Help with some R code

General
#1

Hi folks,

I am a newbie who is self-teaching R. However, I have run into an issue I can't seem to get an answer to.
I have a data frame (df1) with approximately 40 variables and 300 observations. I have run PCA (prcomp(df1)) on it and then turned the pca$scores into a data-frame (df2).

What I want to do now is take the df2 and select data from the df2 by using the values in the first two columns. So if entries in column 1 are between -2 and 2 and values in column 2 are between -5 to 5, I want all of the data in the rows and columns placed into another table.

The reason for this is I want to run PCA again on a subset of the original PCAs data. So I have from the individuals' plots found a cluster of data that is contained within certain PC1 and PC2 corrdinates. See attached graphic.

I am trying to reproduce the double PCA technique discussed in the following paper - "Statistical evaluation of elemental concentrations in shallow-marine deposits (Cretaceous, Lusitanian Basin)" Coimbra et al., 2017.

Thanks in advance for any help you can provide.

data
data1400×1507 40.4 KB

#2

Hi, and welcome!

Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. It can be really difficult reverse engineering a question from just a description, and screenshots are usually not helpful.

#3

Hi

This appears to be a clustering and subsetting question. Have you thought about running a kmeans? Based on your plot you might expect 3 distinct clusters. You can then add the clustering results as an additional vector to your original data frame to then subset on the cluster you wish to perform a pca on.

Example code: 


clusters <- kmeans(data, 3)

# Save the cluster number in the dataset as column 'clusters' 
data$clusters <- as.factor(clusters$cluster)

#Filter data on cluster 2 using tidyverse 
cluster <- data %>%
filter(clusters == "2")
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#6

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#5

Thank you Nusob88, I had not thought about doing it that way. I have run cluster analysis on the data so I should be able to accomplish it.

Thank you again for your help.

#4

I apologize, I will do so in the future

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