{"id":29995,"date":"2024-02-27T17:51:22","date_gmt":"2024-02-27T09:51:22","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29995"},"modified":"2024-02-27T17:51:23","modified_gmt":"2024-02-27T09:51:23","slug":"%e6%8e%8c%e6%8f%a1ggplot2%ef%bc%8c%e8%bd%bb%e6%9d%be%e7%bb%98%e5%88%b6%e5%9f%ba%e5%9b%a0%e5%8c%ba%e9%97%b4%e9%95%bf%e5%ba%a6%e5%9b%be%ef%bc%81-r%e8%af%ad%e8%a8%80%e5%ae%9e%e6%88%98","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=29995","title":{"rendered":"\u638c\u63e1ggplot2\uff0c\u8f7b\u677e\u7ed8\u5236\u57fa\u56e0\u533a\u95f4\u957f\u5ea6\u56fe\uff01\u2014\u2014R\u8bed\u8a00\u5b9e\u6218"},"content":{"rendered":"

\u638c\u63e1ggplot2\uff0c\u8f7b\u677e\u7ed8\u5236\u57fa\u56e0\u533a\u95f4\u957f\u5ea6\u56fe\uff01\u2014\u2014R\u8bed\u8a00\u5b9e\u6218<\/strong><\/p>\n

\u200b \u76f8\u4fe1\u5927\u5bb6\u90fd\u5bf9R\u8bed\u8a00\u4e2d\u7684ggplot2\u5305\u6709\u6240\u4e86\u89e3\uff0c\u90a3\u4e48\u4f60\u4eec\u77e5\u9053\u600e\u4e48\u901a\u8fc7ggplot2\u7ed8\u5236\u57fa\u56e0\u7ec4\u7684\u5404\u67d3\u8272\u4f53\u4e0a\u7684\u533a\u95f4\u7c7b\u578b\u5206\u5e03\u56fe\u561b\uff1f\u4eca\u5929\u5c0f\u679c\u5c31\u6765\u7ed9\u5927\u5bb6\u6f14\u793a\u4e00\u4e0b\uff01<\/p>\n

\u6570\u636e\u96c6\u51c6\u5907<\/strong><\/p>\n

\u200b \u5728\u7ed8\u5236\u4e4b\u524d\uff0c\u6211\u4eec\u9996\u5148\u8981\u51c6\u5907\u8981\u5bfc\u5165\u7684\u6570\u636e\u96c6\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u7684\u60c5\u51b5\u5c06\u5404\u67d3\u8272\u4f53\u4e0a\u8fdb\u884c\u201c\u5206\u533a\u201d\uff0c\u5e76\u6c47\u603b\u6210\u4e00\u4e2atable\u6587\u4ef6\uff0c\u6211\u4eec\u4e00\u8d77\u6765\u770b\u770b\u5c0f\u679c\u51c6\u5907\u7684\u6570\u636e\u6587\u4ef6\u5427\uff01<\/p>\n

\u5728\u8fd9\u4e2a\u6570\u636e\u96c6\u4e2d\uff0c\u4ece\u5de6\u5411\u53f3\u5206\u522b\u4ee3\u8868\u67d3\u8272\u4f53\u7f16\u53f7\u3001\u533a\u57df\u8d77\u70b9\u3001\u533a\u57df\u7ec8\u70b9\u3001\u67d3\u8272\u4f53\u540d\u79f0\u3001\u533a\u57df\u7c7b\u578b\u3001\u533a\u57df\u957f\u5ea6\u3001\u533a\u57df\u53f7\u3002\u600e\u4e48\u6837\uff0c\u4f60\u770b\u61c2\u4e86\u561b\uff1f<\/p>\n

\u5bfc\u5165\u6570\u636e\u96c6\u4ee5\u53ca\u6570\u636e\u5904\u7406<\/strong><\/p>\n

\u9996\u5148\uff0c\u8ba9\u6211\u4eec\u770b\u4e00\u4e0b\u8fd9\u6bb5\u4ee3\u7801\u7684\u4f5c\u7528\u3002\u8fd9\u6bb5\u4ee3\u7801\u53ef\u4ee5\u8bfb\u53d6\u4e00\u4e2a\u540d\u4e3a”newGeneArea-15.txt”\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5e76\u7ed8\u5236\u51fa\u67f1\u72b6\u56fe\uff0c\u5176\u4e2d\u6bcf\u4e2a\u67f1\u5b50\u4ee3\u8868\u4e00\u6bb5\u57fa\u56e0\u533a\u57df\uff0c\u6a2a\u5750\u6807\u8868\u793a\u57fa\u56e0\u6240\u5728\u7684\u67d3\u8272\u4f53\u7f16\u53f7\uff0c\u7eb5\u5750\u6807\u8868\u793a\u57fa\u56e0\u533a\u57df\u7684\u957f\u5ea6\uff0c\u4e0d\u540c\u989c\u8272\u7684\u67f1\u5b50\u4ee3\u8868\u4e0d\u540c\u7684\u57fa\u56e0\u5206\u6bb5\u7c7b\u522b\uff0c\u4f8b\u5982\u91cd\u8981\u6027\u9ad8\u3001\u4e2d\u3001\u4f4e\u7b49\u3002<\/p>\n

