Version: 1.0.0
Title: Machine Learning and Visualization
Date: 2026-03-14
Description: Machine learning and visualization package with an 'S7' backend featuring comprehensive type checking and validation, paired with an efficient functional user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and unsupervised learning. All configuration steps are performed using setup functions and validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017) https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64.
License: GPL (≥ 3)
URL: https://www.rtemis.org, https://docs.rtemis.org/r/, https://docs.rtemis.org/r-api/
BugReports: https://github.com/rtemis-org/rtemis/issues
ByteCompile: yes
Depends: R (≥ 4.1.0)
Imports: grDevices, graphics, stats, methods, utils, S7, data.table, future, htmltools, cli
Suggests: arrow, bit64, car, colorspace, DBI, dbscan, dendextend (≥ 0.18.0), duckdb, e1071, farff, fastICA, flexclust, future.apply, future.mirai, futurize, geosphere, ggplot2, glmnet, geojsonio, glue, grid, gsubfn, haven, heatmaply, htmlwidgets, igraph, jsonlite, leaflet, leaps, lightAUC, lightgbm, matrixStats, mgcv, mice, mirai, missRanger, networkD3, NMF, openxlsx, parallelly, partykit, plotly, pROC, progressr, psych, pvclust, ranger, reactable, readxl, reticulate, ROCR, rpart, Rtsne, seqinr, sf, shapr, survival, tabnet, threejs, testthat (≥ 3.0.0), tibble, timeDate, toml, torch, uwot, vegan, vroom, withr
Encoding: UTF-8
Config/testthat/edition: 3
RoxygenNote: 7.3.3
LazyData: true
NeedsCompilation: no
Packaged: 2026-03-23 09:34:39 UTC; egenn
Author: E.D. Gennatas ORCID iD [aut, cre, cph]
Maintainer: E.D. Gennatas <gennatas@gmail.com>
Repository: CRAN
Date/Publication: 2026-03-26 10:00:02 UTC

rtemis: Advanced Machine Learning and Visualization

Description

Advanced Machine Learning & Visualization made efficient, accessible, reproducible

Online Documentation and Vignettes

https://docs.rtemis.org/r/

System Setup

There are some options you can define in your .Rprofile (usually found in your home directory), so you do not have to define each time you execute a function.

rtemis_theme

General plotting theme; set to e.g. "whiteigrid" or "darkgraygrid"

rtemis_font

Font family to use in plots.

rtemis_palette

Name of default palette to use in plots. See options by running get_palette()

Visualization

Graphics are handled using the draw family, which produces interactive plots primarily using plotly and other packages.

Supervised Learning

By convention, the last column of the data is the outcome variable, and all other columns are predictors. Convenience function set_outcome can be used to move a specified column to the end of the data. Regression and Classification is performed using train(). This function allows you to preprocess, train, tune, and test models on multiple resamples. Use available_supervised to get a list of available algorithms

Classification

For training of binary classification models, the outcome should be provided as a factor, with the second level of the factor being the 'positive' class.

Clustering

Clustering is performed using cluster(). Use available_clustering to get a list of available algorithms.

Decomposition

Decomposition is performed using decomp(). Use available_decomposition to get a list of available algorithms.

Type Documentation

Function documentation includes input type (e.g. "Character", "Integer", "Float"/"Numeric", etc). When applicable, value ranges are provided in interval notation. For example, Float: [0, 1) means floats between 0 and 1 including 0, but excluding 1. Categorical variables may include set of allowed values using curly braces. For example, Character: {"future", "mirai", "none"}.

Tabular Data

rtemis internally uses methods for efficient handling of tabular data, with support for data.frame, data.table, and tibble. If a function is documented as accepting "tabular data", it should work with any of these data structures. If a function is documented as accepting only one of these, then it should only be used with that structure. For example, some optimized data.table operations that perform in-place modifications only work with data.table objects.

Author(s)

Maintainer: E.D. Gennatas gennatas@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

Binary matrix times character vector

Description

Binary matrix times character vector

Usage

x %BC% labels

Arguments

x

A binary matrix or data.frame

labels

Character vector length equal to ncol(x)

Value

a character vector

Author(s)

EDG

Available Draw Functions

Description

Print available draw functions for visualization.

Usage

available_draw()

Value

NULL, invisibly.

Author(s)

EDG

Examples

available_draw()

Available Algorithms

Description

Print available algorithms for supervised learning, clustering, and decomposition.

Usage

available_supervised()

available_clustering()

available_decomposition()

Value

Called for its side effect of printing available algorithms.

Author(s)

EDG

Examples

available_supervised()
available_clustering()
available_decomposition()

Print available rtemis themes

Description

Print available rtemis themes

Usage

available_themes()

Value

Called for its side effect of printing available themes.

Author(s)

EDG

Examples

available_themes()

Calibrate Classification & ClassificationRes Models

Description

Generic function to calibrate binary classification models.

Usage

calibrate(
  x,
  algorithm = "isotonic",
  hyperparameters = NULL,
  verbosity = 1L,
  ...
)

Arguments

x

Classification or ClassificationRes object to calibrate.

algorithm

Character: Algorithm to use to train calibration model.

hyperparameters

Hyperparameters object: Setup using one of ⁠setup_*⁠ functions.

verbosity

Integer: Verbosity level.

...

Additional arguments passed to specific methods.

Details

The goal of calibration is to adjust the predicted probabilities of a binary classification model so that they better reflect the true probabilities (i.e. empirical risk) of the positive class.

Value

Calibrated model object.

Method-specific parameters

For Classification objects:

For ClassificationRes objects:

Author(s)

EDG

Examples

# --- Calibrate Classification ---
dat <- iris[51:150, ]
res <- resample(dat)
dat$Species <- factor(dat$Species)
dat_train <- dat[res[[1]], ]
dat_test <- dat[-res[[1]], ]

# Train GLM on a training/test split
mod_c_glm <- train(
  x = dat_train,
  dat_test = dat_test,
  algorithm = "glm"
)

# Calibrate the `Classification` by defining `predicted_probabilities` and `true_labels`,
# in this case using the training data, but it could be a separate calibration dataset.
mod_c_glm_cal <- calibrate(
  mod_c_glm,
  predicted_probabilities = mod_c_glm$predicted_prob_training,
  true_labels = mod_c_glm$y_training
)
mod_c_glm_cal

# --- Calibrate ClassificationRes ---

# Train GLM with cross-validation
resmod_c_glm <- train(
  x = dat,
  algorithm = "glm",
  outer_resampling_config = setup_Resampler(n_resamples = 3L, type = "KFold")
)

# Calibrate the `ClassificationRes` using the same resampling configuration as used for training.
resmod_c_glm_cal <- calibrate(resmod_c_glm)
resmod_c_glm_cal

Check Data

Description

Check Data

Usage

check_data(
  x,
  name = NULL,
  get_duplicates = TRUE,
  get_na_case_pct = FALSE,
  get_na_feature_pct = FALSE
)

Arguments

x

tabular data: Input to be checked.

name

Character: Name of dataset.

get_duplicates

Logical: If TRUE, check for duplicate cases.

get_na_case_pct

Logical: If TRUE, calculate percent of NA values per case.

get_na_feature_pct

Logical: If TRUE, calculate percent of NA values per feature.

Value

CheckData object.

Author(s)

EDG

Examples

n <- 1000
x <- rnormmat(n, 50, return_df = TRUE)
x$char1 <- sample(letters, n, TRUE)
x$char2 <- sample(letters, n, TRUE)
x$fct <- factor(sample(letters, n, TRUE))
x <- rbind(x, x[1, ])
x$const <- 99L
x[sample(nrow(x), 20), 3] <- NA
x[sample(nrow(x), 20), 10] <- NA
x$fct[30:35] <- NA
check_data(x)

Select an rtemis theme

Description

Select an rtemis theme

Usage

choose_theme(
  x = c("white", "whitegrid", "whiteigrid", "black", "blackgrid", "blackigrid",
    "darkgray", "darkgraygrid", "darkgrayigrid", "lightgraygrid", "mediumgraygrid"),
  override = NULL
)

Arguments

x

Character: Name of theme to select. If not defined, will use getOption("rtemis_theme", "whitegrid").

override

Optional List: Theme parameters to override defaults.

Details

If x is not defined, choose_theme() will use getOption("rtemis_theme", "whitegrid") to select the theme. This allows users to set a default theme for all rtemis plots by setting options(rtemis_theme = "theme_name") at any point.

Value

Theme object.

Author(s)

EDG

Examples

# Get default theme set by options(rtemis_theme = "theme_name").
# If not set, defaults to "whitegrid":
choose_theme()
# Get darkgraygrid theme. Same as `theme_darkgraygrid()`:
choose_theme("darkgraygrid")
# This will use the default theme, and override the foreground color to red:
choose_theme(override = list(fg = "#ff0000"))

Class Imbalance

Description

Calculate class imbalance as given by:

I = K\cdot\sum_{i=1}^K (n_i/N - 1/K)^2

where K is the number of classes, and n_i is the number of instances of class i

Usage

class_imbalance(x)

Arguments

x

Vector, factor: Outcome.

Value

Numeric.

Author(s)

EDG

Examples

# iris is perfectly balanced
class_imbalance(iris[["Species"]])
# Simulate imbalanced outcome
x <- factor(sample(c("A", "B"), size = 500L, replace = TRUE, prob = c(0.9, 0.1)))
class_imbalance(x)

Classification Metrics

Description

Classification Metrics

Usage

classification_metrics(
  true_labels,
  predicted_labels,
  predicted_prob = NULL,
  binclasspos = 2L,
  calc_auc = TRUE,
  calc_brier = TRUE,
  auc_method = "lightAUC",
  sample = character(),
  verbosity = 0L
)

Arguments

true_labels

Factor: True labels.

predicted_labels

Factor: predicted values.

predicted_prob

Numeric vector: predicted probabilities.

binclasspos

Integer: Factor level position of the positive class in binary classification.

calc_auc

Logical: If TRUE, calculate AUC. May be slow in very large datasets.

calc_brier

Logical: If TRUE, calculate Brier_Score.

auc_method

Character: "lightAUC", "pROC", "ROCR".

sample

Character: Sample name.

verbosity

Integer: Verbosity level.

Details

Note that auc_method = "pROC" is the only one that will output an AUC even if one or more predicted probabilities are NA.

Value

ClassificationMetrics object.

Author(s)

EDG

Examples

# Assume positive class is "b"
true_labels <- factor(c("a", "a", "a", "b", "b", "b", "b", "b", "b", "b"))
predicted_labels <- factor(c("a", "b", "a", "b", "b", "a", "b", "b", "b", "a"))
predicted_prob <- c(0.3, 0.55, 0.45, 0.75, 0.57, 0.3, 0.8, 0.63, 0.62, 0.39)

classification_metrics(true_labels, predicted_labels, predicted_prob)
classification_metrics(true_labels, predicted_labels, 1 - predicted_prob, binclasspos = 1L)

Clean column names

Description

Clean column names by replacing all spaces and punctuation with a single underscore

Usage

clean_colnames(x, lowercase = FALSE, uppercase = FALSE, titlecase = FALSE)

Arguments

x

Character vector OR any object with colnames() method, like matrix, data.frame, data.table, tibble, etc.

lowercase

Logical: If TRUE, convert to lowercase.

uppercase

Logical: If TRUE, convert to uppercase.

titlecase

Logical: If TRUE, convert to Title Case.

Value

Character vector with cleaned names.

Author(s)

EDG

Examples

clean_colnames(iris, lowercase = FALSE, uppercase = FALSE, titlecase = FALSE)

Clean names

Description

Clean character vector by replacing all symbols and sequences of symbols with single underscores, ensuring no name begins or ends with a symbol

Usage

clean_names(x, sep = "_", prefix_digits = "V_")

Arguments

x

Character vector.

sep

Character: Separator to replace symbols with.

prefix_digits

Character: prefix to add to names beginning with a digit. Set to NA to skip.

Value

Character vector.

Author(s)

EDG

Examples

x <- c("Patient ID", "_Date-of-Birth", "SBP (mmHg)")
x
clean_names(x)
clean_names(x, sep = " ")

Perform Clustering

Description

Perform clustering on the rows (usually cases) of a dataset.

Usage

cluster(x, algorithm = "KMeans", config = NULL, verbosity = 1L)

Arguments

x

Matrix or data.frame: Data to cluster. Rows are cases to be clustered.

algorithm

Character: Clustering algorithm.

config

List: Algorithm-specific config.

verbosity

Integer: Verbosity level.

Details

See docs.rtemis.org/r for detailed documentation.

