{"id":37781,"date":"2025-02-08T02:26:09","date_gmt":"2025-02-08T07:26:09","guid":{"rendered":"https:\/\/statanalytica.com\/blog\/?p=37781"},"modified":"2025-02-08T02:45:22","modified_gmt":"2025-02-08T07:45:22","slug":"sql-vs-r-for-data-manipulation","status":"publish","type":"post","link":"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/","title":{"rendered":"SQL vs R for Data Manipulation: Which is Faster and More Efficient?"},"content":{"rendered":"\n<p>In today&#8217;s data-driven world, businesses and analysts rely heavily on data manipulation to extract meaningful insights. Whether cleaning data, aggregating statistics, or preparing datasets for machine learning, choosing the right tool can significantly improve performance and efficiency. SQL (Structured Query Language) and R are among the most widely used tools for data manipulation, each excelling in different aspects of data handling.<\/p>\n\n\n\n<p>But which one is faster and more efficient for data manipulation? The answer isn&#8217;t straightforward\u2014it depends on several factors, including the size of the dataset, complexity of operations, available system resources, and the specific task at hand. In this in-depth analysis, we\u2019ll focus on SQL vs R for Data Manipulation in terms of speed, ease of use, scalability, and practical applications. By the end of this blog, you\u2019ll have a clear understanding of which tool to use for different data manipulation tasks and how you can integrate both for optimal results.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"understanding-sql-vs-r-for-data-manipulation\"><\/span><strong>Understanding SQL Vs R for Data Manipulation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a1b4c8b5e2b4\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ff5104;color:#ff5104\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ff5104;color:#ff5104\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a1b4c8b5e2b4\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#understanding-sql-vs-r-for-data-manipulation\" >Understanding SQL Vs R for Data Manipulation<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#what-is-sql\" >What is SQL?<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#key-features-of-sql\" >Key Features of SQL:<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#what-is-r\" >What is R?<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#key-features-of-r\" >Key Features of R:<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#performance-comparison-sql-vs-r-for-data-manipulation\" >Performance Comparison: SQL vs R for Data Manipulation<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#1-data-extraction-and-querying\" >1. Data Extraction and Querying<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#2-aggregation-and-grouping\" >2. Aggregation and Grouping<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#3-complex-data-transformations\" >3. Complex Data Transformations<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#when-to-use-sql-vs-r\" >When to Use SQL vs R?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#which-is-easier-to-learn-sql-or-r\" >Which is easier to learn, SQL or R?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#can-sql-and-r-be-used-together\" >Can SQL and R be used together?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/statanalytica.com\/blog\/sql-vs-r-for-data-manipulation\/#is-sql-better-than-r-for-big-data\" >Is SQL better than R for big data?<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-sql\"><\/span><strong>What is SQL?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>SQL (Structured Query Language) is a powerful language specifically designed for querying and managing structured data stored in relational databases. It is widely used in industries such as finance, healthcare, and e-commerce, where data is stored in well-organized tables and needs to be retrieved, updated, and manipulated efficiently.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"key-features-of-sql\"><\/span><strong>Key Features of SQL:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Efficient Data Retrieval:<\/strong> SQL queries enable quick data extraction using SELECT statements.<\/li>\n\n\n\n<li><strong>Aggregation and Filtering:<\/strong> SQL supports functions like SUM(), AVG(), COUNT(), and GROUP BY for summarizing data.<\/li>\n\n\n\n<li><strong>Joins and Relationships:<\/strong> SQL can merge data from multiple tables using JOIN operations.<\/li>\n\n\n\n<li><strong>Indexing for Speed:<\/strong> Indexing optimizes queries for faster data access.<\/li>\n\n\n\n<li><strong>Scalability:<\/strong> Works well with big data and can handle millions of rows efficiently.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-r\"><\/span><strong>What is R?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>R is a programming language and environment specifically built for statistical computing and data analysis. It is highly popular among data scientists and analysts due to its vast collection of packages and visualization capabilities. Unlike SQL, which primarily works with structured relational databases, R can handle both structured and unstructured data, making it more flexible for data manipulation.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"key-features-of-r\"><\/span><strong>Key Features of R:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Advanced-Data Manipulation:<\/strong> R packages like dplyr and tidyr simplify data wrangling.<\/li>\n\n\n\n<li><strong>Statistical Analysis:<\/strong> Offers extensive statistical functions and machine learning capabilities.<\/li>\n\n\n\n<li><strong>Data Visualization:<\/strong> Libraries like ggplot2 and plotly allow detailed data representation.<\/li>\n\n\n\n<li><strong>Works with Multiple Data Formats:<\/strong> Supports CSV, JSON, Excel, and databases.<\/li>\n\n\n\n<li><strong>Ideal for Data Transformation:<\/strong> Great for handling missing values, reshaping datasets, and complex computations.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"performance-comparison-sql-vs-r-for-data-manipulation\"><\/span><strong>Performance Comparison: SQL vs R for Data Manipulation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-data-extraction-and-querying\"><\/span><strong>1. Data Extraction and Querying<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>SQL is faster and more efficient when it comes to extracting large volumes of data. Since it operates directly on databases, it eliminates the need to load data into memory, making it ideal for querying millions of records in seconds.