{"id":3056,"date":"2023-07-19T10:33:55","date_gmt":"2023-07-19T10:33:55","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e5%88%97%e5%b9%b3%e5%9d%87%e5%80%bc\/"},"modified":"2023-07-19T10:33:55","modified_gmt":"2023-07-19T10:33:55","slug":"r-%e5%88%97%e5%b9%b3%e5%9d%87%e5%80%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e5%88%97%e5%b9%b3%e5%9d%87%e5%80%bc\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97 r \u4e2d\u5217\u7684\u5e73\u5747\u503c\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4efb\u610f\u65b9\u6cd5\u6765\u8ba1\u7b97 R \u4e2d\u5217\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n

 #calculate mean using column name<\/span>\nmean(df$my_column)\n\n#calculate mean using column name (ignore missing values)\n<\/span>mean(df$my_column, na. rm<\/span> = TRUE<\/span> )\n\n#calculate mean using column position<\/span>\nmean(df[, 1])\n\n#calculation mean of all numeric columns\n<\/span>colMeans(df[sapply(df, is. numeric<\/span> )])\n<\/b><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u5c06\u6bcf\u79cd\u65b9\u6cd5\u4e0e\u4ee5\u4e0b\u6570\u636e\u5e27\u4e00\u8d77\u4f7f\u7528\uff1a<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'A', 'B', 'B', 'B'),\n                 points=c(99, 90, 93, 86, 88, 82),\n                 assists=c(33, 28, 31, 39, NA, 30))\n\n#view data frame\n<\/span>df\n\n  team points assists\n1 A 99 33\n2 A 90 28\n3 A 93 31\n4 B 86 39\n5 B 88 NA\n6 B 82 30\n<\/strong><\/pre>\n
\u793a\u4f8b 1\uff1a\u4f7f\u7528\u5217\u540d\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u4f7f\u7528\u5217\u540d\u79f0\u8ba1\u7b97\u201c\u70b9\u201d\u5217\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
 #calculate mean of 'points' column\nmean(df$points)\n\n[1] 89.66667<\/span>\n<\/span><\/strong><\/pre>\n
 \u201c\u70b9\u6570\u201d\u680f\u4e2d\u7684\u5e73\u5747\u503c\u662f89.66667<\/strong> \u3002<\/span><\/p>\n
\u793a\u4f8b 2\uff1a\u4f7f\u7528\u5217\u540d\u79f0\u8ba1\u7b97\u5e73\u5747\u503c\uff08\u5ffd\u7565\u7f3a\u5931\u503c\uff09<\/strong><\/span><\/h3>\n
\u5982\u679c\u6211\u4eec\u5c1d\u8bd5\u8ba1\u7b97\u5305\u542b\u7f3a\u5931\u503c\u7684\u5217\u7684\u5e73\u5747\u503c\uff0c\u6211\u4eec\u5c06\u5f97\u5230NA<\/strong> \uff1a<\/span><\/p>\n
 #attempt to calculate mean of 'assists' column\nmean(df$assists)\n\n[1] NA<\/span>\n<\/span><\/strong><\/pre>\n
\u6211\u4eec\u5728\u8ba1\u7b97\u5217\u5e73\u5747\u503c\u65f6\u9700\u8981\u4f7f\u7528na.rm=TRUE<\/strong>\u6765\u5ffd\u7565\u7f3a\u5931\u503c\uff1a<\/span><\/span><\/p>\n
 #calculate mean of 'assists' column and ignore missing values\nmean(df$assists, na. rm<\/span> = TRUE<\/span> )\n\n[1] 32.2<\/span><\/span><\/strong><\/pre>\n
 \u201c\u52a9\u653b\u201d\u680f\u4e2d\u7684\u5e73\u5747\u503c\u4e3a32.2<\/strong> \u3002<\/span><\/p>\n
\u793a\u4f8b 3\uff1a\u4f7f\u7528\u5217\u4f4d\u7f6e\u8ba1\u7b97\u5e73\u5747\u503c<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u7d22\u5f15\u4f4d\u7f6e 2 \u5904\u7684\u5217\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
 #calculate mean of column in index position 2\nmean(df[, 2])\n\n[1] 89.66667<\/span>\n<\/span><\/strong><\/pre>\n
\u7d22\u5f15\u4f4d\u7f6e 2 \u5904\u7684\u5217\uff08\u201c\u70b9\u201d\u5217\uff09\u7684\u5e73\u5747\u503c\u4e3a89.66667<\/strong> \u3002<\/span><\/p>\n
\u793a\u4f8b 4\uff1a\u8ba1\u7b97\u6240\u6709\u6570\u5b57\u5217\u7684\u5e73\u5747\u503c<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u8ba1\u7b97\u6570\u636e\u6846\u4e2d\u6240\u6709\u6570\u5b57\u5217\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
 #calculate mean of all numeric columns\ncolMeans(df[sapply(df, is. numeric<\/span> )], na. rm<\/span> = TRUE<\/span> )\n\n  assist points \n89.66667 32.20000<\/span>\n<\/span><\/strong><\/pre>\n
\u8f93\u51fa\u663e\u793a\u6570\u636e\u6846\u4e2d\u6bcf\u4e2a\u6570\u5b57\u5217\u7684\u5e73\u5747\u503c\u3002<\/span><\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728R\u4e2d\u8ba1\u7b97\u5176\u4ed6\u5e73\u5747\u503c\uff1a<\/span><\/p>\n
\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u7684\u622a\u5c3e\u5e73\u5747\u503c<\/a>
\u5982\u4f55\u8ba1\u7b97R\u4e2d\u7684\u51e0\u4f55\u5e73\u5747\u503c<\/a>
\u5982\u4f55\u8ba1\u7b97R\u4e2d\u7684\u52a0\u6743\u5e73\u5747\u503c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4efb\u610f\u65b9\u6cd5\u6765\u8ba1\u7b97 R \u4e2d\u5217\u7684\u5e73\u5747\u503c\uff1a #calculate mean using column n […]<\/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-3056","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u67d0\u4e00\u5217\u7684\u5e73\u5747\u503c\uff08\u9644\u793a\u4f8b\uff09- Statory<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u5217\u7684\u5e73\u5747\u503c\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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