Value

Clustering object.

Author(s)

EDG

Examples

iris_km <- cluster(exc(iris, "Species"), algorithm = "KMeans")

Color to Grayscale

Description

Convert a color to grayscale

Usage

col2grayscale(x, what = c("color", "decimal"))

Arguments

x

Color to convert to grayscale

what

Character: "color" returns a hexadecimal color, "decimal" returns a decimal between 0 and 1

Details

Uses the NTSC grayscale conversion: 0.299 * R + 0.587 * G + 0.114 * B

Value

Character: color hex code.

Author(s)

EDG

Examples

col2grayscale("red")
col2grayscale("red", "dec")

Adjust HSV Color

Description

Modify alpha, hue, saturation and value (HSV) of a color

Usage

color_adjust(color, alpha = NULL, hue = 0, sat = 0, val = 0)

Arguments

color

Input color. Any format that grDevices::col2rgb() recognizes

alpha

Numeric: Scale alpha by this amount. Future: replace with absolute setting

hue

Float: How much hue to add to color

sat

Float: How much saturation to add to color

val

Float: How much to increase value of color by

Value

Adjusted color

Author(s)

EDG

Examples

previewcolor(c(teal = "#00ffff", teal50 = color_adjust("#00ffff", alpha = 0.5)))

Format Numbers for Printing

Description

2 Decimal places, otherwise scientific notation

Usage

ddSci(x, decimal_places = 2, hi = 1e+06, as_numeric = FALSE)

Arguments

x

Vector of numbers

decimal_places

Integer: Return this many decimal places.

hi

Float: Threshold at or above which scientific notation is used.

as_numeric

Logical: If TRUE, convert to numeric before returning. This will not force all numbers to print 2 decimal places. For example: 1.2035 becomes "1.20" if as_numeric = FALSE, but 1.2 otherwise This can be helpful if you want to be able to use the output as numbers / not just for printing.

Details

Numbers will be formatted to 2 decimal places, unless this results in 0.00 (e.g. if input was .0032), in which case they will be converted to scientific notation with 2 significant figures. ddSci will return 0.00 if the input is exactly zero. This function can be used to format numbers in plots, on the console, in logs, etc.

Value

Formatted number

Author(s)

EDG

Examples

x <- .34876549
ddSci(x)
# "0.35"
x <- .00000000457823
ddSci(x)
# "4.6e-09"

Collect a lazy-read duckdb table

Description

Collect a table read with ddb_data(x, collect = FALSE)

Usage

ddb_collect(sql, progress = TRUE, returnobj = c("data.frame", "data.table"))

Arguments

sql

Character: DuckDB SQL query, usually output of ddb_data with collect = FALSE

progress

Logical: If TRUE, show progress bar

returnobj

Character: data.frame or data.table: class of object to return

Value

data.frame or data.table.

Author(s)

EDG

Examples

## Not run: 
  # Requires local CSV file; replace with your own path
  sql <- ddb_data("/Data/iris.csv", collect = FALSE)
  ir <- ddb_collect(sql)

## End(Not run)

Read CSV using DuckDB

Description

Lazy-read a CSV file, optionally: filter rows, remove duplicates, clean column names, convert character to factor, collect.

Usage

ddb_data(
  filename,
  datadir = NULL,
  sep = ",",
  header = TRUE,
  quotechar = "",
  ignore_errors = TRUE,
  make_unique = TRUE,
  select_columns = NULL,
  filter_column = NULL,
  filter_vals = NULL,
  character2factor = FALSE,
  collect = TRUE,
  progress = TRUE,
  returnobj = c("data.table", "data.frame"),
  data.table.key = NULL,
  clean_colnames = TRUE,
  verbosity = 1L
)

Arguments

filename

Character: file name; either full path or just the file name, if datadir is also provided.

datadir

Character: Optional path if filename is not full path.

sep

Character: Field delimiter/separator.

header

Logical: If TRUE, first line will be read as column names.

quotechar

Character: Quote character.

ignore_errors

Logical: If TRUE, ignore parsing errors (sometimes it's either this or no data, so).

make_unique

Logical: If TRUE, keep only unique rows.

select_columns

Character vector: Column names to select.

filter_column

Character: Name of column to filter on, e.g. "ID".

filter_vals

Numeric or Character vector: Values in filter_column to keep. filter_column to keep.

character2factor

Logical: If TRUE, convert character columns to factors.

collect

Logical: If TRUE, collect data and return structure class as defined by returnobj.

progress

Logical: If TRUE, print progress (no indication this works).

returnobj

Character: "data.frame" or "data.table" object class to return. If "data.table", data.frame object returned from DBI::dbGetQuery is passed to data.table::setDT; will add to execution time if very large, but then that's when you need a data.table.

data.table.key

Character: If set, this corresponds to a column name in the dataset. This column will be set as key in the data.table output.

clean_colnames

Logical: If TRUE, clean colnames with clean_colnames.

verbosity

Integer: Verbosity level.

Value

data.frame or data.table if collect is TRUE, otherwise a character with the SQL query

Author(s)

EDG

Examples

## Not run: 
  # Requires local CSV file; replace with your own path
  ir <- ddb_data("/Data/massive_dataset.csv",
    filter_column = "ID",
    filter_vals = 8001:9999
  )

## End(Not run)

Perform Data Decomposition

Description

Perform linear or non-linear decomposition of numeric data.

Usage

decomp(x, algorithm = "ICA", config = NULL, verbosity = 1L)

Arguments

x

Matrix or data frame: Input data.

algorithm

Character: Decomposition algorithm.

config

DecompositionConfig: Algorithm-specific config.

verbosity

Integer: Verbosity level.

Details

See docs.rtemis.org/r for detailed documentation.

Value

Decomposition object.

Author(s)

EDG

Examples

iris_pca <- decomp(exc(iris, "Species"), algorithm = "PCA")

Describe rtemis object

Description

This generic is used to provide a description of an rtemis object in plain language.

Usage

describe(x, ...)

Arguments

x

Supervised or SupervisedRes object or list of such objects.

...

Not used.

Value

A character string describing the object.

Author(s)

EDG

Examples

species_lightrf <- train(iris, algorithm = "lightrf")
describe(species_lightrf)

Describe factor

Description

Outputs a single character with names and counts of each level of the input factor.

Arguments

x

factor.

max_n

Integer: Return counts for up to this many levels.

return_ordered

Logical: If TRUE, return levels ordered by count, otherwise return in level order.

Value

Character with level counts.

Author(s)

EDG

Examples

# Small number of levels
describe(iris[["Species"]])

# Large number of levels: show top n by count
x <- factor(sample(letters, 1000, TRUE))
describe(x)
describe(x, 3)
describe(x, 3, return_ordered = FALSE)

Move data frame column

Description

Move data frame column

Usage

df_movecolumn(x, colname, to = ncol(x))

Arguments

x

data.frame.

colname

Character: Name of column you want to move.

to

Integer: Which column position to move the vector to. Default = ncol(x) i.e. the last column.

Value

data.frame

Author(s)

EDG

Examples

ir <- df_movecolumn(iris, colname = "Species", to = 1L)

Unique values per feature

Description

Get number of unique values per features

Usage

df_nunique_perfeat(x, excludeNA = FALSE)

Arguments

x

matrix or data frame input

excludeNA

Logical: If TRUE, exclude NA values from unique count.

Value

Vector, integer of length NCOL(x) with number of unique values per column/feature

Author(s)

EDG

Examples

df_nunique_perfeat(iris)

Interactive 3D Scatter Plots

Description

Draw interactive 3D scatter plots using plotly.

Usage

draw_3Dscatter(
  x,
  y = NULL,
  z = NULL,
  fit = NULL,
  cluster = NULL,
  cluster_config = NULL,
  group = NULL,
  formula = NULL,
  rsq = TRUE,
  mode = "markers",
  order_on_x = NULL,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  zlab = NULL,
  alpha = 0.8,
  bg = NULL,
  plot_bg = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  axes_square = FALSE,
  group_names = NULL,
  font_size = 16,
  marker_col = NULL,
  marker_size = 8,
  fit_col = NULL,
  fit_alpha = 0.7,
  fit_lwd = 2.5,
  tick_font_size = 12,
  spike_col = NULL,
  legend = NULL,
  legend_xy = c(0, 1),
  legend_xanchor = "left",
  legend_yanchor = "auto",
  legend_orientation = "v",
  legend_col = NULL,
  legend_bg = "#FFFFFF00",
  legend_border_col = "#FFFFFF00",
  legend_borderwidth = 0,
  legend_group_gap = 0,
  margin = list(t = 30, b = 0, l = 0, r = 0),
  fit_params = NULL,
  width = NULL,
  height = NULL,
  padding = 0,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  verbosity = 0L,
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1
)

Arguments

x

Numeric, vector/data.frame/list: x-axis data.

y

Numeric, vector/data.frame/list: y-axis data.

z

Numeric, vector/data.frame/list: z-axis data.

fit

Character: Fit method.

cluster

Character: Clustering method.

cluster_config

List: Config for clustering.

group

Factor: Grouping variable.

formula

Formula: Formula for non-linear least squares fit.

rsq

Logical: If TRUE, print R-squared values in legend if fit is set.

mode

Character, vector: "markers", "lines", "markers+lines".

order_on_x

Logical: If TRUE, order x and y on x.

main

Character: Main title.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

zlab

Character: z-axis label.

alpha

Numeric: Alpha for markers.

bg

Background color.

plot_bg

Plot background color.

theme

Theme object.

palette

Character vector: Colors to use.

axes_square

Logical: If TRUE, draw a square plot.

group_names

Character: Names for groups.

font_size

Numeric: Font size.

marker_col

Color for markers.

marker_size

Numeric: Marker size.

fit_col

Color for fit line.

fit_alpha

Numeric: Alpha for fit line.

fit_lwd

Numeric: Line width for fit line.

tick_font_size

Numeric: Tick font size.

spike_col

Spike lines color.

legend

Logical: If TRUE, draw legend.

legend_xy

Numeric: Position of legend.

legend_xanchor

Character: X anchor for legend.

legend_yanchor

Character: Y anchor for legend.

legend_orientation

Character: Orientation of legend.

legend_col

Color for legend text.

legend_bg

Color for legend background.

legend_border_col

Color for legend border.

legend_borderwidth

Numeric: Border width for legend.

legend_group_gap

Numeric: Gap between legend groups.

margin

Numeric, named list: Margins for top, bottom, left, right.

fit_params

Hyperparameters for fit.

width

Numeric: Width of plot.

height

Numeric: Height of plot.

padding

Numeric: Graph padding.

displayModeBar

Logical: If TRUE, display mode bar.

modeBar_file_format

Character: File format for mode bar.

verbosity

Integer: Verbosity level.

filename

Character: Filename to save plot.

file_width

Numeric: Width of saved file.

file_height

Numeric: Height of saved file.

file_scale

Numeric: Scale of saved file.

Details

See docs.rtemis.org/r for detailed documentation.

Note that draw_3Dscatter uses the theme's plot_bg as grid_col.

Value

A plotly object.