<\/p>\n\n\n\n<p><strong>Example:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXegX_2KduiESBFDQ6wOo4lFGdWzaOK1K-5hUV12aGADgn0E0nwsmpYrjuXHNIRPJKWVVTRJTTu4YdGqTIIbp3OHgPuzJemNDfdjkHeHzYOGAcvZergQ1ArM0LEQmG29Dxj-RyEUjQ?key=VqRDxeS0vtzOyTUDVPkz_QZZ\" alt=\"\"\/><\/figure>\n\n\n\n<p>In contrast, R requires loading data into memory first, which can slow down performance, especially with large datasets.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdJclf8h9I-UUBEJR5sb6c5w5MsJs5l0hsr-bDxIog_fh91Vs37Y6wh0eVtjwTKCzY7Vt9A3TotbLKEMZYoTlfINsvDfRlsAPIdTGms4hLI97ToF76KXcS97WQJgTrIAk20GbVDmA?key=VqRDxeS0vtzOyTUDVPkz_QZZ\" alt=\"\"\/><\/figure>\n\n\n\n<p>\ud83d\udd39 <strong>Winner:<\/strong> SQL (faster query execution for large datasets)<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2-aggregation-and-grouping\"><\/span><strong>2. Aggregation and Grouping<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>SQL&#8217;s optimized engine executes aggregation functions (SUM(), AVG(), COUNT(), GROUP BY) efficiently. It also takes advantage of indexing, which speeds up operations.<\/p>\n\n\n\n<p>However, R provides greater flexibility in aggregating data using functions like summarise() in dplyr. If additional computations and transformations are needed post-aggregation, R might be a better choice.<\/p>\n\n\n\n<p>\ud83d\udd39 <strong>Winner:<\/strong> SQL (for basic aggregation), R (for flexible transformations)<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3-complex-data-transformations\"><\/span><strong>3. Complex Data Transformations<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>R excels at complex data transformations, such as reshaping data, handling missing values, and performing advanced computations. The tidyr package makes data restructuring easier than SQL.<\/p>\n\n\n\n<p>Example of reshaping data in R:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXd8hWLf5Ddmp0Edrre846SbImSmo-L0mJLRIOb5WQW2G5_reeVEx4xeN7gs7ugynD5yu38W2Kcsmtxr1pH8zH-WPnlYlDIvMLT3zQ11yhKZ3YMkVMFE-i1HVopf7C6bvq8Sis4TTA?key=VqRDxeS0vtzOyTUDVPkz_QZZ\" alt=\"\"\/><\/figure>\n\n\n\n<p>In SQL, similar operations require multiple joins and subqueries, making them more complicated and less intuitive.<\/p>\n\n\n\n<p>\ud83d\udd39 Winner: R (better flexibility for data transformation tasks)<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"when-to-use-sql-vs-r\"><\/span><strong>When to Use SQL vs R?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Task<\/strong><\/td><td><strong>Best Choice<\/strong><\/td><\/tr><tr><td>Querying Large Databases<\/td><td>SQL<\/td><\/tr><tr><td>Aggregating Large Datasets<\/td><td>SQL<\/td><\/tr><tr><td>Complex Data Transformations<\/td><td>R<\/td><\/tr><tr><td>Statistical Analysis &amp; Modeling<\/td><td>R<\/td><\/tr><tr><td>Data Cleaning &amp; Reshaping<\/td><td>R<\/td><\/tr><tr><td>Handling Big Data Efficiently<\/td><td>SQL<\/td><\/tr><tr><td>Visualization &amp; Reporting<\/td><td>R<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Choosing between SQL and R for data manipulation depends on the nature of your tasks. If you\u2019re working with large relational databases and need fast data retrieval, SQL is the best choice. If you require complex transformations, statistical analysis, and visualization, R is more powerful.<\/p>\n\n\n\n<p>For the best results, combining both SQL and R will help you leverage the strengths of each tool. SQL can be used for extracting and preprocessing large datasets, while R can take over for in-depth analysis and visualization. By mastering both, you can handle any data manipulation task efficiently and effectively!<\/p>\n\n\n\n<p><strong>Also Read: <a href=\"https:\/\/statanalytica.com\/blog\/python-vs-matlab-for-data-analysis\/\">Python vs MATLAB for Data Analysis: The Ultimate Comparison for 2025<\/a><\/strong><\/p>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1738997573435\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"which-is-easier-to-learn-sql-or-r\"><\/span><strong>Which is easier to learn, SQL or R?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>SQL is generally easier to learn than R since it uses structured syntax and mainly focuses on retrieving and managing data. R, on the other hand, has a steeper learning curve due to its wide range of functionalities and packages.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1738997598297\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"can-sql-and-r-be-used-together\"><\/span><strong>Can SQL and R be used together?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes! Many data professionals combine SQL and R by using SQL for data extraction and R for <a href=\"https:\/\/en.wikipedia.org\/?title=Statistical_analysis&amp;redirect=no\" target=\"_blank\" rel=\"noreferrer noopener\">statistical analysis<\/a> and visualization. R can connect to databases using packages like DBI and RSQLite.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1738997614944\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"is-sql-better-than-r-for-big-data\"><\/span><strong>Is SQL better than R for big data?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, SQL performs better with big data since it operates directly on databases. R loads data into memory, which can slow down performance if system resources are limited.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In today&#8217;s data-driven world, businesses and analysts rely heavily on data manipulation to extract meaningful insights. Whether cleaning data, aggregating statistics, or preparing datasets for machine learning, choosing the right tool can significantly improve performance and efficiency. SQL (Structured Query Language) and R are among the most widely used tools for data manipulation, each excelling [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":37783,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[136],"tags":[5116],"class_list":["post-37781","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general","tag-sql-vs-r-for-data-manipulation"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37781","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/comments?post=37781"}],"version-history":[{"count":1,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37781\/revisions"}],"predecessor-version":[{"id":37784,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37781\/revisions\/37784"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media\/37783"}],"wp:attachment":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media?parent=37781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/categories?post=37781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/tags?post=37781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}