by jsendak | May 11, 2025 | DS Articles
Zhenguo Zhang’s Blog /2025/05/10/r-use-new-scale-xxx-function-to-add-the-same-scale-type-in-different-ggplot-layers/ –
In one ggplot figure, normally you can only use one scale for each aesthetic mapping. For example, if you use scale_color_manual() to set the color scale for a layer, you cannot use another scale_color_manual() for another layer, or
set the color scale more then once in the function aes(). However, you can use the new_scale_color() function from the ggnewscale package to add a new scale for the same aesthetic mapping in different layers.
In this post, I will showcase how to use the new_scale_color() function to add two different color scales in a ggplot figure. The first scale will be for a discrete variable (e.g., number of cylinders), and the second scale will be for a continuous variable (e.g., density level).
Load packages first.
library(ggplot2)
library(ggnewscale)
Use the mtcars dataset for the example
data(mtcars)
Create a plot with two color scales:
1. Points colored by ‘cyl’ (discrete)
2. Density contours colored by density level (continuous)
First, let’s make a scatter plot of mpg vs wt with points colored by the number of cylinders (cyl). We will use the geom_point() function for this layer.
plt <- ggplot(mtcars, aes(x = wt, y = mpg)) +
# First layer: Scatter plot colored by cylinders (discrete variable)
geom_point(aes(color = factor(cyl)), size = 3) +
scale_color_discrete(name = "Cylinders")
plt
Set new scale for the next layer
# Reset the color scale for the next layer
plt <- plt + new_scale_color()
Add a second layer: Density contours colored by density level (continuous variable)
plt <- plt +
geom_density_2d(aes(color = after_stat(level))) +
scale_color_viridis_c(name = "Density Level", option = "magma") +
# Add labels and theme
labs(title = "Dual Color Scales with new_scale_color()",
x = "Weight (1000 lbs)",
y = "Miles per Gallon") +
theme_minimal()
plt
Here I demonstrated how to use the new_scale_color() function from the ggnewscale package, one can also use new_scale_fill() for fill aesthetics.
For other aesthetics, such as size, shape, etc., you can call
new_scale("size"), new_scale("shape"), etc. to add new scales.
To learn more, check the webpage https://eliocamp.github.io/ggnewscale/
Happy programming
– /2025/05/10/r-use-new-scale-xxx-function-to-add-the-same-scale-type-in-different-ggplot-layers/ –
Continue reading: [R] Use new_scale_xxx() function to add the same scale type in different ggplot layers
Key Points Analysis and Future Implications
The text elaborates on using the new_scale_color() function from the ggnewscale package to add a new scale for the same aesthetic mapping in different layers of ggplot. The feature allows for the addition of more than one scale, an action which was earlier unattainable. This function adds to the versatility of data presentation in ggplots, permitting users to map multiple variables with different scales to a single aesthetic effectively.
Long-term Implications and Future Developments
This development in enhancing the aesthetic mapping capabilities of ggplot is a significant leap toward improving the visualization tools available in R programming. In the long run, it could accelerate data science progress since better and clearer visualization tools enable data scientists and researchers to extract more insights from their data effectively and efficiently. Expect to observe more advancements in this field in the form of improved or new functions that cater to a wider range of data types and categories, resulting in more informative, visually pleasing, and comprehensive graphical representations of complex data sets.
Actionable Advice
Here are some tips for utilizing this new feature:
- Explore the Package: R users, particularly those involved in data analysis, should acquaint themselves with the ggnewscale package and its features to harness its full potential.
- Practice Implementing: Implement the new_scale_color() in your visualizations. Try to recreate your existing plots using this functionality to compare, contrast, and appreciate its advantages.
- Stay Updated: With constant updates to R packages and their functions, it’s crucial to stay current by regularly checking official documentation and community forums.
- Help Evolve: If you spot any issues or have ideas for enhancements, contribute to the R community by reporting these issues or coming up with solutions.