Author(s)

EDG

Examples


draw_3Dscatter(iris, group = iris$Species, theme = theme_darkgraygrid())

Interactive Barplots

Description

Draw interactive barplots using plotly

Usage

draw_bar(
  x,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  alpha = 1,
  horizontal = FALSE,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  barmode = c("group", "relative", "stack", "overlay"),
  group_names = NULL,
  order_by_val = FALSE,
  ylim = NULL,
  hovernames = NULL,
  feature_names = NULL,
  font_size = 16,
  annotate = FALSE,
  annotate_col = theme[["labs_col"]],
  legend = NULL,
  legend_col = NULL,
  legend_xy = c(1, 1),
  legend_orientation = "v",
  legend_xanchor = "left",
  legend_yanchor = "auto",
  hline = NULL,
  hline_col = NULL,
  hline_width = 1,
  hline_dash = "solid",
  hline_annotate = NULL,
  hline_annotation_x = 1,
  margin = list(b = 65, l = 65, t = 50, r = 10, pad = 0),
  automargin_x = TRUE,
  automargin_y = TRUE,
  padding = 0,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  verbosity = 0L
)

Arguments

x

vector (possibly named), matrix, or data.frame: If matrix or data.frame, rows are groups (can be 1 row), columns are features

main

Character: Main plot title.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

alpha

Float (0, 1]: Transparency for bar colors.

horizontal

Logical: If TRUE, plot bars horizontally

theme

Theme object.

palette

Character vector: Colors to use.

barmode

Character: Type of bar plot to make: "group", "relative", "stack", "overlay". Default = "group". Use "relative" for stacked bars, wich handles negative values correctly, unlike "stack", as of writing.

group_names

Character, vector, length = NROW(x): Group names. Default = NULL, which uses rownames(x)

order_by_val

Logical: If TRUE, order bars by increasing value. Only use for single group data.

ylim

Float, vector, length 2: y-axis limits.

hovernames

Character, vector: Optional character vector to show on hover over each bar.

feature_names

Character, vector, length = NCOL(x): Feature names. Default = NULL, which uses colnames(x)

font_size

Float: Font size for all labels.

annotate

Logical: If TRUE, annotate stacked bars

annotate_col

Color for annotations

legend

Logical: If TRUE, draw legend. Default = NULL, and will be turned on if there is more than one feature present

legend_col

Color: Legend text color. Default = NULL, determined by theme

legend_xy

Numeric, vector, length 2: x and y for plotly's legend

legend_orientation

"v" or "h" for vertical or horizontal

legend_xanchor

Character: Legend's x anchor: "left", "center", "right", "auto"

legend_yanchor

Character: Legend's y anchor: "top", "middle", "bottom", "auto"

hline

Float: If defined, draw a horizontal line at this y value.

hline_col

Color for hline.

hline_width

Float: Width for hline.

hline_dash

Character: Type of line to draw: "solid", "dot", "dash", "longdash", "dashdot", or "longdashdot"

hline_annotate

Character: Text of horizontal line annotation if hline is set

hline_annotation_x

Numeric: x position to place annotation with paper as reference. 0: to the left of the plot area; 1: to the right of the plot area

margin

Named list: plot margins.

automargin_x

Logical: If TRUE, automatically set x-axis margins

automargin_y

Logical: If TRUE, automatically set y-axis margins

padding

Integer: N pixels to pad plot.

displayModeBar

Logical: If TRUE, show plotly's modebar

modeBar_file_format

Character: "svg", "png", "jpeg", "pdf" / any output file type supported by plotly and your system

filename

Character: Path to file to save static plot.

file_width

Integer: File width in pixels for when filename is set.

file_height

Integer: File height in pixels for when filename is set.

file_scale

Numeric: If saving to file, scale plot by this number

verbosity

Integer: Verbosity level.

Details

See docs.rtemis.org/r for detailed documentation.

Value

plotly object.

Author(s)

EDG

Examples


draw_bar(VADeaths, legend_xy = c(0, 1))
draw_bar(VADeaths, legend_xy = c(1, 1), legend_xanchor = "left")
# simple individual bars
a <- c(4, 7, 2)
draw_bar(a)
# if input is a data.frame, each row is a group and each column is a feature
b <- data.frame(x = c(3, 5, 7), y = c(2, 1, 8), z = c(4, 5, 2))
rownames(b) <- c("Jen", "Ben", "Ren")
draw_bar(b)
# stacked
draw_bar(b, barmode = "stack")

Interactive Boxplots & Violin plots

Description

Draw interactive boxplots or violin plots using plotly

Usage

draw_box(
  x,
  time = NULL,
  time_bin = c("year", "quarter", "month", "day"),
  type = c("box", "violin"),
  group = NULL,
  x_transform = c("none", "scale", "minmax"),
  main = NULL,
  xlab = "",
  ylab = NULL,
  alpha = 0.6,
  bg = NULL,
  plot_bg = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  boxpoints = "outliers",
  quartilemethod = "linear",
  xlim = NULL,
  ylim = NULL,
  violin_box = TRUE,
  orientation = "v",
  annotate_n = FALSE,
  annotate_n_y = 1,
  annotate_mean = FALSE,
  annotate_meansd = FALSE,
  annotate_meansd_y = 1,
  annotate_col = theme[["labs_col"]],
  xnames = NULL,
  group_lines = FALSE,
  group_lines_dash = "dot",
  group_lines_col = NULL,
  group_lines_alpha = 0.5,
  labelify = TRUE,
  order_by_fn = NULL,
  font_size = 16,
  ylab_standoff = 18,
  legend = NULL,
  legend_col = NULL,
  legend_xy = NULL,
  legend_orientation = "v",
  legend_xanchor = "auto",
  legend_yanchor = "auto",
  xaxis_type = "category",
  cataxis_tickangle = "auto",
  margin = list(b = 65, l = 65, t = 50, r = 12, pad = 0),
  automargin_x = TRUE,
  automargin_y = TRUE,
  boxgroupgap = NULL,
  hovertext = NULL,
  show_n = FALSE,
  pvals = NULL,
  htest = "none",
  htest_compare = 0,
  htest_y = NULL,
  htest_annotate = TRUE,
  htest_annotate_x = 0,
  htest_annotate_y = -0.065,
  htest_star_col = theme[["labs_col"]],
  htest_bracket_col = theme[["labs_col"]],
  starbracket_pad = c(0.04, 0.05, 0.09),
  use_plotly_group = FALSE,
  width = NULL,
  height = NULL,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  mathjax = NULL
)

Arguments

x

Vector or List of vectors: Input

time

Date or date-time vector

time_bin

Character: "year", "quarter", "month", or "day". Period to bin by

type

Character: "box" or "violin"

group

Factor to group by

x_transform

Character: "none", "scale", or "minmax" to use raw values, scaled and centered values or min-max normalized to 0-1, respectively. Transform is applied to each variable before grouping, so that groups are comparable

main

Character: Plot title.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

alpha

Float (0, 1]: Transparency for box colors.

bg

Color: Background color.

plot_bg

Color: Background color for plot area.

theme

Theme object.

palette

Character vector: Colors to use.

boxpoints

Character or FALSE: "all", "suspectedoutliers", "outliers" See https://plotly.com/r/box-plots/#choosing-the-algorithm-for-computing-quartiles

quartilemethod

Character: "linear", "exclusive", "inclusive"

xlim

Numeric vector: x-axis limits

ylim

Numeric vector: y-axis limits

violin_box

Logical: If TRUE and type is "violin" show box within violin plot

orientation

Character: "v" or "h" for vertical, horizontal

annotate_n

Logical: If TRUE, annotate with N in each box

annotate_n_y

Numeric: y position for annotate_n

annotate_mean

Logical: If TRUE, annotate with mean of each box

annotate_meansd

Logical: If TRUE, annotate with mean (SD) of each box

annotate_meansd_y

Numeric: y position for annotate_meansd

annotate_col

Color for annotations

xnames

Character, vector, length = NROW(x): x-axis names. Default = NULL, which tries to set names automatically.

group_lines

Logical: If TRUE, add separating lines between groups of boxplots

group_lines_dash

Character: "solid", "dot", "dash", "longdash", "dashdot", or "longdashdot"

group_lines_col

Color for group_lines

group_lines_alpha

Numeric: transparency for group_lines_col

labelify

Logical: If TRUE, labelify x names

order_by_fn

Function: If defined, order boxes by increasing value of this function (e.g. median).

font_size

Float: Font size for all labels.

ylab_standoff

Numeric: Standoff for y-axis label

legend

Logical: If TRUE, draw legend.

legend_col

Color: Legend text color. Default = NULL, determined by the theme.

legend_xy

Float, vector, length 2: Relative x, y position for legend.

legend_orientation

"v" or "h" for vertical, horizontal

legend_xanchor

Character: Legend's x anchor: "left", "center", "right", "auto"

legend_yanchor

Character: Legend's y anchor: "top", "middle", "bottom", "auto"

xaxis_type

Character: "linear", "log", "date", "category", "multicategory"

cataxis_tickangle

Numeric: Angle for categorical axis tick labels

margin

Named list: plot margins.

automargin_x

Logical: If TRUE, automatically set x-axis margins

automargin_y

Logical: If TRUE, automatically set y-axis margins

boxgroupgap

Numeric: Sets the gap (in plot fraction) between boxes of the same location coordinate

hovertext

Character vector: Text to show on hover for each data point

show_n

Logical: If TRUE, show N in each box

pvals

Numeric vector: Precomputed p-values. Should correspond to each box. Bypasses htest and htest_compare. Requires group to be set

htest

Character: e.g. "t.test", "wilcox.test" to compare each box to the first box. If grouped, compare within each group to the first box. If p-value of test is less than htest.thresh, add asterisk above/ to the side of each box

htest_compare

Integer: 0: Compare all distributions against the first one; 2: Compare every second box to the one before it. Requires group to be set

htest_y

Numeric: y coordinate for htest annotation

htest_annotate

Logical: if TRUE, include htest annotation

htest_annotate_x

Numeric: x-axis paper coordinate for htest annotation

htest_annotate_y

Numeric: y-axis paper coordinate for htest annotation

htest_star_col

Color for htest annotation stars

htest_bracket_col

Color for htest annotation brackets

starbracket_pad

Numeric: Padding for htest annotation brackets

use_plotly_group

If TRUE, use plotly's group arg to group boxes.

width

Numeric: Force plot size to this width. Default = NULL, i.e. fill available space

height

Numeric: Force plot size to this height. Default = NULL, i.e. fill available space

displayModeBar

Logical: If TRUE, show plotly's modebar

modeBar_file_format

Character: "svg", "png", "jpeg", "pdf"

filename

Character: Path to file to save static plot.

file_width

Integer: File width in pixels for when filename is set.

file_height

Integer: File height in pixels for when filename is set.

file_scale

Numeric: If saving to file, scale plot by this number

mathjax

Optional Character {"local", "cdn"}: Whether to use local or CDN version of MathJax for rendering mathematical annotations.

Details

See docs.rtemis.org/r for detailed documentation.

For multiple box plots, the recommendation is:

If orientation == "h", xlab is applied to y-axis and vice versa. Similarly, x.axist.type applies to y-axis - this defaults to "category" and would not normally need changing.

Value

plotly object.

Author(s)

EDG

Examples


# A.1 Box plot of 4 variables
draw_box(iris[, 1:4])
# A.2 Grouped Box plot
draw_box(iris[, 1:4], group = iris[["Species"]])
draw_box(iris[, 1:4], group = iris[["Species"]], annotate_n = TRUE)
# B. Boxplot binned by time periods
# Synthetic data with an instantenous shift in distributions
set.seed(2021)
dat1 <- data.frame(alpha = rnorm(200, 0), beta = rnorm(200, 2), gamma = rnorm(200, 3))
dat2 <- data.frame(alpha = rnorm(200, 5), beta = rnorm(200, 8), gamma = rnorm(200, -3))
x <- rbind(dat1, dat2)
startDate <- as.Date("2019-12-04")
endDate <- as.Date("2021-03-31")
time <- seq(startDate, endDate, length.out = 400)
draw_box(x[, 1], time, "year", ylab = "alpha")
draw_box(x, time, "year", legend.xy = c(0, 1))
draw_box(x, time, "quarter", legend.xy = c(0, 1))
draw_box(x, time, "month",
  legend.orientation = "h",
  legend.xy = c(0, 1),
  legend.yanchor = "bottom"
)
# (Note how the boxplots widen when the period includes data from both dat1 and dat2)

Draw calibration plot

Description

Draw calibration plot

Usage

draw_calibration(
  true_labels,
  predicted_prob,
  n_bins = 10L,
  bin_method = c("quantile", "equidistant"),
  binclasspos = 2L,
  main = NULL,
  subtitle = NULL,
  xlab = "Mean predicted probability",
  ylab = "Empirical risk",
  show_marginal_x = TRUE,
  marginal_x_y = -0.02,
  marginal_col = NULL,
  marginal_size = 10,
  mode = "markers+lines",
  show_brier = TRUE,
  theme = choose_theme(getOption("rtemis_theme")),
  filename = NULL,
  ...
)

Arguments

true_labels

Factor or list of factors with true class labels

predicted_prob

Numeric vector or list of numeric vectors with predicted probabilities

n_bins

Integer: Number of windows to split the data into

bin_method

Character: "quantile" or "equidistant": Method to bin the estimated probabilities.

binclasspos

Integer: Index of the positive class. The convention used in the package is the second level is the positive class.

main

Character: Main title

subtitle

Character: Subtitle, placed bottom right of plot

xlab

Character: x-axis label

ylab

Character: y-axis label

show_marginal_x

Logical: Add marginal plot of distribution of estimated probabilities

marginal_x_y

Numeric: y position of marginal plot

marginal_col

Character: Color of marginal plot

marginal_size

Numeric: Size of marginal plot

mode

Character: "lines", "markers", "lines+markers": How to plot.

show_brier

Logical: If TRUE, add Brier scores to trace names.

theme

Theme object.

filename

Character: Path to save output.

...

Additional arguments passed to draw_scatter

Value

plotly object.

