Scenario : you want to obtain either the first, or last row, from a set of rows that belong to a particular group.
For example, for a patient admitted to hospital, you may want to capture their first admission, or the entire time they were in a specific hospital (hospital stay), or their journey across multiple hospitals and deparments (Continuous Stay).
The key point is that these admissions have a means of identifying the patient, and the stay itself, and that there will likely be several rows of data for each.
With data.table’s .I syntax, we can grab the first row using .I[1], and the last row, regardless of how many there are, using .I[.N]
See the example function below.
At patient level, I want the first record in the admission, so I can count unique admissions.
This retrieves the first row using the identity column, and joins back to the original dataset, returning the ID and any other supplied columns ( which are passed to the ... argument)
If I want to grab the last row, I switch to the super handy .N function:
This retrieves the last row using the specified identity column(s), joins back to the original data and retrieves any other required columns.
Of course, this is lightning quick, rock solid, and reliable.
get_records <- function(.dt,
position = c("first", "last"),
type = c("patient", "stays" ,"episodes"),
...) {
if (type == "patient") {
idcols <- "PatId"
}
if (type == "stays") {
idcols <- c("PatId", "StayID")
}
if (type == "episodes") {
idcols <- c("PatId", "StayID", "GUID")
}
vars <- eval(substitute(alist(...)), envir = parent.frame())
vars <- sapply(as.list(vars), deparse)
vars <- c(idcols, vars)
if (position == "first") {
res <- .dt[.dt[,.I[1], idcols]$V1][,.SD, .SDcols = vars][]
}
if (position == "last") {
res <- .dt[.dt[,.I[.N], idcols]$V1][,.SD, .SDcols = vars][]
}
res
}
data.table has lots of useful functionality hidden away, so hopefully this shines a light on some of it, and encourages some of you to investigate it for yourself.
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The .I syntax in data.table is a powerful tool for efficiently handling data in R, especially when dealing with large datasets. In the given scenario – identifying specific records within a dataset, such as the first or last row of a particular group – it appears to offer significant benefits in speed and reliability.
Comprehending the potential of data.table’s .I syntax could have considerable implications for the future of data analytics with R. It may permit more comprehensive processing of substantial databases and potentially foster more extensive and robust analyses. Given the growth in data generation across industries, this advancement in handling complex datasets might see increased utilization.
Future Developments
Given its efficiency and convenience for handling large datasets, improvements and expansions of this methodology could be anticipated. These might include the creation of additional functions designed to simplify different aspects of data analysis, or improvements on existing ones for better performance. Furthermore, increased usage could also result in more user feedback that could influence further development of the syntax.
Actionable Advice
To maximize the benefits offered by .I syntax in data.table, here are several points to consider:
Understanding .I syntax: Investing time to understand and experiment with data.table’s .I syntax would assist users in recognizing its potential and apply it effectively when working with large datasets. The syntax is capable of precisely accessing specific rows of data, enhancing the speed and reliability of the operation.
Keeping up-to-date with future developments: With its groundwork already making a mark, remaining informed about updates and new features related to this methodology could help users fully leverage future expansions.
Providing feedback: Actively contributing feedback, reporting issues and suggesting potential improvements for data.table can support its continuous development, thus benefiting the whole R user community.
Careful planning of studies: Anticipating possible limitations of your study and pre-emptively incorporating appropriate .I syntax commands and specifications into your analysis plan can streamline the processing and analyzing of data, saving you time and computational resources in the long run.
In conclusion, taking note of such functions like the .I syntax in data.table, their potential advantages and how to maximize them may open new paths for more effective and efficient data analysis in R.
Tune in to these 5 AI podcasts at the gym or on your commute to keep up to date with the world of AI.
The Long-Term Implications and Future Developments of AI Podcasts
Artificial intelligence (AI) is making waves in nearly all sectors. As a rapidly progressing field, staying ahead of the curve is crucial. One effective way to keep updated is through AI podcasts. These challenges us to think critically about the impact and potential of AI technologies, as well as staying informed about emerging trends, key insights, and thought leadership in the space.
Future developments and long-term implications
As AI becomes more mainstream, there is no doubt that the popularity of AI-centric content such as podcasts will rise, too. As this occurs, the discussion will likely go beyond the technical aspects of AI and delve into cultural, ethical, and social implications. Here are some potential long-term implications and future developments we might expect:
Broader audience scope: As the public becomes more interested and engaged in the world of AI, podcast content may evolve to cater to different audiences – not just those with a technical background. This could pave the way for more diverse discussions about AI.
Rising demand for AI ethics discussion: With AI penetrating multiple sectors, ethical considerations and regulations will become prominent topics. This could result in more podcasts focusing on discussing ethical aspects of AI.
Increasing podcast collaborations: As AI becomes more prevalent, collaborations between different podcast hosts to discuss interdisciplinary applications of AI could increase.
