants of these sessions also came from the professional world and public health. I have been out of the country for a while now, but the R community is booming. More people are interested in learning and using R in different settings and fields. I recently heard that people are fascinated with R for machine learning and artificial intelligence. It is exciting to see that people are integrating R into various fields. There are also a few more training opportunities for R enthusiasts.
Can you tell us about your plans for the R Ladies Cotonou for the new year?
More meetups from our Beninese community, other R-Ladies chapters, and allies.
We are planning a series of meetups that feature students from the training “Science des Données au Féminin en Afrique,” a data science with R program for francophone women organized by the Benin chapter of OWSD (Organization for Women in Science for the Developing World). We have three initial speakers for the series: the student who won the excellence prize and the two grantees from R-Ladies Cotonou. The program is an online training requiring good internet, which is unfortunately expensive and unreliable. If you want good internet, you must pay the price.
R-Ladies Cotonou supported two students (from Benin and Burkina Faso) by creating a small “internet access” grant using the R Consortium grant received in 2020.
The meetup speaker is taking us through a review of the most practical methods of importing and exporting datasets in R. The attendees are listening and taking notes.
This next series of meetups will focus on R tutorials with a bonus. The speakers will additionally share their stories embracing R through the training. The first speaker, Jospine Doris Abadassi, will discuss dashboard creation with Shiny and its potential applications to public health. I hope more folks from the training join the series to share their favorite R tools.
I believe these meetups will assist in expanding not only the R-Ladies but the entire R community. I particularly enjoy it when local people share what they have learned. It further motivates the participants to be bold with R.
About “Science des Données au Féminin en Afrique“, it is the first time I know that a data science training is free for specifically African women from French-speaking areas. Initiated by Dr. Bernice Bancole and Prof. Thierry Warin, the program trains 100 African francophone women in data science using R, emphasizing projects focused on societal problem resolution. The training concluded its first batch and is now recruiting for the second round. So, the community has expanded, and a few more people are using R. I appreciate that the training focuses on helping people develop projects that address societal issues. I believe that it enriches the community.
As I said in my last interview with the R consortium, “In some parts of the world, before expecting to find R users or a vivid R community, you first need to create favorable conditions for their birth – teach people what R is and its usefulness in professional, academic, and even artistic life.” It is especially true in Benin, whose official language is French. English is at least a third language for the average multilingual Beninese. Many people are uncomfortable or restrained in using R since most R materials are in English. I hope this OWSD Benin training receives all the contributions to keep running long-term. You can reach the leading team at owsd.benin@gmail.com.
Our other plan is to collaborate with other R-Ladies chapters and RUGS who speak French. If you speak French and want to teach us something, please email cotonou@rladies.org.
Otherwise, I will be working on welcoming and assisting new organizers for our chapter. So, for anyone interested, please email cotonou@rladies.org.
Are you guys currently hosting your events online or in-person? And what are your plans for hosting events in 2024?
We used to hold in-person events when we started. Then, the COVID-19 pandemic hit, and we had to decide whether to hold events online. Organizing online events became challenging due to Cotonou’s lack of reliable internet access or expensive packages. As a result, we only held one online event with poor attendance. We took a long break from our activities.
Going forward, our events will be hybrid, a mix of in-person and online events. In-person events will allow attendees to use the existing infrastructure of computers and internet access of our allies. It also offers an opportunity to interact with participants. Therefore, I am working with people in Cotonou to identify locations with consistent internet access where attendees can go to attend the meetups. Online events will be necessary to accommodate speakers from outside of the country. It will be open to attendees unable to make it in person.
Any techniques you recommend using for planning for or during the event? (Github, zoom, other) Can these techniques be used to make your group more inclusive to people that are unable to attend physical events in the future?
The techniques and tools should depend on the realities of the community. What language is comfortable for attendees? What meeting modality, online or in person, works best for participants?
As mentioned earlier, I was inexperienced, and organizing a chapter was daunting. My mentoring experience shifted my perspective. I realized that I needed to adjust many available resources/tools. Organizing meetups became easier as I integrated all these factors.
For example, our chapter prioritizes other communication and advertisement tools like regular emails and WhatsApp. The group is mildly active on social media, where the R community is alive (X/Twitter, Mastodon). It is easier to have a WhatsApp group to share information due to its popularity within our community. We recently created an Instagram account and will get LinkedIn and Facebook pages (with more co-organizers). I would love a website to centralize everything related to R-Ladies Cotonou. Using emails is an adjustment to Meetup, which is unpopular in Benin. Getting sponsors or partners and providing a few small grants for good internet would help tremendously our future online events.
Adjusting helps us to reach people where they are. It is imperative to consider the community, its realities, and its needs. I often asked our meetup participants their expectations, “What do you anticipate from us?” “What would you like to see in the future?” Then, I take notes. Also, we have Google Forms to collect comments, suggestions, potential speakers, contributors, and preferred meeting times. It is crucial to encourage people to participate, especially gender minorities less accustomed to such gatherings.
