News
Entertainment
Science & Technology
Life
Culture & Art
Hobbies
News
Entertainment
Science & Technology
Culture & Art
Hobbies
R-bloggers | R news and tutorials contributed by (750) R bloggers - Software
8 | Follower
New book ‘C++ for R Users’ and all my other books for free or 10 USD each | R-bloggers
In line with my previous post, I have decided to release a premilinary edition of ‘C++ for R Users’. The suggested price is 10 USD, these can be downloaded for free, and the offer will last while my scholarship payments are still delayed (it’s be...
RAG (Retrieval Augmented Generation) in R with ragnar workshop | R-bloggers
Join our workshop on RAG (Retrieval Augmented Generation) in R with ragnar, which is a part of our workshops for Ukraine series! Here’s some more info: Title: RAG (Retrieval Augmented Generation) in R with ragnar Date: Thursday, September 11th, 18:00 – 20:00 CEST (Rome, Berlin, Paris timezone) Speaker: Sharon Machlis is a long-time tech journalist … Continue reading RAG (Retrieval Augmented Generation) in R with ragnar workshopRAG (Retrieval Augmented Generation) in R with ragnar workshop was first posted on August 11, 2025 at 11:46 am.
A Personal Message from an Open Source Contributor | R-bloggers
Dear fellow developers and data scientists, If everyone reading this gave just the price of a coffee, I could focus fully on the open source work that serves our community. But not everyone can or will contribute, and that’s okay. Why I’m Writin...
Managing Remote Database Connections from Quarto Documents | R-bloggers
R offers excellent facilities for working interactively with SQL databases on a remote server. It becomes more challenging to do so in a self-contained Quarto document, especially if you wish to be able to render it without running into errors. F...
Adding Shiny app’s parameters to the URL | R-bloggers
Shiny allows to use all of R to visualize information, no matter if it is a sophisticated statistical model or a simple plot. One of the features it does not provide out-of-the-box is adding the selected parameters to the URL, like this: https://...
Conformal Prediction Intervals of XGBoost Model: Bitcoin Peaks Amid Coinbase’s Earnings Struggle | R-bloggers
In the second quarter, Coinbase did not meet Wall Street’s expectations. This decline occurred alongside lower market volatility, despite BTC prices reaching record highs according to Kaiko Research. The chart below indicates that the market priced the aforementioned from August. We can observe a negative decoupling between the Bitcoin and Coinbase Global share prices. Source […]
Shiny in Production 2025: Sponsors | R-bloggers
Shiny in Production Conference wouldn’t be possible without our sponsors, so we wanted to take the time to tell you a little bit about them. Don’t miss out on this great chance to learn from R experts and network with fellow data science enthusiasts! Tickets are available at Shiny in Production website! Posit Posit (formerly known as RStudio) is a software company that builds both open‑source tools and professional solutions enabling teams to create, manage, and share reproducible data work in R, Python, and beyond. ThinkR ThinkR is a consultancy company offering development and training on R, RStudio and Shiny. Datacove Datacove are a Brighton based Data and analytics consultancy, specialising in Customer Analytics (unearthing your most valuable customers), Marketing Analytics (getting more out of your marketing budget) and Process Automation. Newcastle University Solve Newcastle University Solve (NU Solve) has been helping businesses, public sector organisations and industries to find answers to complex challenges for more than three decades. They emerged out of the Industrial Statistics Research Unit, which had successfully engaged with enterprises since 1984. CRC Press CRC Press is a scientific publisher that specializes in science, technology, engineering, mathematics, and medicine. They publish books and digital resources for researchers, academics, professionals, and students. CRC Press is part of the Taylor & Francis Group. R Consortium The central mission of the R Consortium is to work with and provide support to the R Foundation and to the key organizations developing, maintaining, distributing and using R software through the identification, development and implementation of infrastructure projects. For updates and revisions to this article, see the original post
Global Modeling with Automated ML: Impact of One Big Beautiful Bill on Big Tech | R-bloggers
Morgan Stanley analysts believe that the One Big Beautiful Bill will be a boon for Big Tech, as it will provide a cash influx to AI giants, thereby enhancing their dominance in future AI competitions. But Trump’s 1 August tariffs, which compensate for tax cuts in the aforementioned bill, seemed not to benefit the tech […]
clav: R package and Shiny application for cluster analysis validation | R-bloggers
Cluster analysis is a statistical procedure for grouping observations using an observation-centered approach as compared to variable-centric approaches (e.g. PCA, factor analysis). Whether a preprocessing step for predictive modeling or the ...
