Today's artwork generated with #rstats and #ggplot2:

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. The visualization illustrates the essential layers used to create a plot: 1️⃣ Data: The foundation, where you start by defining the dataset. 2️⃣ Aesthetics: Map variables to visual aspects like color, size, and position. 3️⃣ Geometries: Specify the type of plot you want, such as bar, line, or scatter. 4️⃣ Facets: Create subplots for different subsets of your data. 5️⃣ Statistics: Add statistical transformations, like mean lines or trend lines. 6️⃣ Coordinates: Control the plot’s coordinate system, such as flipping axes. 7️⃣ Theme: Adjust the overall appearance, like grid lines, font styles, and background. In the code example shown, each of these layers is combined to produce the boxplot visualization. The process starts with defining the data and aesthetics, then moves through geometries, adding facets to split the data by groups, and even applying statistical transformations to highlight the mean value of each group. Finally, it configures the coordinates and finishes with a clean theme. 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!" Learn more by visiting this link: statisticsglobe.com/online-c… #DataScience #RStats #VisualAnalytics #Rpackage #tidyverse #database #Data #programming #datavis

📊 #TidyTuesday – 2026 W24 | UK Baby Names . 🔗: stevenponce.netlify.app/data… . #rstats | #r4ds | #dataviz | #ggplot2

Traditionally, visualization and statistical testing are handled in separate steps. This makes the workflow slower and the results harder to present clearly. With ggstatsplot in R, both are automatically integrated into a single figure. This helps you work more efficiently and makes your results easier to interpret and communicate. The graphic below demonstrates this using the relationship between living space and property price. Each point represents one observation, and the line shows the overall trend. In addition, the plot automatically includes key statistical information, such as the correlation coefficient, confidence interval, p-value, and sample size. This way, you can see the data and the corresponding statistical conclusions in one place, which makes your findings clearer and easier to share. Looking to improve your data visualizations in R? In my course, Data Visualization in R Using ggplot2 & Friends, I cover ggplot2 and tools like ggstatsplot to help you build clear and effective plots. Check out this link for more details: statisticsglobe.com/online-c… #StatisticalAnalysis #Rpackage #DataViz #DataVisualization #RStats #ggplot2 #coding #Data

Master ggplot2 & R for publication-ready biology visuals! 📊🧬 Join @OmicsLogic's 3-Day Hands-On Workshop on R Programming for Omics Data Analysis. 🔹Map gene expression & clinical data No prior coding required! 🚀Register: forms.gle/kARjd4o5g71T3ZrL7 #RProgramming #Bioinformatics

Looking to create elegant Venn diagrams in R? ComplexUpset, an extension of ggplot2, offers a flexible and customizable approach to visualize set intersections with precision. ✔️ Customize colors, labels, and layouts to fit your analysis style. ✔️ Integrate additional layers of information for deeper insights. ✔️ Easily handle both small and large-scale data sets. Whether you're exploring gene overlaps, survey responses, or experimental group comparisons, ComplexUpset equips you with powerful tools to transform complex intersections into clear, visually engaging diagrams. This visualization is sourced from the package website: krassowski.github.io/complex… Learn how to create impactful visualizations with ggplot2 and its extensions in my online course "Data Visualization in R Using ggplot2 & Friends." More details are available at this link: statisticsglobe.com/online-c… #ggplot2 #datastructure #programmer #RStats #Data

Today's artwork generated with #rstats and #ggplot2:

Struggling to visualize complex intersections in your data? ComplexUpset, an extension of ggplot2, makes it easy to create advanced UpSet plots, offering a clear way to display overlapping sets and their relationships. ✔️ Visualize complex set intersections with clarity. ✔️ Customize plot layouts, colors, and annotations for better readability. ✔️ Integrate additional data layers for deeper insights. ✔️ Handle large and intricate data sets effortlessly. Whether you're analyzing gene sets, customer segments, or survey responses, ComplexUpset helps you uncover meaningful patterns in intersecting groups. The visualization shown here is taken from the package website: github.com/krassowski/comple… Explore how to create impactful visualizations with ggplot2 and its extensions in my online course "Data Visualization in R Using ggplot2 & Friends." See this link for additional information: statisticsglobe.com/online-c… #DataScientist #tidyverse #RStats #rstudioglobal #datavis #programming #VisualAnalytics #DataAnalytics #ggplot2

I have always seen a lot of people treat Python and R like a competition. And it is usually framed as picking a side. But the more I work with data, the more that framing feels incomplete. My current workflow is built around Python, especially in Jupyter notebooks. That is where I do most of my data cleaning and preparation. At the same time, I have been paying closer attention to R. Although not because I am using it (yet), but from what it seems to handle really well. From what I have observed so far: ➛ Statistical analysis feels more native to the language rather than something added on. ➛ Data visualization, especially with ggplot2, looks more structured and easier to turn into presentation-ready charts. ➛ Data exploration appears more direct, with less setup before getting useful insights. ➛ Reporting workflows seem cleaner when combining analysis with output. That is what I am looking to explore next. But the goal is not to replace Python. It is to understand where R fits and how it can complement what I already do. Because at this point, it is less about the tool and more about knowing where each one works best. I will be exploring R properly soon. I am curious to hear from you if you’ve used R. What stood out to you when you started? P.S. kindly check out the thread below and share your thoughts.

I created the breathtaking 3D forest cover map of Africa's majestic forests using the power of R, #ggplot2, & #rayshader. Prepare to be mesmerized by the beauty & importance of our planet's green lungs! #RStats #DataScience #GIS #EnvironmentalScience #DataVisualization #maps

[EVENTO 🏆] - ¡Los 16 clasificados al #MundialDeR2026📦 ya tienen grupos! La convocatoria cerró, contamos los votos y el sorteo está hecho. Estos son los paquetes que compiten en el primer #MundialDeR2026📦: ✓ Grupo A: dplyr · arrow · geoAR · tidyr ✓ Grupo B: eph · openxlsx · srvyr · ggplot2 ✓ Grupo C: leaflet · janitor · sf · pointblank ✓ Grupo D: highcharter · shiny · purrr · beepr El lunes 15 de junio arranca la fase de grupos: 4 partidos por día, uno por grupo. La comunidad vota en stories y encuestas quién avanza. Hay candidatos al título, tapados y cruces que van a dar que hablar. {ggplot2} y {eph} en la misma zona 😱, y el Grupo A tiene un dplyr que llega como gran favorito. ¿Cuál crees que es el grupo de la muerte? ¿Tu candidato llegó? ¿Quién levanta la copa? ¡Que arranquen las predicciones! #MundialDeR2026📦 #RStats #RStatsES #EstacionR

#Day73 of my AI/ML & Quant journey 🚀 Udemy ML: 🔹 Used ggplot2 to plot the training set observations alongside the regression line. 🔹 Visualized test set predictions to compare our model's fit against raw, unseen data. Seeing the visual alignment of the regression line