Type: Package
Title: Heatmap-Integrated Decision Tree Visualizations
Version: 0.2.1
Maintainer: Trang Le <grixor@gmail.com>
Description: Creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. 'treeheatr' utilizes the customizable 'ggparty' package for drawing decision trees.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
Depends: R (≥ 3.5.0)
Imports: ggparty, ggplot2, partykit, dplyr, ggnewscale, gtable, stats, tidyr, cluster, grid, yardstick, seriation
Suggests: forcats, knitr, rmarkdown, rpart, testthat
URL: https://trang1618.github.io/treeheatr/index.html, https://trang1618.github.io/treeheatr-manuscript/
BugReports: https://github.com/trang1618/treeheatr/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-11-19 20:45:18 UTC; ttle
Author: Trang Le [aut, cre] (https://trang.page/), Jason Moore [aut] (http://www.epistasisblog.org/), University of Pennsylvania [cph]
Repository: CRAN
Date/Publication: 2020-11-19 21:00:03 UTC

Align decision tree and heatmap:

Description

Align decision tree and heatmap:

Usage

align_plots(
  dheat,
  dtree,
  heat_rel_height,
  show = c("heat-tree", "heat-only", "tree-only")
)

Arguments

dheat

ggplot2 grob object of the heatmap.

dtree

ggplot2 grob object of the decision tree

heat_rel_height

Relative height of heatmap compared to whole figure (with tree).

show

Character string indicating which components of the decision tree-heatmap should be drawn. Can be 'heat-tree', 'heat-only' or 'tree-only'.

Value

A gtable/grob object of the decision tree (top) and heatmap (bottom).

Performs clustering or features.

Description

Performs clustering or features.

Usage

clust_feat_func(dat, clust_vec, clust_feats = TRUE)

Arguments

dat

Dataframe of the original dataset. Samples may be reordered.

clust_vec

Character vector of variable names to be applied clustering on. Can include class labels.

clust_feats

if TRUE clusters displayed features (passed through 'clust_vec') using the the Gower metric based on the values of all samples and returns the ordered features. When 'clust_samps = FALSE' and 'clust_feats = FALSE', no clustering is performed.

Value

Character vector of reordered features when 'clust_feats == TRUE'.

Performs clustering of samples.

Description

Performs clustering of samples.

Usage

clust_samp_func(leaf_node = NULL, dat, clust_vec, clust_samps = TRUE)

Arguments

leaf_node

Integer value indicating terminal node id.

dat

Dataframe of the original dataset. Samples may be reordered.

clust_vec

Character vector of variable names to be applied clustering on. Can include class labels.

clust_samps

Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.

Value

Dataframe of reordered original dataset when clust_samps == TRUE.

Compute decision tree from data set

Description

Compute decision tree from data set

Usage

compute_tree(
  x,
  data_test = NULL,
  target_lab = NULL,
  task = c("classification", "regression"),
  feat_types = NULL,
  label_map = NULL,
  clust_samps = TRUE,
  clust_target = TRUE,
  custom_layout = NULL,
  lev_fac = 1.3,
  panel_space = 0.001
)

Arguments

x

Dataframe or a 'party' or 'partynode' object representing a custom tree. If a dataframe is supplied, conditional inference tree is computed. If a custom tree is supplied, it must follow the partykit syntax: https://cran.r-project.org/web/packages/partykit/vignettes/partykit.pdf

data_test

Tidy test dataset. Required if 'x' is a 'partynode' object. If NULL, heatmap displays (training) data 'x'.

target_lab

Name of the column in data that contains target/label information.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

feat_types

Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.

label_map

Named vector of the meaning of the target values, e.g., c(‘0' = ’Edible', ‘1' = ’Poisonous').

clust_samps

Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.

clust_target

Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.

custom_layout

Dataframe with 3 columns: id, x and y for manually input custom layout.

lev_fac

Relative weight of child node positions according to their levels, commonly ranges from 1 to 1.5. 1 for parent node perfectly in the middle of child nodes.

panel_space

Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.

