Package: fdm2id 1.0.2

fdm2id: Data Mining and R Programming for Beginners

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Authors:Alexandre Blansché [aut, cre]

fdm2id_1.0.2.tar.gz
fdm2id_1.0.2.zip(r-4.7-any)fdm2id_1.0.2.zip(r-4.6-any)fdm2id_1.0.2.zip(r-4.5-any)
fdm2id_1.0.2.tgz(r-4.6-any)fdm2id_1.0.2.tgz(r-4.5-any)
fdm2id_1.0.2.tar.gz(r-4.7-any)fdm2id_1.0.2.tar.gz(r-4.6-any)
fdm2id_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
fdm2id/json (API)

# Install 'fdm2id' in R:

install.packages('fdm2id', repos = c('https://blansche.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

accident2014 - Sample of car accident location in the UK during year 2014.
alcohol - Alcohol dataset
autompg - Auto MPG dataset
beetles - Flea beetles dataset
birth - Birth dataset
britpop - Population and location of 18 major british cities.
capitals - Capitals dataset
cookies - Cookies dataset
cookies.desc.test - Cookies dataset
cookies.desc.train - Cookies dataset
cookies.y.test - Cookies dataset
cookies.y.train - Cookies dataset
credit - Credit dataset
data1 - "data1" dataset
data2 - "data2" dataset
data3 - "data3" dataset
decathlon - Decathlon dataset
eucalyptus - Eucalyptus dataset
ionosphere - Ionosphere dataset
linsep - Linsep dataset
movies - Movies dataset
ozone - Ozone dataset
reg1 - Reg1 dataset
reg1.test - Reg1 dataset
reg1.train - Reg1 dataset
reg2 - Reg2 dataset
reg2.test - Reg2 dataset
reg2.train - Reg2 dataset
snore - Snore dataset
spine - Spine dataset
spine.test - Spine dataset
spine.train - Spine dataset
temperature - Temperature dataset
titanic - Titanic dataset
universite - University dataset
vowels - Vowels dataset
vowels.test - Vowels dataset
vowels.train - Vowels dataset
wheat - Wheat dataset
wine - Wine dataset
zoo - Zoo dataset

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.05 score 1 stars 47 scripts 834 downloads 116 exports 143 dependencies

Last updated from:46872b8027. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	328
source / vignettes	OK	286
linux-release	OK	291
macos-release	OK	235
macos-oldrel	OK	197
windows-devel	OK	290
windows-release	OK	252
windows-oldrel	OK	268
wasm-release	OK	161

Exports:ADABOOST APRIORI augmentation BAGGING boxclus CA CART cartdepth cartinfo cartleafs cartnodes cartplot CDA closegraphics compare compare.accuracy compare.jaccard compare.kappa confusion cookplot correlated cost.curves data.diag data.gauss data.parabol data.target1 data.target2 data.twomoons data.xor DBSCAN distplot EM evaluation evaluation.accuracy evaluation.adjr2 evaluation.fmeasure evaluation.fowlkesmallows evaluation.goodness evaluation.jaccard evaluation.kappa evaluation.msep evaluation.precision evaluation.r2 evaluation.recall exportgraphics exportgraphics.off exportgraphics.on FEATURESELECTION filter.rules frequentwords GBREG general.rules getvocab GRADIENTBOOSTING HCA intern intern.dunn intern.interclass intern.intraclass kaiser KERREG KMEANS kmeans.getk KNN LDA leverageplot LINREG loadtext LR MCA MEANSHIFT MLP MLPREG NB NMF PAM PCA performance plotavsp plotcloud plotclus plotdata plotzipf POLYREG pseudoF QDA query.docs query.words RANDOMFOREST regplot resplot roc.curves rotation runningtime scatterplot selectfeatures SOM SPECTRAL splitdata stability STUMP SVD SVM SVMl SVMr SVR SVRl SVRr TEXTMINING toggleexport toggleexport.off toggleexport.on treeplot TSNE vectorize.docs vectorize.words

