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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.
Anthropic has released a new series of large language models and an updated Python API to access them.
Anthropic’s Large Language Models and Updated Python API: Unleashing New Potential
In a significant development, Anthropic, a renowned provider of advanced AI solutions, has unveiled a new line of large language models and an enhanced Python API to integrate them. This exciting development promises to deliver both immediate and long-term value by propelling the capabilities of AI to new heights. Here, we explore the potential future implications and developments this could catalyze.
Looking at the Long-Term Implications
With the advent of these large language models and an updated Python API from Anthropic, the potential for advancements in AI and the industries it serves is vast. By utilizing these advanced models, developers can enhance AI’s capability to understand, generate, and interact with human language on a more sophisticated level.
These models are also expected to accelerate the development of intelligent virtual assistants, real-time translators, content genenators, and many other AI applications. The technology could eventually become integral to sectors as diverse as healthcare, education, and entertainment, transforming everyday operations significantly.
Anticipating Future Developments
Given the sheer potential these models and APIs hold, we can anticipate continuous refinement and expansion in the field of AI. As the capability of AI to comprehend and manipulate human language increases, we could witness swift advancements in fields such as natural language processing (NLP), real-time translations, automated journalism, and chatbots.
Moreover, enhanced Python APIs like the one rolled out by Anthropic may very well encourage more developers to explore the potential of AI, thus leading to further innovation and a wider array of advanced AI solutions.
Actionable Advice
Given this significant advancement in AI technology, organizations should consider the following actions:
Invest in AI Applications: With the ongoing advancements in AI language models, there exists a greater opportunity to invest in advanced applications that would significantly enhance operational efficiency.
Upskill Workforce: Organizations should ensure their IT and development teams are well versed with the latest technology, specifically the Python programming language, as most AI development will make use of updated Python APIs.
Stay Abreast of Developments: Organizations need to consistently monitor advancements in AI technology to understand the evolving landscape and make strategic technology investments accordingly.
Anthropic’s new large language models and updated Python API is not just a significant leap for AI but also a promising development for sectors utilizing AI in their operational strategies. As such, organizations should act proactively to leverage and adapt to these advancements.
Unlock Future of Data excellence: intuitive interfaces, seamless integration, advanced transformations for efficient, secure data handling.
Future of Data Excellence: Seamless Integration, Advanced Transformations and Intuitive Interfaces
With the evolution of technology, the future of data excellence is no longer a distant dream. Significant indicators point towards intuitive interfaces, seamless integration, and advanced transformations as key elements that will shape the future of data handling. It is clear that these novel trends will help in ensuring efficient and secure data management.
1. Potential Long-term Implications
The long-term implications of investing in seamless integration, advanced transformations, and intuitive interfaces are significant, influencing a broad range of fields across the technological sphere.
Firstly, intuitive interfaces will make data more accessible to a diverse range of users, not only those with technical expertise. This implies a potential democratization of data-related tasks, empowering more individuals and businesses to harness the power of information.
Simultaneously, seamless integration will lead to increased interoperability between different systems and platforms. Consequently, companies can expect improved efficiency and productivity resulting from streamlined data-sharing processes.
Further, the development of advanced transformations will pave way for sophisticated data analysis and manipulation. This will invariably lead to valuable insights and decision-making tools that can significantly impact a company’s strategic direction.
2. Possible Future Developments
Moving forward, we can anticipate a couple of potential developments influenced by these emerging trends.
There’s likely to be a surge in the creation of comprehensive data management platforms that combine these three pillars: intuitive interfaces, seamless integration, and advanced transformations. Companies will be seeking software solutions that deliver a ‘one-stop-shop’ for their data needs.
Another probable development is the growing emphasis on data security. As more companies turn towards digital solutions for data handling; the need and search for secure, reliable systems will be paramount.
3. Actionable Advice
To unlock the future of data excellence, organizations need to take proactive steps today. Here are some recommendations:
Innovate and invest in intuitive interfaces: Strive to make your data systems user-friendly and intuitive, enabling even non-technical employees to easily navigate and utilize them.
Prioritize seamless integration: Reduce silos between different data systems and encourage integration for streamlined data-sharing and improved efficiency.
Embrace advanced transformations: Utilise advanced tools and technologies for data analysis and manipulation to gain valuable business insights and drive strategic decision making.
Focus on security: As the demand for digitized data solutions escalates, ensuring the security of these systems should be a top priority.
By cultivating a data-centric culture that values integration, advanced transformations, and intuitive systems, organizations can tap into the future of data excellence and thrive in the digital age.
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Introduction
In data analysis with R, subsetting data frames based on multiple conditions is a common task. It allows us to extract specific subsets of data that meet certain criteria. In this blog post, we will explore how to subset a data frame using three different methods: base R’s subset() function, dplyr’s filter() function, and the data.table package.
