# How to Visualize Multivariate Data Analysis

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In this tutorial, we will work with the **factoextra** R package and we will consider the Country dataset. Let’s start:

library(factoextra) df<-read.csv("DataCountries.txt", sep="\t") head(df)

**PCA Analysis**

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 `Country`

.

# 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.

fviz_eig (dfPCA)

**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**

# Eigenvalues eigens_vals <- get_eigenvalue(dfPCA) eigens_vals

**Variables**

# By Variable by_var <- get_pca_var(dfPCA) by_var$coord by_var$contrib by_var$cos2

**Individuals**

# By ndividual by_ind <- get_pca_ind(dfPCA) by_ind$coord by_ind$contrib by_ind$cos2

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