{"id":28050,"date":"2022-12-28T06:45:52","date_gmt":"2022-12-28T01:15:52","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-selecting-rows-in-r-with-yes-in-one-column-of-a-set-of-columns-and-not-yes-in-all-columns-of-another-set-of-columns\/"},"modified":"2022-12-28T06:45:52","modified_gmt":"2022-12-28T01:15:52","slug":"solved-selecting-rows-in-r-with-yes-in-one-column-of-a-set-of-columns-and-not-yes-in-all-columns-of-another-set-of-columns","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-selecting-rows-in-r-with-yes-in-one-column-of-a-set-of-columns-and-not-yes-in-all-columns-of-another-set-of-columns\/","title":{"rendered":"[Solved] Selecting rows in R with &#8220;Yes&#8221; in one column of a set of columns, and NOT &#8220;Yes&#8221; in all columns of another set of columns"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<div id=\"answer-33787219\" class=\"answer js-answer accepted-answer js-accepted-answer\" data-answerid=\"33787219\" data-parentid=\"33786445\" data-score=\"0\" 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>as long as you have the dataframe organized the way you do now i.e., comp1type1,comp1type2,comp1type3,comp2type1,&#8230;,comp[I]type[J]. I am sure you can use the following method.<\/p>\n<pre><code>    ncomp &lt;- 20\n    ntype &lt;- 3\n    vecone &lt;- df[,seq(1,ncomp*ntype,ntype)]\n    vectwo &lt;- df[,seq(2,ncomp*ntype,ntype)]\n    vecthree &lt;- df[,seq(3,ncomp*ntype,ntype)]\n\n    # now that we have the vectors of types seperated into data.frame's\n    # it'll be easier to do what we want\n    # this first condition will find which rows type1\/2 have No or NA for all\n    condition1 &lt;- rowSums(vecone==\"No\" | is.na(vecone)) == ncol(vecone)\n    condition2 &lt;- rowSums(vectwo==\"No\" | is.na(vectwo)) == ncol(vectwo)\n    # this third condition will find which rows type3 have atleast one Yes\n    condition3 &lt;- rowSums(vecthree ==\"Yes\",na.rm=T) &gt;= 1\n<\/code><\/pre>\n<p>Now you may use these conditions to figure out how many rows include a Yes for any type3 and a No\/NA for all type1\/2<\/p>\n<pre><code>sum(condition1 &amp; condition2 &amp; condition3)\n<\/code><\/pre>\n<\/p><\/div>\n<div class=\"mt24\"><\/div>\n<\/div>\n<p>            <span class=\"d-none\" itemprop=\"commentCount\">2<\/span> <\/p><\/div>\n<\/div>\n<p>[ad_2]<\/p>\n<p>solved Selecting rows in R with &#8220;Yes&#8221; in one column of a set of columns, and NOT &#8220;Yes&#8221; in all columns of another set of columns <\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] as long as you have the dataframe organized the way you do now i.e., comp1type1,comp1type2,comp1type3,comp2type1,&#8230;,comp[I]type[J]. I am sure you can use the following method. ncomp &lt;- 20 ntype &lt;- 3 vecone &lt;- df[,seq(1,ncomp*ntype,ntype)] vectwo &lt;- df[,seq(2,ncomp*ntype,ntype)] vecthree &lt;- df[,seq(3,ncomp*ntype,ntype)] # now that we have the vectors of types seperated into data.frame&#8217;s # it&#8217;ll be &#8230; <a title=\"[Solved] Selecting rows in R with &#8220;Yes&#8221; in one column of a set of columns, and NOT &#8220;Yes&#8221; in all columns of another set of columns\" class=\"read-more\" href=\"https:\/\/jassweb.com\/solved\/solved-selecting-rows-in-r-with-yes-in-one-column-of-a-set-of-columns-and-not-yes-in-all-columns-of-another-set-of-columns\/\" aria-label=\"More on [Solved] Selecting rows in R with &#8220;Yes&#8221; in one column of a set of columns, and NOT &#8220;Yes&#8221; in all columns of another set of columns\">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":[321],"class_list":["post-28050","post","type-post","status-publish","format-standard","hentry","category-solved","tag-r"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[Solved] Selecting rows in R with &quot;Yes&quot; in one column of a set of columns, and NOT &quot;Yes&quot; in all columns of another set of columns - 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-selecting-rows-in-r-with-yes-in-one-column-of-a-set-of-columns-and-not-yes-in-all-columns-of-another-set-of-columns\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Selecting rows in R with &quot;Yes&quot; in one column of a set of columns, and NOT &quot;Yes&quot; in all columns of another set of columns - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] as long as you have the dataframe organized the way you do now i.e., comp1type1,comp1type2,comp1type3,comp2type1,&#8230;,comp[I]type[J]. I am sure you can use the following method. ncomp &lt;- 20 ntype &lt;- 3 vecone &lt;- df[,seq(1,ncomp*ntype,ntype)] vectwo &lt;- df[,seq(2,ncomp*ntype,ntype)] vecthree &lt;- df[,seq(3,ncomp*ntype,ntype)] # now that we have the vectors of types seperated into data.frame&#039;s # it&#039;ll be ... 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I am sure you can use the following method. ncomp &lt;- 20 ntype &lt;- 3 vecone &lt;- df[,seq(1,ncomp*ntype,ntype)] vectwo &lt;- df[,seq(2,ncomp*ntype,ntype)] vecthree &lt;- df[,seq(3,ncomp*ntype,ntype)] # now that we have the vectors of types seperated into data.frame's # it'll be ... 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