library(factoextra) df<-read.csv("DataCountries.txt", sep="\t") head(df)
Now we will run a PCA analysis on our dataset. Note that we need to include only the numeric variables. We will also set as row names the column
# set as rownames the column Country rownames(df)<-df$Country # remove the Countrly columns df$Country<-NULL # run a PCA Analysis dfPCA <- prcomp(df, center = TRUE, scale. = TRUE)
Let’s get Scree plot which shows the percentage of explained variance by Principal Component.
Graph of Individual
Let’s plot all the countries into two dimensions by taking into consideration the quality of the individuals on the factor map.
# cos2 = the quality of the individuals on the factor map # Select and visualize some individuals (ind) with select.ind argument. # - ind with cos2 >= 0.96: select.ind = list(cos2 = 0.96) # - Top 20 ind according to the cos2: select.ind = list(cos2 = 20) # - Top 20 contributing individuals: select.ind = list(contrib = 20) # - Select ind by names: select.ind = list(name = c("23", "42", "119") ) fviz_pca_ind(dfPCA, col.ind = "cos2" , repel = TRUE)
Graph of Variables
Let’s see how we can represent the variables into two dimensions by taking into account their contribution.
# select.var = list(contrib = 15) fviz_pca_var(dfPCA, col.var = "contrib", repel = TRUE)
Graph of the Biplot
# Graph of the Biplot fviz_pca_biplot(dfPCA, repel = TRUE)
Eigenvalues, Variables and Individuals
Let’s see how we can get the Eigenvalues and statistics for Variables and Individuals such as the Coordinates, the Contributions to the PCs and the Quality of representation
# Eigenvalues eigens_vals <- get_eigenvalue(dfPCA) eigens_vals
# By Variable by_var <- get_pca_var(dfPCA) by_var$coord by_var$contrib by_var$cos2
# By ndividual by_ind <- get_pca_ind(dfPCA) by_ind$coord by_ind$contrib by_ind$cos2