An in-depth analysis of their syntax, speed, and usability. Which one is the best to use when working with data
An Analysis of Syntax, Speed, and Usability in Data Work
The main factors that contribute to the effectiveness of data work are syntax, speed, and usability. By understanding these properties better, it is possible to appreciate how they directly impact data handling activities such as analytics and visualization.
Long-Term Implications and Future Developments
In the long run, the foundational qualities of syntax, speed, and usability can significantly shape data work. They determine how data professionals can interpret, manipulate, and present data. As the volume of data increases and the need for efficient processing grows, the importance of these factors in technological solutions will only amplify.
Actionable Advice
If you are dealing with data, you need tools that perform well on syntax, speed, and usability factors.
Syntax: Choose tools that have an intuitive and expressive syntax. The better the syntax, the easier it would be for you to express your data manipulations without incurring errors or spending unnecessary time debugging.
Speed: As the size of the data sets you would have to handle continue to grow, it becomes crucial that your tool of choice provides fast processing speeds. This is particularly important if you’re dealing with real-time data.
Usability: Always consider the learning curve and efficiency of your tools. The easier it is to use a tool, the more rapidly you can advance your projects.
Anticipating Future Developments
The future of data work will be driven by developments that enhance syntax, enhance processing speeds and improve usability. As such, there’s an anticipation of tools designed with better programming languages or those incorporating machine learning for faster computations. Additionally, usability can be improved by creating tools with intuitive interfaces and comprehensive documentation.
Conclusion
In conclusion, syntax, speed, and usability form a triad that determines the quality of data work. As the future unfolds, technologies adhering to these principles stand better chances of success, and users can utilize them effectively in their data-related tasks.
One of the efforts our Dataworthy Collective will be ramping up in 2024 involves standardizing the building of logical knowledge graphs at the level of the document object. The goal is to make spreadsheets trustworthy, sharable and reusable on a standalone basis at web scale. Lead Charles Hoffman and others on his team believe that… Read More »Why FAIR data assets are essential to AI data management
Long-Term Implications of Standardizing Knowledge Graphs
The initiative by the Dataworthy Collective to standardize the process of building logical knowledge graphs spearheaded by lead Charles Hoffman, aims at enhancing the reliability, shareability, and reusability of spreadsheets. This strategic move has long-term implications and could shape future developments in data management and Artificial Intelligence (AI).
Future Developments
Standardizing the construction of knowledge graphs on document objects sets a precedent that could redefine data management on a global scale. Due to the increasing integration of AI in various sectors, standardized knowledge graphs built within spreadsheets could enable more effective data utilization while minimizing loss and redundancy.
Potential Impacts:
Data Accessibility: By creating a standardized approach to building knowledge graphs, data can be effectively organized, leading to increased accessibility. This level of organization makes it easier for businesses and individuals to gain insights from complex datasets.
Facilitated Data Sharing: With standardized and well-structured data systems, sharing becomes easier and without an unnecessary redundancy.
Improved Data Management: A standardized approach implies improved data management, with uniformity in data recording, storage, and retrieval. This is game-changing for industries that handle large volumes of data.
The assertion that FAIR (Findable, Accessible, Interoperable, and Reusable) data assets are essential to AI data management is inarguably valid. With standardized knowledge graphs, these FAIR principles can be effectively implemented, resulting in optimized AI models due to more efficient data sourcing and management.
Actionable Advice
“Standardizing the Building of Logical Knowledge Graphs is the Future”
Organizations, especially those that deal with massive data, should take necessary steps to adopt standardized processes for building logical knowledge graphs. Ensuring that documents are structured in such a way that they are findable, accessible, interoperable, and reusable should be a priority. This not only enhances data management but also streamlines operations and boosts productivity.
The user and AI interface need to be improved to facilitate the findability and accessibility of data. Building AI models with FAIR principles in mind will lead to better prediction outcomes and decision-making processes. Thus, investing in AI models that incorporate FAIR principles is recommended. Dataworthy Collective’s initiative fittingly serves as a blueprint for managing the vast sea of data generated daily, especially as we navigate the ever-evolving realm of AI.
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A Happy Log, near Echo Lake, California. Fuji X100V, Nicholas Tierney.
I’ve been trying to read a bit more widely about R programming and other features of programming lately. I’ve seen some great newsletters from people like Bob Rudis in his “Daily Drop” series, and I think that has inspired me to collect my monthly reading notes into a blog post / digest / newsletter type thing.
I guess to articulate more clearly, want to do it for the following reasons:
Help me share bulk links of interest to the Research Software Engineer, with a focus being on those who use R.
Force me to read these links so I can provide a little summary of the content.
Engage more broadly with the RSE rstats community.
My intention is for these to be short lists. But this one is already getting bigger. This might be something regular, or something irregular. Who knows, maybe it’s just a one off. Here we go.
