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Zeitpunkt              Nutzer    Delta   Tröts        TNR     Titel                     Version  maxTL
Mi 03.07.2024 00:00:12    61.917      +2    3.513.906    56,8 Fosstodon                 4.2.9      500
Di 02.07.2024 00:01:44    61.915      -2    3.510.479    56,7 Fosstodon                 4.2.9      500
Mo 01.07.2024 00:00:33    61.917       0    3.507.420    56,6 Fosstodon                 4.2.9      500
So 30.06.2024 00:00:34    61.917      +2    3.504.671    56,6 Fosstodon                 4.2.9      500
Sa 29.06.2024 00:01:13    61.915      +2    3.501.982    56,6 Fosstodon                 4.2.9      500
Fr 28.06.2024 00:01:07    61.913      +3    3.498.459    56,5 Fosstodon                 4.2.9      500
Do 27.06.2024 00:00:32    61.910       0    3.495.444    56,5 Fosstodon                 4.2.9      500
Mi 26.06.2024 00:00:07    61.910      +1    3.494.703    56,4 Fosstodon                 4.2.9      500
Di 25.06.2024 00:00:06    61.909      +1    3.491.246    56,4 Fosstodon                 4.2.9      500
Mo 24.06.2024 00:00:01    61.908       0    3.488.299    56,3 Fosstodon                 4.2.9      500

Mi 03.07.2024 19:13

if you're a data 'anything' and wonder "why is R still a thing?" maybe this answer I wrote on Reddit will help

But to provide an actual answer, R being a domain specific language for data analysis, visualisation and modeling (not to mention field-specific packages for bioinformatics, econometrics, bayesian and geospatial analysis), makes it a prime choice for these tasks. There are also many packages that extend the language to make it usable for other tasks such as the {shiny} package to build full web applications, Quarto for document authoring, {targets} for pipelining, {vetiver} for deployment of machine learning models and it's relatively easy to integrate with other languages like C++, Rust, Julia and Python. It also pioneered things that we take for granted when it comes to data analysis such as data frames, the forward pipe operator or using grammar for data visualisation or manipulation. It’s 30 years old and very robust: you cannot submit a package to CRAN (R’s Pypi so to say) if it breaks another package: if one of your submitted package on CRAN has a dependency that gets updated, and this update somehow breaks your package, you have 2 weeks to update it or it gets taken off CRAN: this ensure that there is no dependency hell when installing R packages. Other crappy practices such as namesquatting or, worse, typosquatting are impossible since packages are reviewed by actual humans on first submission.

But to provide an actual answer, R being a domain specific language for data analysis, visualisation and modeling (not to mention field-specific packages for bioinformatics, econometrics, bayesian and geospatial analysis), makes it a prime choice for these tasks. There are also many packages that extend the language to make it usable for other tasks such as the {shiny} package to build full web applications, Quarto for document authoring, {targets} for pipelining, {vetiver} for deployment of machine learning models and it's relatively easy to integrate with other languages like C++, Rust, Julia and Python. It also pioneered things that we take for granted when it comes to data analysis such as data frames, the forward pipe operator or using grammar for data visualisation or manipulation. It’s 30 years old and very robust: you cannot submit a package to CRAN (R’s Pypi so to say) if it breaks another package: if one of your submitted package on CRAN has a dependency that gets updated, and this update somehow breaks your package, you have 2 weeks to update it or it gets taken off CRAN: this ensure that there is no dependency hell when installing R packages. Other crappy practices such as namesquatting or, worse, typosquatting are impossible since packages are reviewed by actual humans on first submission.

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