Karsten Schmidt<p>Reflecting some more on the Sketchpad & ECS parts of this talk: SideFX Houdini organizes all geometry data in similar vertical silos of points, vertices, edges, faces, prims, each with component IDs, each with its own group of native and user-defined attribs, and with similar powerful "omniscient" visibility/access from anywhere. That structure makes VEX SOPs akin to "systems" in an ECS setup and the handling/scripting itself very fun & powerful. The GUI also provides spreadsheet views of the geometry (again similar to e.g. what FLECS provides for debugging). Considering the age of Houdini, I think this approach is notable...</p><p>Blender's BMesh Radial Mesh implementation[1] is more traditional OOP structured, but the core idea of "discs" (aka bi-directional circular lists) of pointers to vertices & edges now seems somewhat relevant to some Sketchpad ideas too. Also a reminder that I really need to find/make time to update & release my own mesh implementation (from 2018) combining ideas from both Houdini & BMesh... It's already been a year (again) since I last talked about & touched it... 😱</p><p>[1] <a href="https://developer.blender.org/docs/features/objects/mesh/bmesh/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">developer.blender.org/docs/fea</span><span class="invisible">tures/objects/mesh/bmesh/</span></a></p><p><a href="https://mastodon.thi.ng/tags/Blender" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Blender</span></a> <a href="https://mastodon.thi.ng/tags/Houdini" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Houdini</span></a> <a href="https://mastodon.thi.ng/tags/Mesh" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Mesh</span></a> <a href="https://mastodon.thi.ng/tags/Geometry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Geometry</span></a> <a href="https://mastodon.thi.ng/tags/DataStructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataStructure</span></a> <a href="https://mastodon.thi.ng/tags/ECS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ECS</span></a> <a href="https://mastodon.thi.ng/tags/Sketchpad" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Sketchpad</span></a></p>
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Statistics Globe<p>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.</p><p>The attached visual highlights a variety of bar chart styles to inspire your work.</p><p>Take a look here for more details: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/datasciencetraining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datasciencetraining</span></a></p>
Statistics Globe<p>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.</p><p>In the plot below (generated from reproducible code – thanks, Paul!), you can see how different models compare:</p><p>Original post: <a href="https://www.linkedin.com/posts/joachim-schork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">linkedin.com/posts/joachim-sch</span><span class="invisible">ork_coding-bigdata-rprogramming-activity-7242865910405824512-oNmF/</span></a></p><p>Further details: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">eepurl.com/gH6myT</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/database" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>database</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a></p>
Statistics Globe<p>Using dplyr and ggplot2 in R can significantly streamline your data analysis process, making it easier to work with complex data sets.</p><p>I have created a video tutorial in collaboration with Albert Rapp, where I demonstrate how to do this in practice: <a href="https://www.youtube.com/watch?v=EKISB0gnue4" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">youtube.com/watch?v=EKISB0gnue4</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/coding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>coding</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/rprogramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rprogramming</span></a> <a href="https://mastodon.social/tags/dataviz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataviz</span></a> <a href="https://mastodon.social/tags/statisticalanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalanalysis</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a> <a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a></p>
Statistics Globe<p>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.</p><p>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.</p><p>Tutorial: <a href="https://statisticsglobe.com/mean-imputation-for-missing-data/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/mean-imput</span><span class="invisible">ation-for-missing-data/</span></a></p><p>Newsletter: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">eepurl.com/gH6myT</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a></p>
Statistics Globe<p>Mean imputation is a straightforward method for handling missing values in numerical data, but it can significantly distort the relationships between variables.</p><p>For a detailed explanation of mean imputation, its drawbacks, and better alternatives, check out my full tutorial here: <a href="https://statisticsglobe.com/mean-imputation-for-missing-data/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/mean-imput</span><span class="invisible">ation-for-missing-data/</span></a></p><p>More details are available at this link: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">eepurl.com/gH6myT</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>research</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/businessanalyst" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>businessanalyst</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a></p>
Piotr Nowak<p>Designing an Efficient Tree Index on Disaggregated Memory</p><p><a href="https://cacm.acm.org/research-highlights/designing-an-efficient-tree-index-on-disaggregated-memory/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cacm.acm.org/research-highligh</span><span class="invisible">ts/designing-an-efficient-tree-index-on-disaggregated-memory/</span></a></p><p><a href="https://c.im/tags/computing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computing</span></a> <a href="https://c.im/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://c.im/tags/algorithm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>algorithm</span></a> <a href="https://c.im/tags/software" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>software</span></a></p>
