{"id":3214,"date":"2015-09-23T12:08:56","date_gmt":"2015-09-23T11:08:56","guid":{"rendered":"https:\/\/abcdr.guyader.pro\/?p=3214"},"modified":"2018-04-08T00:02:28","modified_gmt":"2018-04-07T23:02:28","slug":"droplevels-ou-comment-se-debarrasser-efficacement-de-niveaux-de-facteurs-inutilises","status":"publish","type":"post","link":"https:\/\/thinkr.fr\/abcdr\/droplevels-ou-comment-se-debarrasser-efficacement-de-niveaux-de-facteurs-inutilises\/","title":{"rendered":"droplevels() ou comment se d\u00e9barrasser efficacement de niveaux de facteurs inutilis\u00e9s"},"content":{"rendered":"<pre><code><br \/> jdd &lt;- data.frame(deslettres=letters[1:10], <br \/> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 desnombres=seq(1:10), <br \/> \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 desfacteurs=c(rep(\"oui\",5), rep(\"non\", 5))) <br \/> <br \/> levels(jdd$deslettres)<br \/> [1] \"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\" \"i\" \"j\"<br \/> # il y a 10 niveaux pour la variable qualitative \"deslettres\"<br \/> <br \/> # en filtrant sur les nombres....<br \/> library(dplyr)<br \/> unextrait&lt;-filter(jdd,desnombres &gt; 5)<br \/> levels(unextrait$deslettres)<br \/> [1] \"a\" \"b\" \"c\" \"d\" \"e\" \"f\" \"g\" \"h\" \"i\" \"j\" <br \/> # ...le nouveau jeu de donn\u00e9es garde les anciens noms de niveaux de \"deslettres\"<br \/> <\/code><\/pre>\n<p> Pour s&rsquo;en d\u00e9barrasser, depuis R 2.12.0, la fonction droplevels() rend cette op\u00e9ration ais\u00e9e&#8230; <\/p>\n<p><\/p>\n<pre><code><br \/> <br \/> # ...sur tout le jeu de donn\u00e9es :\u00a0 <br \/> droplevels(unextrait)<br \/> summary(unextrait) <br \/> <br \/> <br \/> # ...sur une variable en particulier : <br \/> droplevels(unextrait$deslettres)<br \/> summary(unextrait)<br \/> <br \/> # ...sur tout le jeu de donn\u00e9es sauf celle mentionn\u00e9es dans l'argument except : <br \/> droplevels(unextrait, except=\"desfacteurs\")<br \/> \u00a0<br \/> \u00a0<br \/> <\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>jdd &lt;- data.frame(deslettres=letters[1:10], \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 desnombres=seq(1:10), \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 desfacteurs=c(rep(\u00ab\u00a0oui\u00a0\u00bb,5), rep(\u00ab\u00a0non\u00a0\u00bb, 5))) levels(jdd$deslettres) [1] \u00ab\u00a0a\u00a0\u00bb \u00ab\u00a0b\u00a0\u00bb \u00ab\u00a0c\u00a0\u00bb \u00ab\u00a0d\u00a0\u00bb \u00ab\u00a0e\u00a0\u00bb \u00ab\u00a0f\u00a0\u00bb \u00ab\u00a0g\u00a0\u00bb \u00ab\u00a0h\u00a0\u00bb \u00ab\u00a0i\u00a0\u00bb \u00ab\u00a0j\u00a0\u00bb # il y a 10 niveaux pour la variable qualitative \u00ab\u00a0deslettres\u00a0\u00bb # en filtrant sur les nombres&#8230;. library(dplyr) unextrait&lt;-filter(jdd,desnombres &gt; 5) levels(unextrait$deslettres) [1] \u00ab\u00a0a\u00a0\u00bb \u00ab\u00a0b\u00a0\u00bb \u00ab\u00a0c\u00a0\u00bb \u00ab\u00a0d\u00a0\u00bb \u00ab\u00a0e\u00a0\u00bb \u00ab\u00a0f\u00a0\u00bb \u00ab\u00a0g\u00a0\u00bb \u00ab\u00a0h\u00a0\u00bb \u00ab\u00a0i\u00a0\u00bb \u00ab\u00a0j\u00a0\u00bb # &#8230;le nouveau jeu de donn\u00e9es garde les anciens noms de niveaux de \u00ab\u00a0deslettres\u00a0\u00bb Pour s&rsquo;en d\u00e9barrasser, depuis R 2.12.0, la fonction droplevels() rend cette op\u00e9ration ais\u00e9e&#8230; # &#8230;sur tout le jeu de donn\u00e9es :\u00a0 droplevels(unextrait) summary(unextrait) # &#8230;sur une variable en particulier : droplevels(unextrait$deslettres) summary(unextrait) # &#8230;sur tout le jeu<a class=\"more-link\" href=\"https:\/\/thinkr.fr\/abcdr\/droplevels-ou-comment-se-debarrasser-efficacement-de-niveaux-de-facteurs-inutilises\/\">Read More &rarr;<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","rop_custom_images_group":[],"rop_custom_messages_group":[],"rop_publish_now":"initial","rop_publish_now_accounts":{"twitter_399453572_399453572":""},"rop_publish_now_history":[],"rop_publish_now_status":"pending","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[4],"tags":[],"class_list":{"0":"entry","1":"post","2":"publish","3":"author-diane","4":"post-3214","6":"format-standard","7":"category-base-indispensable"},"acf":[],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p9O7Sx-PQ","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3214","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/comments?post=3214"}],"version-history":[{"count":2,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3214\/revisions"}],"predecessor-version":[{"id":4304,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/posts\/3214\/revisions\/4304"}],"wp:attachment":[{"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/media?parent=3214"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/categories?post=3214"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thinkr.fr\/abcdr\/wp-json\/wp\/v2\/tags?post=3214"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}