Dependencies:abind arules arulesViz askpass backports base64enc boot broom bslib ca cachem car carData cli cluster codetools colorspace cowplot cpp11 crosstalk curl data.table Deriv digest doBy dplyr DT ellipse emmeans estimability evaluate FactoMineR farver fastmap flashClust fontawesome foreach forecast Formula fracdiff fs gclus generics ggforce ggplot2 ggraph ggrepel glue graphlayouts gridExtra gtable highr htmltools htmlwidgets httr igraph irlba isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mclust memoise mgcv mime minqa modelr multcompView mvtnorm nlme nloptr nnet numDeriv openssl otel pbkrtest pillar pkgconfig plotly pls polyclip promises purrr qap quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas registry rlang rmarkdown S7 sass scales scatterplot3d seriation showtext showtextdb SparseM stringi stringr survival sys sysfonts systemfonts tibble tidygraph tidyr tidyselect timeDate tinytex TSP tweenr urca utf8 vcd vctrs viridis viridisLite visNetwork withr xfun yaml zoo

Clustering: earthquakes in Fiji

The data | Question 1. Which method produces the most stable clusters? | Question 2. How do the clusters read in depth and magnitude? | Question 3. Where are the shallowest earthquakes? The deepest? | Question 4. How many clusters are there really? | Question 5. Which methods give the most relevant clusters?

Rendered fromv07-clustering-earthquakes.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-28

Ensemble methods: the ionosphere

The data | Question 1. Which method predicts best? | Question 2. How do the two best differ, in false positives and false negatives?

Rendered fromv06-ensemble-methods-ionosphere.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-28

Feature selection: crabs

The data | Question 1. Selecting for the species | Question 2. Selecting for the sex | Question 3. Which variables, in the end?

Rendered fromv08-feature-selection-crabs.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-28

Linear regression: Scottish hill races

The data | Question 1. Which single variable predicts best? Was it foreseeable? | Question 2. One variable or two? | Question 3. What do the residuals say? | Question 4. Can the predictions be improved?

Rendered fromv03-linear-regression-hill-races.Rmdusingknitr::rmarkdown

Last update: 2026-10-05
Started: 2026-08-28

Clustering: wheat varieties

The data | Question 1. Raw data, or centred and scaled? | Question 2. How many clusters do the methods see? | Question 3. With three clusters, which method is the most compact? The most stable? | Question 4. Which method comes closest to the three varieties?

Rendered fromv04-clustering-wheat-varieties.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2026-08-28

Discriminant analysis: the lumbar spine

The data | Question 1. Which method predicts the two-class problem best? | Question 2. And the three-class problem? | Question 3. Is the three-class problem harder than the two-class one? | Question 4. Which classes are hardest to separate? | Going further: are non-linear methods worth it?

Rendered fromv02-discriminant-analysis-spine.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2026-08-28

Principal component analysis: breast tumours

The data | Question 1. How many factorial axes are needed? | Question 2. How should the first two axes be read? | Question 3. What can be said about the two groups of tumours?

Rendered fromv01-pca-breast-tumours.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2026-08-28

Visualisation: cars from three continents

The data | Question 1. Should the data be centred and scaled? | Question 2. What does each view of the data show?