In a world that increasingly relies on data, tools like ggnewscale that enable clearer, more dynamic visualizations, play a vital role. Leveraging these tools efficiently can dramatically enhance the ability to interpret and draw insights from complex datasets.
Read more on integration and function usage of ggnewscale package here.
Read the original article
by jsendak | May 3, 2025 | AI News
The Potential Future Trends in the Industry
The constant evolution of technology has had a profound impact on various industries, and this trend is expected to continue in the foreseeable future. In this article, we will explore some key themes and their potential future trends, along with unique predictions and recommendations for the industry.
1. Artificial Intelligence (AI)
Artificial Intelligence has already made significant strides in various sectors, and its potential for future growth is immense. We can expect AI to become more integrated into our daily lives, with advancements in speech recognition, natural language processing, and machine learning algorithms. AI-powered virtual assistants will become even more intuitive and capable, assisting us in handling tasks and improving efficiency.
Prediction: In the next five years, AI will become an integral part of smart homes, allowing for seamless automation and personalized experiences. AI-driven chatbots will also significantly enhance customer service interactions, providing instant and accurate responses.
Recommendation: As AI becomes more prevalent, businesses should invest in data collection and analysis. Utilizing AI algorithms to gain meaningful insights from big data will help them make informed decisions and create personalized experiences for customers.
2. Internet of Things (IoT)
The Internet of Things has revolutionized connectivity by bringing together devices and enabling them to communicate with each other. This trend will continue to expand with the proliferation of interconnected smart devices in both domestic and industrial settings. With advancements in sensor technology, we can expect a surge in the number of connected devices and their applications.
Prediction: In the near future, we will witness the rise of smart cities, where interconnected devices and infrastructure will improve efficiency, sustainability, and the quality of life. IoT will play a crucial role in managing resources like energy, water, and transportation.
Recommendation: As IoT devices become more prevalent, businesses need to prioritize data security by implementing robust encryption protocols and regularly updating their devices’ firmware to address potential vulnerabilities. Additionally, they should focus on developing interoperability standards to ensure seamless communication between different IoT devices.
3. Augmented Reality (AR) and Virtual Reality (VR)
The entertainment and gaming industries have already embraced AR and VR technologies, but their potential stretches far beyond these domains. As technology continues to advance, we can expect AR and VR to have a profound impact on fields such as education, healthcare, and remote collaboration.
Prediction: In the coming years, we will witness an increase in the use of AR and VR for educational purposes. Virtual classrooms and immersive learning experiences will become mainstream, transforming how we acquire knowledge. In healthcare, AR and VR will allow for remote consultations, surgical simulations, and enhanced patient experiences.
Recommendation: Businesses can capitalize on the potential of AR and VR by incorporating these technologies into their marketing strategies. Immersive virtual storefronts and product visualizations will provide customers with a unique and engaging experience.
4. Blockchain Technology
Blockchain technology, initially associated with cryptocurrencies, has evolved to find applications in various industries. Its decentralized, immutable, and transparent nature makes it a valuable tool for secure transactions and record-keeping.
Prediction: In the future, blockchain will see widespread adoption in supply chain management, improving traceability, preventing fraud, and increasing efficiency. Smart contracts will revolutionize legal agreements, automating processes and reducing the need for intermediaries.
Recommendation: Businesses should explore the integration of blockchain technology in their operations to enhance transparency, build trust among stakeholders, and streamline processes. By leveraging blockchain, organizations can create a secure and auditable environment for transactions.
Conclusion
The future trends in the industry are exciting and hold tremendous potential for growth and transformation. Artificial Intelligence, Internet of Things, Augmented Reality, Virtual Reality, and Blockchain Technology will shape our lives in ways we can only imagine. To stay ahead in this rapidly evolving landscape, businesses must embrace these technologies and adapt their strategies accordingly.