AI introducing newer formats: AI could soon automate the process of creating content or even introducing newer podcast formats, changing the face of podcasts as we know it.
Actionable Advice
Staying informed about AI’s impact, potential, and emerging trends can be facilitated by tuning into AI podcasts. Here are some recommendations to optimize your podcast learning experience:
Choose diverse content: Don’t limit yourself to purely technical AI podcasts. Include podcasts that discuss ethical, social, and cultural implications of AI. This broader scope can enhance your understanding.
Listen actively: Engage with the content. Consider following along with additional resources or taking notes during or after each episode to help solidify your understanding of the topics discussed.
Apply what you learn: Try to think about how the concepts and technologies discussed in the podcast can be applied in your line of work or personal projects. This will make your learning more practical and meaningful.
Conclusion
AI is a rapidly evolving domain and harnessing its potential requires us to stay informed and adaptable. Tuning into these AI podcasts provides an accessible and versatile tool to navigate this changing landscape. So whether you’re in the gym or on your commute, it’s never been easier to plug in and stay connected with the world of AI.
A podcast with CEO Ricky Sun of Ultipa Image by Gerd Altmann from Pixabay Relationship-rich graph structures can be quite complex and resource consuming to process at scale when using conventional technology. This is particularly the case when it comes to searches that demand the computation to reach 30 hops or more into the graphs. … Read More »High-performance computing’s role in real-time graph analytics
Long-term implications and possible future developments in real-time graph analytics
The conversation with CEO Ricky Sun of Ultipa Image emphasises the complexities and resources involved in processing graph structures, especially when computations demand a reach of 30 hops or more into the graphs. Moving forward, high-performance computing can play a significant role in driving efficient and real-time analytics on these relationship-rich graph networks.
Potential Long-Term Implications
The adoption of high-performance computing in graph analytics can open up a wide range of possibilities. Most importantly, these technologies can enhance the capability to process complex queries and manage large datasets efficiently. This could fuel advancements in various sectors, including healthcare, research, cybersecurity, and marketing, where graph analytics has significant potential.
Simultaneously, there may also be potential shortcomings. High-performance computing systems are typically expensive, which may deter smaller businesses or research institutions from exploring their utility. Furthermore, handling such advanced technologies may require a specialized skill set, fostering a talent gap in the field.
Possible Future Developments
As the demand for real-time analytics grows, we expect further developments in high-performance computing. This could include improved algorithms for faster processing and more cost-effective systems creating more accessibility even to smaller organizations. There may also be advancements in software that can work alongside these high-performance systems to streamline graph analytics.
Actionable Advice
The insights from Ricky Sun’s podcast underline three actionable points:
Invest in Education: To leverage high-performance computing in real-time graph analytics, it is essential to understand its use cases and benefits thoroughly. Continuous learning will be a crucial component in this respect.
Adopt Gradually: Instead of a complete technology switch, companies can adopt high-performance computing gradually to allow more time for employees to adjust and reduce workflow disruptions.
Start Small: Considering the cost of high-performance computing systems, starting small may be the best approach. Initial small-scale projects can provide valuable insights into how the technology can benefit your organization before a larger scale adoption.
Overall, high-performance computing seems to have an exciting future in real-time graph analytics. With careful planning and adoption, businesses can harness its full potential and drive significant value.
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The R Consortium recently talked with Natalia Andriychuk, Statistical Data Scientist at Pfizer and co-founder of the RTP R User Group (Research Triangle Park in Raleigh, North Carolina), to get details about her experience supporting the Pfizer R community and starting a local R user group.
She started her R journey over 7 years ago, and since then, she has been passionate about open source development. She is a member of the Interactive Safety Graphics Task Force within the American Statistical Association Biopharmaceutical Safety Working Group, which is developing graphical tools for the drug safety community.
Natalia Andriychuk at posit:conf 2023
Please share your background and involvement with the R community at Pfizer and beyond.
From 2015 to 2022, I worked at a CRO (Contract Research Organization) in various roles, where I discovered my passion for Data Science after being introduced to R, JavaScript, and D3 by my talented colleagues. I became a part of an amazing team where I learned valuable skills.
Later, when I began looking for new career opportunities, I knew that I wanted to focus on R. I sought a role that would deepen my R skills and further advance my R knowledge. This is how I came to join Pfizer in 2022 and became a part of the amazing team. I am a Statistical Data Scientist in the R Center of Excellence SWAT (Scientific Workflows and Analytic Tools) team.
Pfizer SWAT team at posit::conf2023 (left to right: Natalia Andriychuk, Mike K Smith, Sam Parmar, James Kim)
The R Center of Excellence (CoE) supports various business lines at Pfizer. We provide technical expertise, develop training on R and associated tools, promote best practices, and build a community of R users within Pfizer. Our community currently consists of over 1,200 members.
