Ani Lung

"Pessimism of the intellect, optimism of the will” Romain Rolland

[Yan Holtz @R_Graph_Gallery]

#Dataviz with R is wild! 🔥

This is what you can do with a couple of lines of code:

- create 3 graphs (ggplot2 syntax)
- custom layout
- cross-graph hover effect
- tooltip

😳

Thanks to the ggiraph package. ❤️

Code in comments!

[Éric Mauvière @ericMauviere]

Très sympa cette option de @datagouvfr, la plateforme génère automatiquement une copie parquet quand qqun publie un fichier volumineux.

[INRAE Occitanie-Toulouse @INRAE_Tlse]

Évaluation des politiques publiques : quels enjeux pour l’agriculture❓

🗣️Réponses de Thomas Poméon, directeur de l'Observatoire du développement rural qui accompagne les acteurs depuis➕de 15 ans sur le chemin des transitions

📅09/11 à 13h30
📍@INRAE_Tlse & en ligne
🆓Inscription par ici 👉https://t.co/AG93GujiJy

[Joachim Schork @JoachimSchork]

Looking to create stunning, data-rich maps in R? The tidyterra package makes it simple to integrate spatial data with ggplot2, bringing the power of the tidyverse to geospatial analysis. With tidyterra, you can work with spatial data just like any other data set in ggplot2.

✔️ Ease of Use: Simplifies map creation by integrating with ggplot2, using familiar functions and syntax, which reduces the complexity of working with spatial data.
✔️ Flexible Visualizations: Supports a variety of geospatial formats, allowing you to combine spatial data with other data sets, and work seamlessly with both raster and vector data.
✔️ Efficient Data Handling: Leverages dplyr-like functions for filtering, summarizing, and joining, which streamlines data processing and enables smooth integration between spatial and non-spatial data.
✔️ Beautiful Maps: Create detailed, customizable maps with less code and more flexibility, using the grammar of graphics you already know from ggplot2.

The visualizations shown here are taken from the package website and demonstrate how tidyterra simplifies creating detailed maps using ggplot2: https://t.co/s6TmHyj0oc

If you’d like to learn more about ggplot2 and how to create stunning visualizations, check out my online course on “Data Visualization in R Using ggplot2 & Friends,” starting on November 25, 2024! See this link for additional information: https://t.co/ztlEzoEDWv

#RStats #Data #datascienceeducation #ggplot2 #StatisticalAnalysis #datavis #RStudio #DataViz

[Éric Mauvière @ericMauviere]

Innovation #Macarte de @IGNFrance, la méthode de discrétisation head/tail. Bravo et merci, vous êtes précurseurs !

[Joachim Schork @JoachimSchork]

Add beautifully aligned text to paths and lines in your ggplot2 visualizations with geomtextpath, an extension that allows you to embed text directly onto plot lines in R. This package lets you add labels that follow the curvature of lines, making it easier to annotate plots while maintaining readability and style.

Key advantages of geomtextpath include:

✔️ Clear Annotations: Place text along lines for clear, context-specific labeling, reducing the need for separate legends or labels.
✔️ Enhanced Readability: Text follows the line's curvature, ensuring it stays aligned with the visual flow of the plot.
✔️ Flexible Customization: Adjust font, position, angle, and style to match your design needs and maintain a cohesive look.

To use geomtextpath in ggplot2, replace standard line geoms with geom_textpath() or geom_labelpath() and specify your text as an aesthetic. This simple addition allows you to embed text smoothly along plot lines, making it ideal for line graphs, contour plots, and other curved paths.

The example visualization shown here is from the geomtextpath package website and demonstrates these text-on-path capabilities: https://t.co/eul3wLXi5F

If you’re interested in mastering text and annotation techniques in R, consider joining my course, "Data Visualization in R Using ggplot2 & Friends," starting November 25, 2024!

Learn more by visiting this link: https://t.co/ztlEzoEDWv

#tidyverse #RStats #ggplot2 #R4DS #Rpackage #datavis #DataVisualization #statisticians #StatisticalAnalysis

[𝙳𝚊𝚟𝚒𝚍 𝚆𝚞𝚎𝚙𝚙𝚎𝚛 @DavidWuepper]

On my way to @UNIGEnews to talk about the use of satellite data in agricultural, climate, and environmental economics at the "postdoc network in agricultural, climate, and environmental economics" (PoNACEE).

Link to paper: https://t.co/ORCzirCb43

[Robert Finger @RobertFinger1]

'Open Science in Agricultural Economics'

Our paper aims to provide the basis for taking our discipline in new directions towards open science. It has now been accepted for publication in Q Open 🥳

Joint work w @ArneHenningsen, @julia_hoehler, @robehube, J Rommel, C Grebitus

[Yan Holtz @R_Graph_Gallery]

◻️ In grey: lighter cars _slightly_ increase your chances of dying in a car accident.

♦️ In red: if the car in front is large, your risk of dying increases _massively_.