Examples
Using Base R’s subset() Function
Base R provides a handy function called subset() that allows us to subset data frames based on one or more conditions.
# Load the mtcars dataset
data(mtcars)
# Subset data frame using subset() function
subset_mtcars <- subset(mtcars, mpg > 20 & cyl == 4)
# View the resulting subset
print(subset_mtcars)
In the above code, we first load the mtcars dataset. Then, we use the subset() function to create a subset of the data frame where the miles per gallon (mpg) is greater than 20 and the number of cylinders (cyl) is equal to 4. Finally, we print the resulting subset.
Using dplyr’s filter() Function
dplyr is a powerful package for data manipulation, and it provides the filter() function for subsetting data frames based on conditions.
# Load the dplyr package
library(dplyr)
# Subset data frame using filter() function
filter_mtcars <- mtcars %>%
filter(mpg > 20, cyl == 4)
# View the resulting subset
print(filter_mtcars)
In this code snippet, we load the dplyr package and use the %>% operator, also known as the pipe operator, to pipe the mtcars dataset into the filter() function. We specify the conditions within the filter() function to create the subset, and then print the resulting subset.
Using data.table Package
The data.table package is known for its speed and efficiency in handling large datasets. We can use data.table’s syntax to subset data frames as well.
# Load the data.table package
library(data.table)
# Convert mtcars to data.table
dt_mtcars <- as.data.table(mtcars)
# Subset data frame using data.table syntax
dt_subset_mtcars <- dt_mtcars[mpg > 20 & cyl == 4]
# Convert back to data frame (optional)
subset_mtcars_dt <- as.data.frame(dt_subset_mtcars)
# View the resulting subset
print(subset_mtcars_dt)
In this code block, we first load the data.table package and convert the mtcars data frame into a data.table using the as.data.table() function. Then, we subset the data using data.table’s syntax, specifying the conditions within square brackets. Optionally, we can convert the resulting subset back to a data frame using as.data.frame() function before printing it.
Conclusion
In this blog post, we learned three different methods for subsetting data frames in R by multiple conditions. Whether you prefer base R’s subset() function, dplyr’s filter() function, or data.table’s syntax, there are multiple ways to achieve the same result. I encourage you to try out these methods on your own datasets and explore the flexibility and efficiency they offer in data manipulation tasks. Happy coding!
Comprehensive Analysis of Subsetting Data Frames in R
In the realm of data analysis with R, extracting specific subsets of data based on various conditions is a crucial and frequent task. Three different methods highlighted for this purpose are: the use of base R’s subset() function, dplyr’s filter() function, and the data.table package. Familiarity with these methods is fundamental in handling data manipulation tasks with fluency and efficiency.
Key Points from the Original Article
Utilizing Base R’s subset() Function
Base R’s subset() function has been presented as a handy tool for data subsetting depending on one or more conditions. The ‘mtcars’ dataset was used as an example to create a subset where the miles per gallon (mpg) is greater than 20 and the number of cylinders (cyl) equals 4.
Dplyr’s filter() Function
The dplyr’s filter() function can also be used to subset data frames based on specific conditions. By using the pipe operator (%>%), the ‘mtcars’ dataset was piped into the filter() function, and appropriate conditions were specified to complete the subsetting process.
Data Manipulation using the data.table Package
The data.table’s syntax, known for its robustness and efficiency when dealing with large datasets, was also demonstrated for subsetting data frames. After loading the data.table package, the ‘mtcars’ data frame was converted into a data.table to use the specific syntax for subsetting.
Long-term Implications and Future Developments
As data continues to increase in volume and complexity, the need to handle this data efficiently is more than ever. Whether one choose to use Base R’s subset(), dplyr’s filter(), or data.table, users would have an advantage with efficient and powerful tools at their disposal to handle large and complex datasets.
Moving forward, the R community might continue to develop optimized packages and functions that allow analysts and data scientists to cleanly and quickly streamline data. As the field of data science continues to evolve, new packages and improved functions could be released, further aiding in efficient data manipulation.
Actionable Advice
It is recommended that data analysts and data scientists familiarize themselves with multiple ways of subsetting data in R. Proficiency in these techniques allows them to choose the most efficient and suitable method according to the complexity and size of the dataset at hand.
For beginners, starting with the base R’s subset() function might be a good starting point as it is straightforward and easy to grasp. Once familiar with the base R syntax, methods using advanced packages like dplyr and data.table could be explored.
Finally, practicing these methods on various datasets will help one get a commanding understanding of how, when, and where to apply these techniques most effectively.