Introduction to Generalised Linear Models with Prussian Horse Kicks | R-bloggers
Time to finally patch a hole in the leaky roof of my knowledge: what are Generalised Linear Models anyway? Groundwork: what are Linear Models anyway? Generalised Linear Models (GLMs) are a short step from Linear Models, provided you have the ri...
Health Technology notes: Copilot tips and guidance | R-bloggers
1. Introduction 1.1. The use of AI agents and the case for “prompt” choices We introduced the use of the AI pair programmer, Copilot, while working with tidyverse. In this post, we walk the reader through the fundamental driver of successfully...
Animated Maps with {ggplot2} and {gganimate} | R-bloggers
In this blog post, we are going to use data from the {gapminder} R package, along with global spatial boundaries from ‘opendatasoft’. We are going to plot the life expectancy of each country in the Americas and animate it to see the changes from ...
Can Juliet manage without Romeo? How much she should spend, save, and invest to achieve that? | R-bloggers
Meet Juliet Juliet just turned 20 and is ready to start her adult life. She’s landed a stable, well-paid job and her new journey begins. This time, life unfolds without Romeo. Think she can’t handle it? You might be surprised. Juliet feels anxious about her future. Each month she have to choose how to balance spending, saving, and investing for what’s ahead. She’s keen to avoid poor decisions. Juliet seeks clear, rational advice tailored to her needs—not the generic advice that’s all over the internet. She aims to make informed, personalized financial choices that align with her present knowledge and future plans. She needs a clear set of recommendations to know how much to save and how to invest 1. Let’s follow Juliet’s example to craft a straightforward financial report for a single-person household. We will be using our new R4GoodPersonalFinances R package that implements (with our modifications) the theoretical model invented and described in Lifetime Financial Advice: A Personalized Optimal Multilevel Approach by Thomas M. Idzorek and Paul D. Kaplan (Idzorek and Kaplan 2024). Juliet’s assumptions and expectations We aim to explore scenarios that an individual might encounter, based on realistic assumptions about market returns and living expenses in a big Polish city. These scenarios reflect a consistent, median-level income. We focus on income and spending estimates for Poland, using Polish złoty (PLN). However, you can adapt these scenarios to any currency or country-specific factors. Codemonthly_income plot_expected_allocation() Codescenarios |> dplyr::filter(scenario_id == "J-65") |> plot_expected_allocation() In both retirement scenarios, the model advises Juliet to invest all her financial capital in stocks. This makes sense because her small financial capital is offset by significant bond-like human capital. As time progresses, the model recommends moving towards bonds in both scenarios. However, in the later retirement scenario, the shift to stocks occurs later than in the earlier one. This is because Juliet plans to work longer, maintaining a larger human capital that she will convert into financial capital over an extended period. Possible extensions There are several ways in which Juliet can decide to extend the lifecycle model and simulations: We’re using two asset types: the risky asset (stocks) and the safe asset (bonds). Our package supports various assets with specific traits, though for most, it’s likely overkill. Leveraging these two asset classes offers a straightforward, effective approach we endorse, aligning with authors such as (Haghani and White 2023) and (Ibbotson et al. 2007). We use default market assumptions and Poland-specific tax rates. To adapt the model for other countries, update or tailor these elements. You can customize those parameters entirely since the portfolio object functions as an editable data frame, with each asset represented by a single row. We’re modeling two income streams for Juliet—her salary and social security benefits—and one non-discretionary spending stream. This setup isn’t set in stone. You can tweak it to reflect various income and spending streams as Juliet’s life events unfold. We’ve predicted two key moments in Juliet’s life: retirement and claiming social security benefits. We could also consider other significant events, such as relocating, marriage, having children, or landing a promotion with a raise. Each of these events might have related changes to Juliet’s income and spending streams. We’ve based Juliet’s life expectancy on the average for women in Poland. However, this can be adjusted to match her personal life expectancy. For instance, if her parents enjoy long lives, she might anticipate a longer lifespan for herself (and tweak