{"id":25839,"date":"2024-01-23T17:32:13","date_gmt":"2024-01-23T09:32:13","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25839"},"modified":"2024-01-23T17:32:15","modified_gmt":"2024-01-23T09:32:15","slug":"r%e8%af%ad%e8%a8%80%e5%8c%85plyr%e7%9a%84%e4%bf%9d%e5%a7%86%e7%ba%a7%e8%ae%b2%e8%a7%a3%ef%bc%8c%e5%b8%ae%e5%8a%a9%e4%bd%a0%e5%bf%ab%e9%80%9f%e6%8b%86%e5%88%86%e5%ba%94%e7%94%a8%e6%b1%87%e6%80%bb","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25839","title":{"rendered":"R\u8bed\u8a00\u5305plyr\u7684\u4fdd\u59c6\u7ea7\u8bb2\u89e3\uff0c\u5e2e\u52a9\u4f60\u5feb\u901f\u62c6\u5206\u5e94\u7528\u6c47\u603b\u6570\u636e"},"content":{"rendered":"

R\u8bed\u8a00\u662f\u4e00\u79cd\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5efa\u6a21\u5de5\u5177\uff0c\u62e5\u6709\u4f17\u591a\u4f18\u79c0\u7684\u6570\u636e\u5904\u7406\u5305\u3002\u5728\u6570\u636e\u79d1\u5b66\u9886\u57df\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u6b65\u9aa4\u4e4b\u4e00\u3002plyr\u65e8\u5728\u63d0\u4f9b\u4e00\u7ec4\u7b80\u6d01\u800c\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u5e2e\u52a9\u7528\u6237\u8fdb\u884c\u6570\u636e\u7684\u5206\u5272\u3001\u8f6c\u6362\u3001\u6c47\u603b\u548c\u5e94\u7528\u3002plyr\u7684\u8bbe\u8ba1\u54f2\u5b66\u662f”\u5206\u5272-\u5e94\u7528-\u5408\u5e76”\uff08split-apply-combine\uff09\uff0c\u4e5f\u88ab\u79f0\u4e3a”\u62c6\u5206-\u5e94\u7528-\u6c47\u603b”\u3002<\/p>\n

\u9996\u5148\uff0cplyr\u7684”\u62c6\u5206”\u8fc7\u7a0b\u662f\u901a\u8fc7\u6307\u5b9a\u4e00\u4e2a\u6216\u591a\u4e2a\u53d8\u91cf\u6765\u5c06\u6570\u636e\u5206\u5272\u6210\u591a\u4e2a\u5b50\u96c6\u3002\u7528\u6237\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\uff0c\u5c06\u6570\u636e\u6309\u7167\u67d0\u4e2a\u53d8\u91cf\u7684\u503c\u8fdb\u884c\u5206\u7ec4\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u53d8\u91cf\u8fdb\u884c\u5206\u7ec4\u3002\u5176\u6b21\uff0c”\u5e94\u7528”\u9636\u6bb5\u662f\u5bf9\u6bcf\u4e2a\u5b50\u96c6\u5e94\u7528\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\u3002\u7528\u6237\u53ef\u4ee5\u7f16\u5199\u81ea\u5df1\u7684\u51fd\u6570\uff0c\u5bf9\u6bcf\u4e2a\u5b50\u96c6\u8fdb\u884c\u64cd\u4f5c\uff0c\u4f8b\u5982\u8ba1\u7b97\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u7b49\u7edf\u8ba1\u91cf\uff0c\u6216\u8005\u8fdb\u884c\u81ea\u5b9a\u4e49\u7684\u6570\u636e\u8f6c\u6362\u3002\u6700\u540e\uff0c”\u5408\u5e76”\u9636\u6bb5\u5c06\u6240\u6709\u5b50\u96c6\u7684\u5904\u7406\u7ed3\u679c\u5408\u5e76\u6210\u4e00\u4e2a\u6700\u7ec8\u7684\u8f93\u51fa\u3002plyr\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u5408\u5e76\u65b9\u5f0f\uff0c\u4f7f\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u8981\u5c06\u7ed3\u679c\u5408\u5e76\u4e3a\u6570\u636e\u6846\u3001\u5217\u8868\u6216\u5176\u4ed6\u5f62\u5f0f\u3002<\/p>\n

