Clik here to view.
Principal component analysis in R : prcomp() vs. princomp() - R software and...
Packages in R for principal component analysisprcomp() and princomp() functionsInstall factoextra for visualizationPrepare the dataUse the R function prcomp() for PCAVariances of the principal...
View ArticleClik here to view.
Correspondence analysis basics - R software and data mining
Required packageLoad FactoMineR and factoextraData format: Contingency tablesVisualize a contingency table using graphical matrixRow sums and column sumsRow variablesRow profilesDistance (or...
View ArticleClik here to view.
ggplot2 - Easy way to mix multiple graphs on the same page - R software and...
Install and load required packagesInstall and load the package gridExtraInstall and load the package cowplotPrepare some dataCowplot: Publication-ready plotsBasic plotsArranging multiple graphs using...
View ArticleClik here to view.
ggplot2 - Introduction
IntroductionInstall and load ggplot2 packageData formatQuick plot : qplot()UsageScatter plotsBasic scatter plotsScatter plots with linear fitsLinear fits by groupsChange scatter plot colorsChange the...
View ArticleClik here to view.
ade4 and factoextra : Correspondence Analysis - R software and data mining
Required packagesLoad ade4 and factoextraData format: Contingency tablesCorrespondence analysis (CA)Eigenvalues and scree plotExtract the eigenvaluesMake a scree plot using ade4 base graphicsMake the...
View ArticleClik here to view.
Correspondence Analysis in R: The Ultimate Guide for the Analysis, the...
How this article is organized?Required packagesLoad FactoMineR and factoextraData format: Contingency tablesExploratory data analysis (EDA)Visual inspectionVisualize a contingency table using graphical...
View ArticleClik here to view.
MASS package and factoextra : Correspondence Analysis - R software and data...
Required packagesLoad MASS and factoextraData formatCorrespondence analysis (CA)Interpretation of CA outputsEigenvalues and scree plotBiplot of row and column variablesRow variablesColumn...
View ArticleClik here to view.
ca package and factoextra : Correspondence Analysis - R software and data mining
Required packagesLoad ca and factoextraData formatCorrespondence analysis (CA)Summary of CA outputsInterpretation of CA outputsEigenvalues and scree plotBiplot of row and column variablesReferences and...
View ArticleClik here to view.
Multiple Correspondence Analysis Essentials: Interpretation and application...
Required packagesLoad FactoMineR and factoextraData formatExploratory data analysisMultiple Correspondence Analysis (MCA)Summary of MCA outputsInterpretation of MCA outputsEigenvalues/variances and...
View ArticleClik here to view.
factoextra: Reduce overplotting of points and labels - R software and data...
Install required packagesLoad FactoMineR and factoextraMultiple Correspondence Analysis (MCA)Simple Correspondence Analysis (CA)Principal Componet Analysis (PCA)InfosTo reduce overplotting, the...
View ArticleClik here to view.
Clarifying distance measures - Unsupervised Machine Learning
1 Methods for measuring distances2 Distances and scaling3 Data preparation3.1 Descriptive statistics4 R functions for computing distances4.1 The standard dist() function4.2 Correlation based distance...
View ArticleClik here to view.
Partitioning cluster analysis: Quick start guide - Unsupervised Machine Learning
1 Required package2 K-means clustering2.1 Concept2.2 Algorithm2.3 R function for k-means clustering2.4 Data format2.5 Compute k-means clustering2.6 Application of K-means clustering on real data2.6.1...
View ArticleClik here to view.
Hierarchical Clustering Essentials - Unsupervised Machine Learning
1 Required R packages2 Algorithm3 Data preparation and descriptive statistics4 R functions for hierarchical clustering4.1 hclust() function4.2 agnes() and diana() functions4.2.1 R code for computing...
View ArticleClik here to view.
Assessing clustering tendency: A vital issue - Unsupervised Machine Learning
1 Required packages2 Data preparation2.1 faithful dataset2.2 Random uniformly distributed dataset3 Why assessing clustering tendency?4 Methods for assessing clustering tendency4.1 Hopkins...
View ArticleClik here to view.
Determining the optimal number of clusters: 3 must known methods -...
1 Required packages2 Data preparation3 Example of partitioning method results4 Example of hierarchical clustering results5 Three popular methods for determining the optimal number of clusters5.1 Elbow...
View ArticleClik here to view.
ggplot2 scatter plots : Quick start guide - R software and data visualization
Prepare the dataBasic scatter plotsLabel points in the scatter plotAdd regression linesChange the appearance of points and linesScatter plots with multiple groupsChange the point color/shape/size...
View ArticleClik here to view.
Visual Enhancement of Clustering Analysis - Unsupervised Machine Learning
1 Required package2 Data preparation3 Enhanced distance matrix computation and visualization4 Enhanced clustering analysis4.1 eclust() function4.2 Examples5 InfosClustering analysis is used to find...
View ArticleClik here to view.
Clustering Validation Statistics: 4 Vital Things Everyone Should Know -...
1 Required packages2 Data preparation3 Relative measures: Determine the optimal number of clusters4 Clustering analysis4.1 Example of partitioning method results4.2 Example of hierarchical clustering...
View ArticleClik here to view.
How to compute p-value for hierarchical clustering in R - Unsupervised...
1 Concept2 Algoritm3 Required R packages4 Data preparation5 Compute p-value for hierarchical clustering5.1 Description of pvclust() function5.2 Usage of pvclust() function6 Infos1 ConceptClustering...
View Article