Statistics Globe<p>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.</p><p>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.</p><p>More information: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> <a href="https://mastodon.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p>
Statistics Globe<p>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.</p><p>Visualization: <a href="https://cran.r-project.org/web/packages/gggenes/vignettes/introduction-to-gggenes.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">cran.r-project.org/web/package</span><span class="invisible">s/gggenes/vignettes/introduction-to-gggenes.html</span></a></p><p>Click this link for detailed information: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> <a href="https://mastodon.social/tags/datascientists" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascientists</span></a> <a href="https://mastodon.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p>
Karsten Schmidt<p><a href="https://mastodon.thi.ng/tags/ReleaseMonday" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ReleaseMonday</span></a> — One of the recent (already very useful!) new package additions to <a href="https://mastodon.thi.ng/tags/ThingUmbrella" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ThingUmbrella</span></a> is:</p><p><a href="https://thi.ng/leaky-bucket" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">thi.ng/leaky-bucket</span><span class="invisible"></span></a></p><p>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.</p><p>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...</p><p>Related, I've also updated/simplified the rate limiter interceptor in <a href="https://thi.ng/server" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">thi.ng/server</span><span class="invisible"></span></a> to utilize this new package...</p><p><a href="https://mastodon.thi.ng/tags/ThingUmbrella" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ThingUmbrella</span></a> <a href="https://mastodon.thi.ng/tags/DataStructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataStructure</span></a> <a href="https://mastodon.thi.ng/tags/RateLimiting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RateLimiting</span></a> <a href="https://mastodon.thi.ng/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSource</span></a> <a href="https://mastodon.thi.ng/tags/TypeScript" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TypeScript</span></a> <a href="https://mastodon.thi.ng/tags/JavaScript" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>JavaScript</span></a></p>
Statistics Globe<p>I used to think that writing sophisticated R code meant using all the advanced features and chaining long functions together...</p><p>Fancy code can be fun, but clean code makes collaboration and debugging so much easier.</p><p>Stay informed on data science by joining my free newsletter. Check out this link for more details: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">eepurl.com/gH6myT</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/datasciencecourse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datasciencecourse</span></a> <a href="https://mastodon.social/tags/datasciencetraining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datasciencetraining</span></a></p>
Statistics Globe<p>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.</p><p>The visualization below can be generated using the following R code:</p><p>library(mice)<br>my_imp <- mice(boys)<br>densityplot(my_imp)</p><p>Take a look here for more details: <a href="https://statisticsglobe.com/online-workshop-missing-data-imputation-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-wor</span><span class="invisible">kshop-missing-data-imputation-r</span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/statisticalanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalanalysis</span></a> <a href="https://mastodon.social/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.social/tags/visualanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>visualanalytics</span></a> <a href="https://mastodon.social/tags/pythoncoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pythoncoding</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a></p>
Statistics Globe<p>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.</p><p>Video: <a href="https://www.youtube.com/watch?v=5lu4h_CPhi0" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">youtube.com/watch?v=5lu4h_CPhi0</span><span class="invisible"></span></a><br>Website: <a href="https://statisticsglobe.com/avoid-overlap-text-labels-ggplot2-plot-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/avoid-over</span><span class="invisible">lap-text-labels-ggplot2-plot-r</span></a></p><p>Take a look here for more details: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/pythonprogramminglanguage" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>pythonprogramminglanguage</span></a> <a href="https://mastodon.social/tags/statisticalanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statisticalanalysis</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/rstudio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstudio</span></a></p>
naught101<p>Is there a data structure that can sensibly handle multiple hierarchical classification systems?</p><p>e.g. an Orange, in terms of phylogeny is<br>Plantae->Eudicot->...->Citrus->sinensis</p><p>and in terms of usefulness, is <br>Thing->Food->fruit->orange<br>(and it could have multiple parents in this taxonomy, e.g. cleaning product)</p><p>Bonus points for cool visualisations of this kind information.</p><p><a href="https://mastodon.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://mastodon.social/tags/dataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataScience</span></a> <a href="https://mastodon.social/tags/dataStructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataStructure</span></a> <a href="https://mastodon.social/tags/information" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>information</span></a> <a href="https://mastodon.social/tags/hierarchy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hierarchy</span></a> <a href="https://mastodon.social/tags/taxonomy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>taxonomy</span></a> <a href="https://mastodon.social/tags/classification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>classification</span></a> <a href="https://mastodon.social/tags/visualisation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>visualisation</span></a> <a href="https://mastodon.social/tags/dataViz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataViz</span></a></p>