Author(s)

EDG

Examples


# Synthetic data with n cases
n <- 500L
true_labels <- factor(sample(c("A", "B"), n, replace = TRUE))
# Synthetic probabilities where A has mean 0.25 and B has mean 0.75
predicted_prob <- ifelse(true_labels == "A",
  rbeta(n, 2, 6),
  rbeta(n, 6, 2)
)
draw_calibration(true_labels, predicted_prob)

Plot confusion matrix

Description

Plot confusion matrix

Usage

draw_confusion(
  x,
  xlab = "Predicted",
  ylab = "Reference",
  true_col = "#43A4AC",
  false_col = "#FA9860",
  font_size = 18,
  main = NULL,
  main_y = 1,
  main_yanchor = "bottom",
  theme = choose_theme(getOption("rtemis_theme")),
  margin = list(l = 20, r = 5, b = 5, t = 20),
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1
)

Arguments

x

ClassificationMetrics object produced by classification_metrics or confusion matrix where rows are the reference and columns are the estimated classes. For binary classification, the first row and column are the positive class.

xlab

Character: x-axis label. Default is "Predicted".

ylab

Character: y-axis label. Default is "Reference".

true_col

Color for true positives & true negatives.

false_col

Color for false positives & false negatives.

font_size

Integer: font size.

main

Character: plot title.

main_y

Numeric: y position of the title.

main_yanchor

Character: y anchor of the title.

theme

Theme object.

margin

List: Plot margins.

filename

Character: file name to save the plot. Default is NULL.

file_width

Numeric: width of the file. Default is 500.

file_height

Numeric: height of the file. Default is 500.

file_scale

Numeric: scale of the file. Default is 1.

Value

plotly object.

Author(s)

EDG

Examples


# Assume positive class is "b"
true_labels <- factor(c("a", "a", "a", "b", "b", "b", "b", "b", "b", "b"))
predicted_labels <- factor(c("a", "b", "a", "b", "b", "a", "b", "b", "b", "a"))
predicted_prob <- c(0.3, 0.55, 0.45, 0.75, 0.57, 0.3, 0.8, 0.63, 0.62, 0.39)
metrics <- classification_metrics(true_labels, predicted_labels, predicted_prob)
draw_confusion(metrics)

Draw Distributions using Histograms and Density Plots

Description

Draw Distributions using Histograms and Density Plots using plotly.

Usage

draw_dist(
  x,
  type = c("density", "histogram"),
  mode = c("overlap", "ridge"),
  group = NULL,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  col = NULL,
  alpha = 0.75,
  plot_bg = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = getOption("rtemis_palette", "rtms"),
  axes_square = FALSE,
  group_names = NULL,
  font_size = 16,
  font_alpha = 0.8,
  legend = NULL,
  legend_xy = c(0, 1),
  legend_col = NULL,
  legend_bg = "#FFFFFF00",
  legend_border_col = "#FFFFFF00",
  bargap = 0.05,
  vline = NULL,
  vline_col = theme[["fg"]],
  vline_width = 1,
  vline_dash = "dot",
  text = NULL,
  text_x = 1,
  text_xref = "paper",
  text_xanchor = "left",
  text_y = 1,
  text_yref = "paper",
  text_yanchor = "top",
  text_col = theme[["fg"]],
  margin = list(b = 65, l = 65, t = 50, r = 10, pad = 0),
  automargin_x = TRUE,
  automargin_y = TRUE,
  zerolines = FALSE,
  density_kernel = "gaussian",
  density_bw = "SJ",
  histnorm = c("", "density", "percent", "probability", "probability density"),
  histfunc = c("count", "sum", "avg", "min", "max"),
  hist_n_bins = 20,
  barmode = "overlay",
  ridge_sharex = TRUE,
  ridge_y_labs = FALSE,
  ridge_order_on_mean = TRUE,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  width = NULL,
  height = NULL,
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1
)

Arguments

x

Numeric vector / data.frame / list: Input. If not a vector, each column / each element is drawn.

type

Character: "density" or "histogram".

mode

Character: "overlap", "ridge". How to plot different groups; on the same axes ("overlap"), or on separate plots with the same x-axis ("ridge").

group

Vector: Will be converted to factor; levels define group members.

main

Character: Main title for the plot.

xlab

Character: Label for the x-axis.

ylab

Character: Label for the y-axis.

col

Color: Colors for the plot.

alpha

Numeric: Alpha transparency for plot elements.

plot_bg

Color: Background color for plot area.

theme

Theme object.

palette

Character: Color palette to use.

axes_square

Logical: If TRUE, draw a square plot to fill the graphic device. Default = FALSE.

group_names

Character: Names for the groups.

font_size

Numeric: Font size for plot text.

font_alpha

Numeric: Alpha transparency for font.

legend

Logical: If TRUE, draw legend. Default = NULL, which will be set to TRUE if x is a list of more than 1 element.

legend_xy

Numeric, vector, length 2: Relative x, y position for legend. Default = c(0, 1).

legend_col

Color: Color for the legend text.

legend_bg

Color: Background color for legend.

legend_border_col

Color: Border color for legend.

bargap

Numeric: The gap between adjacent histogram bars in plot fraction.

vline

Numeric, vector: If defined, draw a vertical line at this x value(s).

vline_col

Color: Color for vline.

vline_width

Numeric: Width for vline.

vline_dash

Character: Type of line to draw: "solid", "dot", "dash", "longdash", "dashdot", or "longdashdot".

text

Character: If defined, add this text over the plot.

text_x

Numeric: x-coordinate for text.

text_xref

Character: "x": text_x refers to plot's x-axis; "paper": text_x refers to plotting area from 0-1.

text_xanchor

Character: "auto", "left", "center", "right".

text_y

Numeric: y-coordinate for text.

text_yref

Character: "y": text_y refers to plot's y-axis; "paper": text_y refers to plotting area from 0-1.

text_yanchor

Character: "auto", "top", "middle", "bottom".

text_col

Color: Color for text.

margin

List: Margins for the plot.

automargin_x

Logical: If TRUE, automatically adjust x-axis margins.

automargin_y

Logical: If TRUE, automatically adjust y-axis margins.

zerolines

Logical: If TRUE, draw lines at y = 0.

density_kernel

Character: Kernel to use for density estimation.

density_bw

Character: Bandwidth to use for density estimation.

histnorm

Character: NULL, "percent", "probability", "density", "probability density".

histfunc

Character: "count", "sum", "avg", "min", "max".

hist_n_bins

Integer: Number of bins to use if type = "histogram".

barmode

Character: Barmode for histogram. One of "overlay", "stack", "relative", "group".

ridge_sharex

Logical: If TRUE, draw single x-axis when mode = "ridge".

ridge_y_labs

Logical: If TRUE, show individual y labels when mode = "ridge".

ridge_order_on_mean

Logical: If TRUE, order groups by mean value when mode = "ridge".

displayModeBar

Logical: If TRUE, display the mode bar.

modeBar_file_format

Character: File format for mode bar. Default = "svg".

width

Numeric: Force plot size to this width. Default = NULL, i.e. fill available space.

height

Numeric: Force plot size to this height. Default = NULL, i.e. fill available space.

filename

Character: Path to file to save static plot.

file_width

Integer: File width in pixels for when filename is set.

file_height

Integer: File height in pixels for when filename is set.

file_scale

Numeric: If saving to file, scale plot by this number.

Details

See docs.rtemis.org/r for detailed documentation.

If input is data.frame, non-numeric variables will be removed.

Value

plotly object.

Author(s)

EDG

Examples


# Will automatically use only numeric columns
draw_dist(iris)
draw_dist(iris[["Sepal.Length"]], group = iris[["Species"]])

True vs. Predicted Plot

Description

A draw_scatter wrapper for plotting true vs. predicted values

Usage

draw_fit(
  x,
  y,
  xlab = "True",
  ylab = "Predicted",
  fit = "glm",
  se_fit = TRUE,
  axes_square = TRUE,
  axes_equal = TRUE,
  diagonal = TRUE,
  ...
)

Arguments

x

Numeric, vector/data.frame/list: True values. If y is NULL and NCOL(x) > 1, first two columns used as x and y, respectively

y

Numeric, vector/data.frame/list: Predicted values

xlab

Character: x-axis label.

ylab

Character: y-axis label.

fit

Character: Fit method.

se_fit

Logical: If TRUE, include standard error of the fit.

axes_square

Logical: If TRUE, draw a square plot.

axes_equal

Logical: If TRUE, set equal scaling for axes.

diagonal

Logical: If TRUE, add diagonal line.

...

Additional arguments passed to draw_scatter

Value

plotly object.

Author(s)

EDG

Examples


x <- rnorm(500)
y <- x + rnorm(500)
draw_fit(x, y)

Plot graph using networkD3

Description

Plot graph using networkD3

Usage

draw_graphD3(
  net,
  groups = NULL,
  color_scale = NULL,
  edge_col = NULL,
  node_col = NULL,
  node_alpha = 0.5,
  edge_alpha = 0.33,
  zoom = TRUE,
  legend = FALSE,
  palette = get_palette(getOption("rtemis_palette")),
  theme = choose_theme(getOption("rtemis_theme")),
  ...
)

Arguments

net

igraph network.

groups

Vector, length n nodes indicating group/cluster/community membership of nodes in net.

color_scale

D3 colorscale (e.g. networkD3::JS("d3.scaleOrdinal(d3.schemeCategory20b);")).

edge_col

Color for edges.

node_col

Color for nodes.

node_alpha

Float [0, 1]: Node opacity.

edge_alpha

Float [0, 1]: Edge opacity.

zoom

Logical: If TRUE, graph is zoomable.

legend

Logical: If TRUE, display legend for groups.

palette

Character vector: Colors to use.

theme

Theme object.

...

Additional arguments to pass to networkD3.

Value

forceNetwork object.

Author(s)

EDG

Examples


library(igraph)
g <- make_ring(10)
draw_graphD3(g)

Plot network using threejs::graphjs

Description

Interactive plotting of an igraph net using threejs.

Usage

draw_graphjs(
  net,
  vertex_size = 1,
  vertex_col = NULL,
  vertex_label_col = NULL,
  vertex_label_alpha = 0.66,
  vertex_frame_col = NA,
  vertex_label = NULL,
  vertex_shape = "circle",
  edge_col = NULL,
  edge_alpha = 0.5,
  edge_curved = 0.35,
  edge_width = 2,
  layout = c("fr", "dh", "drl", "gem", "graphopt", "kk", "lgl", "mds", "sugiyama"),
  coords = NULL,
  layout_args = list(),
  cluster = NULL,
  groups = NULL,
  cluster_config = list(),
  cluster_mark_groups = TRUE,
  cluster_color_vertices = FALSE,
  main = "",
  theme = choose_theme(getOption("rtemis_theme")),
  palette = getOption("rtemis_palette", "rtms"),
  mar = rep(0, 4),
  filename = NULL,
  verbosity = 1L,
  ...
)

Arguments

net

igraph network.

vertex_size

Numeric: Vertex size.

vertex_col

Color for vertices.

vertex_label_col

Color for vertex labels.

vertex_label_alpha

Numeric: Transparency for vertex_label_col.

vertex_frame_col

Color for vertex border (frame).

vertex_label

Character vector: Vertex labels. Default = NULL, which will keep existing names in net if any. Set to NA to avoid printing vertex labels.

vertex_shape

Character, vector, length 1 or N nodes: Vertex shape. See graphjs("vertex_shape").

edge_col

Color for edges.

edge_alpha

Numeric: Transparency for edges.

edge_curved

Numeric: Curvature of edges.

edge_width

Numeric: Edge thickness.

layout

Character: one of: "fr", "dh", "drl", "gem", "graphopt", "kk", "lgl", "mds", "sugiyama", corresponding to all the available layouts in igraph.

coords

Output of precomputed igraph layout. If provided, layout is ignored.

layout_args

List of arguments to pass to layout function.

cluster

Character: one of: "edge_betweenness", "fast_greedy", "infomap", "label_prop", "leading_eigen", "louvain", "optimal", "spinglass", "walktrap", corresponding to all the available igraph clustering functions.

groups

Output of precomputed igraph clustering. If provided, cluster is ignored.

cluster_config

List of arguments to pass to cluster function.

cluster_mark_groups

Logical: If TRUE, draw polygons to indicate clusters, if groups or cluster are defined.

cluster_color_vertices

Logical: If TRUE, color vertices by cluster membership.

main

Character: Main title.

theme

Theme object.

palette

Color vector or name of rtemis palette.

mar

Numeric vector, length 4: par's margin argument.

filename

Character: If provided, save plot to this filepath.

verbosity

Integer: Verbosity level.

