Error reproducing pca plot using scatter3D

Question

I have performed principal component analysis on mtcars dataset and plotted it using scatter3D using the code given below:

require(rgl)
require(SciViews)
require(plotrix)
require(ggplot2)
require(reshape)
require("gridExtra")
library(plot3D)
cars.pca <- pcomp(~mpg+cyl+disp+hp+drat+wt+qsec, data = mtcars)#,  subset = -(8:14))
mtcars_pca = cbind(cbind(mtcars, cars.pca$scores), car = rownames(mtcars))
k <- kmeans(mtcars, 5, nstart=25, iter.max=1000)
new = cbind(mtcars_pca,cluster = k$cluster)
with(new,scatter3D(PC1,PC2,PC3, col=k$cluster, size=10, type='s', bty = "g", pch = 20, cex = 1.5,xlab="PC1",ylab="PC2",zlab="PC3",lwd = 4,pch = 20,colkey = FALSE))
with(new,text3D(PC1, PC2, PC3,  labels = car,add = TRUE, colkey = FALSE, cex = 0.5,adj=-0.15))

As a result following plot is obtained:

But when I tried to reproduce the same plot for the dataset given below:

"NB1" "NB2" "NB3" "NF1" "NF2" "NF3" "NG1" "NG2" "NG3" "NH1" "NH2" "NH" "NL1" "NL2" "NL3" "NM1" "NM2" "NM3" "NN1" "NN2" "NN3" "NP1" "NP2" "NP3"
"Ca" 1.153 1.29013 1.221565 1.091 1.167239 1.1291195 1.156 1.28064 1.21832 1.108 1.19312 1.15056 1.134 1.013 1.0735 1.308 1.002 1.155 0.898 0.96566 0.93183 0.963 0.773 0.868
"Po" 0.842 0.91382 0.87791 0.602 0.615158 0.608579 0.544 0.55236 0.54818 0.606 0.62084 0.61342 0.689 0.616 0.6525 0.878 0.748 0.813 0.72 0.7574 0.7387 0.701 0.782 0.7415
"Gs" 1.023 1.13283 1.077915 0.853 0.898537 0.8757685 0.96 1.0474 1.0037 0.94 1.0016 0.9708 0.886 0.854 0.87 1.138 0.959 1.0485 1.008 1.09436 1.05118 0.946 0.834 0.89
"Ap" 0.549 0.55929 0.554145 0.525 0.528225 0.5266125 0.655 0.68445 0.669725 0.59 0.6026 0.5963 0.535 0.519 0.527 0.65 0.609 0.6295 0.542 0.54914 0.54557 0.664 0.554 0.609
"So" 1.74518207282913 2.00667030812325 1.87592619047619 2.15723163841808 2.37101451977401 2.26412307909605 2.32591160220995 2.67283480662983 2.49937320441989 2.73303797468354 3.04566329113924 2.88935063291139 1.14326086956522 1.84277044854881 1.49301565905701 2.0930303030303 2.49391061452514 2.29347045877772 4.03489795918367 4.6358306122449 4.33536428571429 2.64142857142857 2.20642228739003 2.4239254294093
"MDC" 0.5531148 0.5524648636 0.5527898318 0.5413232 0.541223504 0.541273352 0.562528 0.5613016 0.5619148 0.5451037 0.545458885 0.5452812925 0.5433668 0.5432226 0.5432947 0.5453266 0.5419368 0.5436317 0.5590145 0.55859421 0.558804355 0.5416098 0.5419624 0.5417861     
"Elt" 72.4806763285024 72.4806763285024 72.4806763285024 74.5671641791045 74.5671641791045 74.5671641791045 51.4309309309309 51.4309309309309 51.4309309309309 83.1568265682657 83.1568265682657 83.1568265682657 73.8212341197822 90.0522388059701 77.368529286626 73.1224783861671 105.763157894737 86.6952861952862 81.1878306878307 81.1878306878307 81.1878306878307 83.2403482210447 86.8753213367609 84.5876607908528
"Hmfwt" 618.357142857143 631.321428571428 624.839285714286 654.071428571429 644.214285714286 649.142857142857 807.642857142857 799.785714285714 803.714285714286 1050.5 1078.78571428571 1064.64285714286 1000.5 793.357142857143 896.928571428571 743.357142857143 746.928571428571 745.142857142857 614.785714285714 597.785714285714 606.285714285714 821.928571428571 721.928571428571 771.928571428571