Value

A list of results from 'partykit::ctree' or provided custom tree, including fit, estimates, smart layout and terminal data.

Examples

fit_tree <- compute_tree(penguins, target_lab = 'species')
fit_tree$fit
fit_tree$layout
dplyr::select(fit_tree$term_dat, - contains('nodedata'))

Diabetes patient records.

Description

http://archive.ics.uci.edu/ml/datasets/diabetes https://www.kaggle.com/uciml/pima-indians-diabetes-database

Usage

diabetes

Format

A data frame with 768 observations and 9 variables: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age and Outcome.

Draws the heatmap.

Description

Draws the heatmap to be placed below the decision tree.

Usage

draw_heat(
  dat,
  fit,
  feat_types = NULL,
  target_cols = NULL,
  target_lab_disp = fit$target_lab,
  trans_type = c("percentize", "normalize", "scale", "none"),
  clust_feats = TRUE,
  feats = NULL,
  show_all_feats = FALSE,
  p_thres = 0.05,
  cont_legend = FALSE,
  cate_legend = FALSE,
  cont_cols = ggplot2::scale_fill_viridis_c,
  cate_cols = ggplot2::scale_fill_viridis_d,
  panel_space = 0.001,
  target_space = 0.05,
  target_pos = "top"
)

Arguments

dat

Dataframe with samples from original dataset ordered according to the clustering within each leaf node.

fit

party object, e.g., as output from partykit::ctree()

feat_types

Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.

target_cols

Character vectors representing the hex values of different level colors for targets, defaults to viridis option B.

target_lab_disp

Character string for displaying the label of target label. If not provided, use 'target_lab'.

trans_type

Character string of 'normalize', 'scale' or 'none'. If 'scale', subtract the mean and divide by the standard deviation. If 'normalize', i.e., max-min normalize, subtract the min and divide by the max. If 'none', no transformation is applied. More information on what transformation to choose can be acquired here: https://cran.rstudio.com/package=heatmaply/vignettes/heatmaply.html#data-transformation-scaling-normalize-and-percentize

clust_feats

Logical. If TRUE, performs cluster on the features.

feats

Character vector of feature names to be displayed in the heatmap. If NULL, display features of which P values are less than 'p_thres'.

show_all_feats

Logical. If TRUE, show all features regardless of 'p_thres'.

p_thres

Numeric value indicating the p-value threshold of feature importance. Feature with p-values computed from the decision tree below this value will be displayed on the heatmap.

cont_legend

Function determining the options for legend of continuous variables, defaults to FALSE. If TRUE, use 'guide_colorbar(barwidth = 10, barheight = 0.5, title = NULL)'. Any other ['guides()'](https://ggplot2.tidyverse.org/reference/guides.html) functions would also work.

cate_legend

Function determining the options for legend of categorical variables, defaults to FALSE. If TRUE, use 'guide_legend(title = NULL)'. Any other ['guides()'](https://ggplot2.tidyverse.org/reference/guides.html) functions would also work.

cont_cols

Function determining color scale for continuous variable, defaults to 'scale_fill_viridis_c(guide = cont_legend)'.

cate_cols

Function determining color scale for nominal categorical variable, defaults to 'scale_fill_viridis_d(begin = 0.3, end = 0.9)'.

panel_space

Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.

target_space

Numeric value indicating spacing between the target label and the rest of the features

target_pos

Character string specifying the position of the target label on heatmap, can be 'top', 'bottom' or 'none'.

Value

A ggplot2 grob object of the heatmap.

Examples

x <- compute_tree(penguins, target_lab = 'species')
draw_heat(x$dat, x$fit)

Draws the conditional decision tree.

Description

Draws the conditional decision tree output from partykit::ctree(), utilizing ggparty geoms: geom_edge, geom_edge_label, geom_node_label.

