• A question on Cross Validated that asks about imputation of missing values for PCA in R. The question provides an example of using the prcomp() function with na.action = na.omit, but notes that this method drops the rows with NA values. The question also considers replacing NA values with the median or a value close to zero, but is not sure about the impact on the PCA analysis. The question receives five answers that suggest different methods of dealing with missing values, such as using a soil correction factor, decomposing a covariance matrix, or using the missMDA or pcaMethods packages1.
• A question on Stack Overflow that asks about PCA analysis using FactoMineR and factoextra packages. The question provides a code snippet that uses the PCA() function from FactoMineR and the fviz_*() functions from factoextra to perform and visualize a PCA. The question also mentions that the ggarrange() function does not work and gives an error. The question receives one answer that suggests that the error is due to the identifier variable being of type character, and advises to remove it from the PCA() function2.
• A website that describes the factoextra package and its features. The website explains that the factoextra package can handle the results of PCA, CA, MCA, MFA, FAMD and HMFA from several packages, and provides functions for extracting and visualizing the most important information contained in the data. The website also provides examples and tutorials on how to use the factoextra package for different types of analyses

I would get rid of R. It is too hard to learn and to program yourself and by the time you work it out, it's too late. Try to obtain a preset program, such as Systat or SPSS, that will just do PCA for you with a simple analysis :)

(The answer by Raghad above proves my point hehe)

Parece que factoextra solo se usa para extraer resultados de otros programas, probablemente hayas usado princomp, en general la mayoría de los paquetes imputan los datos perdidos. Si todas las muestras están representadas en tus resultados es porque imputo los missing data, si faltan muestras es porque descarto las que tienen datos perdidos. Hay muchos métodos para imputar datos (poner el valor promedio es el mas común probablemente aunque no es muy robusto), pero también podes imputar antes de hacer el PCA (por ejemplo usando el paquete missForest), así decidís que método usar y no el que usa el paquete por default. Escribime si necesitas ayuda con R. Saludos.