"Can" 3.10751 3.5507867 2.9510594 7.12473 8.2509341 6.7272462 1.40903 1.5635651 1.3272173 2.80357 3.1951769 2.99937345 4.17967 6.2562 5.217935 5.8482 6.25014 6.04917 3.03144 3.3605272 3.1959836 2.96378 7.63304 5.29841
"Cab" 1.5809 1.764653 1.516046 2.6063 2.964371 2.479922 1.0004 1.085468 0.955364 1.4213 1.577921 1.4996105 2.0747 2.6 2.33735 2.2589 2.3777 2.3183 2.798 3.09674 2.94737 1.886 2.6195 2.25275
"Tc" 4.18841 4.8154397 3.9671054 9.23103 10.7153051 8.7071682 1.90943 2.1490331 1.7825813 3.72487 4.2730979 3.99898395 5.75437 8.3562 7.055285 7.6071 8.12784 7.86747 5.32944 5.9572672 5.6433536 4.34978 9.75254 7.05116
"Crt" 2.40583946363636 2.72983217245455 2.29148909581818 3.79729982525253 4.35784079554545 3.59946183573737 1.36768463333333 1.515191021 1.28959301633333 1.9468614979798 2.19282795263636 2.06984472530808 2.69910104343434 3.56556422222222 3.13233263282828 3.49860908080808 3.51460955151515 3.50660931616162 3.00008494545455 3.32509598836364 3.16259046690909 2.36201111313131 3.9031210969697 3.1325661050505
"Nol" 6.5 5.5 7.5 5.5 7.5 11.5 10.5 9.5 8.5 13.5 11.5 15.5 10.5 14.5 22.5 16.5 13.5 11.5 12.5 17.5 15.5 13.5 15.5 19.5
"Sl" 1.74 1.68 1.62 7.06 9.9 9.1 6.4 5.34 4.28 6.295 6.74 5.85 5.95 8.85 7.45 8.4 7.75 8.1 6.9 6.73 6.815 4.74 3.9 11.73
"Rl" 2.8 2.6735 2.547 5.153 5.576 4.73 5.8 5.48 5.16 7.85 5.4 10.3 6.37 10.37 9.17 6.7 8.75 8.5 8.3 6.06 7.18 7.3 5.4 13.1 
"Lfa" 1.7775 1.41389285714286 1.05028571428571 1.25 1.41028571428571 2.67818181818182 1.967 1.836625 1.70625 2.65254545454545 1.95309090909091 3.352 1.5857 5.34785714285714 5.99945454545455 6.7570625 4.603 4.64545454545454 3.13916666666667 1.84729411764706 2.49323039215686 3.848 3.03866666666667 2.50152631578947
"Stfwt" 0.5825 0.5835 0.5812 0.5422 0.5392 0.5412 0.5472 0.5434 0.5396 0.6444 0.5535 0.7353 0.5515 1.3233 0.8082 1.0485 0.8833 0.7783 0.5752 0.6337 0.60445 0.5877 0.685 0.6978
"Stdwt" 0.563 0.573 0.561 0.536054368932039 0.533491262135922 0.5352 0.5208 0.52015 0.5195 0.6105 0.525 0.696 0.5271 1.1622 0.7424 0.8148 0.8211 0.7366 0.5351 0.5437 0.5394 0.565 0.6598 0.556
"Lfwt" 0.5645 0.5607 0.5569 0.5372 0.5575 1.0131 0.6626 0.65245 0.6423 1.1657 0.675 1.6564 0.6694 2.3376 4.1303 2.6067 2.1981 1.598 1.0212 0.8315 0.92635 1.5245 1.2483 1.0622
"Lfdwt" 0.5398 0.53745519379845 0.535110387596899 0.5152 0.5314 0.565804852686308 0.606123723041998 0.596166677744066 0.586209632446134 0.668353675538858 0.58896499238965 0.747742358688067 0.570931034482759 0.739456110950592 1.10839018138801 1.39596683191146 0.739585612262672 0.88201750547046 0.847367447173044 0.725077490774908 0.786222468973976 0.620431922351038 0.57650553443177 0.928141916859122
"Flfwt" 0.6841 0.6732 0.6641 0.57455 0.6038 0.5453 0.5803 0.58005 0.5798 0.5619 0.5594 0.5644 0.5766 0.9186 0.6897 1.2573 0.6885 0.8109 0.7289 0.6962 0.71255 0.9158 0.598 0.6235
"Fldwt" 0.5488 1.2031 0.7833 0.5601 0.5838 0.5364 0.5692 0.56885 0.5685 0.54875 0.5487 0.5488 0.564 0.8768 0.6708 1.2031 0.6592 0.7833 0.7073 0.6771 0.6922 0.8571 0.5654 0.5944
"Rfwt" 0.5345 0.5325 0.5355 0.5522 0.5543 0.5527 0.5316 0.5315 0.5314 0.58015 0.5339 0.6264 0.532 1.1512 0.6641 1.6326 0.8613 0.8158 0.6577 0.7232 0.69045 0.8303 0.5377 0.6956
"rdw" 0.516 0.513 0.519 0.518819734345351 0.519576850094877 0.519 0.5185 0.5178 0.5171 0.5391 0.5189 0.5593 0.5147 0.8079 0.5672 0.8905 0.6732 0.6135 0.5582 0.5788 0.5685 0.5695 0.5127 0.6144

