{"id":944,"date":"2023-07-28T05:49:15","date_gmt":"2023-07-28T05:49:15","guid":{"rendered":"https:\/\/statorials.org\/nl\/filter-rijen-r\/"},"modified":"2023-07-28T05:49:15","modified_gmt":"2023-07-28T05:49:15","slug":"filter-rijen-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/nl\/filter-rijen-r\/","title":{"rendered":"Rijen filteren in r"},"content":{"rendered":"

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Vaak ben je misschien ge\u00efnteresseerd in een subset van een dataframe op basis van bepaalde voorwaarden in R. Gelukkig is dit eenvoudig te doen met behulp van de functie filter()<\/a> uit het dplyr-<\/a> pakket.<\/span><\/p>\n

 library(dplyr)<\/strong><\/pre>\n
 In deze tutorial worden verschillende voorbeelden uitgelegd van hoe je deze functie praktisch kunt gebruiken met behulp van de ingebouwde dplyr-dataset genaamd starwars<\/strong> :<\/span><\/p>\n
 #view first six rows of starwars dataset<\/span>\nhead(starwars)\n\n# A tibble: 6 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 Luke~ 172 77 blond fair blue 19 male Tatooine \n2 C-3PO 167 75 <NA> gold yellow 112 <NA> Tatooine \n3 R2-D2 96 32 <NA> white, bl~ red 33 <NA> Naboo    \n4 Dart~ 202 136 none white yellow 41.9 male Tatooine \n5 Leia~ 150 49 brown light brown 19 female Alderaan \n6 Owen~ 178 120 brown, gr~ light blue 52 male Tatooine \n# ... with 4 more variables: species , films , vehicles ,\n# starships \n<\/strong><\/pre>\n
 Voorbeeld 1: Filter rijen die gelijk zijn aan een bepaalde waarde<\/span><\/strong><\/h3>\n
 De volgende code laat zien hoe u de gegevensset filtert op rijen waarbij de variabele ’species‘ gelijk is aan Droid.<\/span><\/p>\n
 starwars %>% filter(species == ' Droid<\/span> ')\n\n# A tibble: 5 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 C-3PO 167 75 gold yellow 112 Tatooine \n2 R2-D2 96 32 white, bl~ red 33 Naboo    \n3 R5-D4 97 32 white, red red NA Tatooine \n4 IG-88 200 140 none metal red 15 none        \n5 BB8 NA NA none none black NA none        \n# ... with 4 more variables: species , films , vehicles ,\n# starships \n<\/strong><\/pre>\n
 We kunnen zien dat 5 rijen van de dataset aan deze voorwaarde voldoen, zoals aangegeven door #A tibble: 5 x 13<\/em> .<\/span><\/p>\n
 Voorbeeld 2: rijen filteren met ‚En‘<\/span><\/strong><\/h3>\n
 We kunnen ook rijen filteren waarbij de soort Droid is en<\/strong> de oogkleur rood is:<\/span><\/p>\n
 starwars %>% filter(species == ' Droid<\/span> ' &<\/span> eye_color == ' red<\/span> ')\n\n# A tibble: 3 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 R2-D2 96 32 <NA> white, bl~ red 33 <NA> Naboo    \n2 R5-D4 97 32 <NA> white, red red NA <NA> Tatooine \n3 IG-88 200 140 none metal red 15 none <NA>      \n# ... with 4 more variables: species , films , vehicles ,\n# starships \n<\/strong><\/pre>\n
 We kunnen zien dat 3 rijen in de dataset aan deze voorwaarde voldoen.<\/span><\/p>\n
 Voorbeeld 3: Rijen filteren met ‚Of‘<\/span><\/strong><\/h3>\n
 We kunnen ook rijen filteren waarbij de soort Droid is of<\/strong> de oogkleur rood is:<\/span><\/p>\n
 starwars %>% filter(species == ' Droid<\/span> ' |<\/span> eye_color == ' red<\/span> ')\n\n# A tibble: 7 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 C-3PO 167 75 <NA> gold yellow 112 <NA> Tatooine \n2 R2-D2 96 32 <NA> white, bl~ red 33 <NA> Naboo    \n3 R5-D4 97 32 <NA> white, red red NA <NA> Tatooine \n4 IG-88 200 140 none metal red 15 none <NA>     \n5 Bossk 190 113 none green red 53 male Trandosha\n6 Nute~ 191 90 none mottled g~ red NA male Cato Nei~\n7 BB8 NA NA none none black NA none <NA>     \n# ... with 4 more variables: species , films , vehicles ,\n# starships  \n<\/strong><\/pre>\n
 We kunnen zien dat 7 rijen in de dataset aan deze voorwaarde voldoen.<\/span><\/p>\n
 Voorbeeld 4: Filter rijen met waarden in een lijst<\/span><\/strong><\/h3>\n
 We kunnen ook rijen filteren waarin oogkleur voorkomt in een lijst met kleuren:<\/span><\/p>\n
 starwars %>% filter(eye_color %in%<\/span> c(' blue<\/span> ', ' yellow<\/span> ', ' red<\/span> '))\n\n# A tibble: 35 x 13\n   name height mass hair_color skin_color eye_color birth_year gender\n                               \n 1 Luke~ 172 77 blond fair blue 19 male  \n 2 C-3PO 167 75 <NA> gold yellow 112 <NA> \n 3 R2-D2 96 32 <NA> white, bl~ red 33 <NA>  \n 4 Dart~ 202 136 none white yellow 41.9 male  \n 5 Owen~ 178 120 brown, gr~ light blue 52 male  \n 6 Beru~ 165 75 brown light blue 47 female\n 7 R5-D4 97 32 <NA> white, red red NA <NA> \n 8 Anak~ 188 84 blond fair blue 41.9 male  \n 9 Wilh~ 180 NA auburn, g~ fair blue 64 male  \n10 Chew~ 228 112 brown unknown blue 200 male  \n# ... with 25 more rows, and 5 more variables: homeworld, species,\n# films, vehicles, starships  \n<\/strong><\/pre>\n
 We kunnen zien dat 35 rijen in de dataset blauwe, gele of rode oogkleuren hadden.<\/span><\/p>\n
 Gerelateerd:<\/span><\/strong> Hoe de %in% operator in R te gebruiken (met voorbeelden)<\/a><\/p>\n
 Voorbeeld 5: Filter rijen met kleiner dan of groter dan<\/span><\/strong><\/h3>\n
 We kunnen rijen ook filteren met behulp van kleiner dan of groter dan-bewerkingen op numerieke variabelen:<\/span><\/p>\n