plyr\u5305\u7684\u5f3a\u5927\u4e4b\u5904\u5728\u4e8e\u5b83\u7684\u9ad8\u6027\u80fd\u548c\u53ef\u6269\u5c55\u6027\u3002\u901a\u8fc7\u5185\u90e8\u4f7f\u7528C++\u4ee3\u7801\u548c\u5e76\u884c\u8ba1\u7b97\u6280\u672f\uff0cplyr\u80fd\u591f\u9ad8\u6548\u5730\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u548c\u590d\u6742\u7684\u64cd\u4f5c\u4efb\u52a1\u3002\u5b83\u8fd8\u652f\u6301\u7528\u6237\u81ea\u5b9a\u4e49\u7684\u51fd\u6570\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u6839\u636e\u81ea\u5df1\u7684\u9700\u6c42\u8fdb\u884c\u7075\u6d3b\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n

\u8981\u4f7f\u7528plyr\u5305\uff0c\u53ef\u4ee5\u5728R\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u548c\u52a0\u8f7d\uff1a<\/p>\n

> install.packages(“plyr”) #\u5b89\u88c5plyr\u8bed\u8a00\u5305<\/p>\n

> library(plyr) #\u52a0\u8f7d\u8bed\u8a00\u5305<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

#\u52a0\u8f7dplyr\u8bed\u8a00\u5305<\/p>\n

> library(plyr)<\/p>\n

# \u4f7f\u7528mtcars\u6570\u636e\u96c6<\/p>\n

> data(mtcars)<\/p>\n

# \u6dfb\u52a0\u4e00\u4e2a\u968f\u673a\u5217\u4f5c\u4e3a\u6a21\u62df\u7684\u57fa\u56e0\u8868\u8fbe\u503c<\/p>\n

> set.seed(123)<\/p>\n

> mtcars$gene_expression <- rnorm(nrow(mtcars))<\/p>\n

# \u4f7f\u7528plyr\u5305\u6309\u7167\u8f66\u8f86\u7c7b\u578b\uff08am\u5b57\u6bb5\uff09\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u7c7b\u578b\u4e0b\u57fa\u56e0\u8868\u8fbe\u7684\u5e73\u5747\u503c<\/p>\n

> result <- ddply(mtcars, “am”, summarise, average_expression = mean(gene_expression))<\/p>\n

# \u6253\u5370\u7ed3\u679c<\/p>\n

> print(result)<\/p>\n

\"Screenshot<\/p>\n

\u5728\u4e0a\u8ff0\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b89\u88c5\u5e76\u52a0\u8f7d\u4e86plyr\u5305\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528data(mtcars)\u52a0\u8f7d\u5185\u7f6e\u7684mtcars\u6570\u636e\u96c6\uff0c\u8be5\u6570\u636e\u96c6\u5305\u542b\u6709\u5173\u4e0d\u540c\u6c7d\u8f66\u578b\u53f7\u7684\u4fe1\u606f\u3002\u6211\u4eec\u4e3a\u6a21\u62df\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u6dfb\u52a0\u4e86\u4e00\u4e2a\u540d\u4e3a”gene_expression”\u7684\u968f\u673a\u5217\u3002<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528ddply()\u51fd\u6570\u6309\u7167\u8f66\u8f86\u7c7b\u578b\uff08\u81ea\u52a8\u6321\u548c\u624b\u52a8\u6321\uff09\u5c06\u6570\u636e\u5206\u7ec4\u3002\u5728ddply()\u51fd\u6570\u4e2d\uff0c\u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u6570\u636e\u96c6\u672c\u8eab\uff08mtcars\uff09\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u7528\u4e8e\u5206\u7ec4\u7684\u53d8\u91cf\u540d\uff08”am”\uff09\uff0c\u7136\u540e\u6211\u4eec\u4f7f\u7528summarise\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u7ec4\u4e2d\u57fa\u56e0\u8868\u8fbe\u7684\u5e73\u5747\u503c\uff0c\u5e76\u5c06\u5176\u547d\u540d\u4e3a”average_expression”\u3002<\/p>\n

