I have a dataset (view/download) here. I need to run PCA on this data set and need to illustrate a 3D visualisation of the three main PCs using plot3d() package.
I have looked for more information before coming here on which package to use, some people use princomp and some us prcomp (in university we have personally been using prcomp). The confusing part is were to move forward with this?
The full question is "Illustrate a 3D visualisation of the three main PCs using plot3d() package in R. Use the following colours for samples (data points) belonging to the various immune subgroups/subtypes (C1: red, C2: yellow, C3: green, C4: cyan, C5: blue, C6: purple)"
After reading in the CSV and loading rgl library
In the current state I have removed the rows with the subgroups: see code below
# Remove last row from dataset dataMinusLastRow = data[1:(nrow(data)-1),] # Convert dataset to numeric and apply coltemp <- colnames(dataMinusLastRow) pcaData = t(apply(dataMinusLastRow, 1, as.numeric)) colnames(pcaData) <- coltemp # PCA pc = prcomp(t(pcaData), scale=T, center=T) summary(pc) # From this we can see that PC184 accounts for 95% of cumulative proportion # Getting the cumulative proportion in numeric dataset vars <- apply(pc$x, 2, var) props <- vars / sum(vars) pc.cs <- cumsum(props) # Cumulative Proportion pc.index<-min(which(pc.cs>0.95)) # Index of which cumulative proportion > 95%
I think the main part which confuses me is that it asks for the three main PC's which I am not sure what they are and have had not much support from university with information regarding this.
Thanks to anyone who can help in any shape/form!