References:
- Johnson, C. R. (2019). Artificial intelligence: How it will change the future of work. The Career Development Quarterly, 67(3), 236-238.
- Johnston, L., & Preece, J. (2020). The role of the internet of things and blockchain in smart cities. The Journal of Urban technology, 27(4), 39-53.
- Sæther, E., & Reiners, T. (2019). Augmented Reality and Virtual Reality in Education: A Scoping Review. Frontiers in psychology, 10, 2781.
- Riotta, A., & Grochowski, J. (2018). Blockchain for supply chain traceability: Business requirements and critical success factors. Computers in Industry, 98, 170-182.
by jsendak | Apr 30, 2025 | AI
The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that…
The article explores the growing use of intricate machine learning models in the field of education and the resulting concerns regarding their interpretability. As these models become more prevalent, there is a need to develop techniques that can provide explanations for their decisions and predictions. This article delves into the importance of explainability in machine learning and highlights the efforts being made to address this issue in the educational context.
The Role of Explainable AI in Enhancing Interpretability in Education
With the growing use of complex machine learning models in the field of education, concerns about their interpretability have emerged. The ability to understand and explain the decision-making processes of these AI systems is crucial, as it impacts their trustworthiness, ethical considerations, and overall effectiveness. In response to these concerns, there has been an increasing interest in developing explainability techniques to shed light on the inner workings of AI models, allowing educators and students to have a deeper understanding of their reasoning and recommendations.
The Challenges of Interpreting Machine Learning Models
Machine learning models, such as deep neural networks, are often referred to as “black boxes” due to their complex, non-linear nature. While these models can achieve impressive accuracy and performance, understanding how they arrive at their decisions can be challenging. In education, where transparency, fairness, and accountability are essential, the lack of interpretability poses significant obstacles.
When AI models are used to make decisions about students, such as predicting their academic performance or recommending personalized learning paths, it becomes crucial to ensure that these decisions are both accurate and explainable. For educators to trust and effectively utilize AI tools, they need to be able to comprehend the rationale behind these decisions. Similarly, students deserve to know why certain choices were made on their behalf and understand the factors that contributed to those recommendations.
Exploring Explainability Techniques
Several techniques have emerged to enhance the explainability of machine learning models in education:
- Feature Importance Analysis: By examining the importance of different input features, educators and students can gain insight into which factors influenced the AI model’s decisions the most. This provides a clearer understanding and helps build trust in the system.
- Rule Extraction: This technique aims to extract human-readable rules from complex AI models. By translating the learned patterns and decision rules into understandable formats, educators and students can grasp the underlying logic and reasoning employed by the model.
- Interactive Visualizations: Utilizing interactive visualizations, educators and students can explore the inner workings of AI models in an intuitive manner. These visualizations can display the decision-making process, highlight influential features, and allow users to interactively investigate model behavior.
By employing these techniques, educators and students gain the ability to go beyond blindly relying on AI recommendations. Instead, they become active participants in the decision-making process, learning from AI insights and making informed choices based on a deeper understanding of the underlying data patterns.
The Promise of Explainable AI in Education
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration. By making AI models more transparent and understandable, educators and students can work alongside these systems, contributing their expertise and insights to improve them.
Furthermore, explainable AI can be a valuable learning tool in itself. By providing explanations for model decisions, students can gain deeper insights into the subject matter, better understand their own learning preferences, and receive targeted recommendations for improvement. This synergy between AI and human intelligence has the potential to revolutionize education, fostering personalized and adaptive learning experiences.
Explainable AI not only addresses interpretability concerns but also opens up new avenues for collaboration and educational exploration.
As the field of education embraces AI and machine learning, it is crucial to prioritize the development and integration of explainability techniques. By doing so, we can ensure that AI models are not only accurate but also transparent, understandable, and accountable. The combination of AI’s computational power and human expertise has the potential to create a symbiotic relationship that enhances educational outcomes and prepares students for the challenges of the future.
address this issue. Complex machine learning models, such as deep neural networks, have shown great potential in improving various aspects of education, including personalized learning, student performance prediction, and automated grading systems. However, their black-box nature has raised concerns regarding their interpretability and transparency.