 #find rows where height is greater than 250<\/span>\nstarwars %>% filter(height ><\/span> 250)\n\n# A tibble: 1 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 Yara~ 264 NA none white yellow NA male Quermia  \n# ... with 4 more variables: species , films , vehicles ,\n# starships   \n\n#find rows where height is between 200 and 220<\/span>\nstarwars %>% filter(height ><\/span> 200 &<\/span> height <<\/span> 220)\n\n# A tibble: 5 x 13\n  name height mass hair_color skin_color eye_color birth_year gender homeworld\n                                   \n1 Dart~ 202 136 none white yellow 41.9 male Tatooine \n2 Rugo~ 206 NA none green orange NA male Naboo    \n3 Taun~ 213 NA none gray black NA female Kamino   \n4 Grie~ 216 159 none brown, wh~ green, y~ NA male Kalee    \n5 Tion~ 206 80 none gray black NA male Utapau   \n# ... with 4 more variables: species , films , vehicles ,\n# starships \n\n#find rows where height is above the average height\nstarwars %>% filter(height ><\/span> mean<\/span> (height, na.rm = TRUE<\/span> ))\n\n# A tibble: 51 x 13\n   name height mass hair_color skin_color eye_color birth_year gender\n                               \n 1 Dart~ 202 136 none white yellow 41.9 male  \n 2 Owen~ 178 120 brown, gr~ light blue 52 male  \n 3 Bigg~ 183 84 black light brown 24 male  \n 4 Obi-~ 182 77 auburn, w~ fair blue-gray 57 male  \n 5 Anak~ 188 84 blond fair blue 41.9 male  \n 6 Wilh~ 180 NA auburn, g~ fair blue 64 male  \n 7 Chew~ 228 112 brown unknown blue 200 male  \n 8 Han ~ 180 80 brown fair brown 29 male  \n 9 Jabb~ 175 1358 <NA> green-tan~ orange 600 herma~\n10 Jek ~ 180 110 brown fair blue NA male  \n# ... with 41 more rows, and 5 more variables: homeworld, species,\n# films, vehicles, starships<\/span><\/span>\n<\/strong><\/pre>\n
Vaak ben je misschien ge\u00efnteresseerd in een subset van een dataframe op basis van bepaalde voorwaarden in R. Gelukkig is dit eenvoudig te doen met behulp van de functie filter() uit het dplyr- pakket. library(dplyr) In deze tutorial worden verschillende voorbeelden uitgelegd van hoe je deze functie praktisch kunt gebruiken met behulp van de ingebouwde […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-944","post","type-post","status-publish","format-standard","hentry","category-gids"],"yoast_head":"\nHoe rijen te filteren in R \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"Een eenvoudige uitleg over het filteren van gegevens in R met behulp van de functie filter() uit het dplyr-pakket.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/nl\/filter-rijen-r\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hoe rijen te filteren in R \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"Een eenvoudige uitleg over het filteren van gegevens in R met behulp van de functie filter() uit het dplyr-pakket.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/nl\/filter-rijen-r\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T05:49:15+00:00\" \/>\n<meta name=\"author\" content=\"Dr.benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr.benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"5\u00a0Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/nl\/filter-rijen-r\/\",\"url\":\"https:\/\/statorials.org\/nl\/filter-rijen-r\/\",\"name\":\"Hoe rijen te filteren in R \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/nl\/#website\"},\"datePublished\":\"2023-07-28T05:49:15+00:00\",\"dateModified\":\"2023-07-28T05:49:15+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/nl\/#\/schema\/person\/d4b8842173cca1bb62cdec41860e4219\"},\"description\":\"Een eenvoudige uitleg over het filteren van gegevens in R met behulp van de functie filter() uit het dplyr-pakket.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/nl\/filter-rijen-r\/#breadcrumb\"},\"inLanguage\":\"de\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/nl\/filter-rijen-r\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/nl\/filter-rijen-r\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Thuis\",\"item\":\"https:\/\/statorials.org\/nl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Rijen filteren in r\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/nl\/#website\",\"url\":\"https:\/\/statorials.org\/nl\/\",\"name\":\"Statorials\",\"description\":\"Uw gids voor statistische competentie\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/nl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"de\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/nl\/#\/schema\/person\/d4b8842173cca1bb62cdec41860e4219\",\"name\":\"Dr.benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"de\",\"@id\":\"https:\/\/statorials.org\/nl\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/nl\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/nl\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr.benjamin anderson\"},\"description\":\"Ik ben Benjamin, een gepensioneerde hoogleraar statistiek die nu een toegewijde Statorials-lesgever is. 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 De volledige documentatie voor de functie filter()<\/strong> vindt u hier<\/a> .<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"