\u4e0b\u9762\uff0c\u6211\u5c06\u4e00\u6b65\u4e00\u6b65\u5730\u4e3a\u5927\u5bb6\u89e3\u91ca\u8fd9\u6bb5\u4ee3\u7801\u7684\u5b9e\u73b0\u8fc7\u7a0b\u3002<\/p>\n

    \n
  1. \u5bfc\u5165ggplot2\u5305<\/li>\n<\/ol>\n

    library(ggplot2)<\/p>\n

    ggplot2\u662fR\u8bed\u8a00\u4e2d\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5305\uff0c\u5b83\u63d0\u4f9b\u4e86\u5f88\u591a\u79cd\u56fe\u5f62\u7c7b\u578b\u548c\u53c2\u6570\u8bbe\u7f6e\uff0c\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u53ef\u89c6\u5316\u56fe\u5f62\u54e6\u3002<\/p>\n

      \n
    1. \u8bfb\u53d6\u6570\u636e\u6587\u4ef6<\/li>\n<\/ol>\n

      a<-read.table(file = “newGeneArea-15.txt”, header=F,quote=””,sep=”\\t”,stringsAsFactors=F)<\/p>\n

        \n
      1. \u63d0\u53d6\u67d3\u8272\u4f53\u53f7\u5e76\u52a0\u4e0a”Chr”\u524d\u7f00<\/li>\n<\/ol>\n

        g<-sprintf(“%s”,unique(a[,4]))<\/p>\n

        \u200b \u8fd9\u884c\u4ee3\u7801\u4f7f\u7528unique\u51fd\u6570\u9009\u51fa\u6570\u636e\u4e2d\u552f\u4e00\u7684\u67d3\u8272\u4f53\u53f7\uff0c\u7136\u540e\u4f7f\u7528sprintf\u51fd\u6570\u5c06\u5b83\u4eec\u8f6c\u6362\u4e3a\u5b57\u7b26\u4e32\uff0c\u5e76\u5728\u6bcf\u4e2a\u5b57\u7b26\u4e32\u524d\u52a0\u4e0a”Chr”\u524d\u7f00\u3002\u8fd9\u6837\u505a\u662f\u4e3a\u4e86\u540e\u7eed\u7528\u8fd9\u4e9b\u5b57\u7b26\u4e32\u6765\u6807\u8bb0x\u8f74\u3002<\/p>\n

          \n
        1. \u8ba1\u7b97\u57fa\u56e0\u533a\u57df\u957f\u5ea6<\/li>\n<\/ol>\n

          a[,6]<-a[,3]-a[,2]<\/p>\n

          \u8fd9\u884c\u4ee3\u7801\u5c06\u6570\u636e\u8868a\u4e2d\u7684\u7b2c6\u5217\u8bbe\u7f6e\u4e3a\u7b2c3\u5217\u51cf\u53bb\u7b2c2\u5217\u7684\u5dee\uff0c\u8fdb\u800c\u5f97\u5230\u6bcf\u4e00\u4e2a\u57fa\u56e0\u533a\u57df\u7684\u957f\u5ea6\u3002<\/p>\n

            \n
          1. \u6839\u636e\u67d3\u8272\u4f53\u53f7\u8bbe\u7f6e\u5bf9\u5e94\u7684\u6a2a\u5750\u6807\u4e0a\u7684\u4f4d\u7f6e\u5217\uff1a<\/li>\n<\/ol>\n

            cssCopy code
            \nfor(i in 1:length(a[,1])){
            \nif(a[i,4] == “chr1”){
            \na[i,7]=1
            \n}else if(a[i,4] == “chr2”){
            \na[i,7]=2
            \n}else if(a[i,4] == “chr3”){
            \na[i,7]=3
            \n}else if(a[i,4] == “chr4”){
            \na[i,7]=4
            \n}else if(a[i,4] == “chr5”){
            \na[i,7]=5
            \n}
            \n}<\/p>\n