the Gompertz parameters). Juliet’s household settings use preset values for preferences like consumption impatience, smooth spending, and risk tolerance. You can adjust these to match Juliet’s preferences—for instance, if she wants to spend more early in life and less later, or vice versa. Juliet plans to claim the minimum social security benefits as early as possible during retirement. It’s worth evaluating if delaying her claim could offer more advantages. The next versions of the package will include custom functions to dynamically calculate income streams based on events like age when you begin claiming social security benefits. When to claim social security pension? We assumed Juliet will only claim the minimum social security benefits at retirement. That may be too pessimistic. Even if the average pension replacement rate for young people like Juliet in Poland might be around 40% (OECD 2023, 53–54), Juliet could still receive roughly 2,000 PLN per month at age 60 in today’s terms. That’s a bit more than the current minimum social security benefits (1,710). Moreover, Juliet’s social security pension will grow if she waits before claiming it. Delaying each year boosts her pension by 8-10% 7. Waiting for 10 years, until age 70, could increase Juliet’s pension to 4,000 PLN or more. Should Juliet delay claiming Social Security? That is a topic for another post. But it’s worth considering in her financial model. Social Security works like an inflation-indexed annuity from the recipient’s perspective (Ibbotson et al. 2007, vii). This makes it crucial insurance against outliving ones savings. Conclusion Juliet thrives solo in a single-person household in a bustling Polish city. She navigates life independently, whether or not Romeo is by her side, or if they haven’t crossed paths, or perhaps they did, but he wasn’t quite right for her. Our simulations suggest this single-person household scenario is realistic for Juliet. Juliet bases her decisions on current assumptions about the future market and a median salary that aligns with inflation. The future is uncertain, which means these assumptions might shift. However, decisions must be made today, rooted in what seems most reasonable right now. When the future looks bleak, Juliet will adapt. She might ramp up her savings, cut unnecessary costs, think about retiring later, or find ways to boost her income—like taking on part-time work or seizing business opportunities. But if the future shines bright, she could decide to enjoy more discretionary spending, retire earlier, or even do both. You can see that with the household lifecycle framework you can quite quickly reach an optimal (or near-optimal) recommendation tailored to your own specific situation at a given moment. In future posts, we’ll explore scenarios where Juliet and Romeo choose to spend their life together. Our simulation will include diverse income sources, multiple necessary expenses, and varied life expectancy assumptions. Eventually, we’ll even factor in children. Tip Enjoyed this post? Share it with others who might find it interesting. Subscribe to our newsletter to stay updated. Thanks for reading! Back to topReferences CFP Board, ed. 2015. Financial Planning Competency Handbook. 2nd ed. Hoboken, NJ: John Wiley & Sons. https://doi.org/10.1002/9781119642473. Haghani, Victor, and James White. 2023. The Missing Billionaires: A Guide to Better Financial Decisions. Hoboken, New Jersey: John Wiley & Sons, Inc. Ibbotson, Roger G., Moshe A. Milevsky, Peng Chen, and Kevin X. Zhu. 2007. “Lifetime Financial Advice: Human Capital, Asset Allocation, and Insurance.” Monograph. Research Foundation of CFA Institute. https://rpc.cfainstitute.org/research/foundation/2007/lifetime-financial-advice-human-capital-asset-allocation-and-insurance. Idzorek, Thomas M., and Paul D. Kaplan. 2024. Lifetime Financial Advice: A Personalized Optimal Multilevel Approach. CFA Institute Research Foundation. Milevsky, Moshe A. 2012. Are You a Stock or a Bond? Identify Your Own Human Capital for a Secure Financial Future. Updated and Revised. FT Press. OECD. 2023. Pensions at a Glance 2023: OECD and G20 Indicators. Paris: OECD Publishing. https://doi.org/10.1787/678055dd-en. Footnotes Juliet’s expectation for clear advice seems to be in line with the Financial Planning Competency Handbook by the CFP Board: Presenting planning alternatives, while useful in showing a client the many paths available to reach a financial goal, can create decision paralysis among some clients. This is the reason that the planner should conclude with a clear set of recommendations that leave the client in no doubt as to what must be done next (CFP Board 2015, 606). Using life-cycle model, we can recommend Juliet not only one of many alternatives of recommendations, but a single set of recommendations that should be optimal for her (or at lest close to optimal).