Usage

draw_tree(
  dat,
  fit,
  term_dat,
  layout,
  target_cols = NULL,
  title = NULL,
  tree_space_top = 0.05,
  tree_space_bottom = 0.05,
  print_eval = FALSE,
  metrics = NULL,
  x_eval = 0,
  y_eval = 0.9,
  task = c("classification", "regression"),
  par_node_vars = list(label.size = 0, label.padding = unit(0.15, "lines"), line_list =
    list(aes(label = splitvar)), line_gpar = list(list(size = 9)), ids = "inner"),
  terminal_vars = list(label.padding = unit(0.25, "lines"), size = 3, col = "white"),
  edge_vars = list(color = "grey70", size = 0.5),
  edge_text_vars = list(color = "grey30", size = 3, mapping = aes(label =
    paste(breaks_label, "*NA")))
)

Arguments

dat

Dataframe with samples from original dataset ordered according to the clustering within each leaf node.

fit

party object, e.g., as output from partykit::ctree()

term_dat

Dataframe for terminal nodes, must include these columns: id, x, y and y_hat.

layout

Dataframe of layout of all nodes, must include these columns: id, x, y and y_hat.

target_cols

Character vectors representing the hex values of different level colors for targets, defaults to viridis option B.

title

Character string for plot title.

tree_space_top

Numeric value to pass to expand for top margin of tree.

tree_space_bottom

Numeric value to pass to expand for bottom margin of tree.

print_eval

Logical. If TRUE, print evaluation of the tree performance.

metrics

A set of metric functions to evaluate decision tree, defaults to common metrics for classification/regression problems. Can be defined with 'yardstick::metric_set'.

x_eval

Numeric value indicating x position to print performance statistics.

y_eval

Numeric value indicating y position to print performance statistics.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

par_node_vars

Named list containing arguments to be passed to the 'geom_node_label()' call for non-terminal nodes.

terminal_vars

Named list containing arguments to be passed to the 'geom_node_label()' call for terminal nodes.

edge_vars

Named list containing arguments to be passed to the 'geom_edge()' call for tree edges.

edge_text_vars

Named list containing arguments to be passed to the 'geom_edge_label()' call for tree edge annotations.

Value

A ggplot2 grob object of the decision tree.

Examples

x <- compute_tree(penguins, target_lab = 'species')
draw_tree(x$dat, x$fit, x$term_dat, x$layout)

Print decision tree performance according to different metrics.

Description

Print decision tree performance according to different metrics.

Usage

eval_tree(
  dat,
  target_lab = colnames(dat)[1],
  task = c("classification", "regression"),
  metrics = NULL
)

Arguments

dat

Dataframe with truths (column 'target_lab') and estimates (column 'y_hat') of samples from original dataset.

target_lab

Name of the column in data that contains target/label information.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

metrics

A set of metric functions to evaluate decision tree, defaults to common metrics for classification/regression problems. Can be defined with 'yardstick::metric_set'.

Value

Character string of the decision tree evaluation.

Examples

eval_tree(compute_tree(penguins, target_lab = 'species')$dat)

Galaxy dataset for regression.

Description

Fetched from PMLB.

Usage

galaxy

Format

An object of class data.frame with 323 rows and 5 columns.

Details

#' @format A data frame with 323 observations and 5 variables: eastwest, northsouth, angle, radialposition and target (velocity).

https://www.openml.org/d/690

Get color functions from character vectors

Description

Get color functions from character vectors

Usage

get_cols(my_cols, task, guide = FALSE)

Arguments

my_cols

Character vectors of different hex values

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

guide

A function used to create a guide or its name. Inherit from ['ggplot2::guides()'](https://ggplot2.tidyverse.org/reference/guides.html).

Select the important features to be displayed.

Description

Select features with p-value (computed from decision tree) < 'p_thres' or all features if 'show_all_feats == TRUE'.

