Danielito

PhD student

[Danielito @DanielChGo]

@clover_haruru_ Stream, mine arrives next week.

[Academia Mexicana de Ciencias @amciencias]

🦠 ¡Estrenamos temporada! Acompáñanos a descubrir a los microbios que viven en las plantas crudas en #TuMundoConCiencia con la doctora Esperanza Martínez del @ccg_unam.

🗓️ Jueves, 14 agosto
⏰ 18 h
⭕️Transmisión por FB y YT: https://t.co/b42ielyOIY
@lorealmexico @UNESCOMexico

[Matt Dancho (Business Science) @mdancho84]

RIP Data Scientists.

The Generative AI Data Scientist is NOW what companies want.

This is actually good news. Let me explain:

[MindBodyIsLife @MindBodyIsLife]

La pérdida de grasa es lo más fácil del mundo.

Te hicieron creer que era complicado para venderte programas y dietas sin fin.

Aquí tienes 8 estrategias basadas en evidencia que usé para perder 9 kg en 65 días (según mi experiencia):🧵

1. Dejé de hacer cardio interminable

[Vive con Propósito. @PropositoyVida]

Hilo de las playas más surrealistas del planeta: 🧵

1. Camino a la Atlántida, Tailandia

[CIMAT @cimatoficial]

¿Quieres dar tus primeros pasos en el mundo del análisis de datos?

En el curso Análisis de Datos con R de Educación Continua CIMAT, aprenderás a importar, limpiar, manipular y visualizar datos usando uno de los lenguajes más poderosos en ciencia de datos. 💻📈

[Danielito @DanielChGo]

@clover_haruru_ かわいい

[Joachim Schork @JoachimSchork]

Adding statistical metrics to your plots can transform your visualizations from basic to highly informative. With ggplot2 in R and its versatile extensions, incorporating features like p-values, confidence intervals, and regression lines becomes both straightforward and visually appealing.

These are my top 5 packages for adding statistical metrics in ggplot2:

1️⃣ ggstatsplot: Combines statistical analysis and visualizations, displaying p-values, confidence intervals, and effect sizes directly on your plots.

2️⃣ ggpubr: Simplifies the process of adding p-values, statistical comparisons, and summaries to boxplots, bar charts, and more.

3️⃣ ggsignif: Adds significance brackets with p-values to plots like boxplots and bar charts, making statistical comparisons easy to interpret.

4️⃣ stat_poly_eq: Annotates regression equations, R² values, and p-values on scatter plots, ideal for showcasing relationships in linear models.

5️⃣ gghighlight: Highlights specific data points or groups in plots, drawing attention to key statistical trends or outliers while maintaining context.

With these tools, integrating statistical insights into your ggplot2 visualizations becomes both effective and effortless. In the graph shown here, you can see examples of how these packages enhance your plots: a density plot with group means marked by vertical lines, a crowded line plot with selected series highlighted for clarity, a violin-boxplot hybrid with p-values annotated for group comparisons, and a scatter plot featuring a regression line, confidence intervals, and marginal histograms for added context. These enhancements demonstrate the power of ggplot2 extensions for making statistical insights visually accessible.

If you’d like to learn how to use ggplot2 and these extensions, join my online course, Data Visualization in R Using ggplot2 & Friends. I’ll guide you step-by-step to create visualizations packed with statistical insights! More info: https://t.co/ztlEzoEDWv

#R4DS #datastructure #RStats #tidyverse

[van der Heijden Lab @vandeHeijdenLab]

A fourpartite protist-bacteria-fungal-plant symbiosis as a driver of the mycorrhizal symbiosis?

our study indicates that mycorrhizal fungal hyphae not only associate with specialised bacterial communities (see https://t.co/51fFUVOKSp) but also associate with specific protist communities.
See: https://t.co/9ofPkguYHo
https://t.co/p5XNiAzv05

By Changfeng Zhang; @RL_Berendsen;
@stefan_geisen

[ResearchTweet @ResearchTweet22]

Multiple Postdoctoral Fellowships, Research Assistant, & Research Jobs at Emory University, Atlanta, Georgia, United States

#opportunities #scholarship #research #Postdoctoral #Postdoc #job #UniJobs

[Dr. Asif’s Mol. Biology @asifmolbio]

📢 Applications open!
🎓 Scholarships available
⏳ Deadline: April 30th, 2025
🧫🧬🧑‍💻🧫🧬🧑‍💻🧫🧬🧑‍💻
Next-gen sequencing is vital for developing bioproducts but requires bioinformatics expertise. This course closes the gap for Illumina and Nanopore platforms https://t.co/uGCzR7b3NK

[Joachim Schork @JoachimSchork]

Adding statistical test results directly to your plots is a powerful way to make your findings clear and accessible at a glance. With tidyplots, functions like add_test_pvalue and add_test_asterisks make it easy to display p-values or significance levels on your charts, helping viewers quickly understand the statistical differences between groups.

Using add_test_pvalue, you can place exact p-values between groups, as shown in the example plot, where p-values highlight the differences between treatment groups A, B, C, and D. Alternatively, add_test_asterisks enables you to display significance levels with asterisks, conveying statistical insights without detailed numbers.

✔️ Quick Setup: With just a few lines of code, you can emphasize significant differences between groups.

✔️ Clear Communication: These functions enhance readability by allowing viewers to easily interpret the significance of results.

✔️ Flexible Options: Customize the format, choose specific groups for comparison, and display either exact p-values or simplified asterisks.

