intubate (version 1.0.0)

intubate-package: Interface to Popular R Functions for Data Science Pipelines.

Description

The aim of intubate (logo <||>) is to offer a painless way to add R functions that that are not pipe-aware to data science pipelines implemented by `magrittr` with the operator %>%, without having to rely on workarounds of varying complexity. It also implements three extensions (experimental), called `intubOrders`, `intuEnv`, and `intuBags`.

For a gentle introduction to intubate, please see the vignette that is included with the package. Currently, there are 461 interfaces for: adabag: Multiclass AdaBoost.M1, SAMME and Bagging AER: Applied Econometrics with R aod: Analysis of Overdispersed Data

ape: Analyses of Phylogenetics and Evolution

arm: Data Analysis Using Regression and Multilevel/Hierarchical Models betareg: Beta Regression

brglm: Bias reduction in binomial-response generalized linear models

caper: Comparative Analyses of Phylogenetics and Evolution in R car: Companion to Applied Regression caret: Classification and Regression Training coin: Conditional Inference Procedures in a Permutation Test Framework CORElearn: Classification, Regression and Feature Evaluation drc: Analysis of Dose-Response Curves

e1071: Support Vector Machines

earth: Multivariate Adaptive Regression Splines EnvStats: Environmental Statistics, Including US EPA Guidance fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling flexmix: Flexible Mixture Modeling

forecast: Forecasting Functions for Time Series and Linear Models

frontier: Stochastic Frontier Analysis gam: Generalized Additive Models gbm: Generalized Boosted Regression Models gee: Generalized Estimation Equation Solver glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models glmx: Generalized Linear Models Extended gmnl: Multinomial Logit Models with Random Parameters gplots: Various R Programming Tools for Plotting Data graphics: The R Graphics Package gss: General Smoothing Splines hdm: High-Dimensional Metrics

Hmisc: Harrell Miscellaneous

ipred: Improved Predictors

iRegression: Regression Methods for Interval-Valued Variables ivfixed: Instrumental fixed effect panel data model kernlab: Kernel-Based Machine Learning Lab

kknn: Weighted k-Nearest Neighbors

klaR: Classification and Visualization

lars: Least Angle Regression, Lasso and Forward Stagewise

lattice: Trellis Graphics for R latticeExtra: Extra Graphical Utilities Based on Lattice leaps: Regression Subset Selection lfe: Linear Group Fixed Effects

lme4: Linear Mixed-Effects Models using 'Eigen' and S4 lmtest: Testing Linear Regression Models

MASS: Robust Regression, Linear Discriminant Analysis, Ridge Regression, Probit Regression, ... MCMCglmm: MCMC Generalised Linear Mixed Models

mda: Mixture and Flexible Discriminant Analysis metafor: Meta-Analysis Package for R mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness Estimation mhurdle: Multiple Hurdle Tobit Models minpack.lm: R Interface to the Levenberg-Marquardt Nonlinear Least-Squares Algorithm Found in MINPACK, Plus Support for Bounds mlogit: Multinomial logit model mnlogit: Multinomial Logit Model modeltools: Tools and Classes for Statistical Models nlme: Linear and Nonlinear Mixed Effects Models nlreg: Higher Order Inference for Nonlinear Heteroscedastic Models nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models ordinal: Regression Models for Ordinal Data party: A Laboratory for Recursive Partytioning partykit: A Toolkit for Recursive Partytioning plotrix: Various Plotting Functions pls: Partial Least Squares and Principal Component Regression

pROC: Display and Analyze ROC Curves

pscl: Political Science Computational Laboratory, Stanford University

psychomix: Psychometric Mixture Models psychotools: Infrastructure for Psychometric Modeling

psychotree: Recursive Partitioning Based on Psychometric Models

quantreg: Quantile Regression

randomForest: Random Forests for Classification and Regression Rchoice: Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters

rminer: Data Mining Classification and Regression Methods rms: Regression Modeling Strategies robustbase: Basic Robust Statistics rpart: Recursive Partitioning and Regression Trees RRF: Regularized Random Forest RWeka: R/Weka Interface sampleSelection: Sample Selection Models sem: Structural Equation Models spBayes: Univariate and Multivariate Spatial-temporal Modeling

stats: The R Stats Package (glm, lm, loess, lqs, nls, ...) strucchange: Testing, Monitoring, and Dating Structural Changes

survey: Analysis of Complex Survey Samples

survival: Survival Analysis SwarmSVM: Ensemble Learning Algorithms Based on Support Vector Machines systemfit: Estimating Systems of Simultaneous Equations tree: Classification and Regression Trees vcd: Visualizing Categorical Data vegan: Community Ecology Package The aim is to provide interfaces to most methodologies used in data science. intubate core depends only on base, stats, and utils libraries. To keep it as lean as possible, intubate will not install not load any library. You need to make sure that the library containing the functions to be interfaced are loaded (before or after intubate). Moreover, you can interface the functions of any library directly without the need to create interfaces (see ntbt) so perhaps in the future that will be the preferred way of using intubate.

intubate is still a work in progress. As such, the implementation may change in future versions until stabilization.

Details

Package:

intubate

Type:

Package

Version:

1.0.0

Date:

2016-08-27

License:

GPL (>=2)

See examples of use below.

See Also

intubate

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# 
# ######### Interface to lm #########
# ## Original function to interface
# lm(conc ~ uptake, CO2)
# 
# ## The interface reverses the order of data and formula
# ntbt_lm(CO2, conc ~ uptake)
# 
# ## so it can be used easily in a pipeline.
# CO2 %>%
#   ntbt_lm(conc ~ uptake)
# 
# CO2 %>%
#   ntbt_lm(conc ~ uptake) %>%
#   summary()
# 
# ######### Interface to cor.test #########
# ## Original function to interface
# cor.test(~ CONT + INTG, data = USJudgeRatings)
# 
# ## The interface reverses the order of data and formula
# ntbt_cor.test(data = USJudgeRatings, ~ CONT + INTG)
# 
# ## so it can be used easily in a pipeline.
# USJudgeRatings %>%
#   ntbt_cor.test(~ CONT + INTG)
#   
# ######### Interfaces to aggregate and xtabs #########
# ## Original function to interface
# ag <- aggregate(len ~ ., data = ToothGrowth, mean)
# xtabs(len ~ ., data = ag)
# 
# ## The interface reverses the order of data and formula
# ag <- ntbt_aggregate(ToothGrowth, len ~ ., mean)
# ntbt_xtabs(ag, len ~ .)
# 
# ## so it can be used easily in a pipeline.
# ToothGrowth %>%
#   ntbt_aggregate(len ~ ., mean) %>%
#   ntbt_xtabs(len ~ .)
# ## End(Not run)