...

Extra arguments to pass to igraph::plot.igraph().

Value

scatterplotThree object.

Author(s)

EDG

Examples


library(igraph)
g <- make_ring(10)
draw_graphjs(g)

Interactive Heatmaps

Description

Draw interactive heatmaps using heatmaply.

Usage

draw_heatmap(
  x,
  Rowv = TRUE,
  Colv = TRUE,
  cluster = FALSE,
  symm = FALSE,
  cellnote = NULL,
  colorgrad_n = 101,
  colors = NULL,
  space = "rgb",
  lo = "#18A3AC",
  lomid = NULL,
  mid = NULL,
  midhi = NULL,
  hi = "#F48024",
  k_row = 1,
  k_col = 1,
  grid_gap = 0,
  limits = NULL,
  margins = NULL,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  key_title = NULL,
  showticklabels = NULL,
  colorbar_len = 0.7,
  plot_method = "plotly",
  theme = choose_theme(getOption("rtemis_theme")),
  row_side_colors = NULL,
  row_side_palette = NULL,
  col_side_colors = NULL,
  col_side_palette = NULL,
  font_size = NULL,
  padding = 0,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  ...
)

Arguments

x

Input matrix.

Rowv

Logical or dendrogram. If Logical: Compute dendrogram and reorder rows. Defaults to FALSE. If dendrogram: use as is, without reordering. See more at heatmaply::heatmaply("Rowv").

Colv

Logical or dendrogram. If Logical: Compute dendrogram and reorder columns. Defaults to FALSE. If dendrogram: use as is, without reordering. See more at heatmaply::heatmaply("Colv").

cluster

Logical: If TRUE, set Rowv and Colv to TRUE.

symm

Logical: If TRUE, treat x symmetrically - x must be a square matrix.

cellnote

Matrix with values to be displayed on hover. Defaults to ddSci(x).

colorgrad_n

Integer: Number of colors in gradient. Default = 101.

colors

Character vector: Colors to use in gradient.

space

Character: Color space to use. Default = "rgb".

lo

Character: Color for low values. Default = "#18A3AC".

lomid

Character: Color for low-mid values.

mid

Character: Color for mid values.

midhi

Character: Color for mid-high values.

hi

Character: Color for high values. Default = "#F48024".

k_row

Integer: Number of desired number of groups by which to color dendrogram branches in the rows. Default = 1.

k_col

Integer: Number of desired number of groups by which to color dendrogram branches in the columns. Default = 1.

grid_gap

Integer: Space between cells. Default = 0 (no space).

limits

Float, length 2: Determine color range. Default = NULL, which automatically centers values around 0.

margins

Float, length 4: Heatmap margins.

main

Character: Main title.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

key_title

Character: Title for the color key.

showticklabels

Logical: If TRUE, show tick labels.

colorbar_len

Numeric: Length of the colorbar.

plot_method

Character: Plot method to use. Default = "plotly".

theme

Theme object.

row_side_colors

Data frame: Column names will be label names, cells should be label colors. See heatmaply::heatmaply("row_side_colors").

row_side_palette

Color palette function. See heatmaply::heatmaply("row_side_palette").

col_side_colors

Data frame: Column names will be label names, cells should be label colors. See heatmaply::heatmaply("col_side_colors").

col_side_palette

Color palette function. See heatmaply::heatmaply("col_side_palette").

font_size

Numeric: Font size.

padding

Numeric: Padding between cells.

displayModeBar

Logical: If TRUE, display the plotly mode bar.

modeBar_file_format

Character: File format for image exports from the mode bar.

filename

Character: File name to save the plot.

file_width

Numeric: Width of exported image.

file_height

Numeric: Height of exported image.

file_scale

Numeric: Scale of exported image.

...

Additional arguments to be passed to heatmaply::heatmaply.

Details

See docs.rtemis.org/r for detailed documentation. 'heatmaply' unfortunately forces loading of the 'colorspace' namespace.

Value

plotly object.'

Author(s)

EDG

Examples


x <- rnormmat(200, 20)
xcor <- cor(x)
draw_heatmap(xcor)

Plot interactive choropleth map using leaflet

Description

Plot interactive choropleth map using leaflet

Usage

draw_leaflet(
  fips,
  values,
  names = NULL,
  fillOpacity = 1,
  color_mapping = c("Numeric", "Bin"),
  col_lo = "#0290EE",
  col_hi = "#FE4AA3",
  col_na = "#303030",
  col_highlight = "#FE8A4F",
  col_interpolate = c("linear", "spline"),
  col_bins = 21,
  domain = NULL,
  weight = 0.5,
  color = "black",
  alpha = 1,
  bg_tile_provider = leaflet::providers[["CartoDB.Positron"]],
  bg_tile_alpha = 0.67,
  fg_tile_provider = leaflet::providers[["CartoDB.PositronOnlyLabels"]],
  legend_position = c("topright", "bottomright", "bottomleft", "topleft"),
  legend_alpha = 0.8,
  legend_title = NULL,
  init_lng = -98.5418083333333,
  init_lat = 39.2074138888889,
  init_zoom = 3,
  stroke = TRUE
)

Arguments

fips

Character vector: FIPS codes. (If numeric, it will be appropriately zero-padded).

values

Values to map to fips.

names

Character vector: Optional county names to appear on hover along values.

fillOpacity

Float: Opacity for fill colors.

color_mapping

Character: "Numeric" or "Bin".

col_lo

Overlay color mapped to lowest value.

col_hi

Overlay color mapped to highest value.

col_na

Color mapped to NA values.

col_highlight

Hover border color.

col_interpolate

Character: "linear" or "spline".

col_bins

Integer: Number of color bins to create if color_mapping = "Bin".

domain

Limits for mapping colors to values. Default = NULL and set to range.

weight

Float: Weight of county border lines.

color

Color of county border lines.

alpha

Float: Overlay transparency.

bg_tile_provider

Background tile (below overlay colors), one of leaflet::providers.

bg_tile_alpha

Float: Background tile transparency.

fg_tile_provider

Foreground tile (above overlay colors), one of leaflet::providers.

legend_position

Character: One of: "topright", "bottomright", "bottomleft", "topleft".

legend_alpha

Float: Legend box transparency.

legend_title

Character: Defaults to name of values variable.

init_lng

Float: Center map around this longitude (in decimal form). Default = -98.54180833333334 (US geographic center).

init_lat

Float: Center map around this latitude (in decimal form). Default = 39.207413888888894 (US geographic center).

init_zoom

Integer: Initial zoom level (depends on device, i.e. window, size).

stroke

Logical: If TRUE, draw polygon borders.

Value

leaflet object.

Author(s)

EDG

Examples


fips <- c(06075, 42101)
population <- c(874961, 1579000)
names <- c("SF", "Philly")
draw_leaflet(fips, population, names)

Interactive Pie Chart

Description

Draw interactive pie charts using plotly.

Usage

draw_pie(
  x,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  alpha = 0.8,
  bg = NULL,
  plot_bg = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  category_names = NULL,
  textinfo = "label+percent",
  font_size = 16,
  labs_col = NULL,
  legend = TRUE,
  legend_col = NULL,
  sep_col = NULL,
  margin = list(b = 50, l = 50, t = 50, r = 20),
  padding = 0,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1
)

Arguments

x

data.frame: Input: Either a) 1 numeric column with categories defined by rownames, or b) two columns, the first is category names, the second numeric or c) a numeric vector with categories defined using the category.names argument.

main

Character: Plot title. Default = NULL, which results in colnames(x)[1].

xlab

Character: x-axis label.

ylab

Character: y-axis label.

alpha

Numeric: Alpha for the pie slices.

bg

Character: Background color.

plot_bg

Character: Plot background color.

theme

Theme object.

palette

Character vector: Colors to use.

category_names

Character, vector, length = NROW(x): Category names. Default = NULL, which uses either rownames(x), or the first column of x if ncol(x) = 2.

textinfo

Character: Info to show over each slice: "label", "percent", "label+percent".

font_size

Integer: Font size for labels.

labs_col

Character: Color of labels.

legend

Logical: If TRUE, show legend.

legend_col

Character: Color for legend.

sep_col

Character: Separator color.

margin

List: Margin settings.

padding

Numeric: Padding between cells.

displayModeBar

Logical: If TRUE, display the plotly mode bar.

modeBar_file_format

Character: File format for image exports from the mode bar.

filename

Character: File name to save plot.

file_width

Integer: Width for saved file.

file_height

Integer: Height for saved file.

file_scale

Numeric: Scale for saved file.

Value

plotly object.

Author(s)

EDG

Examples


draw_pie(VADeaths[, 1, drop = FALSE])

Plot an amino acid sequence with annotations

Description

Plot an amino acid sequence with multiple site and/or region annotations.

Usage

draw_protein(
  x,
  site = NULL,
  region = NULL,
  ptm = NULL,
  cleavage_site = NULL,
  variant = NULL,
  disease_variants = NULL,
  n_per_row = NULL,
  main = NULL,
  main_xy = c(0.055, 0.975),
  main_xref = "paper",
  main_yref = "paper",
  main_xanchor = "middle",
  main_yanchor = "top",
  layout = c("simple", "grid", "1curve", "2curve"),
  show_markers = TRUE,
  show_labels = TRUE,
  font_size = 18,
  label_col = NULL,
  scatter_mode = "markers+lines",
  marker_size = 28,
  marker_col = NULL,
  marker_alpha = 1,
  marker_symbol = "circle",
  line_col = NULL,
  line_alpha = 1,
  line_width = 2,
  show_full_names = TRUE,
  region_scatter_mode = "markers+lines",
  region_style = 3,
  region_marker_size = marker_size,
  region_marker_alpha = 0.6,
  region_marker_symbol = "circle",
  region_line_dash = "solid",
  region_line_shape = "line",
  region_line_smoothing = 1,
  region_line_width = 1,
  region_line_alpha = 0.6,
  theme = choose_theme(getOption("rtemis_theme")),
  region_palette = getOption("rtemis_palette", "rtms"),
  region_outline_only = FALSE,
  region_outline_pad = 2,
  region_pad = 0.35,
  region_fill_alpha = 0.1666666,
  region_fill_shape = "line",
  region_fill_smoothing = 1,
  bpadcx = 0.5,
  bpadcy = 0.5,
  site_marker_size = marker_size,
  site_marker_symbol = marker_symbol,
  site_marker_alpha = 1,
  site_border_width = 1.5,
  site_palette = getOption("rtemis_palette", "rtms"),
  variant_col = "#FA6E1E",
  disease_variant_col = "#E266AE",
  showlegend_ptm = TRUE,
  ptm_col = NULL,
  ptm_symbol = "circle",
  ptm_offset = 0.12,
  ptm_pad = 0.35,
  ptm_marker_size = marker_size/4.5,
  clv_col = NULL,
  clv_symbol = "triangle-down",
  clv_offset = 0.12,
  clv_pad = 0.35,
  clv_marker_size = marker_size/4,
  annotate_position_every = 10,
  annotate_position_alpha = 0.5,
  annotate_position_ay = -0.4 * marker_size,
  position_font_size = font_size - 6,
  legend_xy = c(0.97, 0.954),
  legend_xanchor = "left",
  legend_yanchor = "top",
  legend_orientation = "v",
  legend_col = NULL,
  legend_bg = "#FFFFFF00",
  legend_border_col = "#FFFFFF00",
  legend_borderwidth = 0,
  legend_group_gap = 0,
  margin = list(b = 0, l = 0, t = 0, r = 0, pad = 0),
  showgrid_x = FALSE,
  showgrid_y = FALSE,
  automargin_x = TRUE,
  automargin_y = TRUE,
  xaxis_autorange = TRUE,
  yaxis_autorange = "reversed",
  scaleanchor_y = "x",
  scaleratio_y = 1,
  hoverlabel_align = "left",
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  scrollZoom = TRUE,
  filename = NULL,
  file_width = 1320,
  file_height = 990,
  file_scale = 1,
  width = NULL,
  height = NULL,
  verbosity = 1L
)

Arguments

x

Character vector: amino acid sequence (1-letter abbreviations) OR a3 object OR Character: path to JSON file OR Character: UniProt accession number.

site

Named list of lists with indices of sites. These will be highlighted by coloring the border of markers.

region

Named list of lists with indices of regions. These will be highlighted by coloring the markers and lines of regions using the palette colors.

ptm

List of post-translational modifications.

cleavage_site

List of cleavage sites.

variant

List of variant information.

disease_variants

List of disease variant information.

n_per_row

Integer: Number of amino acids to show per row.

main

Character: Main title.