I will present Pfizer’s R CoE progress and initiatives during the R Consortium R Adoption Series Webinar on February 8th at 3:00 pm EST.
My first introduction to the R community was through the posit::conf (previously known as rstudio::conf) in 2018. Attending the conference allowed me to witness the welcoming nature of the R community. Five years later, in 2023, I made it to the speakers’ list and presented at the posit::conf 2023. It was an incredible experience!
I also follow several other avenues to connect with R community members. As the name suggests, I read R Weekly weekly and attend the Data Science Hangout led by Rachael Dempsey at Posit. Every Thursday, Rachael invites a data science community leader to be a featured guest and share their unique experiences with the audience. Fortunately, I was invited as a featured guest to one of the Posit Data Science Hangouts. I shared my experience organizing and hosting an internal R at Pfizer Hangout.
Can you share your experience of starting the RTP (Research Triangle Park) R User Group?
Nicholas Masel and I co-organize the RTP R User Group in our area. We formed the RTP R User Group in 2023 and have held three meetings: meet-and-greet, social hour, and a posit::conf 2023 watch party.
RTP R User Group Social Hour Gathering.
We hope to expand and increase attendance at our meetups in 2024. We currently have approximately 74 members who joined the online meetup group, and we look forward to meeting all of them in person moving forward.
Can you share what the R community is like in the RTP area
Nicholas and I both work in the pharmaceutical industry, and thus far, our in-person user group meetings have predominantly included individuals from this field. However, we want to emphasize that our user group is open to everyone, regardless of industry or background.
The RTP area has great potential for a thriving R User Group. We are surrounded by three major universities (University of North Carolina at Chapel Hill, Duke University, and North Carolina State University), the growing high-technology community and a notable concentration of life science companies. We anticipate attracting more students in the coming year, especially those studying biostatistics or statistics and using R in their coursework. We also look forward to welcoming individuals from various industries and backgrounds to foster a rich and collaborative R user community.
Please share about a project you are working on or have worked on using the R language. Goal/reason, result, anything interesting, especially related to the industry you work in?
I am an open source development advocate, believing in the transformative power of collaborative innovation and knowledge sharing. I am a member of the Interactive Safety Graphics (ISG) Task Force, part of the American Statistical Association Biopharmaceutical Safety Working Group. The group comprises volunteers from the pharmaceutical industry, regulatory agencies, and academia to develop creative and innovative interactive graphical tools following the open source paradigm. Our task force is developing a collection of R packages for clinical trial safety evaluation. The {safetyGraphics} package we developed provides an easy-to-use shiny interface for creating shareable safety graphics for any clinical study.
{safetyGraphics} supports multiple chart typesincluding web-based interactive graphics using {htmlwidgets}
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 65,000 members in 35 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.
Natalia Andriychuk, Statistical Data Scientist at Pfizer and co-founder of the RTP R User Group, shares her insights on fostering the open-source community with the R dialect of the S programming language (R), particularly in a pharmaceutical context.
The Role of Socialization and Collaboration
One of the crucial takeaways from Natalia’s experiences is that collaboration and user groups play a tremendous role in expanding the reach and application of the R language. She started her journey with R about seven years ago, a journey enmeshed with discussions, collaborations, and practice.
The RTP R User Group
Starting an R User Group has been a significant milestone for Natalia in providing a collaborative platform for R users. The RTP R User Group, co-organized by Natalia and Nicholas Masel, was launched in 2023 and has so far conducted three meetings successfully. Currently boasting a digital membership of about 74 individuals, the RTP group plans to scale its numbers further in 2024. Open to everyone, regardless of industry or background, it is a testament to the community’s inclusive and progressive spirit.
The Power of Open Source Development
Possibly the most exciting aspect of this exchange is Natalia’s advocacy for open source development. As a member of the Interactive Safety Graphics (ISG) Task Force within the American Statistical Association Biopharmaceutical Safety Working Group, Natalia is involved in creating innovative interactive graphical tools, adhering to the open-source paradigm. The open-source project {safetyGraphics}, has already catalyzed progress in analyzing clinical trial safety evaluations.
Implications & Future Developments
Indeed, the future seems promising for the R language and relevant collaborations. They will likely surge in the years to come, propelled by the need for refined and accessible open-source tools within various industries and academica alike. The potential for growth is especially evident around the RTP (Research Triangle Park) area, with a confluence of high-tech companies, universities, and life science firms to create a rich tapestry of opportunities.
Takeaways and Actionable Advice
Capitalizing on the power of community: User groups and meetings will significantly contribute to skills development and extending the application of R, regardless of industry or background.
Advocating open source development: Open source projects such as {safteyGraphics} have demonstrated the capability of making technical tasks more accessible and shareable within the community. Such actionable measures could be included in short- and long-term strategies when dealing with R.