Learn how to enhance the quality of your machine learning code using Scikit-learn Pipeline and ColumnTransformer.
Exploring the Future of Machine Learning with Scikit-learn Pipeline and ColumnTransformer
Machine learning and artificial intelligence are dynamic sectors constantly under the influence of technological upgrades and enhancements. Scikit-learn Pipeline and ColumnTransformer are tools designed to optimize the quality of your machine learning code, and they play a significant role in the ongoing evolution of these sectors.
The Role of Scikit-learn Pipeline and ColumnTransformer in Machine Learning
Significantly, the Scikit-learn Pipeline offers a way to streamline a lot of the common and repeatable processes involved in machine learning. On the other hand, ColumnTransformer is principally aimed at transforming features or datasets to optimize their utility within various machine learning frameworks.
Long-term implications and Future Developments
The advancements in machine learning, facilitated by Scikit-learn Pipeline and ColumnTransformer, have far-reaching implications. As machine learning efforts develop and grow more complex, tools like these are vital for maintaining efficiency and quality in coding processes. In the future, we can expect to see a continued expansion and fine-tuning of tools similar to these in order to meet the growing needs of machine learning projects.
Actionable Advice for Effective Use Of Scikit-learn Tools
Stay updated with the new advancements and updates: Like all digital tools, Scikit-learn Pipeline and ColumnTransformer are regularly updated. Keeping up with these updates will allow you to take full advantage of these tools and improve your machine learning efforts.
Improve your understanding of these tools: To fully utilize Scikit-learn Pipeline and ColumnTransformer, first dedicate some time to understanding their full range of applications and opportunities for enhancement. There are many resources available online, including tutorials and communities of users that can offer guidance and insight.
Implement these tools in your own projects: The only way to truly understand the benefits and challenges of Scikit-learn Pipeline and ColumnTransformer is to use them. Start by incorporating these tools into your existing projects and gradually build your expertise.
In conclusion, the use of Scikit-learn Pipeline and ColumnTransformer in improving the quality of machine learning code marks a significant step forward in the field. Being open to learning and integrating these tools into your coding practices is key to staying ahead in the vibrant and rapidly developing sector of artificial intelligence and machine learning.
Image source: Dall-e This week, the tech community has been abuzz with the announcement of the latest model from Mistral being closed source. This revelation confirms a suspicion held by many: the concept of open-source Large Language Models (LLMs) today is more a marketing term than a substantive promise. Historically, open source has been championed… Read More »Open source LLMs – no more than a marketing term?
Analysis of Closed Source Approach by Mistral: Implications and Future Developments
Over the past week, there has been significant discussion in the tech community about Mistral’s announcement that its latest model will follow a “closed source” approach. This surprised a number of observers, notably due to the present prominence of open-source Large Language Models (LLMs) in the field. Contrary to the open-source ideal of freely available and modifiable code, Mistral’s decision indicates a potential shift in the industry. In this context, suspicions that the heralded concept of open-source LLMs is more of a marketing term than a genuine commitment have been validated.
Implications of a Closed Source LLM
The move by Mistral implies a significant strategy pivot and may indicate a broader industry trend. Though the open-source model has historically been celebrated for fostering innovation, transparency, and collective problem-solving, the shift of such a pivotal player to a more reserved, ‘closed source’ model raises potential concerns for the ongoing openness of LLMs.
Potential Challenges
Reduced Transparency: With the source code not openly available, there is less opportunity for oversight and for ensuring that LLMs are free from bias and manipulation.
Fewer learning opportunities: The closed source approach also means that those who wish to study or build upon existing models will not have the opportunity to do so.
Collaboration and Creativity: A key advantage of the open-source model is the innovation that springs from diverse minds working collaboratively. Closing the source code could potentially stifle this.
Future Developments and Actonable Insights
Despite the potential challenges, the future is not necessarily bleak. The industry has often shown its capacity to adapt and evolve in response to shifts such as these. Integral to this evolution, however, is the need for informed debates about the implications of such moves and how to mitigate any potential drawbacks.
Adapting to a Closed Source Model
Advocacy for Transparency: It is now more essential than ever to lobby for greater transparency within the AI and LLM industry, irrespective of the source model utilized.
Greater Regulation: If more companies decide to follow Mistral’s path, there will be an increasing need for regulation to ensure that LLMs are unbiased and safe.
Industry Collaboration: Increased cooperation between open and closed source proponents could ensure that development and learning opportunities remain available.
Conclusively, while Mistral’s decision to move to a closed-source model poses potential challenges in terms of transparency and collaboration, it may also represent a chance for the tech community to push for responsible AI development practices and greater regulation. With these actions, it’s possible to mitigate potential drawbacks and continue fostering innovation in the space.