Using the same code given above I get following errors:

require(rgl)
require(SciViews)
require(plotrix)
require(ggplot2)
require(reshape)
require("gridExtra")
final_df <- read.table("dataset")
mydata.pca <-prcomp(~ NB1+ NB2+ NB3+ NF1+ NF2+ NF3+ NG1+ NG2+ NG3+NH1+NH2+NH+ NL1+ NL2+NL3+ NM1+ NM2+ NM3+ NN1+ NN2+ NN3+ NP1+ NP2+NP3,data=final_df)

This pca on my dataset has not generating any pca scores as given below:

> mydata.pca$scores
NULL

Maybe this is the cause of error in the next statement:

mydata_pca = cbind(cbind(final_df, mydata.pca$scores), car = rownames(final_df))

Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 24, 0

How this error can be removed and above plot can be reproduced?

Shown below is output of dput() for my dataset

dput(final)
structure(list(NB1 = c(1.153, 0.842, 1.023, 0.549, 1.74518207282913, 
0.5531148, 72.4806763285024, 618.357142857143, 3.10751, 1.5809, 
4.18841, 2.40583946363636, 6.5, 1.74, 2.8, 1.7775, 0.5825, 0.563, 
0.5645, 0.5398, 0.6841, 0.5488, 0.5345, 0.516), NB2 = c(1.29013, 
0.91382, 1.13283, 0.55929, 2.00667030812325, 0.5524648636,       72.4806763285024, 
631.321428571428, 3.5507867, 1.764653, 4.8154397, 2.72983217245455, 
5.5, 1.68, 2.6735, 1.41389285714286, 0.5835, 0.573, 0.5607,  0.53745519379845, 
0.6732, 1.2031, 0.5325, 0.513), NB3 = c(1.221565, 0.87791, 1.077915, 
0.554145, 1.87592619047619, 0.5527898318, 72.4806763285024,   624.839285714286, 
2.9510594, 1.516046, 3.9671054, 2.29148909581818, 7.5, 1.62, 
2.547, 1.05028571428571, 0.5812, 0.561, 0.5569, 0.535110387596899, 
0.6641, 0.7833, 0.5355, 0.519), NF1 = c(1.091, 0.602, 0.853, 
0.525, 2.15723163841808, 0.5413232, 74.5671641791045, 654.071428571429, 
7.12473, 2.6063, 9.23103, 3.79729982525253, 5.5, 7.06, 5.153, 
1.25, 0.5422, 0.536054368932039, 0.5372, 0.5152, 0.57455, 0.5601, 
0.5522, 0.518819734345351), NF2 = c(1.167239, 0.615158, 0.898537, 
0.528225, 2.37101451977401, 0.541223504, 74.5671641791045,   644.214285714286, 
8.2509341, 2.964371, 10.7153051, 4.35784079554545, 7.5, 9.9, 
5.576, 1.41028571428571, 0.5392, 0.533491262135922, 0.5575, 0.5314, 
0.6038, 0.5838, 0.5543, 0.519576850094877), NF3 = c(1.1291195, 
0.608579, 0.8757685, 0.5266125, 2.26412307909605, 0.541273352, 
74.5671641791045, 649.142857142857, 6.7272462, 2.479922, 8.7071682, 
3.59946183573737, 11.5, 9.1, 4.73, 2.67818181818182, 0.5412, 
0.5352, 1.0131, 0.565804852686308, 0.5453, 0.5364, 0.5527, 0.519
), NG1 = c(1.156, 0.544, 0.96, 0.655, 2.32591160220995, 0.562528, 
51.4309309309309, 807.642857142857, 1.40903, 1.0004, 1.90943, 
1.36768463333333, 10.5, 6.4, 5.8, 1.967, 0.5472, 0.5208, 0.6626, 
0.606123723041998, 0.5803, 0.5692, 0.5316, 0.5185), NG2 = c(1.28064, 
0.55236, 1.0474, 0.68445, 2.67283480662983, 0.5613016, 51.4309309309309, 
799.785714285714, 1.5635651, 1.085468, 2.1490331, 1.515191021, 
9.5, 5.34, 5.48, 1.836625, 0.5434, 0.52015, 0.65245, 0.596166677744066, 
0.58005, 0.56885, 0.5315, 0.5178), NG3 = c(1.21832, 0.54818, 
1.0037, 0.669725, 2.49937320441989, 0.5619148, 51.4309309309309, 
803.714285714286, 1.3272173, 0.955364, 1.7825813, 1.28959301633333, 
8.5, 4.28, 5.16, 1.70625, 0.5396, 0.5195, 0.6423, 0.586209632446134, 
0.5798, 0.5685, 0.5314, 0.5171), NH1 = c(1.108, 0.606, 0.94, 
0.59, 2.73303797468354, 0.5451037, 83.1568265682657, 1050.5, 
2.80357, 1.4213, 3.72487, 1.9468614979798, 13.5, 6.295, 7.85, 
2.65254545454545, 0.6444, 0.6105, 1.1657, 0.668353675538858, 
0.5619, 0.54875, 0.58015, 0.5391), NH2 = c(1.19312, 0.62084, 
1.0016, 0.6026, 3.04566329113924, 0.545458885, 83.1568265682657, 
1078.78571428571, 3.1951769, 1.577921, 4.2730979, 2.19282795263636, 
11.5, 6.74, 5.4, 1.95309090909091, 0.5535, 0.525, 0.675, 0.58896499238965, 
0.5594, 0.5487, 0.5339, 0.5189), NH = c(1.15056, 0.61342, 0.9708, 