Usage

get_disp_feats(fit, feat_names, show_all_feats, p_thres)

Arguments

fit

constparty object of the decision tree.

feat_names

Character vector specifying the feature names in dat.

show_all_feats

Logical. If TRUE, show all features regardless of 'p_thres'.

p_thres

Numeric value indicating the p-value threshold of feature importance. Feature with p-values computed from the decision tree below this value will be displayed on the heatmap.

Value

A character vector of feature names.

———————————————————————————— Get the fitted tree depending on the input 'x'.

Description

If 'x' is a data.frame object, computes conditional tree from partkit::ctree(). If 'x' is a partynode object specifying the customized tree, fit 'x' on 'data_test'. If 'x' is a party (or constparty) object specifying the precomputed tree, simply coerce 'x' to have class constparty.

Usage

get_fit(x, ...)

## Default S3 method:
get_fit(x, ...)

## S3 method for class 'partynode'
get_fit(x, data_test, target_lab, ...)

## S3 method for class 'party'
get_fit(x, data_test, target_lab, task, ...)

## S3 method for class 'data.frame'
get_fit(x, data_test, target_lab, ...)

Arguments

x

Dataframe or a 'party' or 'partynode' object representing a custom tree. If a dataframe is supplied, conditional inference tree is computed. If a custom tree is supplied, it must follow the partykit syntax: https://cran.r-project.org/web/packages/partykit/vignettes/partykit.pdf

...

Further arguments passed to each method.

data_test

Tidy test dataset. Required if 'x' is a 'partynode' object. If NULL, heatmap displays (training) data 'x'.

target_lab

Name of the column in data that contains target/label information.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

Value

Fitted object as a list with prepped 'data_test' if available.

Draws and aligns decision tree and heatmap.

Description

heat_tree() alias.

Usage

heat_tree(
  x,
  target_lab = NULL,
  data_test = NULL,
  task = c("classification", "regression"),
  feat_types = NULL,
  label_map = NULL,
  target_cols = NULL,
  target_legend = FALSE,
  clust_samps = TRUE,
  clust_target = TRUE,
  custom_layout = NULL,
  show = "heat-tree",
  heat_rel_height = 0.2,
  lev_fac = 1.3,
  panel_space = 0.001,
  print_eval = (!is.null(data_test)),
  ...
)

treeheatr(
  x,
  target_lab = NULL,
  data_test = NULL,
  task = c("classification", "regression"),
  feat_types = NULL,
  label_map = NULL,
  target_cols = NULL,
  target_legend = FALSE,
  clust_samps = TRUE,
  clust_target = TRUE,
  custom_layout = NULL,
  show = "heat-tree",
  heat_rel_height = 0.2,
  lev_fac = 1.3,
  panel_space = 0.001,
  print_eval = (!is.null(data_test)),
  ...
)

Arguments

x

Dataframe or a 'party' or 'partynode' object representing a custom tree. If a dataframe is supplied, conditional inference tree is computed. If a custom tree is supplied, it must follow the partykit syntax: https://cran.r-project.org/web/packages/partykit/vignettes/partykit.pdf

target_lab

Name of the column in data that contains target/label information.

data_test

Tidy test dataset. Required if 'x' is a 'partynode' object. If NULL, heatmap displays (training) data 'x'.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

feat_types

Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.

label_map

Named vector of the meaning of the target values, e.g., c(‘0' = ’Edible', ‘1' = ’Poisonous').

target_cols

Character vectors representing the hex values of different level colors for targets, defaults to viridis option B.

target_legend

Logical. If TRUE, target legend is drawn.

clust_samps

Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.

clust_target

Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.

custom_layout

Dataframe with 3 columns: id, x and y for manually input custom layout.

show

Character string indicating which components of the decision tree-heatmap should be drawn. Can be 'heat-tree', 'heat-only' or 'tree-only'.

heat_rel_height

Relative height of heatmap compared to whole figure (with tree).

lev_fac

Relative weight of child node positions according to their levels, commonly ranges from 1 to 1.5. 1 for parent node perfectly in the middle of child nodes.

panel_space

Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.

print_eval

Logical. If TRUE, print evaluation of the tree performance. Defaults to TRUE when 'data_test' is supplied.