↩︎ The median salary in Poland in 2024 was 6’842 PLN gross per month, and about 4’995 PLN net per month. See: https://stat.gov.pl/obszary-tematyczne/rynek-pracy/pracujacy-zatrudnieni-wynagrodzenia-koszty-pracy/rozklad-wynagrodzen-w-gospodarce-narodowej-w-listopadzie-2024-r-,32,11.html↩︎ The net minimum social security benefits in Poland in 2025 is 1’878.91 PLN per month gross, which is about 1’709.81 PLN net. See: https://www.gov.pl/web/rodzina/waloryzacja-rent-i-emerytur-w-2025-roku–wzrost-swiadczen-dla-milionow-polakow↩︎ The costs of leaving for a single in a big city in Poland in 2025 were estimated at 4’000 PLN per month. This included also discretionary spending like 450 PLN for cinema tickets, eating out, subscriptions. Based on that we assumed that non-discretionary spending is about 3’500 PLN. See: https://www.bankier.pl/smart/ile-kosztuje-zycie-singla-vs-rodziny-w-2025-budzety-ktore-otwieraja-oczy (in Polish)↩︎ Read more about human capital in (Milevsky 2012), (Ibbotson et al. 2007), and (Idzorek and Kaplan 2024).↩︎ Constant discretionary spending—certainty equivalent constant level of spending that would result in the same lifetime utility as a series of (non-constant) future spending in a given scenario.↩︎ See article (in Polish): https://businessinsider.com.pl/praca/emerytury/emerytura-jak-uzyskac-wyzsze-swiadczenie-ike-ppk-odroczenie-emerytury-prezes-zus/cx88knm. The similar situation is in US, where Social Security payments increase 8 percent for every year after full retirement age that a retiree waits to claim benefits. A retiree who des not take early Social Security benefits may enjoy a benefit up to 30 percent grater than one who receives benefits at the earliest possible age. (CFP Board 2015, 437)↩︎ CitationBibTeX citation:@online{wais2025, author = {Wais, Kamil and Wais, Olesia}, title = {Can {Juliet} Manage Without {Romeo?} {How} Much She Should Spend, Save, and Invest to Achieve That?}, date = {2025-07-27}, url = {https://r4good.academy/en/blog/financial-report-individual-minimal-example/index.en.html}, langid = {en} } For attribution, please cite this work as: Wais, Kamil, and Olesia Wais. 2025. “Can Juliet Manage Without Romeo? How Much She Should Spend, Save, and Invest to Achieve That?” July 27, 2025. https://r4good.academy/en/blog/financial-report-individual-minimal-example/index.en.html.
Part II – Using R in Excel – Linear Regression | R-bloggers
Introduction In the first part of this series, I looked at using R in Excel to obtain descriptive statistics. In this second part of the series I am going to look at using R in Excel to perform linear regression, specifically using the lm() function. l...
Numbers of the Beast: Sasquatch Distribution Modelling | R-bloggers
The third OII Halloween Lecture sinks bodily into the tortured mass of data concerning cryptozoological sightings in North America. Drawing on over a century of shadow-haunted sightings documenting the curiously repellant presence of the North America Sasquatch, or Bigfoot, we aim to identify the factors associated with its presence, delineate the confounding presence of other dread manifestations, and cast our minds globally for a faded glimpse of its remote and scarcely-conceived brethren. [...]
Efficient R – How to write faster code workshop | R-bloggers
Join our workshop on Efficient R – How to write faster code, which is a part of our workshops for Ukraine series! Here’s some more info: Title: Efficient R – How to write faster code Date: Thursday, August 28th, 18:00 – 20:00 CEST (Rome, Berlin, Paris timezone) Speaker: Selina Baldauf, Selina is a theoretical ecologist … Continue reading Efficient R – How to write faster code workshopEfficient R – How to write faster code workshop was first posted on July 28, 2025 at 3:24 pm.
Part IV – Using R in Excel – Calling Python | R-bloggers
Introduction In the last post in this series I am going to look at calling Python from R. Even though Excel now provides a means of calling Python scripts directly, using the =PY() formula in a worksheet, there are still occasions when it is beneficial...
Part I – Using R in Excel – Descriptive Statistics | R-bloggers
Introduction The purpose of this series of posts is to demonstrate some use-cases for R in Excel using the ExcelRAddIn component (disclaimer: I am the developer of this add-in: ExcelRAddIn). The fundamental rationale for the add-in is that it allows ac...
Tidyverse with GitHub Copilot for Healthcare Analytics – Part 2 | R-bloggers
1. Introduction and recap Healthcare analytics offers significant benefits to a wide range of stakeholders, including health administrators, organizations, and most importantly, doctors, nurses, and healthcare workers. In the first part of this...