Evans Mudibo

☕ Take a break, grab a coffee, and dive into the latest research! Stroll through the abstract e-posters in the Exhibition Area and make the most of your #NG2025 experience. While you’re there, don’t forget to snap a photo at our booth! 📸

When applying PCA, understanding each principal component's contribution and how it reveals the data structure is essential. Visualizing more than two components can be challenging, but 3D PCA plots offer a useful solution. A 3D plot visualizes high-dimensional data reduced to three principal components, revealing patterns, clusters, and outliers that may not be visible in 2D plots. It makes the data more interpretable and provides deeper insights into the underlying structure. Here's a quick guide to implementing a 3D PCA plot in R: 1️⃣ Prepare Data: Clean and preprocess your data, ensuring all variables are numeric by removing any categorical variables. 2️⃣ Perform PCA: Apply PCA to the preprocessed data using prcomp() and extract the component scores. 3️⃣ Create the 3D Plot: Use plot3d() from the rgl library to visualize the results in three dimensions, creating an interactive 3D scatter plot. For a detailed step-by-step tutorial, check out my tutorial created in collaboration with Paula Villasante Soriano: statisticsglobe.com/3d-plot-pca-r I have also developed an extensive online course on PCA, which explains the theoretical concepts and practical applications in R programming. Click this link for detailed information: statisticsglobe.com/online-course-… #RStudio #datavis #DataViz #datascienceenthusiast #Statistics #DataAnalytics #RStats

@C_NyaKundiH This was the story in Kilifi a few months ago. In our apartment in Kilifi we lost over 6 @MawinguInternet radios. We were asked to repay for reinstallation. This is a common story with @MawinguInternet a need for a more reliable internet provider is needed.

Outraged Clients Cry Foul Over Mysterious Satellite Dish Thefts Linked to Internet Provider Multiple clients of "Mawingu" internet service provider in Kirinyaga County have reported the theft of their satellite dishes in what they suspect to be a coordinated scheme involving local thieves and possible insider complicity within the company. Residents reported that the dishes were removed from rooftops with remarkable precision, leaving the cables and metallic posts untouched. Victims allege that Mawingu's response has been dismissive, with the company demanding payment for replacements and reinstallation instead of addressing the thefts or introducing preventive measures such as tracking systems. One frustrated client reported encountering eight others at the Kutus Police Station, all filing similar complaints about the thefts. Some say their calls to Mawingu were abruptly terminated when they mentioned the issue. "Hi Nyakundi. Please expose this company Mawingu. Apparently, after ushering in the new year, we woke up to find network dishes missing from our homes. It's puzzling how someone can climb a three-story building and remove the dishes from the roof. This is a true story from Kutus, Kirinyaga region, and it appears that dishes across the whole county were stolen. Strangely, when we contacted the company to ask about measures they are taking, they told us to pay for a new dish and reinstallation. Moreover, after multiple calls, whenever you mention the theft, the unprofessional and rude customer service representatives hang up. When I went to report the matter, I found eight other people at the Kutus police station with the same complaint. The manner in which the satellite dishes were removed showed a lot of expertise. The cables and metallic posts were left untouched, which makes us suspect the company and their local collaborators may be involved in this theft. It's disheartening. I thought these were just rumours but now, barely a month after installing this network, I have experienced it firsthand. Mawingu is the worst! They are aware that the dishes are being stolen but have not taken any measures, such as installing tracking systems on their equipment. They are quick to ask customers to pay for replacements after theft while ignoring their concerns and providing poor service. People should boycott this company."

📚Our #ResearchHighlight: @mudibo_evans et al. have a newly published article titled "Systemic biological mechanisms underpin poor post-discharge growth among severely wasted children with HIV" See the summary and read more! nature.com/articles/s4146…

Our JSS article is out! And now I get to focus on {marginaleffects} 1.0.0. Stay tuned. jstatsoft.org/article/view/v…

"Why do children with #HIV & severe #malnutrition recover slower? Our study using #proteomics reveals how HIV alters bone mineralization, growth factors & immunity, hindering #growth. @Njunge__james @jayberkley @ChainNetwork @KEMRI_Wellcome

New @Nature A mechanism for why durable weight loss is so difficult: epigenetic memory of fat cells nature.com/articles/s4158… @vonMeyenn @laurahinte

iModMix: Integrative Module Analysis for Multi-omics Data biorxiv.org/content/10.110…

Our revolution is back,this Time round ni kufinish kumalo and leave Must Go going to hideout in Cayman islands. #OccupyParliament

Circular barplot? 🌀 ➡️ There are now plenty of tutorials in the R and Python graph galleries! - Python with @matplotlib : python-graph-gallery.com/circular-barpl… - R with ggplot2: r-graph-gallery.com/circular-barpl…

Check out this fun #rstats package @nrennie35 built to take clean datasets and make them more messy. github.com/nrennie/messy