Statistics Globe<p>In statistics, Frequentist and Bayesian approaches are two major methods of inference. While they aim to solve similar problems, they differ in their interpretation of probability and handling of uncertainty.</p><p>Frequentists interpret probability as the long-run frequency of events. Parameters (like the mean) are fixed but unknown, and inference relies on analyzing repeated samples.</p><p>Learn more: <a href="http://eepurl.com/gH6myT" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">http://</span><span class="">eepurl.com/gH6myT</span><span class="invisible"></span></a></p><p><a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a> <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/analysisskill" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>analysisskill</span></a></p>
Statistics Globe<p>Bring your visualizations to life with see, a dynamic R package from the easystats ecosystem that extends ggplot2 to create modern and intuitive graphics. Whether you're visualizing statistical models or exploring data, see simplifies the process and enhances the presentation of your insights.</p><p>Visualizations: <a href="https://github.com/easystats/see" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">github.com/easystats/see</span><span class="invisible"></span></a></p><p>Take a look here for more details: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/rprogramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rprogramming</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> <a href="https://mastodon.social/tags/coding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>coding</span></a></p>
Statistics Globe<p>Dimensionality reduction simplifies high-dimensional data while retaining its essential features. It’s a powerful tool for improving data analysis, visualization, and machine learning performance.</p><p>Image credit to Wikipedia: <a href="https://en.wikipedia.org/wiki/Dimensionality_reduction#/media/File:PCA_Projection_Illustration.gif" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Dimensio</span><span class="invisible">nality_reduction#/media/File:PCA_Projection_Illustration.gif</span></a></p><p>I've developed an in-depth course on PCA theory and its application in R programming. Check out this link for more details: <a href="https://statisticsglobe.com/online-course-pca-theory-application-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-pca-theory-application-r</span></a></p><p><a href="https://mastodon.social/tags/rstudio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstudio</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>programming</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/statistical" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistical</span></a> <a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a></p>
Statistics Globe<p>Understanding the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can be challenging!</p><p>Visualization source: <a href="https://en.wikipedia.org/wiki/Deep_learning#/media/File:AI-ML-DL.svg" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Deep_lea</span><span class="invisible">rning#/media/File:AI-ML-DL.svg</span></a></p><p><a href="https://mastodon.social/tags/database" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>database</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a> <a href="https://mastodon.social/tags/dataanalytic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytic</span></a></p>
Statistics Globe<p>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.</p><p>Course link: <a href="https://statisticsglobe.com/online-course-data-visualization-ggplot2-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-data-visualization-ggplot2-r</span></a></p><p><a href="https://mastodon.social/tags/dataanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytics</span></a> <a href="https://mastodon.social/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> <a href="https://mastodon.social/tags/dataanalytic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataanalytic</span></a> <a href="https://mastodon.social/tags/datavisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datavisualization</span></a> <a href="https://mastodon.social/tags/package" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>package</span></a> <a href="https://mastodon.social/tags/datasciencetraining" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datasciencetraining</span></a> <a href="https://mastodon.social/tags/visualanalytics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>visualanalytics</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a> <a href="https://mastodon.social/tags/tidyverse" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tidyverse</span></a> <a href="https://mastodon.social/tags/ggplot2" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ggplot2</span></a></p>
Statistics Globe<p>The Student's t-test is a crucial statistical method used to determine if there are significant differences between the means of two groups. It is widely applied in various fields to analyze small data sets, providing valuable insights when used correctly.</p><p>This visualization is based on the images of this Wikipedia article: <a href="https://en.wikipedia.org/wiki/Student%27s_t-test" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Student%</span><span class="invisible">27s_t-test</span></a></p><p>Further details: <a href="https://statisticsglobe.com/online-course-statistical-methods-r" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticsglobe.com/online-cou</span><span class="invisible">rse-statistical-methods-r</span></a></p><p><a href="https://mastodon.social/tags/bigdata" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bigdata</span></a> <a href="https://mastodon.social/tags/rprogramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rprogramming</span></a> <a href="https://mastodon.social/tags/datastructure" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datastructure</span></a></p>
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