Investing in the future: By considering the potential hotspots for R collaboration, such as the RTP area, individuals and companies can foster community growth and pave the way for future advancements.
Conclusion
The future of R communities is indeed bright, signifying an increased emphasis on collaboration, open-source development, and inclusiveness. As an actionable measure, more individuals and organizations should consider forming or becoming part of User Groups to tap into this global trend’s potential benefits.
Navigating Complex Data Structures with Python’s json_normalize.
Understanding the Future of Json_Normalize in Python
One of the increasingly crucial aspects of programming and data analysis, especially in working with APIs or other complex data sources, has been the use of Python’s json_normalize function. This function has been a powerful tool within Python’s Pandas library for flattening semi-structured JSON data into a flat table. We now invite you to delve deeper into this feature, investigate its long-term implications, as well as potential future developments.
Implications and Developments
For data analysts and developers, understanding, manipulating and managing complex data structures is no mean feat. Python’s json_normalize has offered an effective solution for handling such complex data structures and is likely to continue playing a significant role in the future, especially as we continue experiencing an increase in JSON data structure usage.
Given its ability to break down complex semi-structured JSON data into familiar table structures for easier manipulation and analysis, the future may hold even more sophisticated and versatile functionality. A focus on iterative development and improvements could lead to the creation of new functions or enhancement of json_normalize to handle a wider array of data complexities. The capability to process more intricate structured data types in real-time would be a beneficial advancement.
A move towards automated data flattening is also a future possibility. Automation would streamline significant data-processing tasks, boosting productivity and efficiency, eventually translating into better business decisions and strategies based on accurate, timely data analysis.
Actionable Advice
In light of these possibilities, there is good reason for businesses and individuals involved in data processing to pay close attention to Python’s json_normalize enhancements and its positioning in data handling practice.
Invest in knowledge: Keep skills in Python’s json_normalize and related tools updated. Keeping pace with enhancements and new trends in programming and data science will allow you to make the most of these tools.
Streamline your data handling: If you’re working with complex data structures, start integrating Python’s json_normalize into your workflows, if you haven’t yet. It not only simplifies the tasks but also makes them more efficient
Participation in community: Be part of active communities that discuss, share, and solve issues related to Python json_normalize. This way, you can learn from others’ experiences and stay up-to-date with any new changes or upgrades.
Being proactive in these areas will provide a significant edge when dealing with complex data structures as advancements in json_normalize and other Python tools continue to evolve.
Avoid the AI siren song[1]. Avoid the advice that leads you to believe an artificial intelligence (AI) project is just like any other IT project and that the approach you used for your ERP / MRP / BFA / CRM implementations will work here. Be cautious of the “start small” advice. Instead, think: Start small,… Read More »Your AI Journey: Start Small AND Strategic – Part 1
Understanding The Misconception About Artificial Intelligence (AI) Projects
Many are misguided into treating an AI project akin to their typical IT projects: whether it’s an Enterprise Resource Planning (ERP), Material Requirements Planning (MRP), Budget Financial and Administrative (BFA), or Customer Relationship Management (CRM) implementation. It is crucial to acknowledge the complexity and unique nature of AI initiatives, which to a large extent differs from the traditional IT projects.
A Strategic Approach Towards AI, Avoiding the “Start Small” Misapprehension
The commonplace advice given to newcomers in the AI field is often to “start small.” But the suggestion might be misleading. AI projects shouldn’t just be about “starting small” but starting strategically-that means taking a more holistic view about integrating AI into your organization’s business model and operational structure.
Long-term Implications and Future Developments
The Inevitability of Complexity in AI Projects
As AI continues to evolve and companies globally seek to leverage its potential, one cannot ignore the innate complexity these projects harbor. They differ significantly from standard IT implementations; they necessitate a concrete understanding of the technology, a clear goal, and consistent data feeding. The future will possibly witness more specializations emerging in the AI sector, thereby confirming this complexity.
AI Integration: A Need for Strategic Consideration
The notion of starting small in AI projects could take on a more strategic definition. It’s not just about taking one small step at a time, but implementing that small step in alignment with the overall business strategy. In essence, a merger of strategy and execution is necessary for successful AI projects in the long term. The future might see increased advocacy for a balanced approach towards these projects, with equal emphasis on strategic alignment and feasible execution.
Actionable advice
Embrace the complexity: Accept that an AI project is not your regular IT project. It needs a thorough understanding of the technology and its implications on your business.
Link AI plans with strategy: Don’t “start small” without a calculated strategy. Ensure that every step, however small or big, aligns with your business goal.
Invest in learning: AI is an evolving field. This necessitates continuous learning and upskilling to keep up with the industry’s transformations.
Integrated approach: Balance between strategic planning and execution. Develop a strategy that revolves around your business model and simultaneously enforce an execution plan that’s manageable and practical.