Such a powerful chart by The Economist.

[Éric Mauvière @ericMauviere]

Nouveau dans #DBeaver avec #DuckDB : la présence d'une colonne 'color' permet de faire de la carto thématique, par ex. en quantiles :
FROM st_read('myGeoJSON.json')
SELECT geom, ntile(5) over(ORDER BY part_rn) qt,
['#ccebc5','#a8ddb5','#7bccc4','#43a2ca','#0868ac'][qt] AS color;

[INRAE Occitanie-Toulouse @INRAE_Tlse]

Doctorante en #génétique animale et #épigénétique, @stacy_rousse est spécialisée dans la transmission des mécanismes épigénétiques chez la caille

Découvrez son #portrait en 📽️
➡️ses activités 🧪
➡️son parcours d'étude 👩‍🎓
➡️ses motivations 🤩
https://t.co/k4tmns46fx

[Kyle Walker @kyle_e_walker]

One of my favorite #rstats packages is {exactextractr} which calculates lightning-fast zonal statistics over raster datasets

You can performantly summarize rasters that are multiple GB in size without having to wait around all day

Learn more: https://t.co/exzYMWhP4g

[Joachim Schork @JoachimSchork]

The GWalkR package in R is a powerful tool for data visualization and exploration. Designed to simplify complex data analysis, GWalkR transforms raw data into interactive visualizations, making it easier to understand and interpret your data sets.

Here are some key features of GWalkR:

1️⃣ Interactive Plots: Generate dynamic and interactive plots with minimal code. This helps in identifying trends and patterns in your data quickly.

2️⃣ Ease of Use: With a user-friendly interface, GWalkR is accessible even for those who are new to R. The package integrates seamlessly with other R libraries, enhancing your data analysis workflow.

3️⃣ Customization: GWalkR offers a variety of customization options, allowing you to tailor visualizations to meet your specific needs. From color schemes to plot types, you have complete control over the appearance of your data.

4️⃣ Efficiency: Save time and effort in data analysis by automating the creation of visualizations. GWalkR processes large data sets efficiently, ensuring smooth performance even with extensive data.

5️⃣ Community Support: Benefit from a growing community of users and contributors who share tips, tricks, and support. This makes it easier to troubleshoot issues and stay updated with the latest features.

Here is the package documentation: https://t.co/BM9nfFuV5s

If you prefer not to use RStudio, Ifeanyi Idiaye has developed a web application that lets you use GWalkR directly in your browser: https://t.co/A0mrL4wUOU

#rstats #webapp #datascience #statistics

[. @gamonasterioo]

The value of ‘wasting time’ on deep thinking is often overlooked in a scientific ecosystem increasingly tainted by Wall Street’s productivity mindset.

https://t.co/hNTA74DNOr

[Joachim Schork @JoachimSchork]

Check out this R Shiny App designed to explore causality and multiple regression: https://t.co/IAmIm0X4RQ

This tool helps you understand possible relationships between variables, including noise, confusion, alternative effects, mediation, and interaction/moderation. A very nice tool for anyone looking to deepen their analysis skills!

Big thanks to Ben Rottman for creating this invaluable resource.

You might also enjoy my free newsletter, where I regularly post insights and tips on statistics, data science, and programming in R and Python. More details are available at this link: https://t.co/X93SeCeygJ

#datasciencetraining #StatisticalAnalysis #DataAnalytics #R4DS #RStats

[Kyle Walker @kyle_e_walker]

Now available in the #rstats {mapgl} package: the `compare()` function allows you to swipe between layers on your map!

Compare demographic changes, development scenarios, alternative color palettes (shown here) and much more

Learn how: https://t.co/3uzOYMFmlT

[Éric Mauvière @ericMauviere]

Produisez vous-même en qqs secondes de superbes cartes lissées avec le kit décrit dans cet article🚀.

Appliqué aux #européennes2024, il valorise la grille hexagonale H3. Ce kit s'utilise avec #DuckDB et #Mapshaper, des outils simples, véloces et sexys !
https://t.co/YQ1BaHF6iV

[Kyle Walker @kyle_e_walker]

New {mapgl} Shiny example just addressed over in the GitHub issues:

Seamlessly map different columns in your dataset with different colors and an updated legend with no performance lag.

Try out the example here: https://t.co/Y6AX2szT77

#rstats

[Éric Mauvière @ericMauviere]

Cartographie statistique : @InseeFr passe à la vitesse supérieure avec la technologie @maplibre :
11 indicateurs sociaux (source Filosofi) au carreau de 200 m pour toute la France, avec une fluidité spectaculaire🚀.
https://t.co/BVEBwczlFG
Secret statistique respecté, bien sûr.

[Kyle Walker @kyle_e_walker]

One of my favorite features of the new {mapgl} #rstats package is its flexible interface for hover effects.

Specify a named list of `hover_options()` and you can change the hovered-over feature's look in many different ways.

Try out this tutorial: https://t.co/rlAqWrXN5r