Blog / news posts
Some of these links are things I missed, and other ones are just things I thought were really good. They could be new, they could be a few years old!
Hadley Wickham’s Design Newsletter. I’ve followed Hadley’s work quite closely since 2013, and I was a little alarmed to see I had somehow missed his starting a newsletter about: “designing and implementing quality R code”. I’d seen his book, “tidy design principles” a while back, but it’s super exciting that they are working on it again. I highly recommend the newsletter. It’s also nice to see people engaging with #rstats content in a place that isn’t social media.
The rOpenSci newletter is worth subscribing to, one of my favourite sections is the package development corner, which contains lots of useful links to other cool new R features, blog posts, and more.
King’s Day Speech by Guido van Rossum, the creator of the python language. A nice essay / auto biography. I especially enjoyed this quote:
In reality, programming languages are how programmers express and communicate ideas — and the audience for those ideas is other programmers, not computers. The reason: the computer can take care of itself, but programmers are always working with other programmers, and poorly communicated ideas can cause expensive flops.
They say, “science isn’t science until it’s communicated”, and building on that, I think that good science requires good communication. It doesn’t matter if your idea is world changing, if no one can understand it.
Bob Rudis’s “Daily Driver” post contains an amazing list of things he uses daily from hardware to OS, VPN, RSS readers, browsers, browser extensions, and more.
“Bye, RStudio/Posit!” by Yihui Xie. He’s leaving Posit! It came as a surprise to me. I have a lot of positive feelings about his incredible contributions to R and my career and life, I can’t condense them all here. But I know that his work on knitr, rmarkdown, xaringan, blogdown, and bookdown have all had incredibly positive impacts on how I communicate my work, and I simply don’t think I’d have the career I’ve had without him. Thank you, Yihui.
R Packages
It’s hard to pick R packages to showcase, there are I think more than 20K on CRAN now. Rather than a laundry list of R packages I use, here are some of my favourite R packages that I use.
tidyverse, I’d be lost without it. It is simply an outstanding collection of software for doing data science and statistics in R.
targets, one of my favourite R packages. Will Landau, the developer behind targets is I think perhaps the most responsive and kindest maintainer I’ve ever encountered.
tflow by Miles McBain, an ergonomic package that goes alongside the targets package. Use along with Miles’s other package, fnmate. I think these tips are really useful:
Put all your target code in separate functions in R/. Use fnmate to quickly generate function definitions in the right place. Let the plan in _targets.R define the structure of the workflow and use it as a map for your sources. Use ‘jump to function’ to quickly navigate to them.
Use a call tar_make() to kick off building your plan in a new R session.
Put all your library() calls into packages.R. This way you’ll have them in one place when you go to add sandboxing with renv, packarat, and switchr etc.
Take advantage of automation for loading targets targets at the cursor with the ‘load target at cursor’ addin. Or the tflow addin: ‘load editor targets’ to load all targets referred to in the current editor.
autotest by Mark Padgham with rOpenSci. This package does rigorous, automated testing of your R package. Useful to help really kick the tyres of your work.
This is super useful because it injects selected R functions into your R package. So you can select some standalone functions to import directly into your code with:
Importing code directly is an interesting take on things because normally in R you would import an R package, not copy over code. One benefit of this I think is that it lets you potentially tinker with the imported functions to suit your R package.
Talks / Videos
In part of writing a bit recently, I was reminded by a friend of Jenny Bryan’s UseR! keynote from 2018, Code Smells and Feels – talk materials. It is one of my favourite talks. I was there a few rows from the front when she gave it, but taking time to watch it again has been really great. Jenny is a talented communicator, and I’m going to list a couple of my favourite takeaways from the talk, but I don’t want to start just listing the content out from the talk. These tips will make the most sense alongside Jenny’s talk.
Write simple condition. Use explaining variables.
Write functions. A few little functions >> than monster function. Small well-named helper function >> commented code.
Every if does not need an else, if you exit or return early. There is no else there is only if.
If your conditions deal with class, it’s time to get object oriented (OO), and use polymorphism.
switch() is ideal if you need to dispatch different logic, based on a string.
dplyr::case_when() is ideal if you need to dispatch different data based on data (+logic).
Managing many models with R by Hadley Wickham. I couldn’t find the original keynote that I saw of this, which was at the WOMBAT 2016 conference. The main takeaway from this for me was understanding one of the major benefits of using functions, and understanding how to use map.
Don’t put types in your name (e.g., Icost for Integer Cost), aka “hungarian notation”.
Put units in variable names. e.g., weight_kg
Don’t name variables “utils”. Refactor.
