scenarios when attempting to apply PCA using `prcomp` on a large dataset with missing values
Could someone explain I'm writing unit tests and I'm converting an old project and I've spent hours debugging this and I'm working on a project and hit a roadblock..... I'm trying to perform Principal Component Analysis (PCA) using the `prcomp` function in R on a dataset with around 10,000 observations and 300 variables. However, I keep running into issues due to missing values in my data. When I run the following code: ```r # Load necessary libraries library(tidyverse) # Simulated dataset with NA values set.seed(123) data <- as.data.frame(matrix(rnorm(1000000), ncol=300)) data[sample(1:10000, 500), sample(1:300, 5)] <- NA # Introduce NA values # Attempting PCA pca_result <- prcomp(data, center = TRUE, scale. = TRUE) ``` I receive the following behavior message: ``` behavior in cov.wt(x, wt = weights, ...): 'x' must be numeric ``` I've tried to handle the missing values using different approaches, such as using `na.omit(data)` and `imputeTS::na_mean(data)`, but Iām not sure how to properly integrate these methods into the PCA step. Also, I would like to retain as much information as possible from the dataset without losing too many rows. What is the best practice for addressing missing values in this context? Should I use imputation, or is there a better method to prepare my data for PCA? I would appreciate any insights or examples on how to handle this effectively, especially for larger datasets. I'm currently using R version 4.2.2. How would you solve this? I'm developing on CentOS with R. The stack includes R and several other technologies. Is there a simpler solution I'm overlooking? I'm coming from a different tech stack and learning R. Any suggestions would be helpful. My development environment is Windows 10.