Rendered fromv05-visualisation-cars.Rmdusingknitr::rmarkdown

Last update: 2026-09-04
Started: 2026-08-28

Help page	Topics
Sample of car accident location in the UK during year 2014.	accident2014
Classification using AdaBoost	ADABOOST
Alcohol dataset	alcohol
Classification using APRIORI	APRIORI
APRIORI classification model	apriori-class
Duplicate and add noise to a dataset	augmentation
Auto MPG dataset	autompg
Classification using Bagging	BAGGING
Flea beetles dataset	beetles
Birth dataset	birth
Boosting methods model	boosting-class
Clustering Box Plots	boxclus
Population and location of 18 major british cities.	britpop
Correspondence Analysis (CA)	CA
Capitals dataset	capitals
Classification using CART	CART
Depth	cartdepth
CART information	cartinfo
Number of Leafs	cartleafs
Number of Nodes	cartnodes
CART Plot	cartplot
Classification using Canonical Discriminant Analysis	CDA
Canonical Disciminant Analysis model	cda-class
Close a graphics device	closegraphics
Comparison of two sets of clusters	compare
Comparison of two sets of clusters, using accuracy	compare.accuracy
Comparison of two sets of clusters, using Jaccard index	compare.jaccard
Comparison of two sets of clusters, using kappa	compare.kappa
Confusion matrix	confusion
Cookies dataset	cookies cookies.desc.test cookies.desc.train cookies.y.test cookies.y.train
Plot the Cook's distance of a linear regression model	cookplot
Correlated variables	correlated
Plot Cost Curves	cost.curves
Credit dataset	credit
Square dataset	data.diag
Gaussian mixture dataset	data.gauss
Parabol dataset	data.parabol
Target1 dataset	data.target1
Target2 dataset	data.target2
Two moons dataset	data.twomoons
XOR dataset	data.xor
"data1" dataset	data1
"data2" dataset	data2
"data3" dataset	data3
Training set and test set	dataset-class
DBSCAN model	dbs-class
DBSCAN clustering method	DBSCAN
Decathlon dataset	decathlon
Plot a k-distance graphic	distplot
Expectation-Maximization clustering method	EM
Expectation-Maximization model	em-class
Eucalyptus dataset	eucalyptus
Evaluation of classification or regression predictions	evaluation
Accuracy of classification predictions	evaluation.accuracy
Adjusted R2 evaluation of regression predictions	evaluation.adjr2
F-measure	evaluation.fmeasure
Fowlkes–Mallows index	evaluation.fowlkesmallows
Goodness	evaluation.goodness
Jaccard index	evaluation.jaccard
Kappa evaluation of classification predictions	evaluation.kappa
MSEP evaluation of regression predictions	evaluation.msep
Precision of classification predictions	evaluation.precision
R2 evaluation of regression predictions	evaluation.r2
Recall of classification predictions	evaluation.recall
Open a graphics device	exportgraphics
Toggle graphic exports	exportgraphics.off exportgraphics.on toggleexport toggleexport.off toggleexport.on
Factorial analysis results	factorial-class
Classification with Feature selection	FEATURESELECTION
Filtering a set of rules	filter.rules
Frequent words	frequentwords
Regression using Gradient Boosting	GBREG
Remove redundancy in a set of rules	general.rules
Extract words and phrases from a corpus	getvocab
Classification using Gradient Boosting	GRADIENTBOOSTING
Hierarchical Cluster Analysis method	HCA
Clustering evaluation through internal criteria	intern
Clustering evaluation through Dunn's index	intern.dunn
Clustering evaluation through interclass inertia	intern.interclass
Clustering evaluation through intraclass inertia	intern.intraclass
Ionosphere dataset	ionosphere
Kaiser rule	kaiser
Kernel Regression	KERREG
K-means method	KMEANS
Estimation of the number of clusters for _K_-means	kmeans.getk
Classification using k-NN	KNN
K Nearest Neighbours model	knn-class
Classification using Linear Discriminant Analysis	LDA
Plot the leverage points of a linear regression model	leverageplot
Linear Regression	LINREG
Linsep dataset	linsep
load a text file	loadtext
Classification using Logistic Regression	LR
Multiple Correspondence Analysis (MCA)	MCA
MeanShift method	MEANSHIFT
MeanShift model	meanshift-class
Classification using Multilayer Perceptron	MLP
Multi-Layer Perceptron Regression	MLPREG
Generic classification or regression model	model-class