...

Further arguments passed to 'draw_tree()' and/or 'draw_heat()'.

Value

A gtable/grob object of the decision tree (top) and heatmap (bottom).

Examples

heat_tree(penguins, target_lab = 'species')


heat_tree(
  x = galaxy[1:100, ],
  target_lab = 'target',
  task = 'regression',
  terminal_vars = NULL,
  tree_space_bottom = 0)

treeheatr(penguins, target_lab = 'species')

treeheatr(
  x = galaxy[1:100, ],
  target_lab = 'target',
  task = 'regression',
  terminal_vars = NULL,
  tree_space_bottom = 0)

Data of three different species of penguins.

Description

Collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network.

Usage

penguins

Format

A data frame with 344 observations and 7 variables: species, island, culmen_length_mm, culmen_depth_mm, flipper_length_mm, body_mass_g and sex.

Gorman KB, Williams TD, Fraser WR (2014). Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis). PLoS ONE 9(3): e90081. doi:10.1371/journal.pone.0090081

Details

Fetched from https://github.com/allisonhorst/penguins.

Creates smart node layout.

Description

Create node layout using a bottom-up approach (literally) and overwrites ggparty-precomputed positions in plot_data.

Usage

position_nodes(plot_data, terminal_data, custom_layout, lev_fac, panel_space)

Arguments

plot_data

Dataframe output of 'ggparty:::get_plot_data()'.

terminal_data

Dataframe of terminal node information including id and raw terminal node size.

custom_layout

Dataframe with 3 columns: id, x and y for manually input custom layout.

lev_fac

Relative weight of child node positions according to their levels, commonly ranges from 1 to 1.5. 1 for parent node perfectly in the middle of child nodes.

panel_space

Spacing between facets relative to viewport, recommended to range from 0.001 to 0.01.

Value

Dataframe with 3 columns: id, x and y of smart layout combined with custom_layout.

Apply the predicted tree on either new test data or training data.

Description

Select features with p-value (computed from decision tree) < 'p_thres' or all features if 'show_all_feats == TRUE'.

Usage

prediction_df(fit, task, clust_samps, clust_target)

Arguments

fit

constparty object of the decision tree.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

clust_samps

Logical. If TRUE, hierarchical clustering would be performed among samples within each leaf node.

clust_target

Logical. If TRUE, target/label is included in hierarchical clustering of samples within each leaf node and might yield a more interpretable heatmap.

Value

A dataframe of prediction values with scaled columns and clustered samples.

———————————————————————————— Prepare dataset

Description

———————————————————————————— Prepare dataset

Usage

prep_data(data, target_lab, task, feat_types = NULL)

Arguments

data

Original data frame with features to be converted to correct types.

target_lab

Name of the column in data that contains target/label information.

task

Character string indicating the type of problem, either 'classification' (categorical outcome) or 'regression' (continuous outcome).

feat_types

Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.

Value

List of dataframes (training + test) with proper feature types and target name.

Prepares the feature dataframes for tiles.

Description

If R does not recognize a categorical feature (input from user) as factor, converts to factor.

Usage

prepare_feats(dat, disp_feats, feat_types, clust_feats, trans_type)

Arguments

dat

Dataframe with samples from original dataset ordered according to the clustering within each leaf node.

disp_feats

Character vector specifying features to be displayed.

feat_types

Named vector indicating the type of each features, e.g., c(sex = 'factor', age = 'numeric'). If feature types are not supplied, infer from column type.

clust_feats

Logical. If TRUE, performs cluster on the features.

trans_type

Character string of 'normalize', 'scale' or 'none'. If 'scale', subtract the mean and divide by the standard deviation. If 'normalize', i.e., max-min normalize, subtract the min and divide by the max. If 'none', no transformation is applied. More information on what transformation to choose can be acquired here: https://cran.rstudio.com/package=heatmaply/vignettes/heatmaply.html#data-transformation-scaling-normalize-and-percentize

Value

A list of two dataframes (continuous and categorical) from the original dataset.