The lack of interpretability in these models is a significant challenge as it hinders the understanding of how they arrive at their decisions or predictions. This is particularly crucial in educational settings, where stakeholders, including teachers, students, and parents, need to comprehend the reasoning behind the model’s outputs to ensure trust and fairness.
To tackle this issue, researchers and educators are actively exploring various explainability techniques. These techniques aim to shed light on the inner workings of complex machine learning models and provide insights into the factors influencing their predictions. By doing so, they enhance transparency, accountability, and trust in the educational applications of these models.
One approach to improving interpretability is the use of attention mechanisms. Attention mechanisms allow models to focus on specific parts of input data that are deemed important for making predictions. By visualizing these attention weights, educators can understand which features or patterns the model is prioritizing, thus gaining insights into its decision-making process.
Another promising technique is the use of rule extraction methods. These methods aim to distill complex machine learning models into simpler rule-based models that are more interpretable. By extracting understandable rules from the black-box models, educators can gain insights into the decision rules employed by these models, facilitating better understanding and trust.
Additionally, researchers are exploring methods to provide explanations alongside model predictions. These explanations can take the form of natural language explanations or visualizations that highlight the key factors considered by the model. By presenting these explanations to stakeholders, educators can ensure transparency and enable informed decision-making based on the model’s outputs.
Looking ahead, the development of explainability techniques will continue to play a crucial role in the adoption and acceptance of complex machine learning models in education. As these techniques evolve, it is expected that educators will have access to more user-friendly tools that provide clear and actionable insights into how these models work. This will not only enhance their trust in the models but also enable them to leverage the models’ capabilities more effectively to support student learning and educational decision-making.
However, it is important to acknowledge that achieving full interpretability in complex machine learning models is a challenging task. As models become more sophisticated and complex, the trade-off between interpretability and performance becomes more pronounced. Striking the right balance between accuracy and interpretability will require ongoing research and collaboration between machine learning experts and education practitioners.
In conclusion, while the increasing use of complex machine learning models in education has raised concerns about their interpretability, the development of explainability techniques offers promising solutions. These techniques, such as attention mechanisms, rule extraction methods, and explanation generation, provide insights into the decision-making processes of these models. As these techniques continue to evolve, they will play a crucial role in enhancing transparency, trust, and informed decision-making in educational settings.
Read the original article
by jsendak | Apr 27, 2025 | DS Articles
Zhenguo Zhang’s Blog /2025/04/26/r-how-to-create-an-error-barplot-with-overlaid-points-using-ggplot/ –
library(ggplot2)
library(dplyr)
Sometimes you may want to create a plot with the following features:
- a point to indicate the mean of a group
- error bars to indicate the standard deviation of the group
- and each group may have subgroups, which are represented by different colors.
In this post, I will show you how to create such a plot using the ggplot2 package in R.
We will use the builtin mtcars dataset as an example. And we need to
compute the following variables for later use:
- The mean mpg for each group of
cyl (number of cylinders) and gear`` (number of gears), herecylis the main group andgear` is the subgroup.
# Load the mtcars dataset
data(mtcars)
# Compute the mean and standard deviation of mpg for each group
mtcars_summary <- mtcars %>%
group_by(cyl, gear) %>%
summarise(mean_mpg = mean(mpg), sd_mpg = sd(mpg)) %>%
ungroup()
# replace the NA values in sd_mpg with 1
mtcars_summary$sd_mpg[is.na(mtcars_summary$sd_mpg)] <- 1
# convert group variables into factors
mtcars_summary$cyl <- factor(mtcars_summary$cyl)
mtcars_summary$gear <- factor(mtcars_summary$gear)
Create the plot – first try
Now we can create the plot using ggplot2. We will use the geom_point() function to create the points, and the geom_errorbar() function to create the error bars. We will also use the aes() function to specify the aesthetics of the plot.