main_xy

Numeric vector, length 2: x and y coordinates for title. e.g. if main_xref and main_yref are "paper": c(0.055, .975) is top left, c(.5, .975) is top and middle.

main_xref

Character: xref for title.

main_yref

Character: yref for title.

main_xanchor

Character: xanchor for title.

main_yanchor

Character: yanchor for title.

layout

Character: "1curve", "grid": type of layout to use.

show_markers

Logical: If TRUE, show amino acid markers.

show_labels

Logical: If TRUE, annotate amino acids with elements.

font_size

Integer: Font size for labels.

label_col

Color for labels.

scatter_mode

Character: Mode for scatter plot.

marker_size

Integer: Size of markers.

marker_col

Color for markers.

marker_alpha

Numeric: Alpha for markers.

marker_symbol

Character: Symbol for markers.

line_col

Color for lines.

line_alpha

Numeric: Alpha for lines.

line_width

Numeric: Width for lines.

show_full_names

Logical: If TRUE, show full names of amino acids.

region_scatter_mode

Character: Mode for scatter plot.

region_style

Integer: Style for regions.

region_marker_size

Integer: Size of region markers.

region_marker_alpha

Numeric: Alpha for region markers.

region_marker_symbol

Character: Symbol for region markers.

region_line_dash

Character: Dash for region lines.

region_line_shape

Character: Shape for region lines.

region_line_smoothing

Numeric: Smoothing for region lines.

region_line_width

Numeric: Width for region lines.

region_line_alpha

Numeric: Alpha for region lines.

theme

Theme object.

region_palette

Named list of colors for regions.

region_outline_only

Logical: If TRUE, only show outline of regions.

region_outline_pad

Numeric: Padding for region outline.

region_pad

Numeric: Padding for region.

region_fill_alpha

Numeric: Alpha for region fill.

region_fill_shape

Character: Shape for region fill.

region_fill_smoothing

Numeric: Smoothing for region fill.

bpadcx

Numeric: Padding for region border.

bpadcy

Numeric: Padding for region border.

site_marker_size

Integer: Size of site markers.

site_marker_symbol

Character: Symbol for site markers.

site_marker_alpha

Numeric: Alpha for site markers.

site_border_width

Numeric: Width for site borders.

site_palette

Named list of colors for sites.

variant_col

Color for variants.

disease_variant_col

Color for disease variants.

showlegend_ptm

Logical: If TRUE, show legend for PTMs.

ptm_col

Named list of colors for PTMs.

ptm_symbol

Character: Symbol for PTMs.

ptm_offset

Numeric: Offset for PTMs.

ptm_pad

Numeric: Padding for PTMs.

ptm_marker_size

Integer: Size of PTM markers.

clv_col

Color for cleavage site annotations.

clv_symbol

Character: Symbol for cleavage site annotations.

clv_offset

Numeric: Offset for cleavage site annotations.

clv_pad

Numeric: Padding for cleavage site annotations.

clv_marker_size

Integer: Size of cleavage site annotation markers.

annotate_position_every

Integer: Annotate every nth position.

annotate_position_alpha

Numeric: Alpha for position annotations.

annotate_position_ay

Numeric: Y offset for position annotations.

position_font_size

Integer: Font size for position annotations.

legend_xy

Numeric vector, length 2: x and y coordinates for legend.

legend_xanchor

Character: xanchor for legend.

legend_yanchor

Character: yanchor for legend.

legend_orientation

Character: Orientation for legend.

legend_col

Color for legend.

legend_bg

Color for legend background.

legend_border_col

Color for legend border.

legend_borderwidth

Numeric: Width for legend border.

legend_group_gap

Numeric: Gap between legend groups.

margin

List: Margin settings.

showgrid_x

Logical: If TRUE, show x grid.

showgrid_y

Logical: If TRUE, show y grid.

automargin_x

Logical: If TRUE, use automatic margin for x axis.

automargin_y

Logical: If TRUE, use automatic margin for y axis.

xaxis_autorange

Logical: If TRUE, use automatic range for x axis.

yaxis_autorange

Character: If TRUE, use automatic range for y axis.

scaleanchor_y

Character: Scale anchor for y axis.

scaleratio_y

Numeric: Scale ratio for y axis.

hoverlabel_align

Character: Alignment for hover label.

displayModeBar

Logical: If TRUE, display mode bar.

modeBar_file_format

Character: File format for mode bar.

scrollZoom

Logical: If TRUE, enable scroll zoom.

filename

Character: File name to save plot.

file_width

Integer: Width for saved file.

file_height

Integer: Height for saved file.

file_scale

Numeric: Scale for saved file.

width

Integer: Width for plot.

height

Integer: Height for plot.

verbosity

Integer: Verbosity level.

Value

plotly object.

Author(s)

EDG

Examples

## Not run: 
  # Reads sequence from UniProt server
  tau <- seqinr::read.fasta("https://rest.uniprot.org/uniprotkb/P10636.fasta",
    seqtype = "AA"
  )
  draw_protein(as.character(tau[[1]]))

  # or directly using the UniProt accession number:
  draw_protein("P10636")

## End(Not run)

Barplot p-values using draw_bar

Description

Plot 1 - p-values as a barplot

Usage

draw_pvals(
  x,
  xnames = NULL,
  yname = NULL,
  p_adjust_method = "none",
  pval_hline = 0.05,
  hline_col = rt_red,
  hline_dash = "dash",
  ...
)

Arguments

x

Float, vector: p-values.

xnames

Character, vector: feature names.

yname

Character: outcome name.

p_adjust_method

Character: method for p.adjust.

pval_hline

Float: Significance level at which to plot horizontal line.

hline_col

Color for pval_hline.

hline_dash

Character: type of line to draw.

...

Additional arguments passed to draw_bar.

Value

plotly object.

Author(s)

EDG

Examples


draw_pvals(c(0.01, 0.02, 0.03), xnames = c("Feature1", "Feature2", "Feature3"))

Draw ROC curve

Description

Draw ROC curve

Usage

draw_roc(
  true_labels,
  predicted_prob,
  multiclass_fill_labels = TRUE,
  main = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  legend = TRUE,
  legend_title = "Group (AUC)",
  legend_xy = c(1, 0),
  legend_xanchor = "right",
  legend_yanchor = "bottom",
  auc_dp = 3L,
  xlim = c(-0.05, 1.05),
  ylim = c(-0.05, 1.05),
  diagonal = TRUE,
  diagonal_col = NULL,
  axes_square = TRUE,
  filename = NULL,
  ...
)

Arguments

true_labels

Factor: True outcome labels.

predicted_prob

Numeric vector [0, 1]: Predicted probabilities for the positive class (i.e. second level of outcome). Or, for multiclass, a matrix of predicted probabilities with one column per class. Or, a list of such vectors/matrices to draw multiple ROC curves on the same plot.

multiclass_fill_labels

Logical: If TRUE, fill in labels for multiclass ROC curves. If FALSE, column names of predicted_prob must match levels of true_labels.

main

Character: Main title for the plot.

theme

Theme object.

palette

Character vector: Colors to use.

legend

Logical: If TRUE, draw legend.

legend_title

Character: Title for the legend.

legend_xy

Numeric vector: Position of the legend in the form c(x, y).

legend_xanchor

Character: X anchor for the legend.

legend_yanchor

Character: Y anchor for the legend.

auc_dp

Integer: Number of decimal places for AUC values.

xlim

Numeric vector: Limits for the x-axis.

ylim

Numeric vector: Limits for the y-axis.

diagonal

Logical: If TRUE, draw diagonal line.

diagonal_col

Character: Color for the diagonal line.

axes_square

Logical: If TRUE, make axes square.

filename

Character: If provided, save the plot to this file.

...

Additional arguments passed to draw_scatter.

Value

plotly object.

Author(s)

EDG

Examples


# Binary classification
true_labels <- factor(c("A", "B", "A", "A", "B", "A", "B", "B", "A", "B"))
predicted_prob <- c(0.1, 0.4, 0.35, 0.8, 0.65, 0.2, 0.9, 0.55, 0.3, 0.7)
draw_roc(true_labels, predicted_prob)

Interactive Scatter Plots

Description

Draw interactive scatter plots using plotly.

Usage

draw_scatter(
  x,
  y = NULL,
  fit = NULL,
  se_fit = FALSE,
  se_times = 1.96,
  include_fit_name = TRUE,
  cluster = NULL,
  cluster_config = list(k = 2),
  group = NULL,
  rsq = TRUE,
  mode = "markers",
  order_on_x = NULL,
  main = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  alpha = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  axes_square = FALSE,
  group_names = NULL,
  font_size = 16,
  marker_col = NULL,
  marker_size = 8,
  symbol = "circle",
  fit_col = NULL,
  fit_alpha = 0.8,
  fit_lwd = 2.5,
  line_shape = "linear",
  se_col = NULL,
  se_alpha = 0.4,
  scatter_type = "scatter",
  show_marginal_x = FALSE,
  show_marginal_y = FALSE,
  marginal_x = x,
  marginal_y = y,
  marginal_x_y = NULL,
  marginal_y_x = NULL,
  marginal_col = NULL,
  marginal_alpha = 0.333,
  marginal_size = 10,
  legend = NULL,
  legend_title = NULL,
  legend_trace = TRUE,
  legend_xy = c(0, 0.98),
  legend_xanchor = "left",
  legend_yanchor = "auto",
  legend_orientation = "v",
  legend_col = NULL,
  legend_bg = "#FFFFFF00",
  legend_border_col = "#FFFFFF00",
  legend_borderwidth = 0,
  legend_group_gap = 0,
  x_showspikes = FALSE,
  y_showspikes = FALSE,
  spikedash = "solid",
  spikemode = "across",
  spikesnap = "hovered data",
  spikecolor = NULL,
  spikethickness = 1,
  margin = list(b = 65, l = 65, t = 50, r = 10, pad = 0),
  main_y = 1.01,
  main_yanchor = "bottom",
  subtitle_x = 0.02,
  subtitle_y = 0.99,
  subtitle_xref = "paper",
  subtitle_yref = "paper",
  subtitle_xanchor = "left",
  subtitle_yanchor = "top",
  automargin_x = TRUE,
  automargin_y = TRUE,
  xlim = NULL,
  ylim = NULL,
  axes_equal = FALSE,
  diagonal = FALSE,
  diagonal_col = NULL,
  diagonal_dash = "dot",
  diagonal_alpha = 0.66,
  fit_params = NULL,
  vline = NULL,
  vline_col = theme[["fg"]],
  vline_width = 1,
  vline_dash = "dot",
  hline = NULL,
  hline_col = theme[["fg"]],
  hline_width = 1,
  hline_dash = "dot",
  hovertext = NULL,
  width = NULL,
  height = NULL,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  scrollZoom = TRUE,
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  verbosity = 0L
)

Arguments

x

Numeric, vector/data.frame/list: x-axis data. If y is NULL and NCOL(x) > 1, first two columns used as x and y, respectively.

y

Numeric, vector/data.frame/list: y-axis data.

fit

Character: Fit method.

se_fit

Logical: If TRUE, include standard error of the fit.

se_times

Numeric: Multiplier for standard error.

include_fit_name

Logical: If TRUE, include fit name in legend.

cluster

Character: Clustering method.

cluster_config

List: Config for clustering.

group

Factor: Grouping variable.

rsq

Logical: If TRUE, print R-squared values in legend if fit is set.

mode

Character, vector: "markers", "lines", "markers+lines".

order_on_x

Logical: If TRUE, order x and y on x.

main

Character: Main title.

subtitle

Character: Subtitle.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

alpha

Numeric: Alpha for markers.

theme

Theme object.

palette

Character vector: Colors to use.

axes_square

Logical: If TRUE, draw a square plot.

group_names

Character: Names for groups.

font_size

Numeric: Font size.

marker_col

Color for markers.

marker_size

Numeric: Marker size.

symbol

Character: Marker symbol.

fit_col

Color for fit line.

fit_alpha

Numeric: Alpha for fit line.

fit_lwd

Numeric: Line width for fit line.

line_shape

Character: Line shape for line plots. Options: "linear", "hv", "vh", "hvh", "vhv".

se_col

Color for standard error band.

se_alpha

Numeric: Alpha for standard error band.

scatter_type

Character: Scatter plot type.

show_marginal_x

Logical: If TRUE, add marginal distribution line markers on x-axis.

show_marginal_y

Logical: If TRUE, add marginal distribution line markers on y-axis.

marginal_x

Numeric: Data for marginal distribution on x-axis.

marginal_y

Numeric: Data for marginal distribution on y-axis.