\u6700\u540e\uff0c\u6211\u4eec\u5c06\u7ed3\u679c\u6253\u5370\u51fa\u6765\uff0c\u5f97\u5230\u4e86\u6309\u7167\u8f66\u8f86\u7c7b\u578b\u5206\u7ec4\u540e\u6bcf\u4e2a\u7ec4\u4e2d\u57fa\u56e0\u8868\u8fbe\u7684\u5e73\u5747\u503c\u3002<\/p>\n

\u8fd9\u4e2a\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528plyr\u5305\u5bf9\u751f\u7269\u5b66\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u548c\u8ba1\u7b97\u3002\u901a\u8fc7plyr\u7684\u7075\u6d3b\u6027\u548c\u7b80\u6d01\u6027\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u548c\u5206\u6790\u66f4\u590d\u6742\u7684\u751f\u7269\u5b66\u6570\u636e\u96c6\uff0c\u4ece\u800c\u83b7\u5f97\u5bf9\u57fa\u56e0\u8868\u8fbe\u7b49\u5173\u952e\u751f\u7269\u5b66\u8fc7\u7a0b\u7684\u6df1\u5165\u7406\u89e3\u3002<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

#\u52a0\u8f7d\u6240\u9700\u8981\u7684\u8bed\u8a00\u5305<\/p>\n

> library(plyr)<\/p>\n

> library(ggplot2)<\/p>\n

# \u521b\u5efa\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n

> data <- data.frame(<\/p>\n

+ category = rep(c(“A”, “B”, “C”), each = 4),<\/p>\n

+ value = rnorm(12)<\/p>\n

+ )<\/p>\n

# \u4f7f\u7528plyr\u5305\u6309\u7167category\u53d8\u91cf\u5206\u7ec4\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u503c<\/p>\n

> grouped_data <- ddply(data, “category”, summarise, avg_value = mean(value))<\/p>\n

# \u4f7f\u7528ggplot2\u7ed8\u5236\u67f1\u72b6\u56fe<\/p>\n

> ggplot(grouped_data, aes(x = category, y = avg_value)) +<\/p>\n

+ geom_bar(stat = “identity”, fill = “steelblue”) +<\/p>\n

+ labs(x = “Category”, y = “Average Value”, title = “Average Value by Category”)<\/p>\n

\"34940f6e-fad6-499a-afa9-a70b2d42752f\"<\/p>\n

\u5728\u4e0a\u8ff0\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b89\u88c5\u5e76\u52a0\u8f7d\u4e86plyr\u548cggplot2\u5305\u3002\u7136\u540e\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6data\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u4e2a\u540d\u4e3a”category”\u7684\u5206\u7ec4\u53d8\u91cf\u548c\u4e00\u4e2a\u540d\u4e3a”value”\u7684\u6570\u503c\u53d8\u91cf\u3002<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u4f7f\u7528ddply()\u51fd\u6570\u6309\u7167”category”\u53d8\u91cf\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u503c\u3002\u7ed3\u679c\u5b58\u50a8\u5728grouped_data\u4e2d\u3002<\/p>\n

\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528ggplot2\u5305\u6765\u7ed8\u5236\u67f1\u72b6\u56fe\u3002\u5728ggplot()\u51fd\u6570\u4e2d\uff0c\u6211\u4eec\u6307\u5b9a\u4e86\u6570\u636e\u96c6grouped_data\u548c\u7ed8\u56fe\u6240\u9700\u7684\u6620\u5c04\u5173\u7cfb\u3002\u901a\u8fc7geom_bar()\u51fd\u6570\uff0c\u6211\u4eec\u5c06\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u503c\u4f5c\u4e3a\u67f1\u72b6\u56fe\u7684\u9ad8\u5ea6\uff0c\u5e76\u4f7f\u7528fill\u53c2\u6570\u6307\u5b9a\u67f1\u72b6\u56fe\u7684\u586b\u5145\u989c\u8272\u3002labs()\u51fd\u6570\u7528\u4e8e\u8bbe\u7f6e\u5750\u6807\u8f74\u6807\u7b7e\u548c\u56fe\u8868\u6807\u9898\u3002<\/p>\n

\u8fd0\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e\uff0c\u5c06\u751f\u6210\u4e00\u4e2a\u57fa\u4e8e\u6570\u636e\u5206\u7ec4\u7684\u67f1\u72b6\u56fe\uff0c\u5176\u4e2d\u6bcf\u4e2a\u7ec4\u7684\u5e73\u5747\u503c\u8868\u793a\u4e3a\u67f1\u72b6\u56fe\u7684\u9ad8\u5ea6\u3002\u8fd9\u4e2a\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u7ed3\u5408plyr\u5305\u548cggplot2\u5305\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u53ef\u89c6\u5316\uff0c\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u5448\u73b0\u6570\u636e\u3002<\/p>\n

