{"id":31826,"date":"2023-03-11T06:02:00","date_gmt":"2023-03-11T14:02:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=31826"},"modified":"2023-03-11T10:32:23","modified_gmt":"2023-03-11T18:32:23","slug":"video-highlights-copilot-for-r","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/03\/11\/video-highlights-copilot-for-r\/","title":{"rendered":"Video Highlights: Copilot for R"},"content":{"rendered":"\n<p>Our video highlights selection for today is by data science industry luminary David Smith who made a presentation to the\u00a0<a href=\"https:\/\/nyhackr.org\/past-talks\" target=\"_blank\" rel=\"noreferrer noopener\">NYC Data Hackers<\/a>\u00a0on the topic of Copilot for R. If you haven&#8217;t come across\u00a0<a href=\"https:\/\/github.com\/features\/copilot\/\" target=\"_blank\" rel=\"noreferrer noopener\">Copilot<\/a>\u00a0before, it&#8217;s like an AI-based pair programmer that suggests new lines of code, and perhaps entire functions, based on context. In the presentation (video below) Smith shows how while he was editing in Visual Studio Code with Copilot enabled, it suggested tidyverse functions for cleaning a data set, and even the code for performing an analysis of variance:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-4-3 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\"  id=\"_ytid_46999\"  width=\"480\" height=\"360\"  data-origwidth=\"480\" data-origheight=\"360\" src=\"https:\/\/www.youtube.com\/embed\/XQ4Negbmtk4?enablejsapi=1&#038;autoplay=0&#038;cc_load_policy=0&#038;cc_lang_pref=&#038;iv_load_policy=1&#038;loop=0&#038;modestbranding=0&#038;rel=1&#038;fs=1&#038;playsinline=0&#038;autohide=2&#038;theme=dark&#038;color=red&#038;controls=1&#038;\" class=\"__youtube_prefs__  epyt-is-override  no-lazyload\" title=\"YouTube player\"  allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen data-no-lazy=\"1\" data-skipgform_ajax_framebjll=\"\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>It&#8217;s fun to  take a behind-the-scenes look to see how Copilot uses Generative AI to make its suggestions. With the\u00a0<a href=\"https:\/\/learn.microsoft.com\/en-us\/azure\/cognitive-services\/openai\/overview?WT.mc_id=aiml-88899-davidsmi\" target=\"_blank\" rel=\"noreferrer noopener\">Azure OpenAI Service<\/a>, you can access the underlying OpenAI Codex model directly, and generate code suggestions via its API. You can review an\u00a0<a href=\"https:\/\/github.com\/revodavid\/copilot-for-r\/blob\/main\/openai-sample-script.R\" target=\"_blank\" rel=\"noreferrer noopener\">R script to access the OpenAI API directly<\/a>\u00a0using the httr2 package, and also an\u00a0<a href=\"https:\/\/github.com\/revodavid\/copilot-for-r\/blob\/main\/openai.R\" target=\"_blank\" rel=\"noreferrer noopener\">R function to call an OpenAI model<\/a>. <\/p>\n\n\n\n<p>GitHub (revodavid):&nbsp;<a href=\"https:\/\/github.com\/revodavid\/copilot-for-r\" target=\"_blank\" rel=\"noreferrer noopener\">Copilot for R<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Our video highlights selection for today is by data science industry luminary David Smith who made a presentation to the\u00a0NYC Data Hackers\u00a0on the topic of Copilot for R. If you haven&#8217;t come across\u00a0Copilot\u00a0before, it&#8217;s like an AI-based pair programmer that suggests new lines of code, and perhaps entire functions, based on context. <\/p>\n","protected":false},"author":10513,"featured_media":31716,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,182,170,180,67,268,56,1,85],"tags":[133,277,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Video Highlights: Copilot for R - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/03\/11\/video-highlights-copilot-for-r\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Video Highlights: Copilot for R - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Our video highlights selection for today is by data science industry luminary David Smith who made a presentation to the\u00a0NYC Data Hackers\u00a0on the topic of Copilot for R. 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The data sets have examples in healthcare, but he plans to widen to include other types of data.","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2022\/01\/MLDataR_logo.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":26202,"url":"https:\/\/insidebigdata.com\/2021\/05\/16\/video-highlights-generalized-additive-models-allowing-for-some-wiggle-room-in-your-models\/","url_meta":{"origin":31826,"position":5},"title":"Video Highlights: Generalized Additive Models &#8211; Allowing for some wiggle room in your models","date":"May 16, 2021","format":false,"excerpt":"In this video presentation, we'll unpack GAMs as an extension of generalized linear models, learn about the role of splines in these models, and explore the many choices available to define and fit these models. We'll be using data on traffic stops to investigate racially-biased policing in South Carolina as\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/06\/Data-Scientist-shutterstock_768047488.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/31826"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=31826"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/31826\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/31716"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=31826"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=31826"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=31826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}