Data Analysis by R

Recipe collection of Data Analysis by R


Data Science for Environment and Quality
Data Analysis by R-EDA1
Data Analysis by Python
Data Analysis by Excel

Web app R-EDA1
Web app R-QCA1


Exploratory data analysis

Visualization of entire data

Visualization of entire data : Line charts by variables, heatmaps, expandable line charts

Analysis of variable similarity

Analysis of variable similarity : scatter plot of roundabouts, (correlation coefficient, graphical rasuu, number of associations) x network graph, principal component analysis x multidimensional scaling, log-linear analysis

Analysis of Variable Importance : Stepwise and Lasso regression

Analysis of hidden variables : Principal component analysis, Independent component analysis

Analysis of anomaly quantification : MT method, (principal component analysis, kernel principal component analysis) * MT method, LOF, minimum distance method, multidimensional scaling

Analysis of similarity of individual categories

Analysis of similarity of individual categories : Correspondence analysis * multidimensional scaling, association analysis * network graph

Analysis of sample similarity

Visualization by compressing high dimensions into two dimensions : (multidimensional scaling, t-SNE, self-organizing map) * cluster analysis

Outlier Detection

Visualization by compressing high-dimensional into two dimensions with regression analysis system by R

Analysis of similarity between items in rows and columns

Analysis of similarity between items in rows and columns : Bipartite graph, correspondence analysis * multidimensional scaling * simultaneous attachment

Text mining

Text mining

Time series data

Analysis of quasi-periodic data

Analysis of periodic data

Dimensionality reduction analysis of time series data

Validate data analysis

Analysis of the presence or absence of difference by R : Graph of one-dimensional distribution by stratification, test of difference in mean value, test of difference in variation, test of difference in ratio, test of independence

Analysis of normality : Shapiro-Wilk test

Analysis of prediction interval

Control chart : Applied line graph

Gage R and R : Applied ANOVA

Analysis use the method deeply

Principal component regression analysis : Principal component regression analysis, Factor analysis

Decision tree : Bibary tree, N-try tree, Random forest

Cluster analysis : Hierarchical, Non-hierarchical (k-means, X-means, mixture distribution, DBSCAN)

Multidimensional scaling : Multidimensional scaling, Networked multidimensional scaling

Generalized linear mixed model : Generalized linear model, Linear mixed model

Log-Linear Analysis : Log-Linear Analysis

Principal component analysis : Principal component analysis, Principal component analysis of categorical data

Correspondence analysis : Correspondence analysis

Factor analysis : Factor analysis


Item Response Theory : Item Response Theory(IRT)

Logistic regression analysis : Logistic regression analysis

Bayesian Network : Bayesian Network analysis

Spline : Spline interpolation, Smoothing spline, Multivariate adaptive regression spline

Survival Analysis : Spline interpolation, Smoothing spline, Multivariate adaptive regression spline

Canonical Correlation Analysis : Canonical Correlation Analysis (CCA), Kernel CCA

High-dimensional regression analysis using intervals by R

Vector quantization label classification by R

Draw a graph

Standard graph function



Network graph


Pareto chart

Data preprocessing

Variable conversion

Cross tabulation

Sample data

Pre-processing of missing values

Reachability Matrix