{"id":14468,"date":"2022-10-07T23:28:02","date_gmt":"2022-10-07T17:58:02","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-matching-data-tables-by-five-columns-to-change-a-value-in-another-column\/"},"modified":"2022-10-07T23:28:02","modified_gmt":"2022-10-07T17:58:02","slug":"solved-matching-data-tables-by-five-columns-to-change-a-value-in-another-column","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-matching-data-tables-by-five-columns-to-change-a-value-in-another-column\/","title":{"rendered":"[Solved] Matching Data Tables by five columns to change a value in another column"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<div id=\"answer-39176820\" class=\"answer js-answer accepted-answer js-accepted-answer\" data-answerid=\"39176820\" data-parentid=\"39176643\" data-score=\"4\" data-position-on-page=\"1\" data-highest-scored=\"1\" data-question-has-accepted-highest-score=\"1\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n<div class=\"post-layout\">\n<div class=\"votecell post-layout--left\"><\/div>\n<div class=\"answercell post-layout--right\">\n<div class=\"s-prose js-post-body\" itemprop=\"text\">\n<p>In R it is always preferable to avoid loops wherever possible, as they are usually much slower than alternative vectorized solutions.<\/p>\n<p>This operation can be done with a data.table join. Basically, when you run<\/p>\n<pre><code>dt1[dt2];\n<\/code><\/pre>\n<p>you are performing a right-join between the two data.tables. The preset key columns of <code>dt1<\/code> determine which columns to join on. If <code>dt1<\/code> has no preset key, the operation fails. But you can specify the <code>on<\/code> argument to manually select the key columns on-the-fly:<\/p>\n<pre><code>key &lt;- paste0('V',1:5);\ndt1[dt2,on=key];\n<\/code><\/pre>\n<p>(The alternative of course is to preset a key, using either <code>setkey()<\/code> or <code>setkeyv()<\/code>.)<\/p>\n<p>The above operation will actually just return a merged table containing data from both <code>dt1<\/code> and <code>dt2<\/code>, which is not what you want. But we can make use of the <code>j<\/code> argument of the data.table indexing function and the <code>:=<\/code> in-place assignment syntax to assign the <code>id<\/code> column of <code>dt2<\/code> to the <code>id<\/code> column of <code>dt1<\/code>. Because we have a name conflict, we must use <code>i.id<\/code> to reference the <code>id<\/code> column of <code>dt2<\/code>, while the unmodified name <code>id<\/code> still refers to the <code>id<\/code> column of <code>dt1<\/code>. This is simply the mechanism provided by data.table for disambiguating conflicting names. Hence, you&#8217;re looking for:<\/p>\n<pre><code>dt1[dt2,on=key,id:=i.id];\n<\/code><\/pre>\n<p>Here&#8217;s an example that uses only two key columns and just a few rows of data (for simplicity). I also generated the keys to include some non-matching rows, just to demonstrate that the non-matching rows will have their ids left untouched by the operation.<\/p>\n<pre><code>set.seed(1L);\ndt1 &lt;- data.table(id=1:12,expand.grid(V1=1:3,V2=1:4),blah1=rnorm(12L));\ndt2 &lt;- data.table(id=13:18,expand.grid(V1=1:2,V2=1:3),blah2=rnorm(6L));\ndt1;\n##     id V1 V2      blah1\n##  1:  1  1  1 -0.6264538\n##  2:  2  2  1  0.1836433\n##  3:  3  3  1 -0.8356286\n##  4:  4  1  2  1.5952808\n##  5:  5  2  2  0.3295078\n##  6:  6  3  2 -0.8204684\n##  7:  7  1  3  0.4874291\n##  8:  8  2  3  0.7383247\n##  9:  9  3  3  0.5757814\n## 10: 10  1  4 -0.3053884\n## 11: 11  2  4  1.5117812\n## 12: 12  3  4  0.3898432\ndt2;\n##    id V1 V2       blah2\n## 1: 13  1  1 -0.62124058\n## 2: 14  2  1 -2.21469989\n## 3: 15  1  2  1.12493092\n## 4: 16  2  2 -0.04493361\n## 5: 17  1  3 -0.01619026\n## 6: 18  2  3  0.94383621\nkey &lt;- paste0('V',1:2);\ndt1[dt2,on=key,id:=i.id];\ndt1;\n##     id V1 V2      blah1\n##  1: 13  1  1 -0.6264538\n##  2: 14  2  1  0.1836433\n##  3:  3  3  1 -0.8356286\n##  4: 15  1  2  1.5952808\n##  5: 16  2  2  0.3295078\n##  6:  6  3  2 -0.8204684\n##  7: 17  1  3  0.4874291\n##  8: 18  2  3  0.7383247\n##  9:  9  3  3  0.5757814\n## 10: 10  1  4 -0.3053884\n## 11: 11  2  4  1.5117812\n## 12: 12  3  4  0.3898432\n<\/code><\/pre>\n<\/p><\/div>\n<div class=\"mt24\"><\/div>\n<\/div>\n<p>            <span class=\"d-none\" itemprop=\"commentCount\">5<\/span> <\/p><\/div>\n<\/div>\n<p>[ad_2]<\/p>\n<p>solved Matching Data Tables by five columns to change a value in another column <\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] In R it is always preferable to avoid loops wherever possible, as they are usually much slower than alternative vectorized solutions. This operation can be done with a data.table join. Basically, when you run dt1[dt2]; you are performing a right-join between the two data.tables. The preset key columns of dt1 determine which columns to &#8230; <a title=\"[Solved] Matching Data Tables by five columns to change a value in another column\" class=\"read-more\" href=\"https:\/\/jassweb.com\/solved\/solved-matching-data-tables-by-five-columns-to-change-a-value-in-another-column\/\" aria-label=\"More on [Solved] Matching Data Tables by five columns to change a value in another column\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[1327,1350,1163,321],"class_list":["post-14468","post","type-post","status-publish","format-standard","hentry","category-solved","tag-amazon-web-services","tag-data-table","tag-parallel-processing","tag-r"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[Solved] Matching Data Tables by five columns to change a value in another column - JassWeb<\/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:\/\/jassweb.com\/solved\/solved-matching-data-tables-by-five-columns-to-change-a-value-in-another-column\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Matching Data Tables by five columns to change a value in another column - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] In R it is always preferable to avoid loops wherever possible, as they are usually much slower than alternative vectorized solutions. 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