marginal_x_y

Numeric: Y position of marginal markers on x-axis.

marginal_y_x

Numeric: X position of marginal markers on y-axis.

marginal_col

Color for marginal markers.

marginal_alpha

Numeric: Alpha for marginal markers.

marginal_size

Numeric: Size of marginal markers.

legend

Logical: If TRUE, draw legend.

legend_title

Character: Title for legend.

legend_trace

Logical: If TRUE, draw legend trace. (For when you have fit and don't want a trace for the markers.)

legend_xy

Numeric: Position of legend.

legend_xanchor

Character: X anchor for legend.

legend_yanchor

Character: Y anchor for legend.

legend_orientation

Character: Orientation of legend.

legend_col

Color for legend text.

legend_bg

Color for legend background.

legend_border_col

Color for legend border.

legend_borderwidth

Numeric: Border width for legend.

legend_group_gap

Numeric: Gap between legend groups.

x_showspikes

Logical: If TRUE, show spikes on x-axis.

y_showspikes

Logical: If TRUE, show spikes on y-axis.

spikedash

Character: Dash type for spikes.

spikemode

Character: Spike mode.

spikesnap

Character: Spike snap mode.

spikecolor

Color for spikes.

spikethickness

Numeric: Thickness of spikes.

margin

List: Plot margins.

main_y

Numeric: Y position of main title.

main_yanchor

Character: Y anchor for main title.

subtitle_x

Numeric: X position of subtitle.

subtitle_y

Numeric: Y position of subtitle.

subtitle_xref

Character: X reference for subtitle.

subtitle_yref

Character: Y reference for subtitle.

subtitle_xanchor

Character: X anchor for subtitle.

subtitle_yanchor

Character: Y anchor for subtitle.

automargin_x

Logical: If TRUE, automatically adjust x-axis margins.

automargin_y

Logical: If TRUE, automatically adjust y-axis margins.

xlim

Numeric: Limits for x-axis.

ylim

Numeric: Limits for y-axis.

axes_equal

Logical: If TRUE, set equal scaling for axes.

diagonal

Logical: If TRUE, add diagonal line.

diagonal_col

Color for diagonal line.

diagonal_dash

Character: "solid", "dash", "dot", "dashdot", "longdash", "longdashdot". Dash type for diagonal line.

diagonal_alpha

Numeric: Alpha for diagonal line.

fit_params

Hyperparameters for fit.

vline

Numeric: X position for vertical line.

vline_col

Color for vertical line.

vline_width

Numeric: Width for vertical line.

vline_dash

Character: Dash type for vertical line.

hline

Numeric: Y position for horizontal line.

hline_col

Color for horizontal line.

hline_width

Numeric: Width for horizontal line.

hline_dash

Character: Dash type for horizontal line.

hovertext

List: Hover text for markers.

width

Numeric: Width of plot.

height

Numeric: Height of plot.

displayModeBar

Logical: If TRUE, display mode bar.

modeBar_file_format

Character: File format for mode bar.

scrollZoom

Logical: If TRUE, enable scroll zoom.

filename

Character: Filename to save plot.

file_width

Numeric: Width of saved file.

file_height

Numeric: Height of saved file.

file_scale

Numeric: Scale of saved file.

verbosity

Integer: Verbosity level.

Value

plotly object.

Author(s)

EDG

Examples


draw_scatter(iris$Sepal.Length, iris$Petal.Length,
  fit = "gam", se_fit = TRUE, group = iris$Species
)

Interactive Spectrogram

Description

Draw interactive spectrograms using plotly

Usage

draw_spectrogram(
  x,
  y,
  z,
  colorgrad_n = 101,
  colors = NULL,
  xlab = "Time",
  ylab = "Frequency",
  zlab = "Power",
  hover_xlab = xlab,
  hover_ylab = ylab,
  hover_zlab = zlab,
  zmin = NULL,
  zmax = NULL,
  zauto = TRUE,
  hoverlabel_align = "right",
  colorscale = "Jet",
  colorbar_y = 0.5,
  colorbar_yanchor = "middle",
  colorbar_xpad = 0,
  colorbar_ypad = 0,
  colorbar_len = 0.75,
  colorbar_title_side = "bottom",
  showgrid = FALSE,
  space = "rgb",
  lo = "#18A3AC",
  lomid = NULL,
  mid = NULL,
  midhi = NULL,
  hi = "#F48024",
  grid_gap = 0,
  limits = NULL,
  main = NULL,
  key_title = NULL,
  showticklabels = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  font_size = NULL,
  padding = 0,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  ...
)

Arguments

x

Numeric: Time.

y

Numeric: Frequency.

z

Numeric: Power.

colorgrad_n

Integer: Number of colors in the gradient.

colors

Character: Custom colors for the gradient.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

zlab

Character: z-axis label.

hover_xlab

Character: x-axis label for hover.

hover_ylab

Character: y-axis label for hover.

hover_zlab

Character: z-axis label for hover.

zmin

Numeric: Minimum value for color scale.

zmax

Numeric: Maximum value for color scale.

zauto

Logical: If TRUE, automatically set zmin and zmax.

hoverlabel_align

Character: Alignment of hover labels.

colorscale

Character: Color scale.

colorbar_y

Numeric: Y position of colorbar.

colorbar_yanchor

Character: Y anchor of colorbar.

colorbar_xpad

Numeric: X padding of colorbar.

colorbar_ypad

Numeric: Y padding of colorbar.

colorbar_len

Numeric: Length of colorbar.

colorbar_title_side

Character: Side of colorbar title.

showgrid

Logical: If TRUE, show grid.

space

Character: Color space for gradient.

lo

Character: Low color for gradient.

lomid

Character: Low-mid color for gradient.

mid

Character: Mid color for gradient.

midhi

Character: Mid-high color for gradient.

hi

Character: High color for gradient.

grid_gap

Integer: Space between cells.

limits

Numeric, length 2: Determine color range. Default = NULL, which automatically centers values around 0.

main

Character: Main title.

key_title

Character: Title of the key.

showticklabels

Logical: If TRUE, show tick labels.

theme

Theme object.

font_size

Numeric: Font size.

padding

Numeric: Padding between cells.

displayModeBar

Logical: If TRUE, display the plotly mode bar.

modeBar_file_format

Character: File format for image exports from the mode bar.

filename

Character: Filename to save the plot. Default is NULL.

file_width

Numeric: Width of exported image.

file_height

Numeric: Height of exported image.

file_scale

Numeric: Scale of exported image.

...

Additional arguments to be passed to heatmaply::heatmaply.

Details

To set custom colors, use a minimum of lo and hi, optionally also lomid, mid, midhi colors and set colorscale = NULL.

Value

plotly object.

Author(s)

EDG

Examples


# Example data
time <- seq(0, 10, length.out = 100)
freq <- seq(1, 100, length.out = 100)
power <- outer(time, freq, function(t, f) sin(t) * cos(f))
draw_spectrogram(
  x = time,
  y = freq,
  z = power
)

Draw a survfit object

Description

Draw a survfit object using draw_scatter.

Usage

draw_survfit(
  x,
  mode = "lines",
  symbol = "cross",
  line_shape = "hv",
  xlim = NULL,
  ylim = NULL,
  xlab = "Time",
  ylab = "Survival",
  main = NULL,
  legend_xy = c(1, 1),
  legend_xanchor = "right",
  legend_yanchor = "top",
  theme = choose_theme(getOption("rtemis_theme")),
  nrisk_table = FALSE,
  filename = NULL,
  ...
)

Arguments

x

survfit object created by survival::survfit.

mode

Character, vector: "markers", "lines", "markers+lines".

symbol

Character: Symbol to use for the points.

line_shape

Character: Line shape for line plots. Options: "linear", "hv", "vh", "hvh", "vhv".

xlim

Numeric vector of length 2: x-axis limits.

ylim

Numeric vector of length 2: y-axis limits.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

main

Character: Main title.

legend_xy

Numeric: Position of legend.

legend_xanchor

Character: X anchor for legend.

legend_yanchor

Character: Y anchor for legend.

theme

Theme object.

nrisk_table

Logical: If TRUE, subplot a table of the number at risk at each time point.

filename

Character: Filename to save plot.

...

Additional arguments passed to draw_scatter.

Value

plotly object.

Author(s)

EDG

Examples


# Get the lung dataset
data(cancer, package = "survival")
sf1 <- survival::survfit(survival::Surv(time, status) ~ 1, data = lung)
draw_survfit(sf1)
sf2 <- survival::survfit(survival::Surv(time, status) ~ sex, data = lung)
draw_survfit(sf2)
# with N at risk table
draw_survfit(sf2)

Simple HTML table

Description

Draw an html table using plotly

Usage

draw_table(
  x,
  .ddSci = TRUE,
  main = NULL,
  main_col = "black",
  main_x = 0,
  main_xanchor = "auto",
  fill_col = "#18A3AC",
  table_bg = "white",
  bg = "white",
  line_col = "white",
  lwd = 1,
  header_font_col = "white",
  table_font_col = "gray20",
  font_size = 14,
  font_family = "Helvetica Neue",
  margin = list(l = 0, r = 5, t = 30, b = 0, pad = 0)
)

Arguments

x

data.frame: Table to draw

.ddSci

Logical: If TRUE, apply ddSci to numeric columns.

main

Character: Table tile.

main_col

Color: Title color.

main_x

Float [0, 1]: Align title: 0: left, .5: center, 1: right.

main_xanchor

Character: "auto", "left", "right": plotly's layout xanchor for title.

fill_col

Color: Used to fill header with column names and first column with row names.

table_bg

Color: Table background.

bg

Color: Background.

line_col

Color: Line color.

lwd

Float: Line width.

header_font_col

Color: Header font color.

table_font_col

Color: Table font color.

font_size

Integer: Font size.

font_family

Character: Font family.

margin

List: plotly's margins.

Value

plotly object.

Author(s)

EDG

Examples


 df <- data.frame(
   Name = c("Alice", "Bob", "Charlie"),
   Age = c(25, 30, 35),
   Score = c(90.5, 85.0, 88.0)
)
p <- draw_table(
  df,
  main = "Sample Table",
  main_col = "#00b2b2"
)

Interactive Timeseries Plots

Description

Draw interactive timeseries plots using plotly

Usage

draw_ts(
  x,
  time,
  window = 7L,
  group = NULL,
  roll_fn = c("mean", "median", "max", "none"),
  roll_col = NULL,
  roll_alpha = 1,
  roll_lwd = 2,
  roll_name = NULL,
  alpha = NULL,
  align = "center",
  group_names = NULL,
  xlab = "Time",
  n_xticks = 12,
  scatter_type = "scatter",
  legend = TRUE,
  x_showspikes = TRUE,
  y_showspikes = FALSE,
  spikedash = "solid",
  spikemode = "across",
  spikesnap = "hovered data",
  spikecolor = NULL,
  spikethickness = 1,
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  theme = choose_theme(getOption("rtemis_theme")),
  palette = getOption("rtemis_palette", "rtms"),
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  ...
)

Arguments

x

Numeric vector of values to plot or list of vectors

time

Numeric or Date vector of time corresponding to values of x

window

Integer: apply roll_fn over this many units of time

group

Factor defining groups

roll_fn

Character: "mean", "median", "max", or "sum": Function to apply on rolling windows of x

roll_col

Color for rolling line

roll_alpha

Numeric: transparency for rolling line

roll_lwd

Numeric: width of rolling line

roll_name

Rolling function name (for annotation)

alpha

Numeric [0, 1]: Transparency

align

Character: "center", "right", or "left"

group_names

Character vector of group names

xlab

Character: x-axis label

n_xticks

Integer: number of x-axis ticks to use (approximately)

scatter_type

Character: "scatter" or "lines"

legend

Logical: If TRUE, show legend

x_showspikes

Logical: If TRUE, show x-axis spikes on hover

y_showspikes

Logical: If TRUE, show y-axis spikes on hover

spikedash

Character: dash type string ("solid", "dot", "dash", "longdash", "dashdot", or "longdashdot") or a dash length list in px (eg "5px,10px,2px,2px")

spikemode

Character: If "toaxis", spike line is drawn from the data point to the axis the series is plotted on. If "across", the line is drawn across the entire plot area, and supercedes "toaxis". If "marker", then a marker dot is drawn on the axis the series is plotted on

spikesnap

Character: "data", "cursor", "hovered data". Determines whether spikelines are stuck to the cursor or to the closest datapoints.

spikecolor

Color for spike lines

spikethickness

Numeric: spike line thickness

displayModeBar

Logical: If TRUE, display plotly's modebar

modeBar_file_format

Character: modeBar image export file format

theme

Theme object.

palette

Character: palette name, or list of colors

filename

Character: Path to filename to save plot

file_width

Numeric: image export width

file_height

Numeric: image export height

file_scale

Numeric: image export scale

...

