Statistics Globe

Here are some graphs that we've created in my recent live webinar. Topic: Data Analysis & Visualization in R I’ve developed a mini-course based on this webinar, where I provide the live session recording, exercises with solutions, and additional resources. Check out this link for more details: https://lnkd.in/dcW8Wq9u #datavis #RStats #DataScience #Statistics

Creating plots and running statistical tests separately is a common workflow, but it often results in unnecessary effort and inconsistencies between your visualizations and results. With the ggstatsplot R package, both steps can be combined into a single, streamlined workflow. Instead of running statistical tests first and then adding the results manually, everything is integrated directly into the visualization. Some advantages of using ggstatsplot: ✔ Key results such as p-values, confidence intervals, and effect sizes are included automatically ✔ Pairwise comparisons are added without extra steps ✔ A wide range of statistical tests and plot types are supported ✔ Outputs remain consistent and fully reproducible ✔ Clean, publication-ready graphics with relatively little code This approach saves time, reduces the risk of errors, and makes your results easier to interpret and communicate. I recently released a new module in the Statistics Globe Hub where I show step by step how to apply this workflow in R. The visualization below presents examples of graphs created in this module. If you join the Hub before the end of March, you will get immediate access to this module and all other modules released this month. More info about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #Statistics #DataScience #RStats #DataVisualization #MachineLearning #AI

Today is your last chance to secure access to all Statistics Globe Hub modules released in March. If you join by the end of today, you will get immediate access to all March content, including: 🔹 Feature Selection Using Random Forest 🔹 Data Visualization with tidyplots in R 🔹 Sample Size Calculation Using Power Analysis 🔹 Create Reports with Quarto in R 🔹 Graphs and Statistics with ggstatsplot in R After today, these March modules will no longer be available to new members. Only members who joined in March will retain access. The visualization below shows some graphs and topics we covered this month. In addition, new modules are already planned for the coming weeks: 🔹 Draw Synthetic Datasets with drawdata in Python 🔹 Monte Carlo Simulation 🔹 AI-Assisted Coding with gander in R 🔹 Animated Visualization with magick in R 🔹 Simpson’s Paradox By joining now, you not only secure access to the March modules, you will also get access to all upcoming modules as they are released. You can find the full overview and more details here: https://lnkd.in/e5YB7k4d #statistics #datascience #ai #rstats #python #statisticsglobehub

Automating report creation can save a significant amount of time, especially when reports need to be updated regularly (e.g. monthly or yearly). A very useful tool for this is Quarto in R. It is a modern publishing system that allows you to combine text, code, and results in a single document. Your R code is executed directly inside the document, and outputs such as tables, figures, and summaries are automatically included in the final report. You can also choose the output format, for example HTML, PDF, or DOCX. If you are creating the same report repeatedly, you simply update the input data and run the Quarto document again. That’s it. I recently released a new module in the Statistics Globe Hub that focuses on creating reports with Quarto in R. The module includes a video lecture, hands-on examples, and exercises to guide you through building reproducible reports step by step. Not a Statistics Globe Hub member yet? The Hub is an ongoing program with a new module released every week, covering topics in statistics, data science, AI, R, and Python. Join the Hub now to get access to the Quarto module along with all other modules released this month. More information about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #rstats #datascience #statistics #quarto #reporting #reproducibility #statisticsglobehub

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. I recently published a new module in the Statistics Globe Hub where I walk through how to build informative visualizations with ggstatsplot in R, include statistical results automatically, and use this workflow in practice. The Statistics Globe Hub is my ongoing learning program with weekly modules on statistics, data science, AI, and programming in R and Python. More info: https://lnkd.in/e5YB7k4d #Statistics #DataScience #RStats #DataVisualization #ggstatsplot

Combining data visualization and statistical analysis is often done in separate steps, but with the ggstatsplot package, both can be handled within a single workflow. Instead of running statistical tests separately and then adding results to your plots, ggstatsplot integrates everything directly into the visualization: ✔ p-values, confidence intervals, and effect sizes are included automatically ✔ Pairwise comparisons are added without extra steps ✔ Results remain consistent and fully reproducible This reduces manual work, lowers the risk of errors, and makes your analysis easier to interpret and communicate. I recently released a new module in the Statistics Globe Hub where I show step by step how to use ggstatsplot in R. The visualization below shows examples of graphs created in this module. Register by the end of March to get immediate access to the Hub module on ggstatsplot. More info: https://lnkd.in/e5YB7k4d #Statistics #DataScience #RStats #DataVisualization #MachineLearning #AI

Creating visualizations in R can sometimes require quite a lot of code, especially when building more complex plots step by step. The tidyplots package provides a structured and consistent approach that can make this process much easier. It follows a tidyverse-style workflow in which plots are built through a sequence of clear and readable functions. This often results in shorter code that is easier to understand and maintain. To make the workflow easier to learn, the tidyplots developers provide a cheat sheet that summarizes the most important functions and plotting steps. More information about tidyplots and the cheat sheet: https://tidyplots.org/ The cheat sheet highlights: 🔹 The tidyplots workflow from dataset to finished plot 🔹 Functions for adding data layers and summary statistics 🔹 Tools for annotations and statistical tests 🔹 Options to adjust axes, labels, colors, and layout 🔹 Plot splitting and export functions I recently released a Statistics Globe Hub module where I explain tidyplots in detail, including practical examples and exercises. Not a Statistics Globe Hub member yet? The Hub is an ongoing program where I publish a new module every week on topics related to statistics, data science, AI, R, and Python. Join the Hub now to get immediate access to the tidyplots module and all other modules released this month. More information about the Statistics Globe Hub: https://lnkd.in/e5YB7k4d #rstats #datascience #datavisualization #tidyverse #ggplot2 #statisticsglobehub

Are you creating your plots and statistical tests separately? This is a common workflow in data analysis, but it can be inefficient and error prone because your visualizations and statistical results are disconnected. ❌ You need to run tests manually and copy the results into your plots. ❌ Updating your analysis requires repeating multiple steps. The ggstatsplot package solves this problem by combining visualization and statistical analysis in a single step. The visualization below shows an example. It displays the distribution of prices across groups and automatically includes the corresponding statistical test results, effect sizes, confidence intervals, and pairwise comparisons directly in the plot. This allows you to see both the data and the statistical conclusions in one place. This approach saves time, reduces errors, and makes your analysis more transparent. I recently released a new module in the Statistics Globe Hub where I show step by step how to create informative visualizations with ggstatsplot in R, how to include statistical results automatically, and how to apply this workflow in real projects. Haven’t heard about the Statistics Globe Hub yet? It is my ongoing learning program with weekly modules on statistics, data science, AI, and programming in R and Python. More info: https://lnkd.in/e5YB7k4d #Statistics #DataScience #RStats #DataVisualization #ggstatsplot

Check out the Statistics Globe Hub: https://lnkd.in/e5YB7k4d The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #quarto #rstats #datascience #reproducibility #reporting #statisticsglobehub

Check out the Statistics Globe Hub: https://lnkd.in/e5YB7k4d The Statistics Globe Hub is an ongoing learning program that helps you stay up to date with statistics, data science, AI, and programming using R and Python. #tidyplots #datavisualization #rstats #ggplot2 #datascience #statisticsglobehub