            \u7ed8\u5236\u56fe\u5f62<\/strong><\/p>\n

            \u200b \u73b0\u5728\uff0c\u6211\u4eec\u5df2\u7ecf\u5b8c\u6210\u4e86\u6570\u636e\u7684\u9884\u5904\u7406\u548c\u6574\u7406\uff0c\u5e76\u4e14\u5df2\u7ecf\u5b9a\u4e49\u4e86\u6211\u4eec\u8981\u4f7f\u7528\u7684\u56fe\u5f62\u5143\u7d20\u548c\u914d\u8272\u65b9\u6848\u3002\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u7528 ggplot2 \u5305\u4e2d\u7684 ggplot \u51fd\u6570\u521b\u5efa\u4e00\u4e2a\u7ed8\u56fe\u5bf9\u8c61\uff0c\u7528\u4e8e\u7ed8\u5236\u6211\u4eec\u7684\u56fe\u5f62\u3002\u6211\u4eec\u5c06\u5728\u6b64\u51fd\u6570\u4e2d\u6307\u5b9a\u6570\u636e\u6846\u8868a \u4f5c\u4e3a\u6570\u636e\u6e90\uff0c\u540c\u65f6\u6307\u5b9a\u5728 x \u548c y \u8f74\u4e0a\u7ed8\u5236\u7684\u53d8\u91cf\u3002<\/p>\n

            p <- ggplot(a, aes(x = a[,7], y = a[,6], fill = a[,5], group = a[,4])) + \u00a0#\u6307\u5b9a\u53d8\u91cf a[,7] \u4f5c\u4e3a x \u8f74\u53d8\u91cf\uff0ca[,6] \u4f5c\u4e3a y \u8f74\u53d8\u91cf
            \ngeom_bar(stat = “identity”) +
            \ntheme_bw() + \u00a0#\u4e3a\u7ed8\u56fe\u5bf9\u8c61\u6dfb\u52a0\u4e00\u4e2a\u57fa\u672c\u7684\u767d\u8272\u80cc\u666f
            \ntheme(panel.grid.major = element_blank(), \u00a0#\u4f7f\u7528 element_blank \u51fd\u6570\u6765\u9690\u85cf x \u8f74\u548c y \u8f74\u4e0a\u7684\u4e3b\u8981\u7f51\u683c\u7ebf
            \nlegend.position = c(0.9, 0.9),
            \nlegend.title = element_blank()) +
            \nscale_fill_manual(values = colors) + \u00a0#\u51fd\u6570\u8bbe\u7f6e\u6761\u5f62\u586b\u5145\u989c\u8272
            \nscale_x_continuous(breaks = c(1:5), labels = g) +labs(x = “”, y = “”) + #\u5b9a\u4e49 x \u8f74\u7684\u523b\u5ea6\u503c
            \nscale_y_continuous(labels = c(0, “10MB”, “20MB”, “30MB”)) #\u5b9a\u4e49 y \u8f74\u7684\u523b\u5ea6\u503c<\/p>\n

            \u5728\u4e0a\u8ff0\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528 geom_bar \u51fd\u6570\u7ed8\u5236\u6761\u5f62\u56fe\u3002\u6211\u4eec\u4f7f\u7528 “identity” \u53c2\u6570\uff0c\u4ee5\u4f7f ggplot2 \u5305\u77e5\u9053\u6211\u4eec\u5df2\u7ecf\u4f7f\u7528\u4e86\u9884\u5904\u7406\u7684\u6761\u5f62\u9ad8\u5ea6\u3002\u6211\u4eec\u8fd8\u4f7f\u7528 theme \u51fd\u6570\u6765\u8fdb\u4e00\u6b65\u5b9a\u5236\u7ed8\u56fe\u5bf9\u8c61\u7684\u5916\u89c2\u3002<\/p>\n

            \"\u56fe\u8868, \u597d\u5566\uff0c\u6211\u4eec\u4eca\u5929\u7684\u7ed8\u56fe\u5de5\u4f5c\u5df2\u7ecf\u57fa\u672c\u5b8c\u6210\uff0c\u6211\u4eec\u4e00\u8d77\u6765\u548c\u5c0f\u679c\u770b\u4e00\u4e0b\u7ed8\u5236\u597d\u7684\u56fe\u957f\u4ec0\u4e48\u6837\u5b50\u5427\uff01<\/p>\n

            \u56fe\u7247\u63cf\u8ff0<\/strong><\/p>\n

            \u5728\u8fd9\u4e2a\u56fe\u4e2d\uff0c\u4e3b\u8981\u5411\u5927\u5bb6\u5c55\u793a\u4e86\u62df\u5357\u82a5\u5b8c\u6574\u57fa\u56e0\u7ec4\u76845\u6761\u67d3\u8272\u4f53\u4e0a\u5bf9\u5e94\u7684\u201c\u65b0\u533a\u57df\u201d\u548c\u8001\u533a\u57df\u7684\u4f4d\u7f6e\u5206\u5e03\u56fe\uff0c\u5176\u4e2d\u84dd\u8272\u7684\u90e8\u5206\u4ee3\u8868\u201c\u8001\u533a\u57df\u201d\uff0c\u7ea2\u8272\u7684\u90e8\u5206\u4ee3\u8868\u201c\u65b0\u533a\u57df\u201d\u3002\u540c\u5b66\u4eec\u4e5f\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u60f3\u8981\u5c55\u793a\u7684\u5206\u533a\u6765\u8fdb\u884c\u8bbe\u7f6e\u6570\u636e\u96c6\u54e6\uff01<\/p>\n