0.5963, 2.88935063291139, 0.5452812925, 83.1568265682657, 1064.64285714286, 
2.99937345, 1.4996105, 3.99898395, 2.06984472530808, 15.5, 5.85, 
10.3, 3.352, 0.7353, 0.696, 1.6564, 0.747742358688067, 0.5644, 
0.5488, 0.6264, 0.5593), NL1 = c(1.134, 0.689, 0.886, 0.535, 
1.14326086956522, 0.5433668, 73.8212341197822, 1000.5, 4.17967, 
2.0747, 5.75437, 2.69910104343434, 10.5, 5.95, 6.37, 1.5857, 
0.5515, 0.5271, 0.6694, 0.570931034482759, 0.5766, 0.564, 0.532, 
0.5147), NL2 = c(1.013, 0.616, 0.854, 0.519, 1.84277044854881, 
0.5432226, 90.0522388059701, 793.357142857143, 6.2562, 2.6, 8.3562, 
3.56556422222222, 14.5, 8.85, 10.37, 5.34785714285714, 1.3233, 
1.1622, 2.3376, 0.739456110950592, 0.9186, 0.8768, 1.1512, 0.8079
), NL3 = c(1.0735, 0.6525, 0.87, 0.527, 1.49301565905701, 0.5432947, 
77.368529286626, 896.928571428571, 5.217935, 2.33735, 7.055285, 
3.13233263282828, 22.5, 7.45, 9.17, 5.99945454545455, 0.8082, 
0.7424, 4.1303, 1.10839018138801, 0.6897, 0.6708, 0.6641, 0.5672
), NM1 = c(1.308, 0.878, 1.138, 0.65, 2.0930303030303, 0.5453266, 
73.1224783861671, 743.357142857143, 5.8482, 2.2589, 7.6071,   3.49860908080808, 
16.5, 8.4, 6.7, 6.7570625, 1.0485, 0.8148, 2.6067, 1.39596683191146, 
1.2573, 1.2031, 1.6326, 0.8905), NM2 = c(1.002, 0.748, 0.959, 
0.609, 2.49391061452514, 0.5419368, 105.763157894737, 746.928571428571, 
6.25014, 2.3777, 8.12784, 3.51460955151515, 13.5, 7.75, 8.75, 
4.603, 0.8833, 0.8211, 2.1981, 0.739585612262672, 0.6885, 0.6592, 
0.8613, 0.6732), NM3 = c(1.155, 0.813, 1.0485, 0.6295, 2.29347045877772, 
0.5436317, 86.6952861952862, 745.142857142857, 6.04917, 2.3183, 
7.86747, 3.50660931616162, 11.5, 8.1, 8.5, 4.64545454545454, 
0.7783, 0.7366, 1.598, 0.88201750547046, 0.8109, 0.7833, 0.8158, 
0.6135), NN1 = c(0.898, 0.72, 1.008, 0.542, 4.03489795918367, 
0.5590145, 81.1878306878307, 614.785714285714, 3.03144, 2.798, 
5.32944, 3.00008494545455, 12.5, 6.9, 8.3, 3.13916666666667, 
0.5752, 0.5351, 1.0212, 0.847367447173044, 0.7289, 0.7073, 0.6577, 
0.5582), NN2 = c(0.96566, 0.7574, 1.09436, 0.54914, 4.6358306122449, 
0.55859421, 81.1878306878307, 597.785714285714, 3.3605272, 3.09674, 
5.9572672, 3.32509598836364, 17.5, 6.73, 6.06, 1.84729411764706, 
0.6337, 0.5437, 0.8315, 0.725077490774908, 0.6962, 0.6771, 0.7232, 
0.5788), NN3 = c(0.93183, 0.7387, 1.05118, 0.54557, 4.33536428571429, 
0.558804355, 81.1878306878307, 606.285714285714, 3.1959836, 2.94737, 
5.6433536, 3.16259046690909, 15.5, 6.815, 7.18, 2.49323039215686, 
0.60445, 0.5394, 0.92635, 0.786222468973976, 0.71255, 0.6922, 
0.69045, 0.5685), NP1 = c(0.963, 0.701, 0.946, 0.664, 2.64142857142857, 
0.5416098, 83.2403482210447, 821.928571428571, 2.96378, 1.886, 
4.34978, 2.36201111313131, 13.5, 4.74, 7.3, 3.848, 0.5877, 0.565, 
1.5245, 0.620431922351038, 0.9158, 0.8571, 0.8303, 0.5695), NP2 = c(0.773, 
0.782, 0.834, 0.554, 2.20642228739003, 0.5419624, 86.8753213367609, 
721.928571428571, 7.63304, 2.6195, 9.75254, 3.9031210969697, 
15.5, 3.9, 5.4, 3.03866666666667, 0.685, 0.6598, 1.2483, 0.57650553443177, 
0.598, 0.5654, 0.5377, 0.5127), NP3 = c(0.868, 0.7415, 0.89, 
0.609, 2.4239254294093, 0.5417861, 84.5876607908528, 771.928571428571, 
5.29841, 2.25275, 7.05116, 3.1325661050505, 19.5, 11.73, 13.1, 
2.50152631578947, 0.6978, 0.556, 1.0622, 0.928141916859122, 0.6235, 
0.5944, 0.6956, 0.6144)), .Names = c("NB1", "NB2", "NB3", "NF1", 
"NF2", "NF3", "NG1", "NG2", "NG3", "NH1", "NH2", "NH", "NL1", 
"NL2", "NL3", "NM1", "NM2", "NM3", "NN1", "NN2", "NN3", "NP1", 
"NP2", "NP3"), class = "data.frame", row.names = c("Ca", "Po", 
"Gs", "Ap", "So", "MDC", "Elt", "Hmfwt", "Can", "Cab", "Tc", 
"Crt", "Nol", "Sl", "Rl", "Lfa", "Stfwt", "Stdwt", "Lfwt", "Lfdwt", 
"Flfwt", "Fldwt", "Rfwt", "rdw"))

