Hello,

I am using Rstudio for PCA on geochemical data (quanti.var = elements ) and I want to color my points by the sample they refer to.

Could anyone help me, I saw some tutorials onligne but it doesn't work.

Tkank you

Hello,

I am using Rstudio for PCA on geochemical data (quanti.var = elements ) and I want to color my points by the sample they refer to.

Could anyone help me, I saw some tutorials onligne but it doesn't work.

Tkank you

We really need a lot more information. See

We really need a lot more information. We need some idea of the data and the code you are using to perform the PCA. See

I see ... here is the script that I used

=> matrice_cor_<-cor(data[,1:10], use = "everything")

=> corrplot(matrice_cor_budget, method = "number", type = "upper")

=> res<-PCA(budget, scale.unit=TRUE, ncp=5, graph = FALSE,quali.sup = c(10:10))

10 being the sample's name column

=> fviz_eig(res, choice = "variance", addlabels = TRUE)

=> corrplot(res[["var"]][["cos2"]],is.corr = FALSE)

[score plot] => plot.PCA(res, axes=c(1, 2), choix="ind", habillage = 10,

label=c("ind", "ind.sup", "quali"),new.plot=TRUE, title="score plot")

here is the probleme , it doesn't take into account the column 10 (samples)

the error displayed ( Error in plot.PCA(res, axes = c(1, 2), choix = "ind", habillage = 1, label = c("ind", :

The variable Echantillon is not quantitative)

My dataset include 9 quantitative variables ( elements : Ca, Fe, Si, Li, Ba ... + Distance from the mineralization) the 10th column represents the sample name for each row ( 400 row by sample).

I am sorry but that is, basically, incomprehensible.

We need clean code, just post the actual code between ```

and supply some sample data

A handy way to supply sample data is to use the dput() function. See ?dput. If you have a very large dataset then something like head(dput(myfile), 100) will likely supply enough data for us to work with.

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