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Repost: The Modern R Stack for Production AI

Stephen Turner
[This article was first published on Getting Genetics Done, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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< mark> Reposted from the original at: https://blog.stephenturner.us/p/r-production-ai

…

Python isn’t the only game in town anymore: R can interact with local and cloud LLM APIs, inspect and modify your local R environment and files, implement RAG, computer vision, NLP, evals, & much more.

There was a time in late 2023 to early 2024 when I and probably many others in the R community felt like R was falling woefully behind Python in tooling for development using AI and LLMs. This is no longer the case. The R community, and Posit in particular, have been on an absolute tear bringing new packages online to take advantage of all the capabilities that LLMs provide. Here are a few that I’ve used and others I’m keeping a close eye on as they mature.

< picture style="--tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-color: #fff; --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-offset-width: 0px; --tw-ring-shadow: 0 0 #0000; --tw-rotate: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-shadow-colored: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-skew-x: 0; --tw-skew-y: 0; --tw-translate-x: 0; --tw-translate-y: 0;">< source sizes="100vw" srcset_temp="https://substackcdn.com/image/fetch/w_424,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe2fba2c7-5c59-4fff-9b92-889dab350b84_1037x741.png 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe2fba2c7-5c59-4fff-9b92-889dab350b84_1037x741.png 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe2fba2c7-5c59-4fff-9b92-889dab350b84_1037x741.png 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe2fba2c7-5c59-4fff-9b92-889dab350b84_1037x741.png 1456w" style="--tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-color: #fff; --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-offset-width: 0px; --tw-ring-shadow: 0 0 #0000; --tw-rotate: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-shadow-colored: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-skew-x: 0; --tw-skew-y: 0; --tw-translate-x: 0; --tw-translate-y: 0;" type="image/webp">
< svg class="lucide lucide-maximize2" fill="none" height="20" stroke-linecap="round" stroke-linejoin="round" stroke-width="2" stroke="currentColor" viewbox="0 0 24 24" width="20" xmlns="http://www.w3.org/2000/svg">< polyline points="15 3 21 3 21 9">< polyline points="9 21 3 21 3 15">

  • I recently used the mall package to run a quick sentiment analysis of #Rstats posts on Bluesky:

    < picture style="--tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-color: #fff; --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-offset-width: 0px; --tw-ring-shadow: 0 0 #0000; --tw-rotate: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-shadow-colored: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-skew-x: 0; --tw-skew-y: 0; --tw-translate-x: 0; --tw-translate-y: 0;">< source srcset_temp="https://substackcdn.com/image/fetch/w_140,h_140,c_fill,f_webp,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feab756f1-d8a5-452d-9d2d-44343a8110f7_700x500.png" style="--tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-ring-color: rgb(59 130 246 / 0.5); --tw-ring-offset-color: #fff; --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-offset-width: 0px; --tw-ring-shadow: 0 0 #0000; --tw-rotate: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-scroll-snap-strictness: proximity; --tw-shadow-colored: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-skew-x: 0; --tw-skew-y: 0; --tw-translate-x: 0; --tw-translate-y: 0;" type="image/webp">

    Functions in the mall package integrate smoothly with piped workflows in dplyr. For example:

    reviews |>
  llm_sentiment(review)
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