            \u4fdd\u5b58\u56fe\u50cf<\/strong><\/p>\n

            \u73b0\u5728\uff0c\u6211\u4eec\u5df2\u7ecf\u5b8c\u6210\u4e86\u56fe\u5f62\u7684\u7ed8\u5236\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684\u4efb\u4f55\u7ed8\u56fe\u8bbe\u5907\u5c06\u56fe\u5f62\u4fdd\u5b58\u5230\u6587\u4ef6\u4e2d\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 png \u51fd\u6570\u5c06\u56fe\u5f62\u4fdd\u5b58\u4e3a PNG \u6587\u4ef6\u3002<\/p>\n

            # \u8f93\u51fa\u56fe\u7247
            \npng(filename = “newGeneAreas111.jpg”, type = “cairo”, height = 800, width = 1100)
            \nprint(p)
            \ndev.off()<\/p>\n

            \u5b8c\u6574\u4ee3\u7801\uff1a<\/strong><\/p>\n

            library(ggplot2)
            \n# \u8bfb\u53d6\u6570\u636e
            \na <- read.table(file = “newGeneArea-15.txt”, header = F, quote = “”, sep = “\\t”, stringsAsFactors = F)
            \n# \u63d0\u53d6\u67d3\u8272\u4f53\u53f7
            \ng <- sprintf(“Chr%s”, unique(a[, 4]))
            \n# \u63d0\u53d6\u6bcf\u6bb5\u957f\u5ea6
            \na[, 6] <- a[, 3] – a[, 2]
            \n# \u6807\u8bb0\u67d3\u8272\u4f53\u53f7
            \nfor (i in 1:length(a[, 1])) {
            \nif (a[i, 4] == “chr1”) {
            \na[i, 7] = 1
            \n} else if (a[i, 4] == “chr2”) {
            \na[i, 7] = 2
            \n} else if (a[i, 4] == “chr3”) {
            \na[i, 7] = 3
            \n} else if (a[i, 4] == “chr4”) {
            \na[i, 7] = 4
            \n} else if (a[i, 4] == “chr5”) {
            \na[i, 7] = 5
            \n}
            \n}
            \n# \u8bbe\u7f6e\u989c\u8272
            \ncolors <- c(“lightsteelblue2”, “orangered3”)
            \n# \u7ed8\u56fe
            \np <- ggplot(a, aes(x = a[, 7], y = a[, 6], fill = a[, 5], group = a[, 4])) +
            \ngeom_bar(stat = “identity”) +
            \ntheme_bw() +
            \ntheme(panel.grid.major = element_blank(),
            \nlegend.position = c(0.9, 0.9),
            \nlegend.title = element_blank()) +
            \nscale_fill_manual(values = colors) +
            \nscale_x_continuous(breaks = c(1:5), labels = g) +
            \nlabs(x = “”, y = “”) +
            \nscale_y_continuous(labels = c(0, “10MB”, “20MB”, “30MB”))
            \n# \u8f93\u51fa\u56fe\u7247
            \npng(filename = “newGeneAreas111.jpg”, type = “cairo”, height = 800, width = 1100)
            \nprint(p)
            \ndev.off()
            \nsvg(filename = “newGeneAreas111.svg”, height = 7, width = 14)
            \nprint(p)
            \ndev.off()<\/p>\n

            \u600e\u4e48\u6837\uff0c\u4eca\u5929\u7684\u5206\u533a\u56fe\u8868\u7ed8\u5236\u4f60\u5b66\u4f1a\u600e\u4e48\u7528\u4e86\u5417\uff01<\/p>\n

            \u66f4\u591aggplot\u7ed8\u56fe\u5e94\u7528\u5b66\u4e60\u8d44\u6e90\u8bf7\u5927\u5bb6\u79fb\u6b65\u5c0f\u679c\u4e13\u5c5e\u4e91\u751f\u4fe1\u5e73\u53f0\u641c\u7d22\u66f4\u591a\u8d44\u6e90\u54e6\uff01<\/p>\n

            \u5c0f\u679c\u4e13\u5c5e\u4e91\u751f\u4fe1\u5e73\u53f0\uff1a<\/strong>\u4e91\u751f\u4fe1 \u00a0– \u5b66\u751f\u7269\u4fe1\u606f\u5b66 (biocloudservice.com)<\/a><\/p>\n

            ggplot\u7ed8\u5236\u4e92\u4f5c\u7f51\u7edc\u56fe\uff1a<\/strong>http:\/\/www.biocloudservice.com\/703\/703.php<\/p>\n

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