The example plot shown here illustrates the use of add_test_pvalue to show p-values between treatment groups. Each line represents a comparison, with p-values displayed above. This example is taken from the tidyplots website: https://t.co/fgIWzxFVZI

To dive deeper into data visualization in R, consider joining my Data Visualization in R Using ggplot2 & Friends course. Check out this link for more details: https://t.co/ztlEzoEDWv

#DataScience #VisualAnalytics #DataVisualization #ggplot2

[Joachim Schork @JoachimSchork]

Create clear and informative box plots with added statistical insights using ggpubr! This package makes it easy to design polished plots that effectively showcase group comparisons and data distributions.

✔️ Visualize Group Comparisons: Box plots are ideal for comparing distributions across categories, showing medians, quartiles, and outliers. Combined with dot plots, they provide a detailed view of individual data points and variability.
✔️ Comprehensive Statistical Annotations: Add statistical comparisons, such as p-values and significance brackets, directly on the plot. The example here includes results from a Kruskal-Wallis test, with pairwise comparisons displayed above to indicate where significant differences exist between groups.
✔️ Customizable Design: Adjust colors, shapes, and labels to make your plots visually appealing and easy to interpret, ensuring they convey the right message.
✔️ Seamless Integration with ggplot2: Works directly with ggplot2, letting you build on your existing plots and enhance them with statistical details without the need for complex syntax.

The visualization shown here is from the package website, demonstrating how ggpubr can create polished, publication-ready plots with detailed statistical annotations: https://t.co/Dx49IJJnv5

Ready to master ggplot2 and its powerful extensions to create stunning visualizations? Enroll in my online course, “Data Visualization in R Using ggplot2 & Friends!”

See this link for additional information: https://t.co/ztlEzoEDWv

#DataViz #Rpackage #R #Statistical #DataAnalytics #RStats #ggplot2

[Joachim Schork @JoachimSchork]

Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

Why use gggenomes?

✔️ Genomic-focused visualizations: Specifically designed for handling genomic data, including features, alignments, and comparative analysis.
✔️ Versatile and modular: Create detailed and layered plots for diverse genomic scenarios with flexibility for customization.
✔️ Built on ggplot2: Leverages ggplot2’s familiar framework, making it easy for users to adapt and enhance their visualizations.

The example visualization shown here is taken directly from the gggenomes GitHub repository, demonstrating how it transforms genomic data into compelling plots: https://t.co/FzE2wNzHeP

Curious to learn more about creating data visualizations in R and using tools like ggplot2 and its extensions? Check out my online course, "Data Visualization in R Using ggplot2 & Friends!" Take a look here for more details: https://t.co/ztlEzoEDWv

#datascienceeducation #Statistical #datastructure #RStats

[Rosana Ferrero 📈📊🙌 @RosanaFerrero]

🚀 ¡Crea tablas de datos profesionales en R listas para publicar! 📊
📦 tbl_summary() crea resúmenes descriptivos con una sola línea de código
✨ Divide las estadísticas por grupos (con by=) y agrega pruebas estadísticas para comparar grupos con add_p()💥
https://t.co/X25FUU5pXC

[Dr. Guy Prochilo 🏳️‍🌈 @GuyProchilo]

#Rstats: datapasta is SO COOL! Easily copy data from excel (or even html tables from the web) & paste in R as fully-formatted dataframes!

1. install.packages("datapasta")
2. Set a useful keyboard paste shortcut

https://t.co/hq9mHtRj5B

With thanks to @FaustoBustos :)

#phdchat

[Joachim Schork @JoachimSchork]

The ggplot2 package has become so popular over the years that there are now over 100 extension packages specifically designed to enhance ggplot2 visualizations. These extensions make the ggplot2 syntax even more efficient and help create stunning graphs with ease.

In the upcoming Statistics Globe online course, we’ll explore some of the most useful extensions. I’ve created dedicated course modules on the following:

- geomtextpath: Combines text and paths for creative labeling along lines.
- gganimate: Adds dynamic animations to your visualizations.
- ggdist: Enhances visual representation of distributions and uncertainty.
- gghighlight: Highlights specific data points or groups in your plots.
- ggiraph: Makes your plots interactive with hover and click features.
- ggpubr: Simplifies publication-ready plot creation with user-friendly functions.
- ggrepel: Improves readability by preventing overlapping text labels.
- ggstatsplot: Integrates statistical tests directly into your visualizations.
- ggtext: Adds rich text formatting capabilities to your plots.
- patchwork: Easily combines multiple plots into cohesive layouts.

To give you a sneak peek of my favorite extensions, I’ve created a new YouTube video introducing my personal Top 5. Do you agree with my list? Let me know in the comments!

Watch the video here: https://t.co/8OTp81s9sa

Interested in diving deeper? Join the comprehensive Statistics Globe online course on Data Visualization in R Using ggplot2 & Friends, starting November 25.

More information about the course: https://t.co/ztlEzoEDWv

Talk to you soon.

Joachim

#rstats #ggplot2 #dataviz #statistics #datascience

[Bastián Olea 🌸 @bastimapache]

Estoy empezando a subir tutoriales de R para principiantes! Dentro de estos días también voy a subir unos para empezar R desde cero, sin conocimiento previo 🫢 Por ahora, hay sobre manipular datos con {dplyr} y hacer apps con {shiny} 👩🏻‍💻 https://t.co/sw1hQuNHm5
#rstats

[Matt Dancho (Business Science) @mdancho84]

Fortunately, I stumbled upon this new R package to help.

I made a full tutorial here: https://t.co/BSCTg65Zlt