Print a ggHeatTree object. Adopted from https://github.com/daattali/ggExtra/blob/master/R/ggMarginal.R#L207-L244.

Description

ggHeatTree objects are created from heat_tree(). This is the S3 generic print method to print the result of the scatterplot with its marginal plots.

Usage

## S3 method for class 'ggHeatTree'
print(x, newpage = is.null(vp), vp = NULL, ...)

Arguments

x

ggHeatTree (gtable grob) object.

newpage

Should a new page (i.e., an empty page) be drawn before the ggHeatTree is drawn?

vp

viewpoint

...

ignored

Performs transformation on continuous variables.

Description

Performs transformation on continuous variables for the heatmap color scales.

Usage

scale_norm(x, trans_type = c("percentize", "normalize", "scale", "none"))

Arguments

x

Numeric vector.

trans_type

Character string of 'normalize', 'scale' or 'none'. If 'scale', subtract the mean and divide by the standard deviation. If 'normalize', i.e., max-min normalize, subtract the min and divide by the max. If 'none', no transformation is applied. More information on what transformation to choose can be acquired here: https://cran.rstudio.com/package=heatmaply/vignettes/heatmaply.html#data-transformation-scaling-normalize-and-percentize

Value

Numeric vector of the transformed 'x'.

Examples

scale_norm(1:5)
scale_norm(1:5, 'normalize')

Determines terminal node position.

Description

Create node layout using a bottom-up approach (literally) and overwrites ggparty-precomputed positions in plot_data.

Usage

term_node_pos(plot_data, dat)

Arguments

plot_data

Dataframe output of 'ggparty:::get_plot_data()'.

dat

Dataframe of prediction values with scaled columns and clustered samples.

Value

Dataframe with terminal node information.

External test dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18.

Description

External test dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18.

Usage

test_covid

Format

A data frame with 110 observations and 7 XGBoost-selected variables: PATIENT_ID, Lactate dehydrogenase, High sensitivity C-reactive protein, (%)lymphocyte, Admission time, Discharge time and outcome.

An interpretable mortality prediction model for COVID-19 patients. Yan et al. https://doi.org/10.1038/s42256-020-0180-7 https://github.com/HAIRLAB/Pre_Surv_COVID_19

Training dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18. Containing NAs.

Description

Training dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18. Containing NAs.

Usage

train_covid

Format

A data frame with 375 observations and 77 variables.

An interpretable mortality prediction model for COVID-19 patients. Yan et al. https://doi.org/10.1038/s42256-020-0180-7 https://github.com/HAIRLAB/Pre_Surv_COVID_19

Results of a chemical analysis of wines grown in a specific area of Italy.

Description

Three types of wine are represented in the 178 samples, with the results of 13 chemical analyses recorded for each sample.

Usage

wine

Format

A data frame with 178 observations and 14 variables: Alcohol, Malic, Ash, Alcalinity, Magnesium, Phenols, Flavanoids, Nonflavanoids, Proanthocyanins, Color, Hue, Dilution, Proline and Type (target).

Details

Import with data(wine, package = 'rattle'). Dependent variable: Type. https://rdrr.io/cran/rattle.data/man/wine.html http://archive.ics.uci.edu/ml/datasets/wine

Red variant of the Portuguese "Vinho Verde" wine.

Description

Fetched from PMLB. Physicochemical and quality of wine.

Usage

wine_quality_red

Format

A data frame with 1599 observations and 12 variables: fixed.acidity, volatile.acidity, citric.acid, residual.sugar, chlorides, free.sulfur.dioxide, total.sulfur.dioxide, density, pH, sulphates, alcohol and target (quality).

http://archive.ics.uci.edu/ml/datasets/Wine+Quality

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.