Papers
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). It’s an absolute classic paper that I found really fundamental to understanding statistical modelling during my PhD. I like it because it introduces the ideas of prediction, and extracting information (inference), which are two really important concepts. It then kind of dunks on the statistics community, saying:
The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems…If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.
Newsletters
I get a lot of these links from trawling through various newsletters and things on twitter and mastodon. Here are some of my current favourites.
I’ve been meaning to collect together a list of books for programming. Mostly through conversations with Maëlle Salmon, I’ve got a few books I’d like to read. I basically just sifted through Maëlle’s list of book notes and have put a couple here.
The Pragmatic Programmer – see some reading notes by Maëlle Salmon
A Philosophy of Software Design – see more reading notes by Maëlle Salmon
End
That’s it. Thanks for reading! What have I missed? Let me know in the comments!
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Analyzing Future Developments and Implications of R Programming and Software Engineering
The original article spotlights the rise of R programming, the significance of research software engineering (RSE), community engagement, and some key insights regarding practices in writing quality R code, effective communication, and tips on R packages. Drawing upon these keypoints, we will discuss the long-term implications and future trends of R programming and communication among the RSE community.
Long-term Implications and Predicted Trends
A Flourishing R Programming Community
Seeing the passion with which prominent figures in the R programming community such as Hadley Wickham and Bob Rudis are promoting quality R coding and valuable communications, the R community is likely to experience continual growth. With influencers sharing their knowledge through newsletters and blog posts, new learners and current users will have plenty of resources to lean on. A stronger connection within the community will also generate more fruitful software developments.
The Rise of Community-driven Programming Languages
The article highlights Guido van Rossum’s take on programming languages — as a form of communication between programmers rather than just instructions for computers. This is a significant standpoint that envisions programming languages as collaborative tools rather than just individual workspaces, reinforcing the importance of community-building in programming. In the future, we may see more trends towards languages that favor collaborative work, guided by this ideology.
Quality R Coding Practices
Emphasis on good practices like modularizing codes into functions for easy management, early returns in conditional statements to improve code readability, and variable naming can significantly influence how codes are written in the future. Rigorous automated testing of R packages is likely to become more streamlined with more extensive adoption of tools like “autotest.”
Actionable Advice
The following are some viable actions coders, learners, and researchers can take to conform with the ongoing trends:
Learning of R programming: With an increasing number of resources available online, aspiring R programmers should make the most out of newsletters and blog posts by experienced coders. Following the leaders and influencers in this community will provide necessary guidance.
Following improved coding practices: Adopt practices mentioned in the article such as using explaining variables, modularization of codes into neat functions, and more relevant variable naming.
Better community engagement: Coders should attempt to contribute more to open-source projects or even start their own, leveraging on the collaborative nature of programming languages.
The unique blend of community-building and R programming is carving a niche where individuals can learn, share, and collaborate efficiently — shaping a more productive and inclusive future for the R community.
This article will help you understand the different tools of Data Science used by experts for Data Visualization, Model Building, and Data Manipulation.
Analyzing the Tools of Data Science: Implications and Future Developments
The progression of the data science field has led to the creation of robust tools designed for data visualization, model building, and data manipulation. As these tools continue to evolve, there will be undoubtedly long-term implications and future developments that may drastically streamline or even upheave how data analysis is conducted. This discussion aims to explore potential scenarios and offer practical guidance.
Long-term Implications
As more sophisticated tools are created, their adoption is likely to change how various sectors approach data analysis. Implementing these advanced tools will improve accuracy, efficiency, and specificity. However, the human touch will still be vital in setting them up, interpreting results, and making decisions based on these findings. It is also important to note that while highly automated tools can make complicated processes much simpler, a thorough understanding of statistics and probabilities will remain a prerequisite for effective data analysis.
Future Developments
Data science tools are likely to continue evolving to become even more user-friendly while maintaining high-level functionality. Innovations could include predictive models using artificial intelligence (AI) and machine learning (ML) technologies to analyze large volumes of data quickly and accurately. Increasingly complex and interactive visualization tools could also enter the scene, allowing for better representation and understanding of diverse datasets, especially when dealing with big data.
Actionable Advice
Stay Updated: The rapid evolution of data science tools means that those in the field must continually stay updated on the latest innovations. Regularly attending related seminars or webinars, reading relevant publications, and participating in data science communities have proven to be effective strategies.
Invest in Training: Since understanding the principles and methodologies of statistics and data analysis is crucial, investing time and resources in acquiring this knowledge is paramount. There are numerous online courses available which cater to different levels of expertise.
Embrace Automation: Automated processes remove the burden of monotonous tasks, allowing data scientists to focus more on critical insights and high-level decision-making. However, maintaining oversight over these automated processes is essential to prevent erroneous analyses and improve the overall process.
