Missing values at df of PCA

[heitor]

Hi evebody!

Im trying to run a scrip of PCA but, in my data frame, i have a lot of dates of soil components with different numbers of repetitions. For example: soil pH, 3 observations for all areas; soil Ds, 4 observations for all areas.
The problem is, when i run the scrip, the variables that no have 4 observation, the Rstudio imputed this 'new four value' with the mean of the observations . I dont want it happen.
What i need to do?

PCA_TOTAL <- read_excel("C:/analises/pca/PCA_total.xlsx")
View(PCA_TOTAL)

df = PCA_TOTAL

res.pca = FactoMineR::PCA(df[,(-1:-5)], graph = FALSE)

Warning message:

In FactoMineR::PCA(df[, (-1:-5)], graph = FALSE) :
Missing values are imputed by the mean of the variable: you should use the imputePCA function of the missMDA package

[williaml]

Can you provide a reproducible example?

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…