Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.
I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: https://www.youtube.com/watch?v=EKISB0gnue4
#coding #datavisualization #rprogramming #dataviz #statisticalanalysis #package #datastructure #ggplot2 #bigdata #tidyverse
Lisp-stat: Lisp environment for statistical computing
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Final reminder that registration for all Statistics Globe online courses closes today and won’t reopen until the end of July.
You can find all courses here: https://statisticsglobe.com/courses
#statistics #datascience #rasts #dataviz #statisticalanalysis
When handling missing values, selecting an imputation method that balances simplicity, variability, and accuracy is essential. Deterministic Regression, Stochastic Regression, and Predictive Mean Matching (PMM) are three widely used methods, each with strengths and limitations depending on the data's structure.
The attached plot compares these methods using a non-linear data example.
Tutorial: https://statisticsglobe.com/predictive-mean-matching-imputation-method/.
More: http://eepurl.com/gH6myT
What is predictive mean matching? This article explains the impuation method step by step - Predictive mean matching in R - Programming example with real data - Predictive mean matching as single & multiple imputation - Advantages compared to other missing data methods
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
#bigdata #visualanalytics #tidyverse #programming #statisticalanalysis #datavisualization #package #data #ggplot2
Making your data analysis more insightful and informative is effortless with ggstatsplot. This powerful ggplot2 extension in R combines statistical analysis and data visualization in a single workflow, helping you generate plots that include statistical summaries directly on the visualizations.
The attached visual, which I created using ggstatsplot, showcases its capabilities.
Learn more: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
#programming #statisticalanalysis #datavisualization #dataanalytics
Today is the final day to register for my courses before a 3-month break with no new enrollments, and your last chance to get a 33% discount.
Here are the courses you can join: https://statisticsglobe.com/courses
#rstats #statistics #datascience #dataviz #statisticalanalysis
Comprehensive Online Courses Our online courses are structured to fit various learning styles and skill levels, ensuring that whether you’re a beginner or a seasoned professional, you’ll find content that resonates with your educational needs. By engaging with our courses, you will gain hands-on experience and comprehensive knowledge, essential for navigating the complexities of today’s...
If you're still using raw R outputs for presentations, it's time for an upgrade! Tools like gtsummary bring your statistical results to life, making them much more digestible for non-technical audiences.
The visualization included here was originally shared in a post by Dr. Alexander Krannich. Thanks to Alexander for inspiring me to create this post.
More details are available at this link: http://eepurl.com/gH6myT
Dumb statistical models, always making people look bad
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