\"comparison_lm_lmm<\/p>\n

\"D3xA5\"<\/p>\n

\"iris1\"<\/p>\n

\u4ee5\u4e0a\u5c31\u662f\u5bf9R\u8bed\u8a00\u5305plyr\u7684\u7b80\u5355\u4ecb\u7ecd\u5566\uff0cplyr\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684R\u8bed\u8a00\u5305\uff0c\u63d0\u4f9b\u4e86\u7b80\u6d01\u800c\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\u3002\u5b83\u7684”\u62c6\u5206-\u5e94\u7528-\u5408\u5e76”\u7b56\u7565\u4f7f\u6570\u636e\u5904\u7406\u53d8\u5f97\u66f4\u52a0\u76f4\u89c2\u548c\u53ef\u63a7\u3002\u901a\u8fc7plyr\uff0c\u7528\u6237\u53ef\u4ee5\u66f4\u8f7b\u677e\u5730\u8fdb\u884c\u6570\u636e\u8f6c\u6362\u3001\u5206\u6790\u548c\u5efa\u6a21\uff0c\u4ece\u800c\u66f4\u597d\u5730\u7406\u89e3\u548c\u5229\u7528\u6570\u636e\u3002\u65e0\u8bba\u662f\u5728\u5b66\u672f\u7814\u7a76\u3001\u6570\u636e\u5206\u6790\u8fd8\u662f\u4e1a\u52a1\u51b3\u7b56\u4e2d\uff0cplyr\u90fd\u662f\u4e00\u4e2a\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u5f3a\u5927\u80fd\u529b\u3002<\/p>\n

\u5c0f\u4f19\u4f34\u4eec\uff0c\u4eca\u5929\u6709\u6ca1\u6709\u5b66\u5230\u65b0\u77e5\u8bc6\u5462\uff0c\u60f3\u8981\u7ee7\u7eed\u4e86\u89e3R\u8bed\u8a00\u5185\u5bb9\u53ef\u4ee5\u6301\u7eed\u5173\u6ce8\u5c0f\u679c\u54e6\uff5e\uff5e\u6216\u8005\u4e5f\u53ef\u4ee5\u5173\u6ce8\u6211\u4eec\u7684\u5b98\u7f51\u4e5f\u4f1a\u6301\u7eed\u66f4\u65b0\u7684\u54e6\uff5e http:\/\/www.biocloudservice.com\/home.html<\/a><\/p>\n

References:<\/p>\n

    \n
  1. https:\/\/www.google.com.hk\/url?sa=i&url=https%3A%2F%2Fonunicornsandgenes.blog%2F2021%2F08%2F08%2Fusing-r-plyr-to-purrr%2F&psig=AOvVaw2yIfb1MxHi1Oh22qY5xMCU&ust=1689749833222000&source=images&cd=vfe&opi=89978449&ved=0CA4QjRxqFwoTCNjauubWl4ADFQAAAAAdAAAAABAI<\/a><\/li>\n
  2. https:\/\/www.google.com.hk\/url?sa=i&url=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F12603988%2Fwhat-is-the-ggplot2-plyr-way-to-calculate-statistical-tests-between-two-subgroup&psig=AOvVaw2yIfb1MxHi1Oh22qY5xMCU&ust=1689749833222000&source=images&cd=vfe&opi=89978449&ved=0CA4QjRxqFwoTCNjauubWl4ADFQAAAAAdAAAAABAQ<\/a><\/li>\n
  3. https:\/\/www.google.com.hk\/url?sa=i&url=https%3A%2F%2Fbridgewater.wordpress.com%2F2011%2F06%2F10%2Fsimple-plyrggplot-example-of-cummulative-distribution-plots%2F&psig=AOvVaw2yIfb1MxHi1Oh22qY5xMCU&ust=1689749833222000&source=images&cd=vfe&opi=89978449&ved=0CA4QjRxqFwoTCNjauubWl4ADFQAAAAAdAAAAABAY<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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