如何从主成分制作 PCA？

如何解决如何从主成分制作 PCA？

我正在使用一个程序，当我想要 2D 时，它会生成非常丑陋的 PCA 和 3D。

这就是为什么我更喜欢 ggplot2 生成的 PCA。 问题是程序只允许我导出坐标数据。

这是导出的示例：

你知道如何使用坐标在 ggplot2 中做 PCA 吗？我总是使用prcomp函数来获取主要组件然后绘图。

这是一个有代表性的例子：

data=data.frame("File Name"=c("P1","P1-PE","P2","P2-PE","P3","P3-PE","P4","P4-PE","P14","P14-PE","P18","P18-PE","P24","P24-PE","P28","P28-PE","P30","P30-PE","P31","P31-PE","P32","P32-PE","P34","P34-PE","P35-12W","P35","P36","P36-PE","P37","P37-PE","P39","P39-PE","P40-12W","P40","P41","P41-PE","P44","P44-PE","P45","P45-PE","P46","P46-PE","P48","P48-PE","P49","P49-PE","P50","P50-PE","P51","P51-PE","P53","P53-PE","P54-12W","P54","P56","P56-PE","P58","P58-PE","P59","P59-PE","P60","P60-PE","P61","P61-PE","P62","P62-PE","P64","P64-PE","P65","P65-PE","P66","P66-PE","P67-12W","P67","P69","P69-PE","P70","P70-PE","P71","P71-PE","P72","P72-PE","P73","P73-PE","P75","P75-PE","P77","P77-PE","P78","P78-PE","P80","P80-PE","P83-12W","P83","P84","P84-PE","P85","P85-PE","P86","P86-PE","P87","P87-PE","P88","P88-PE","P90","P90-PE","P92-12W","P92","P93","P93-PE","P95","P95-PE","P99","P99-PE","P102","P102-PE","P103","P103-PE","P106","P106-PE","P107","P107-PE","P109","P109-PE"),"Condition"=c("Basal","Progression","Basal","Progression"),PCA1=c(-10.9579,10.24589,-8.764295,5.585884,-8.982729,8.502527,-6.529874,5.524674,-7.894153,9.948769,-1.519172,-0.4629961,-5.006678,0.6448399,-5.04341,5.302773,-4.785826,5.277316,-8.099407,6.013574,-3.778463,7.03541,-1.923054,-1.717664,6.588612,-7.557001,-1.71925,3.773229,-4.167824,-0.2879927,-4.457466,3.998076,9.374546,-8.256836,-3.410376,-0.5678116,-7.508178,4.362628,-4.72132,2.776236,-2.224811,-0.9225485,-11.40413,10.78403,-3.749314,6.325803,-4.356707,1.078547,-5.781744,6.295918,-7.177786,7.816585,1.346956,2.12475,-11.24952,12.31908,-10.34716,13.30816,-6.990423,8.894703,-0.5486832,0.7178469,-5.48499,5.4066,-2.716906,1.271463,-3.778792,0.2715062,-7.080106,7.762402,-9.714196,9.986426,8.053446,-7.547626,-9.062915,10.30081,-9.581569,7.866413,-8.211257,7.962976,2.346143,0.02758015,0.1042574,1.367131,-6.998948,5.848056,-5.346948,1.662225,-8.752507,7.724463,-9.419344,9.36804,10.91515,-8.63832,-10.49049,12.98536,-7.956654,5.288613,-4.104696,5.480603,-5.197438,4.963965,-10.1821,11.36656,-9.614145,7.53374,-3.186569,1.286804,-2.835104,0.694433,-4.662269,8.17853,-2.700518,5.658089,-1.248439,3.514149,-4.258534,7.986657,-5.36409,9.535872,-2.65027,6.865205,-5.228459,9.335647),PCA2=c(4.644869,-0.9159487,7.163043,-5.944604,6.02665,-6.755553,0.3515455,-3.770467,-0.7126837,0.8632411,-0.1792105,0.6209372,0.04683616,-1.150393,4.546656,-7.346647,2.846132,-7.89389,5.566605,-8.430508,-6.421134,8.166632,0.4366642,1.196585,1.807035,-0.6658779,-3.345687,0.2684199,0.6142479,1.567705,-5.730934,10.93456,1.828797,-1.292148,-1.682971,4.84792,-1.613619,3.184234,-3.607687,-2.222474,-0.2981905,0.8006021,2.056089,0.9938269,-5.199895,0.8800701,1.869372,0.8634705,1.547989,-2.571634,-4.737968,8.177111,3.483726,-9.545896,-0.5694538,5.440719,1.554078,-4.512132,-4.451881,2.793466,-0.1228153,-4.190213,1.563418,-8.30045,0.2423486,-0.7233638,4.243596,-0.5168899,4.96524,-5.191572,3.227186,-0.3496252,7.088329,-2.097333,-3.546338,6.866013,2.137526,1.951131,1.231237,4.960348,0.3655342,-2.997336,-3.611928,-3.991728,-1.215066,-3.199702,-0.3067392,0.001128751,3.566546,1.047726,2.136769,3.459396,5.00845,-0.9095625,0.5970888,0.2759086,1.056065,-1.522866,-0.7580019,-3.944234,-3.201455,-0.7292663,-3.513115,-0.596421,-2.33597,6.346397,6.263683,-0.5857566,-3.155167,6.150011,1.467739,-6.402255,-6.736921,8.226869,-3.641266,4.970406,-1.55765,-2.7993,0.7692839,3.003215,-1.474759,-1.136726,-1.727654,-2.519902)
)

提前致谢。