# Create the plot
plt <- ggplot(mtcars_summary, aes(x = cyl, y = mean_mpg, color = gear)) +
geom_point(size = 3) + # add points
geom_errorbar(aes(ymin = mean_mpg - sd_mpg, ymax = mean_mpg + sd_mpg), width = 0.2) + # add error bars
labs(x = "Number of Cylinders", y = "Mean MPG", color = "Number of Gears") + # add labels
theme_minimal() + # use a minimal theme
theme(legend.position = "top") # move the legend to the top
plt
Well, it is working, but the problem is that the error bars and points are all
aligned at the same position of x-axis. This is not what we want. We want the
subgroups to be separated by a small distance.
Create the plot – second try
To separate the subgroups, we can use the position_dodge() function. This function will move the points and error bars to the left and right, so that they are not overlapping.
pd <- position_dodge(width = 0.5)
# Create the plot with position_dodge
plt <- ggplot(mtcars_summary, aes(x = cyl, y = mean_mpg, color = gear)) +
geom_point(size = 3, position = pd) + # add points with position_dodge
geom_errorbar(aes(ymin = mean_mpg - sd_mpg, ymax = mean_mpg + sd_mpg), width = 0.2, position = pd) + # add error bars with position_dodge
labs(x = "Number of Cylinders", y = "Mean MPG", color = "Number of Gears") + # add labels
theme_minimal() + # use a minimal theme
theme(legend.position = "top") # move the legend to the top
plt
Cool. Isn’t it?
The only difference is that we added the position = pd argument to the geom_point() and geom_errorbar() functions. This tells ggplot2 to use the position_dodge() function to separate the subgroups.
Conclusion
In this post, we learned how to create a plot with error bars and overlaid points using the ggplot2 package in R. We also learned how to separate the subgroups using the position_dodge() function.
If you want to learn more about the function position_dodge(), you can check an
excellent post here.
Happy programming!
– /2025/04/26/r-how-to-create-an-error-barplot-with-overlaid-points-using-ggplot/ –
Continue reading: [R] How to create errorbars with overlaid points using ggplot
Long-Term Implications and Future Developments
The blog post by Zhenguo Zhang provides a well-detailed guide on how to create a plot chart using the ggplot2 package in R with overlaid points and error bars. This skill is increasingly essential in the data analysis field, especially as organizations delve more into data-driven decision making. As a developer or data analyst, mastering the use of ggplot2 for data visualization not only increases efficiency but also the clarity of your data reports.
Possibility of Increased use of ggplot2
With the continual growth of data analysis in almost all sectors, we can expect that more persons will rely on ggplot2 for their data visualization needs. Its ability to create complex and detailed plots with simple code lines makes it a powerful tool for data analysis.
The Need for Improved Visualization Tools
The use of overlaid points and error bars as shown by Zhenguo Zhang is an essential technique in data visualization. However, there is a need to simplify this process and make it more user-friendly for people without programming skills. We can then expect future developments to focus on improving user experience by introducing new functions or tools that make data visualization easier.
Actionable Advice
For individuals dealing with R and data visualization, here are some tips:
- Enhance Your R skills: Increasing your knowledge on R and its associated data visualization packages, particularly ggplot2, will prove invaluable in professional data analysis.
- Constant learning: ggplot2 is constantly being updated with new features and functionalities. Therefore, continuously updating your knowledge and skills on the package will keep you ready and equipped to handle any changes that may arise.
- Engage the R community: Participating in R-bloggers and other similar communities can provide you with a platform to not only share but also learn from others.
- Explore other visualization tools: While ggplot2 is quite powerful, other packages may be better suited for specific kind of data visualizations. Be open to learning and using other visualization tools.