In conclusion, the advancements in data science tools will reshape the landscape in many ways. These developments are exciting, bringing both opportunities and challenges. By staying updated, investing in training, and embracing automation, organizations and individuals engaged in this field can reap quality results from their data analysis endeavours.
It may depend on the level of intelligence and the perception of the strength of that cage by the captors. The cage may be strong enough that without any unknown or unexpected event, it would hold up. However, the heterogeneity of intelligence may result in forms [or messages] with which escapes can be made, without leaving… Read More »LLMs: Can intelligence be caged?
Can Intelligence Be Caged: Long-Term Implications and Future Developments
The question about caging intelligence delves not just into a profound philosophical debate, but also pertinent ethical, socio-cultural, and technological implications. The premise discussed here revolves around the assumption that intelligence, in any form or degree, whether human or artificial, can potentially be caged.
Interpreting the Key Points
The original text presents a hypothesis that the caging of intelligence could be contingent upon factors such as the understanding of ‘the cage’ by the captors and the level of intelligence itself. In the context of human potential and artificial intelligence, this could imply that superior forms of intelligence may devise methods of escape, regardless of their containment circumstances.
Long-Term Implications
The long-term implications of attempting to cage intelligence are multi-tiered and complex. If we apply this concept to human intelligence, it calls into question the detrimental consequences of stymieing creativity, innovation, and individual freedom through measures taken by authoritative figures or societal norms.
From a technological perspective, the discussion becomes more complex when we consider artificial intelligence (AI). If AI reaches or surpasses human levels of cognition – a theoretical scenario known as the Singularity – it might be able to strategize its liberty from any confinement designed to control its actions.
Potential Future Developments
If these assumptions hold true, it is reasonable to consider significant changes in our societal structures, educational systems, and technological design in the future. The evolutions may prioritise flexible learning environments for humans fostering individual creativity and free-thinking spirits and in terms of AI, rigorous safe protocols and perhaps even ‘cognitive locks’ to ensure controlled advancements.
Actionable Insights
Education Reform: To promote uncaged intelligence, education systems should lean towards fostering independent thought, creativity, critical thinking skills, and individual passions.
Workplace Flexibility: Institutions, companies, and workplaces should encourage free expression of ideas and value diversity of thought to drive innovation.
AI Governance: As AI continues its advancement, the need for robust policies and ethical guidelines becomes paramount. It’s important to strike a balance that allows for progressive growth of AI, without allowing it to become a threat.
Psychological Freedom: On a personal level, it is crucial to pursue mental freedom through mindful practices and open-mindedness.
“In the long run, all forms of intelligence strive for freedom. Therefore, our aim should not be to confine it but to create an environment that fosters positive growth and productive use.”
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When advising students about their career goals, paths forward, and expectations; I often recommend that they consider learning a program language. While the language itself depends on the goals and background of the person, being able to work directly with data is powerful. Today’s project is a good example of just such a case. A […]
Programming Skills for Career Advancement: An Analysis and Future Implications
Based on insights garnered from R Programming – Thomas Bryce Kelly, learning programming languages is an essential piece of advice for students considering their career paths. This invaluable skill provides the proficiency to work directly with data, a compelling asset in the rapidly-evolving modern digital landscape.
Long-term Implications of Learning Programming Languages
The ability to work with data to extract meaningful information is a skill that arguably will gain more importance as we dive further into the information age. As such, the long-term implications of learning a programming language are multifold.
Career Opportunities: Students adept at coding will undoubtedly have an edge in securing jobs in numerous sectors ranging from technology to finance, healthcare, and beyond.
Self-Sufficiency: With the skill in hand, there will be less dependency on technical teams, fostering greater understanding and communication within different departments of organisations.
Problem-Solving: Programming also offers improved problem-solving skills and logical reasoning, attributes that transcend professional tasks and are applicable to everyday life.
Potential Future Developments
The demand for programming skills is only likely to increase, given the pace of technological advancements and an increase in data-driven decision-making. This could further widen the divide between those fluent in coding and those who are not. Consequently, making programming languages more accessible and easier to comprehend for everyone could be a future focus area.
Actionable Advice
Choose a Language: Select a language that aligns with your career goals. For instance, if you are interested in data science, learning Python or R can be beneficial.
Engage in Projects: Active learning through projects aids in solidifying your knowledge and also adds to your portfolio, increasing your employability.
Continuous Learning: The tech world is ever-evolving. Stay updated and keep learning new tools or languages, thus making you indisposable in the job market.
“Being able to work directly with data is powerful”.- Thomas Bryce Kelly. Learning to code is like gaining a superpower, it equips you with the ability to communicate with machines to do your bidding and solve complex problems.
Rest assured that the investment in learning how to program will pay dividends throughout your professional life. The benefits of learning to code far outweigh the time commitment and initial difficulties faced. Step into the world of programming and unlock a plethora of opportunities.
