Download pca col3/11/2024 pca.data <- PCA(data, scale.unit = TRUE, graph = FALSE) The second principal component (PC2) corresponds to the directions with the second maximum amount of variation in the data set and third, forth, etc. PCA() function first standardize the values then creates a new principal component table where first principal component (PC1) corresponds to the directions with the maximum amount of variation in the data set. Install.packages(c("factoextra", "FactoMineR")) Now we need to install and load two R package which will allow us to do PCA in R #intall ![]() Then you can upload it into R by using the command below: data <- read.csv(" A:R/20/data.csv", row.names = 1) #Make sure to change the file destination according to where you saved the file First you need to download the table and prepare it as shown above and save as a CSV format ( data.csv).
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