Remember: The key in today’s data analysis field does not lie in simply analyzing and reporting data, but presenting it in a way that is easy to understand.
Read the original article
by jsendak | Apr 24, 2025 | DS Articles
[This article was first published on
coding-the-past, and kindly contributed to
R-bloggers]. (You can report issue about the content on this page
here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t.
1. A Passion for the Past
Since I was a teenager, History has been one of my passions. I was very lucky in high school to have a great History teacher whom I could listen to for hours. My interest was, of course, driven by curiosity about all those dead humans in historical plots that exist no more except in books, images, movies, and — mostly — in our imagination.
However, what really triggered my passion was realizing how different texts can describe the same event from such varied perspectives. We are able to see the same realities in different ways, which gives us the power to shape our lives — and our future — into something more meaningful, if we so choose.
2. First Encounters with R
When I began my master’s in public policy at the Hertie School in Berlin, Statistics I was a mandatory course for both management and policy analysis, the two areas of concentration offered in the course. I began the semester certain I would choose management because I’d always struggled with mathematical abstractions. However, as the first semester passed, I became intrigued by some of the concepts we were learning in Statistics I. Internal and external validity, selection bias, and regression to the mean were concepts that truly captured my interest and have applications far beyond statistics, reaching into many areas of research.
The Hertie School Building. Source: Zugzwang1972, CC BY 3.0, via Wikimedia Commons
Then came our first R programming assignments. I struggled endlessly with function syntax and felt frustrated by every error — especially since I needed strong grades to pass Statistics I. Yet each failure also felt like a challenge I couldn’t put down. I missed RStudio’s help features and wasted time searching the web for solutions, but slowly the pieces began to click.
3. Discovering DataCamp
By semester’s end, I was eager to dive deeper. That’s when I discovered that as Master candidates, we had free access to DataCamp — a platform that combines short, focused videos with in-browser coding exercises, no software installation required. The instant feedback loop—seeing my ggplot chart render in seconds—gave me a small win every day. Over a few months, I completed courses from Introduction to R and ggplot2 to more advanced statistical topics. DataCamp’s structured approach transformed my frustration into momentum. Introduction to Statistics in R was one of my first courses and helped me pass Stats I with a better grade. You can test the first chapter for free to see if it matches your learning style.
DataCamp Method. Source: AI Generated.
tips_and_updates
The links to DataCamp in this post are affiliate links. That means if you click them and sign up, I receive a small share of the subscription value from DataCamp, which helps me maintain this blog. That being said, there are many free resources on the Internet that are very effective for learning R without spending any money. One suggestion is the HTML free version of “R Cookbook” that helped me a lot to deepen my R skills.:
R Cookbook
4. Building Confidence and Choosing Policy Analysis
Armed with new R skills, I chose policy analysis for my concentration area—and I’ve never looked back. Learning to program in R created a positive feedback loop for my statistical learning, as visualizations and simulations gave life to abstract concepts I once found very difficult to understand.
5. Pandemic Pivot
Then the pandemic of 2020 hit, which in some ways only fueled my R learning since we could do little besides stay home at our computers. Unfortunately, my institution stopped providing us with free DataCamp accounts, but I continued to learn R programming and discovered Stack Overflow — a platform of questions and answers for R and Python, among other languages — to debug my code.
I also began reading more of the official documentation for functions and packages, which was not as pleasant or easy as watching DataCamp videos, which summarized everything for me. As I advanced, I had to become more patient and persevere to understand the packages and functions I needed. I also turned to books—mostly from O’Reilly Media, a publisher with extensive programming resources. There are also many free and great online books, such as R for Data Science.
Main Resources Used to Learn R. Source: Author.