Residuals and fitted values of a model	fitted.model model-methods residuals.model rstudent.model
Movies dataset	movies
Classification using Naive Bayes	NB
Non-negative Matrix Factorization	NMF
Ozone dataset	ozone
Clustering using _K_-medoids (PAM)	PAM
Learning Parameters	params-class
Principal Component Analysis (PCA)	PCA
Performance estimation	performance
Plot function for apriori-class	plot.apriori
Plot function for cda-class	plot.cda
Plot function for factorial-class	plot.factorial
Plot function for hca-class	plot.hca
Plot a feature selection	plot.selection
Plot function for som-class	plot.som
Plot actual vs. predictions	plotavsp
Plot word cloud	plotcloud
Generic Plot Method for Clustering	plotclus
Advanced plot function	plotdata
Plot rank versus frequency	plotzipf
Polynomial Regression	POLYREG
Model predictions	predict.apriori
Model predictions	predict.boosting
Model predictions	predict.cda
Predict function for DBSCAN	predict.dbs
Predict function for EM	predict.em
Projection of new observations into a factorial space	predict.factorial
Predict function for hierarchical clustering	predict.hca
Predict function for K-means	predict.kmeans
Model predictions	predict.knn
Predict function for MeanShift	predict.meanshift
Model predictions	predict.model
Predict function for PAM	predict.pam
Model predictions	predict.selection
Predict function for a self-organising map	predict.som
Predict function for Spectral clustering	predict.spectral
Model predictions	predict.textmining
Print a classification model obtained by APRIORI	print.apriori
Print an ensemble model	print.boosting
Print a canonical discriminant analysis	print.cda
Print a training/test split	print.dataset
Print a DBSCAN clustering	print.dbs
Print an EM clustering	print.em
Plot function for factorial-class	print.factorial
Print a _K_-nearest-neighbours model	print.knn
Print a mean shift clustering	print.meanshift
Print a classification or regression model	print.model
Print tuned method parameters	print.params
Print a feature selection result	print.selection
Print a self-organising map	print.som
Print a spectral clustering	print.spectral
Pseudo-F	pseudoF
Classification using Quadratic Discriminant Analysis	QDA
Document query	query.docs
Word query	query.words
Classification using Random Forest	RANDOMFOREST
reg1 dataset	reg1 reg1.test reg1.train
reg2 dataset	reg2 reg2.test reg2.train
Plot function for a regression model	regplot
Plot the studentized residuals of a linear regression model	resplot
Plot ROC Curves	roc.curves
Rotation	rotation
Running time	runningtime
Clustering Scatter Plots	scatterplot
Feature selection for classification	selectfeatures
Feature selection	selection-class
Snore dataset	snore
Self-Organizing Maps clustering method	SOM
Self-Organizing Maps model	som-class
Spectral clustering method	SPECTRAL
Spectral clustering model	spectral-class
Spine dataset	spine spine.test spine.train
Splits a dataset into training set and test set	splitdata
Clustering evaluation through stability	stability
Classification using one-level decision tree	STUMP
Print summary of a classification model obtained by APRIORI	summary.apriori
Summary of a classification or regression model	summary.model
Singular Value Decomposition	SVD
Classification using Support Vector Machine	SVM
Classification using Support Vector Machine with a linear kernel	SVMl
Classification using Support Vector Machine with a radial kernel	SVMr
Regression using Support Vector Machine	SVR
Regression using Support Vector Machine with a linear kernel	SVRl
Regression using Support Vector Machine with a radial kernel	SVRr
Temperature dataset	temperature
Text mining	TEXTMINING
Text mining object	textmining-class
Titanic dataset	titanic
Dendrogram Plots	treeplot
t-distributed Stochastic Neighbor Embedding	TSNE
University dataset	universite
Document vectorization	vectorize.docs
Word vectorization	vectorize.words
Document vectorization object	vectorizer-class
Vowels dataset	vowels vowels.test vowels.train
Wheat dataset	wheat
Wine dataset	wine
Zoo dataset	zoo

Package: fdm2id 1.0.2

fdm2id: Data Mining and R Programming for Beginners

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)