I have also attempted to make the meetups more welcoming and friendly in recent years. I always had some food/snacks and drinks available (thanks to friends and allies). It helps make people feel at ease and focus better. I hope the tradition continues for in-person meetups. It is valuable to make the meetups welcoming and friendly. How people feel is essential. If they come and feel like it is a regular lecture or course, they may decide to skip it. But, if they come to the meetup and learn while having fun, or at the very least, enjoy it a little, it benefits everyone.
These are some of the key aspects to consider when organizing a meetup. It is critical to consider the people since you are doing it for them. Also, make sure you have support and many co-organizers if possible.
All materials live on our GitHub page for people who can’t attend physical events. Another solution would be recording and uploading the session on the R-Ladies Global YouTube or our channel.
What industry are you currently in? How do you use R in your work?
I am now a Ph.D. student in Ecology and Evolutionary Biology at the University of Tennessee in Knoxville.
R has no longer been my first programming language since I started graduate school. I still use R for data tidying data analysis but less extensively. I worked a lot with R as a master’s student and Biostatistician. It was constant learning and growth as a programmer. I had a lot of fun writing my first local package. However, I now work more with mathematical software like Maple and Mathematica. I wish R were as smooth and intuitive as this software for mathematical modeling. I like translating Maple code to R code, especially when I need to make visualizations.
I am addicted to ggplot2 for graphs. I love learning new programming languages but am really attached to R (it’s a 9-year-old relationship now). I developed many skills while programming in R. R helped me become intuitive, a fast learner, and sharp with other programming languages.
My most recent project that utilized R, from beginning to end, was a project in my current lab on the evolutionary strategies of plants in stochastic environments. We used R for demographic data tidying and wrangling. Data analysis was a mix of statistical and mathematical models. It was a good occasion to practice writing functions and use new packages. I enjoy writing functions for any task to automate repetitive tasks, which reduces the need for copying and pasting code. I also learned more subtleties in analyzing demographic data from my advisor and colleagues who have used R longer.
How do I Join?
R Consortium’s R User Group and Small Conference Support Program (RUGS) provides grants to help R groups organize, share information, and support each other worldwide. We have given grants over the past four years, encompassing over 68,000 members in 33 countries. We would like to include you! Cash grants and meetup.com accounts are awarded based on the intended use of the funds and the amount of money available to distribute.
Potential Long-term Implications and Future Developments in Data Science Community Building
In a recent interview, Nadejda Sero, the founder of the R Ladies Cotonou chapter in Benin, West Africa, shared her experiences learning the R programming language and organizing a local R User Group (RUG). As part of the broader global R community, Sero has navigated the challenges of leading data science initiatives in a developing country and has set ambitious plans for the future.
As such, her story provides critical insights into contributing factors for successful community development and offers invaluable lessons to the broader data science community.
Lessons from the R Ladies Cotonou Experience
The experiences of Sero and the R Ladies Cotonou could pave the way for future growth of data science communities, particularly in developing countries. Their strategies on overcoming language and technological obstacles have proven successful and can provide a roadmap for others facing similar challenges.
The necessity of adapting resources to local needs is paramount. Sero has emphasized how improvising with available tools and adjusting them to suit local realities can be beneficial. This mindset could encourage other organizers to think creatively about their resources.
The effort to promote diversity and inclusive participation, particularly within gender minorities, is another noteworthy effort. It demonstrates that fostering an inclusive environment is central to a thriving data science community.
Finally, ensuring events are enjoyable and not just educational can boost attendance and involvement. A positive and fun atmosphere creates a more attractive community for potential members.
Future Developments: Bringing Data Science to More Communities
With data science as an increasingly sought-after skill across various industries, communities like R Ladies Cotonou serve a critical role in advancing technology inclusion, particularly in areas with limited resources. Initiatives that focus on local languages, such as French in Benin, can increase accessibility for non-English speakers and therefore broaden the reach of data science training.
Looking ahead, remote learning initiatives will likely continue to be a crucial part of community-building in data science. Good internet access is often an ongoing challenge, so strategies for boosting online participation will play an essential role in community growth. Hybrid events that mix in-person and online learning could be a promising solution.
Taking Action: Advice Based on These Insights
Based on the insights shared by Sero, here are some actionable steps relevant to anyone interested in establishing or developing a data science community:
Adapt resources to suit local conditions: Existing resources may not fit perfectly into every setting. Be prepared to customize them to suit the unique needs of the local community.
Promote inclusiveness: Exert deliberate efforts to create an inclusive environment that encourages participation from all sections of society, particularly those underrepresented in tech.
Make it fun: Create an engaging atmosphere where members do not just learn but can also enjoy themselves.
User-friendly online infrastructure: Considering the increasing reliance on remote participation, good online infrastructure should be a priority. This includes stable internet access and user-friendly platforms for online meetings.
Encourage voluntary involvement: Foster a sense of collective ownership by encouraging members to contribute freely. This can enhance community cohesion and sustainability.
In conclusion, community building in data science requires consideration of local realities, commitment to inclusive participation, creative use of resources, and strategic use of online platforms. By harnessing these insights effectively, budding communities can thrive and contribute to the broader goal of creating a diverse, global data science network.