Simplify the creation of publication-ready plots! The ggpubr package builds on ggplot2, providing easy-to-use functions for creating clean and stylish visualizations, perfect for reports, articles, and presentations. ✔️ Publication-Ready Graphics: Designed to help you generate high-quality plots with minimal effort, ensuring your visuals are polished and professional. ✔️ Easy Statistical Annotations: Easily add statistical comparisons, p-values, and significance markers to your plots, making it simple to highlight key differences. ✔️ Streamlined Plot Customization: Includes intuitive functions for adding titles, labels, themes, and more, so you can quickly fine-tune your plots to match your style. ✔️ Seamless Integration with ggplot2: Works directly with ggplot2, allowing you to enhance and customize your plots without learning a new syntax. The visualizations shown here are from the package website, demonstrating how ggpubr can simplify the creation of beautiful, informative plots: rpkgs.datanovia.com/ggpubr/ Want to master ggplot2 and its powerful extensions to create stunning visualizations? Enroll in my online course, “Data Visualization in R Using ggplot2 & Friends,” starting on November 25, 2024! Early bird promotion available for a limited time, through November 6. Further details: statisticsglobe.com/online-course-… #Rpackage #tidyverse #DataVisualization #VisualAnalytics #ggplot2 #programmer

Video lectures, UC Berkeley Data 8 Foundations of Data Science spring 2022, fall 2022, spring 2023, by Joseph Gonzalez, Swupnil Sahai, John DeNero data8.org/sp23/ data8.org/fa22/ data8.org/sp22/

Misuse of p-values is a prevalent issue in scientific research. P-values are often misunderstood and misapplied, leading to incorrect conclusions. A p-value measures the probability of obtaining results at least as extreme as the observed ones, assuming that the null hypothesis is true. Common problems with p-values include: ✅ Overemphasis on significance: Researchers often focus on whether p-values are below a threshold (e.g., 0.05), ignoring the effect size and practical significance. ✅ P-hacking: Manipulating data or experimental conditions to achieve statistically significant p-values. ✅ Misinterpretation: Believing that a low p-value proves the alternative hypothesis or that a high p-value confirms the null hypothesis. ✅ Ignoring context: Failing to consider the broader context of the study, including prior evidence and the research design. The graph shown in this post is a modified version of this Wikipedia image: es.wikipedia.org/wiki/Valor_p#/… Want to deepen your understanding of statistics with R? Sign up for my online course, "Statistical Methods in R." Further details: eepurl.com/gH6myT #database #DataVisualization #datascienceenthusiast #statisticians

Rethinking p-Values in Scientific Research This paper addresses the ongoing debate about p-values and offers innovative solutions to improve statistical practice. Let’s explore the key insights! #Statistics #Research #OpenScience #DataScience #d1e143" target="_blank" rel="nofollow noopener">tandfonline.com/doi/full/10.10…

Causal econometrics in R is a game changer. 🚀 Master experiments, DAGs, differences-in-differences, and more. Don’t miss out—level up your skills now: donskerclass.github.io/CausalEconomet…

Robust standard errors improve the reliability of regression analysis by adjusting for variability in errors (heteroscedasticity). Unlike regular standard errors, which assume constant variance, robust standard errors provide more accurate estimates when the spread of residuals varies. Advantages: ✔️ More accurate confidence intervals, especially when data sets exhibit heteroscedasticity. ✔️ Prevents overconfidence in predictions by correctly reflecting areas of increased uncertainty. ✔️ Ensures reliable statistical inferences, leading to better decision-making and more dependable results. Visualization: The graph below illustrates regular vs. robust standard errors. The green area shows regular confidence intervals, assuming constant variance. The red area represents robust confidence intervals, which expand where variability increases, providing a clearer picture of uncertainty. Handling Robust Standard Errors in Practice: 🔹 R: Use packages like sandwich for calculating robust standard errors and ggplot2 for visualization. 🔹 Python: Use modules such as statsmodels for robust standard errors and matplotlib or seaborn for creating clear visualizations. For more insights on data science, statistics, Python, and R programming, check out my email newsletter. Take a look here for more details: eepurl.com/gH6myT #Rpackage #ggplot2 #datastructure #RStudio #RStats #DataVisualization

The function is ggstats::ggcoef_model(). It turns your Linear Regression into an easy-to-understand coefficient report. I don't know how many times I've done this the hard way.

The ggmulti package is an extension for ggplot2 that allows you to combine multiple visualizations into a single plot. It simplifies the process of overlaying different types of graphs, such as scatter plots, line graphs, and histograms, to compare multiple data sets at once. ✔️ Multiple Plots in One: ggmulti lets you layer various plot types, helping you present different perspectives on your data within a single visual. ✔️ Clear Data Comparison: Ideal for situations where you need to compare or highlight relationships between multiple data sets in a compact format. ✔️ Effortless Integration: Works smoothly with ggplot2, making it easy to add multiple layers to your existing plots without much adjustment. For those times when you need to convey complex data relationships in a single view, ggmulti provides a powerful, streamlined solution. The example visualization shown here is taken from the package website: great-northern-diver.github.io/ggmulti/ If you’re looking for regular insights on data science, Python, R, and statistics, be sure to sign up for my free newsletter: eepurl.com/gH6myT #ggplot2 #dataviz #rstats #statistics #datascience

This new R package is wild. It's called ggalign. Here's what it can do: