At first glance, bar charts might seem like a simple visualization type. But with a little creativity, they can be enhanced in countless ways to reveal deeper insights and make your data shine.
The attached visual highlights a variety of bar chart styles to inspire your work.
Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
I recently made a very popular LinkedIn post about Simpson's Paradox, which resulted in an engaging conversation. Paul Julian made a great comment on the relationship between Mixed Effects Models and Simpson's Paradox that I wanted to share with you.
In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:
Original post: https://www.linkedin.com/posts/joachim-schork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/
Further details: http://eepurl.com/gH6myT
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
Harnessing the Power of Data Structure to Build Resilient Power Apps
Too often, Power Apps makers jump into app building—without giving enough thought to data structure. In Ep. 55 of #PowerTalks, Griffin Lickfeldt from Citizen Developer explains why getting this step right from the start is critical for building scalable, secure, and AI-ready apps.
The Superpower of Dataverse
Strategic Table Structuring
Future-Proof Your Apps
Security as a Cornerstone
Mean imputation is a common method for handling missing values in numerical data. It replaces missing values with the mean of the observed values, ensuring the data set remains complete and easy to use.
The image below illustrates the impact of mean imputation. The black line represents the original data distribution before imputation, while the red line shows the data distribution after imputation.
Tutorial: https://statisticsglobe.com/mean-imputation-for-missing-data/
Newsletter: http://eepurl.com/gH6myT
Thread on sorting algorithms.
Starting with #StupidSort 
, an inefficient algorithm that sorts by randomly shuffling until the list is ordered.
(1/3) 
Shoutout to my friend Daniel, who is not on mastodon. He created an uber efficient slice implementation for joining/modifying/copying lists at scale. The blogpost and github README describe the algorithm. It's an interesting technical read if you have 10-20 minutes of spare time.
Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.
For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: https://statisticsglobe.com/mean-imputation-for-missing-data/
More details are available at this link: http://eepurl.com/gH6myT
Designing an Efficient Tree Index on Disaggregated Memory
https://cacm.acm.org/research-highlights/designing-an-efficient-tree-index-on-disaggregated-memory/
gganimate is a powerful extension for ggplot2 that transforms static visualizations into dynamic animations. By adding a time dimension, it allows you to illustrate trends, changes, and patterns in your data more effectively.
The attached animated visualization, which I created with gganimate, showcases a ranked bar chart of the top 3 countries for each year based on inflation since 1980.
More information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Visualizing gene structures in R? gggenes, an extension of ggplot2, simplifies the process of creating clear and informative gene diagrams, making genomic data easier to interpret and share.
Visualization: https://cran.r-project.org/web/packages/gggenes/vignettes/introduction-to-gggenes.html
Click this link for detailed information: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
Red Green Syntax Trees - an Overview | by Will Speak (aka Plingdollar):
https://willspeak.me/2021/11/24/red-green-syntax-trees-an-overview.html
Wow, this crate looks like the most feature-rich tree crate I've ever seen!
It seems very underrated (only ~1000 downloads and one star on GitHub (by me)).
Thank you for the suggestion!
#ReleaseMonday — One of the recent (already very useful!) new package additions to #ThingUmbrella is:
Leaky buckets are commonly used in communication networks for rate limiting, traffic shaping and bandwidth control, but are equally useful in other domains requiring similar constraints.
A Leaky Bucket is a managed counter with an enforced maximum value (i.e. bucket capacity). The counter is incremented for each a new event to check if it can/should be processed. If the bucket capacity has already been reached, the bucket will report an overflow, which we can then handle accordingly (e.g. by dropping or queuing events). The bucket also has a configurable time interval at which the counter is decreasing (aka the "leaking" behavior) until it reaches zero again (i.e. until the bucket is empty). Altogether, this setup can be utilized to ensure both an average rate, whilst also supporting temporary bursting in a controlled fashion...
Related, I've also updated/simplified the rate limiter interceptor in https://thi.ng/server to utilize this new package...
#Development #Guides
Bloom filter · What they are and why they are so powerful https://ilo.im/162mpl
_____
#Programming #Coding #BloomFilter #HashTable #DataStructure #JavaScript #Database #WebDev #Frontend #Backend
I used to think that writing sophisticated R code meant using all the advanced features and chaining long functions together...
Fancy code can be fun, but clean code makes collaboration and debugging so much easier.
Stay informed on data science by joining my free newsletter. Check out this link for more details: http://eepurl.com/gH6myT
Ordered map на Go
Omap — это пакет Golang для работы с потокобезопасными упорядоченными map. Упорядоченная map содержит map golang, list и mutex для выполнения функций упорядоченной map. Упорядоченная map— это map, которая запоминает порядок элементов. Map можно итерировать для извлечения элементов в том порядке, в котором они были добавлены.
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
#ITByte: Algorithms and data structures are central to #ComputerScience.
Here is a quick refresher on #DataStructure and #Algorithms. #CS101
https://knowledgezone.co.in/trends/explorer?topic=Data-Structure-Algorithms
Avoiding text overlap in plots is essential for clarity, and R offers a great solution with the ggplot2 and ggrepel packages. By automatically repositioning labels, ggrepel keeps your plot clean and easy to interpret.
Video: https://www.youtube.com/watch?v=5lu4h_CPhi0
Website: https://statisticsglobe.com/avoid-overlap-text-labels-ggplot2-plot-r
Take a look here for more details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r