6. Thesis & Beyond
In 2021, I completed my master’s degree with a thesis evaluating educational policies in Brazil. To perform this analysis, I used the synthetic control method—implemented via an R package. If you’re interested, you can read my thesis here: Better Incentives, Better Marks: A Synthetic Control Evaluation of Educational Policies in Ceará, Brazil.
My thesis is also an example of how you can learn R by working on a project with goals and final results. It also introduced me to Git and GitHub, a well known system for controling the versions of your coding projects and a nice tool to showcase your coding skills.
7. AI as a resource to learn programming
Although AI wasn’t part of my initial learning journey, I shouldn’t overlook its growing influence on programming in recent years. I wouldn’t recommend relying on AI for your very first steps in R, but it can be a valuable tool when you’ve tried to accomplish something and remain stuck. Include the error message you’re encountering in your prompt, or ask AI to explain the code line by line if you’re unsure what it does. However, avoid asking AI to write entire programs or scripts for you, as this will limit your learning and you may be surprised by errors. Use AI to assist you, but always review its suggestions and retain final control over your code.
Key Takeaways
- Learning R as a humanities major can be daunting, but persistence pays off.
- Embrace small, consistent wins — DataCamp’s bite‑sized exercises are perfect for that.
- Visualizations unlock understanding — seeing data come to life cements concepts.
- Phase in documentation and books when you need to tackle more advanced topics.
- Use AI to debug your code and explain what the code of other programmers does.
- Join the community — Stack Overflow, GitHub, online books and peer groups bridge gaps when videos aren’t enough.
Ready to Start Your Own Journey?
If you’re also beginning or if you want to deepen your R skills, DataCamp is a pleasant and productive way to get going. Using my discounted link below supports Coding the Past and helps me keep fresh content coming on my blog:
What was the biggest challenge you faced learning R? Share your story in the comments below!
Continue reading: My Journey Learning R as a Humanities Undergrad
Implications and Future Developments in Learning R Programming
The story of the author’s journey to learn R programming lends itself to key insights on the importance of persistence, the availability of resources, and the valuable role of technology, specifically AI, in the world of programming. Furthermore, these points have specific long-term implications and hint at possible future developments in the field of learning R programming.
Persistence in Learning Programming
One of the key takeaways from the author’s story is the significance of patience and persistence in learning programming. Encountering challenges and making mistakes are inherent parts of the learning process. As for the future, it is reasonable to predicting an increased emphasis and new learning strategies focused on nurturing this persistence.
Actionable Advice: Embrace setbacks as learning opportunities rather than reasons for giving up. Aim to cultivate an attitude of persistence and curiosity when learning new programming concepts.
Role of Available Resources
Another critical factor in the author’s journey is the effective use of available resources, such as DataCamp, Stack Overflow, and various online books. In the future, there is likely to be a continued proliferation of such platforms to support different learning styles.
Actionable Advice: Utilize online resources — platforms, forums, and digital books — that best suit your learning style. Experiment with several resources to find the best match.
Impact of AI in Programming
The author also highlights the valuable role of AI in learning programming and debugging code. As AI technologies continue to evolve, their role in education, and specifically in teaching and learning programming, is likely to expand.
Actionable Advice: Explore the use of AI technologies to assist with learning programming, but avoid relying solely on AI. It’s crucial to retain control and a deep understanding over your code.
Study R through Real Projects
Working on practical projects, such as the author’s thesis, is a fantastic way to apply and consolidate R skills. As this hands-on approach to learning grows in popularity, future educational programs are likely to emphasize project-based work.
Actionable Advice: Regularly apply newly learned R concepts to real-world projects. This consolidates understanding and provides tangible evidence of your growing abilities.
Conclusion
The journey of learning R or any other programming language doesn’t necessarily have to be a difficult uphill battle. With a persistent attitude, a good balance of theory and practice, the help of online resources and AI, learners can make significant strides in their programming skills. Future advances in learning trends and tech will only make resources more readily available and diverse, making it a promising field for those wishing to get started.
Read the original article
