Alexander Rolwes hat am 16. Juni 2025 seine Dissertation mit dem Titel "Geovisuelle Ansätze zur Analyse von Raum-Zeit-Zusammenhängen in urbanen Anwendungsfällen" erfolgreich verteidigt. Dazu gratulieren wir ihm sehr herzlich!
https://i3mainz.hs-mainz.de/news/2025/06/20/erfolgreich-promoviert-herzlichen-gluckwunsch-alex.html
#mobilität #VisualAnalytics #HochschuleMainz #HochschuleRheinMain #Geoanalyse
#Erreichbarkeitsanalyse #Öffnungszeitenanalyse #Attraktivitätsanalyse
The "Grammar of Graphics" is a powerful concept that ggplot2 in R is built on. It breaks down the process of data visualization into layers, making it easier to customize and understand how to build effective charts.
Want to dive deeper into creating beautiful and informative visuals with ggplot2? Check out my online course on "Data Visualization in R Using ggplot2 & Friends!" Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
I recently discovered the tidyplots package in R, and it’s impressive how effortlessly it enables you to create beautiful, publication-ready plots.
The example visualizations shown here were created by the package author, Jan Broder Engler, and are featured on the tidyplots website: https://jbengler.github.io/tidyplots/
Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
CfP: VIS4DH 2025
The VIS4DH workshop will be held in conjunction with IEEE VIS in Vienna, Austria. It invites submissions at the intersection of visualization and (digital) humanities.
This year’s theme: Visualizing Peace and Conflict
More details: https://vis4dh.dbvis.de
#VIS4DH #IEEEVIS #DigitalHumanities #InformationVisualization #VisualAnalytics #CfP #PeaceResearch #ConflictStudies #HumanitiesData #DataHumanities #DH2025 #CriticalVisualization
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.
With these tools, integrating statistical insights into your ggplot2 visualizations becomes both effective and effortless.
More details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Make your plots more stylish and visually appealing! The ggthemes package offers a variety of pre-built themes that help you customize the look of your ggplot2 visualizations, drawing inspiration from popular design standards.
The visualization shown here is from the package website: https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/
More: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Creating publication-ready plots in R is easier than ever with ggpubr. This extension for ggplot2 simplifies the process of generating clean and professional graphics, especially for exploratory data analysis and reporting.
The attached visual, which I created using ggpubr, demonstrates its versatility.
Additional information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Visual analytics (Cartography 
)
Visual analytics is a multidisciplinary science and technology field that emerged from information visualization and scientific visualization. It focuses on how analytical reasoning can be facilitated by interactive visual interfaces.
Am 2. April 2025 wurde Johannes Frank vom @vdi_news Rheingau-Bezirksverein für seine herausragende Masterarbeit mit dem Titel „Analyse von Einflussfaktoren auf die Unfallschwere von Unfalldaten mittels KI und #xai ausgezeichnet.
Johannes Frank widmete sich einem hochrelevanten Thema der #Verkehrssicherheit und nutzte dabei moderne Technologien, insbesondere #kunstlicheintelligenz , #VisualAnalytics und Ansätze der Erklärbaren KI. Herzlichen Glückwunsch, Johannes!
Basic boxplots are often not the best way to visualize your data! They can hide important information, such as the distribution of individual data points or group-specific differences.
The attached visual showcases several ways to enhance boxplots.
All of these examples were created using ggplot2 and extensions in R.
Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Working with text in ggplot2 plots can be a mess, especially when dealing with overlapping labels, busy backgrounds, or the need for custom formatting. Thankfully, several powerful ggplot2 extensions make text manipulation and annotation much easier and more effective.
With these tools, text in ggplot2 becomes much more manageable and visually appealing.
In missing data imputation, it is crucial to compare the distributions of imputed values against the observed data to better understand the structure of the imputed values.
The visualization below can be generated using the following R code:
library(mice)
my_imp <- mice(boys)
densityplot(my_imp)
Take a look here for more details: https://statisticsglobe.com/online-workshop-missing-data-imputation-r
#IEEE VIS: Visualization & Visual Analytics Conference coming to #Vienna, Austria
November 2 to 7, 2025
Abstract submission March 21, 2025
Paper submission March 31, 2025
https://ieeevis.org/year/2025/welcome
Follow @ieeevis for details
When it comes to learning data science, statistics, and programming, having the right resources is essential.
You can find all links and further descriptions here: https://statisticsglobe.com/statistics-data-science-programming-resources
If you are looking for a structured course that helps you get started with these topics, you may check out my introduction to R programming course. Further details: https://statisticsglobe.com/online-course-r-introduction
Creating publication-ready plots in R is easier than ever with ggpubr. This extension for ggplot2 simplifies the process of generating clean and professional graphics, especially for exploratory data analysis and reporting.
Course link: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
[BLOG] NATO Winter Meeting "Visual Analytics for Complex Systems" #News #VisualAnalytics https://cylab.be/blog/375/nato-winter-meeting-visual-analytics-for-complex-systems
Want to analyze the distribution of a single variable and test its mean against a specified value? The gghistostats() function from the ggstatsplot package is your go-to tool.
The visualization shown here is from the package website, demonstrating how gghistostats() effectively combines data distribution with statistical testing: https://github.com/IndrajeetPatil/ggstatsplot
Further details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
The R programming language is, in my opinion, the best tool for statistical analysis and data visualization.
By enrolling in the course, you’ll receive lifetime access to:
- 20 video lessons on statistical methods & their application in R.
- Exclusive group chat for questions, support, and networking.
- Quizzes, projects, scripts, and additional resources to enhance your skills.
Link: https://statisticsglobe.com/online-course-statistical-methods-r
Bayesian logistic regression is a powerful method for predicting binary outcomes (such as yes/no decisions). It differs from traditional logistic regression by incorporating prior beliefs and quantifying uncertainty using posterior distributions. This makes Bayesian logistic regression ideal for situations where you want to explicitly account for uncertainty or include prior knowledge.
Further details: http://eepurl.com/gH6myT
Logged GDP per capita, social support, freedom to make life choices, and perceptions of corruption are pivotal determinants of happiness.
This graph illustrates these metrics for the top 10 happiest countries, arranged from left to right, based on the World Happiness Report 2023.
See this link for additional information: https://statisticsglobe.com/webinar-data-analysis-visualization-r
