--- title: PCA in R: Principal Component Analysis Step-by-Step... description: A comprehensive step-by-step tutorial on performing Principal Component Analysis (PCA) in R using the built-in prcomp() function on the USArrests dataset.... canonical: https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: PCA in R: Principal Component Analysis Step-by-Step (prcomp + ggplot2) | daily.dev og:description: A comprehensive step-by-step tutorial on performing Principal Component Analysis (PCA) in R using the built-in prcomp() function on the USArrests dataset.... og:url: https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn og:image: https://api.daily.dev/og/posts/3fJCt60wN.png og:image:alt: PCA in R: Principal Component Analysis Step-by-Step (prcomp + ggplot2) og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # PCA in R: Principal Component Analysis Step-by-Step (prcomp + ggplot2) **[R-bloggers](https://daily.dev/sources/rbloggers)** · 14 min read · 0 upvotes · 0 comments ## Summary A comprehensive step-by-step tutorial on performing Principal Component Analysis (PCA) in R using the built-in prcomp() function on the USArrests dataset. Covers assumption checking (normality, correlation), data scaling, running PCA with SVD, deciding how many components to keep via three methods (scree plot, Kaiser criterion, cumulative variance threshold), and visualizing results with both base R and ggplot2/factoextra biplots. Also includes a PCA vs. Factor Analysis comparison table, APA-style reporting template, common mistakes, and downstream uses of component scores. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Principal Component Analysis \(PCA\) in Machine Learning](https://daily.dev/posts/principal-component-analysis-pca-in-machine-learning-xb1oeiyld) · DigitalOcean Community · 0 upvotes · 0 comments - [PCA: an embedding shrink-ray](https://daily.dev/posts/pca-an-embedding-shrink-ray-ayowx3qqb) · Software Doug · 0 upvotes · 0 comments - [How to Create Boxplots and Model Data in R Using ggplot2](https://daily.dev/posts/how-to-create-boxplots-and-model-data-in-r-using-ggplot2-zzgaiupyd) · freeCodeCamp · 2 upvotes · 0 comments - [How to Create Scatterplots and Model Data in R Using ggplot2](https://daily.dev/posts/how-to-create-scatterplots-and-model-data-in-r-using-ggplot2-isburylnh) · freeCodeCamp · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#r](https://daily.dev/tags/r), [#data-visualization](https://daily.dev/tags/data-visualization) [View this post on daily.dev](https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"PCA in R: Principal Component Analysis Step-by-Step (prcomp + ggplot2)","url":"https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn"},"datePublished":"2026-06-20T23:52:02.827Z","dateModified":"2026-06-20T23:52:23.802Z","description":"A comprehensive step-by-step tutorial on performing Principal Component Analysis (PCA) in R using the built-in prcomp() function on the USArrests dataset....","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8ca104e16722f991673e778ae52a4b86?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/8ca104e16722f991673e778ae52a4b86?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"R-bloggers","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"R-bloggers","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/98bcbbdf72774292a55ceda8f2277f1e","url":"https://daily.dev/sources/rbloggers"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/pca-in-r-principal-component-analysis-step-by-step-prcomp-ggplot2--3fjct60wn","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"machine-learning,data-science,r,data-visualization","timeRequired":"PT14M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"R-bloggers","item":"https://daily.dev/sources/rbloggers"},{"@type":"ListItem","position":3,"name":"PCA in R: Principal Component Analysis Step-by-Step (prcomp + ggplot2)"}]} ```