Answer 1

We can try this. First of all the scores are not in mydata.pca$scores, because, as you can see typing ?prcomp you do not have it, but you can have $x , and use it.

So with your model:

require(rgl)
require(car)
mydata.pca <-prcomp(~ NB1+ NB2+ NB3+ NF1+ NF2+ NF3+ NG1+ NG2+ NG3+NH1+NH2+NH+ NL1+ NL2+NL3+ NM1+ NM2+ NM3+ NN1+ NN2+ NN3+ NP1+ NP2+NP3,data=final)

mydata_pc <- mydata.pca$x[,1:3]

The first three PC. Note that it seems you have something not good if you send this:

summary(mydata.pca)

Because you simply need one PC to explain all the variability, making the other rather questionable to care about (but it's my opinion, probably you should not agree, however it points out you should look at it, and also to cor(final) ).

Now you have the k means output:

k <- kmeans(final, 5, nstart=25, iter.max=1000)

# bind together the outputs
new <- data.frame(mydata_pc,cluster = k$cluster)

Again, it seems an output to see

summary(k)

Lastly, you can plot everything:

scatter3d(PC1~PC2+PC3, data=new, group=as.factor(new$cluster), size=10, type='s', bty = "g", pch = 20, cex = 1.5,xlab="PC1",ylab="PC2",zlab="PC3",lwd = 4,pch = 20,colkey = FALSE)

As said here , you can find a more nice way to plot them.