Recipe collection of Data Analysis by R

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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

JAPANESE

**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

**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**

**Time series data**

**Analysis of quasi-periodic data**

**Dimensionality reduction analysis of time series data**

**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

**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

**LiNGAM** : LiNGAM

**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**

**Pre-processing of missing values**