{"id":5691,"date":"2018-01-16T14:19:25","date_gmt":"2018-01-16T14:19:25","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=5691"},"modified":"2018-09-18T10:43:37","modified_gmt":"2018-09-18T05:13:37","slug":"data-type-mapping-between-r-and-spark","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-type-mapping-between-r-and-spark\/","title":{"rendered":"Data Type Mapping Between R and Spark | Learn R and Spark"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:2052,&quot;href&quot;:&quot;https:\\\/\\\/spark.apache.org&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20251009215151\\\/https:\\\/\\\/spark.apache.org\\\/&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-11 00:11:34&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2025-12-14 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05:37:55&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-08 05:37:55&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<h2>1. Objective<\/h2>\n<p><span style=\"font-weight: 400\">Today, in this Spark tutorial, we will learn the Data type mapping between <a href=\"https:\/\/data-flair.training\/blogs\/r-programming-tutorial\/\"><strong>R<\/strong><\/a> and<a href=\"https:\/\/data-flair.training\/blogs\/apache-spark-for-beginners\/\"><strong> Spark<\/strong><\/a>. Before, them we will also learn a brief introduction to<a href=\"https:\/\/data-flair.training\/blogs\/sparkr\/\"><strong> SparkR<\/strong><\/a>.\u00a0<\/span><\/p>\n<p>So, let&#8217;s start Data Type Mapping Between R and Spark.<\/p>\n<div id=\"attachment_5692\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-5692\" class=\"wp-image-5692 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01.jpg\" alt=\"Data type mapping between R and Spark\" width=\"1200\" height=\"628\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01.jpg 1200w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/01\/Data-type-mapping-between-R-and-Spark-01-1024x536.jpg 1024w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-5692\" class=\"wp-caption-text\">Data type mapping between R and Spark<\/p><\/div>\n<h2>2. What is SparkR?<\/h2>\n<p>Apache Spark 1.4 releases <strong>SparkR<\/strong>. One of the major components\u00a0of SparkR is <strong><a href=\"https:\/\/data-flair.training\/blogs\/sparkr-dataframe\/\">SparkR DataFrame<\/a><\/strong>. Basically, it is nothing but fundamental <strong><a href=\"https:\/\/data-flair.training\/blogs\/data-structures-in-r\/\">data structure for data processing in R<\/a><\/strong>. Moreover, <a href=\"https:\/\/data-flair.training\/blogs\/apache-spark-sql-dataframe-tutorial\/\"><strong>DataFrames<\/strong><\/a> concept extends to other languages with libraries, for example, Pandas etc.<br \/>\nIn addition, <a href=\"https:\/\/data-flair.training\/blogs\/career-growth-in-r-programming\/\"><strong>R<\/strong><\/a>\u00a0offers several software facilities for data manipulation, calculation, and graphical display. Therefore, the\u00a0key concept behind SparkR was to explore different techniques to integrate the usability of R with the scalability of Spark. Basically, it is the R package.\u00a0Also gives light-weight frontend to use Apache Spark from R.<br \/>\nMoreover, Using SparkR is beneficial in the following ways:<\/p>\n<h3>a.\u00a0SparkR Data Sources API<\/h3>\n<p>Basically, API SparkR can read in data from a variety of sources. It is possible by tying into <a href=\"https:\/\/data-flair.training\/blogs\/spark-sql-tutorial\/\"><strong>Spark SQL\u2019s<\/strong><\/a> data sources. For example, <a href=\"https:\/\/data-flair.training\/blogs\/apache-hive-tutorial\/\">Hive tables<\/a>, JSON files, Parquet files etc.<\/p>\n<h3>b. SparkR Data Frame Optimizations<\/h3>\n<p>Moreover, it inherits\u00a0all the optimizations made to the computation engine. That is in terms of code generation, memory management.<\/p>\n<h3>c. SparkR Scalability to Many Cores and Machines<\/h3>\n<p>Although, those operations which execute on SparkR DataFrames get distributed across all the cores and machines\u00a0over the<a href=\"https:\/\/data-flair.training\/blogs\/r-clustering-tutorial\/\"><strong>Spark cluster<\/strong><\/a>. Therefore, SparkR DataFrames can run on terabytes of data and clusters with thousands of machines.<\/p>\n<h2>3. Data type mapping between R and Spark<\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>R<\/b><\/td>\n<td><b>Spark<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">byte<\/span><\/td>\n<td><span style=\"font-weight: 400\">byte<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">integer<\/span><\/td>\n<td><span style=\"font-weight: 400\">integer<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">float<\/span><\/td>\n<td><span style=\"font-weight: 400\">float<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">double<\/span><\/td>\n<td><span style=\"font-weight: 400\">double<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">numeric<\/span><\/td>\n<td><span style=\"font-weight: 400\">double<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">character<\/span><\/td>\n<td><span style=\"font-weight: 400\">string<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">string<\/span><\/td>\n<td><span style=\"font-weight: 400\">string<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">binary<\/span><\/td>\n<td><span style=\"font-weight: 400\">binary<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">raw<\/span><\/td>\n<td><span style=\"font-weight: 400\">binary<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">logical<\/span><\/td>\n<td><span style=\"font-weight: 400\">boolean<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">POSIXct<\/span><\/td>\n<td><span style=\"font-weight: 400\">timestamp<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">POSIXlt<\/span><\/td>\n<td><span style=\"font-weight: 400\">timestamp<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">Date<\/span><\/td>\n<td><span style=\"font-weight: 400\">date<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">array<\/span><\/td>\n<td><span style=\"font-weight: 400\">array<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">list<\/span><\/td>\n<td><span style=\"font-weight: 400\">array<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400\">env<\/span><\/td>\n<td><span style=\"font-weight: 400\">map<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>So, this was all in Spark and R data type mapping. Hope you like our explanation.<\/p>\n<h2>4. Conclusion<\/h2>\n<p>Hence, we have learned about Data type mapping between <strong><a href=\"https:\/\/data-flair.training\/blogs\/why-you-should-learn-r-programming\/\">R<\/a> <\/strong>and <strong><a href=\"https:\/\/data-flair.training\/blogs\/limitations-of-apache-spark\/\">Spark<\/a><\/strong>. Also, learned about SparkR. However, if any query occurs, feel free to ask in the comment section. I assure you that\u00a0we will get back to you.<br \/>\nBest Books for <strong><a href=\"https:\/\/data-flair.training\/blogs\/best-apache-spark-scala-books\/\">learning Spark<\/a><\/strong>.<br \/>\n<strong><a href=\"https:\/\/spark.apache.org\/\">For reference<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. Objective Today, in this Spark tutorial, we will learn the Data type mapping between R and Spark. Before, them we will also learn a brief introduction to SparkR.\u00a0 So, let&#8217;s start Data Type&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":6418,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[1793,16596,11147,16597,15967],"class_list":["post-5691","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-spark","tag-benefits-of-sparkr","tag-data-type-mapping-in-spark-and-r","tag-r-and-spark","tag-spark-and-r","tag-what-is-sparkr"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data Type Mapping Between R and Spark | Learn R and Spark - DataFlair<\/title>\n<meta name=\"description\" content=\"Data type mapping between R and Spark,what is SparkR,SparkR Benefits:Data Sources API,Data Frame Optimizations,SparkR Scalability to Cores and Machines\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/data-type-mapping-between-r-and-spark\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Type Mapping Between R and Spark | Learn R and Spark - 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