Additional arguments to be passed to draw_scatter

Value

plotly object.

Author(s)

EDG

Examples


time <- sample(seq(as.Date("2020-03-01"), as.Date("2020-09-23"), length.out = 140))
x1 <- rnorm(140)
x2 <- rnorm(140, 1, 1.2)
# Single timeseries
draw_ts(x1, time)
# Multiple timeseries input as list
draw_ts(list(Alpha = x1, Beta = x2), time)
# Multiple timeseries grouped by group, different lengths
time1 <- sample(seq(as.Date("2020-03-01"), as.Date("2020-07-23"), length.out = 100))
time2 <- sample(seq(as.Date("2020-05-01"), as.Date("2020-09-23"), length.out = 140))
time <- c(time1, time2)
x <- c(rnorm(100), rnorm(140, 1, 1.5))
group <- c(rep("Alpha", 100), rep("Beta", 140))
draw_ts(x, time, 7, group)

Interactive Variable Importance Plot

Description

Plot variable importance using plotly

Usage

draw_varimp(
  x,
  names = NULL,
  main = NULL,
  type = c("bar", "line"),
  xlab = NULL,
  ylab = NULL,
  plot_top = 1,
  orientation = "v",
  line_width = 12,
  labelify = TRUE,
  alpha = 1,
  palette = get_palette(getOption("rtemis_palette")),
  mar = NULL,
  font_size = 16,
  axis_font_size = 14,
  theme = choose_theme(getOption("rtemis_theme")),
  showlegend = TRUE,
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1
)

Arguments

x

Numeric vector: Input.

names

Vector, string: Names of features.

main

Character: Main title.

type

Character: "bar" or "line".

xlab

Character: x-axis label.

ylab

Character: y-axis label.

plot_top

Integer: Plot this many top features.

orientation

Character: "h" or "v".

line_width

Numeric: Line width.

labelify

Logical: If TRUE, labelify feature names.

alpha

Numeric: Transparency.

palette

Character vector: Colors to use.

mar

Vector, numeric, length 4: Plot margins in pixels (NOT inches).

font_size

Integer: Overall font size to use (essentially for the title at this point).

axis_font_size

Integer: Font size to use for axis labels and tick labels.

theme

Theme object.

showlegend

Logical: If TRUE, show legend.

filename

Character: Path to save the plot image.

file_width

Numeric: Width of the saved plot image.

file_height

Numeric: Height of the saved plot image.

file_scale

Numeric: Scale of the saved plot image.

Details

A simple plotly wrapper to plot horizontal barplots, sorted by value, which can be used to visualize variable importance, model coefficients, etc.

Value

plotly object.

Author(s)

EDG

Examples


# synthetic data
x <- rnorm(10)
names(x) <- paste0("Feature_", seq(x))
draw_varimp(x)
draw_varimp(x, orientation = "h")

Volcano Plot

Description

Volcano Plot

Usage

draw_volcano(
  x,
  pvals,
  xnames = NULL,
  group = NULL,
  x_thresh = 0,
  p_thresh = 0.05,
  p_adjust_method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  p_transform = function(x) -log10(x),
  legend = NULL,
  legend_lo = NULL,
  legend_hi = NULL,
  label_lo = "Low",
  label_hi = "High",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  margin = list(b = 65, l = 65, t = 50, r = 10, pad = 0),
  xlim = NULL,
  ylim = NULL,
  alpha = NULL,
  hline = NULL,
  hline_col = NULL,
  hline_width = 1,
  hline_dash = "solid",
  hline_annotate = NULL,
  hline_annotation_x = 1,
  theme = choose_theme(getOption("rtemis_theme")),
  annotate = TRUE,
  annotate_col = theme[["labs_col"]],
  font_size = 16,
  palette = NULL,
  legend_x_lo = NULL,
  legend_x_hi = NULL,
  legend_y = 0.97,
  annotate_n = 7L,
  ax_lo = NULL,
  ay_lo = NULL,
  ax_hi = NULL,
  ay_hi = NULL,
  annotate_alpha = 0.7,
  hovertext = NULL,
  displayModeBar = "hover",
  filename = NULL,
  file_width = 500,
  file_height = 500,
  file_scale = 1,
  verbosity = 1L,
  ...
)

Arguments

x

Numeric vector: Input values, e.g. log2 fold change, coefficients, etc.

pvals

Numeric vector: p-values.

xnames

Character vector: x names.

group

Optional factor: Used to color code points. If NULL, significant points below x_thresh, non-significant points, and significant points above x_thresh will be plotted with the first, second and third color of palette.

x_thresh

Numeric x-axis threshold separating low from high.

p_thresh

Numeric: p-value threshold of significance.

p_adjust_method

Character: p-value adjustment method. "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Default = "holm". Use "none" for raw p-values.

p_transform

function.

legend

Logical: If TRUE, show legend. Will default to FALSE, if group = NULL, otherwise to TRUE.

legend_lo

Character: Legend to annotate significant points below the x_thresh.

legend_hi

Character: Legend to annotate significant points above the x_thresh.

label_lo

Character: label for low values.

label_hi

Character: label for high values.

main

Character: Main title.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

margin

Named list of plot margins. Default = list(b = 65, l = 65, t = 50, r = 10, pad = 0).

xlim

Numeric vector, length 2: x-axis limits.

ylim

Numeric vector, length 2: y-axis limits.

alpha

Numeric: point transparency.

hline

Numeric: If defined, draw a horizontal line at this y value.

hline_col

Color for hline.

hline_width

Numeric: Width for hline.

hline_dash

Character: Type of line to draw: "solid", "dot", "dash", "longdash", "dashdot", or "longdashdot".

hline_annotate

Character: Text of horizontal line annotation if hline is set.

hline_annotation_x

Numeric: x position to place annotation with paper as reference. 0: to the left of the plot area; 1: to the right of the plot area.

theme

Theme object.

annotate

Logical: If TRUE, annotate significant points.

annotate_col

Color for annotations.

font_size

Integer: Font size.

palette

Character vector: Colors to use. If group is NULL, the first, second and third colors will be used for significant points with negative coefficients, non-significant points, and significant points with positive coefficients, respectively. If group is not NULL, colors will be assigned to groups, in order of appearance.

legend_x_lo

Numeric: x position of legend_lo.

legend_x_hi

Numeric: x position of legend_hi.

legend_y

Numeric: y position for legend_lo and legend_hi.

annotate_n

Integer: Number of significant points to annotate.

ax_lo

Numeric: Sets the x component of the arrow tail about the arrow head for significant points below x_thresh.

ay_lo

Numeric: Sets the y component of the arrow tail about the arrow head for significant points below x_thresh.

ax_hi

Numeric: Sets the x component of the arrow tail about the arrow head for significant points above x_thresh.

ay_hi

Numeric: Sets the y component of the arrow tail about the arrow head for significant points above x_thresh.

annotate_alpha

Numeric: Transparency for annotations.

hovertext

Character vector: Text to display on hover.

displayModeBar

Logical: If TRUE, display plotly mode bar.

filename

Character: Path to save the plot image.

file_width

Numeric: Width of the saved plot image.

file_height

Numeric: Height of the saved plot image.

file_scale

Numeric: Scale of the saved plot image.

verbosity

Integer: Verbosity level.

...

Additional arguments passed to draw_scatter.

Value

plotly object.

Author(s)

EDG

Examples


set.seed(2019)
y <- rnormmat(500, 500, return_df = TRUE)
x <- data.frame(x = y[, 3] + y[, 5] - y[, 9] + y[, 15] + rnorm(500))
mod <- massGLM(x, y)
draw_volcano(summary(mod)[["Coefficient_x"]], summary(mod)[["p_value_x"]])

Plot timeseries data

Description

Plot timeseries data

Usage

draw_xt(
  x,
  y,
  x2 = NULL,
  y2 = NULL,
  which_xy = NULL,
  which_xy2 = NULL,
  shade_bin = NULL,
  shade_interval = NULL,
  shade_col = NULL,
  shade_x = NULL,
  shade_name = "",
  shade_showlegend = FALSE,
  ynames = NULL,
  y2names = NULL,
  xlab = NULL,
  ylab = NULL,
  y2lab = NULL,
  xunits = NULL,
  yunits = NULL,
  y2units = NULL,
  yunits_col = NULL,
  y2units_col = NULL,
  zt = NULL,
  show_zt = TRUE,
  show_zt_every = NULL,
  zt_nticks = 18L,
  main = NULL,
  main_y = 1,
  main_yanchor = "bottom",
  x_nticks = 0,
  y_nticks = 0,
  show_rangeslider = NULL,
  slider_start = NULL,
  slider_end = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  palette = get_palette(getOption("rtemis_palette")),
  font_size = 16,
  yfill = "none",
  y2fill = "none",
  fill_alpha = 0.2,
  yline_width = 2,
  y2line_width = 2,
  x_showspikes = TRUE,
  spike_dash = "solid",
  spike_col = NULL,
  x_spike_thickness = -2,
  tickfont_size = 16,
  x_tickmode = "auto",
  x_tickvals = NULL,
  x_ticktext = NULL,
  x_tickangle = NULL,
  legend_x = 0,
  legend_y = 1.1,
  legend_xanchor = "left",
  legend_yanchor = "top",
  legend_orientation = "h",
  margin = list(l = 75, r = 75, b = 75, t = 75),
  x_standoff = 20L,
  y_standoff = 20L,
  y2_standoff = 20L,
  hovermode = "x",
  displayModeBar = TRUE,
  modeBar_file_format = "svg",
  scrollZoom = TRUE,
  filename = NULL,
  file_width = 960,
  file_height = 500,
  file_scale = 1
)

Arguments

x

Datetime vector or list of vectors.

y

Numeric vector or named list of vectors: y-axis data.

x2

Datetime vector or list of vectors, optional: must be provided if y2 does not correspond to values in x. A single x-axis will be drawn for all values in x and x2.

y2

Numeric vector, optional: If provided, a second y-axis will be added to the right side of the plot.

which_xy

Integer vector: Indices of x and y to plot. If not provided, will select up to the first two x-y traces.

which_xy2

Integer vector: Indices of x2 and y2 to plot. If not provided, will select up to the first two x2-y2 traces.

shade_bin

Integer vector {0, 1}: Time points in x to shade on the plot. For example, if there are 10 time points in x, and you want to shade time points 3 to 7, shade_bin = c(0, 0, 1, 1, 1, 1, 1, 0, 0, 0). Only set shade_bin or shade_interval, not both.

shade_interval

List of numeric vectors: Intervals to shade on the plot. Only set shade_bin or shade_interval, not both.

shade_col

Color: Color to shade intervals.

shade_x

Numeric vector: x-values to use for shading.

shade_name

Character: Name for shaded intervals.

shade_showlegend

Logical: If TRUE, show legend for shaded intervals.

ynames

Character vector, optional: Names for each vector in y.

y2names

Character vector, optional: Names for each vector in y2.

xlab

Character: x-axis label.

ylab

Character: y-axis label.

y2lab

Character: y2-axis label.

xunits

Character: x-axis units.

yunits

Character: y-axis units.

y2units

Character: y2-axis units.

yunits_col

Color for y-axis units.

y2units_col

Color for y2-axis units.

zt

Numeric vector: Zeitgeber time. If provided, will be shown on the x-axis instead of x. To be used only with a single x vector and no x2.

show_zt

Logical: If TRUE, show zt on x-axis, if zt is provided.

show_zt_every

Optional integer: Show zt every show_zt_every ticks. If NULL, will be calculated to be x_nticks +/- 1 if x_nticks is not 0, otherwise 12 +/- 1.

zt_nticks

Integer: Number of zt ticks to show. Only used if show_zt_every is NULL. The actual number of ticks shown will depend on the periodicity of zt, so that zt = 0 is always included.

main

Character: Main title.

main_y

Numeric: Y position of main title.

main_yanchor

Character: "top", "middle", "bottom".

x_nticks

Integer: Number of ticks on x-axis.

y_nticks

Integer: Number of ticks on y-axis.

show_rangeslider

Logical: If TRUE, show a range slider.

slider_start

Numeric: Start of range slider.

slider_end

Numeric: End of range slider.

theme

Theme object.

palette

Character vector: Colors to be used to draw each vector in y and y2, in order.

font_size

Numeric: Font size for text.

yfill

Character: Fill type for y-axis: "none", "tozeroy", "tonexty".

y2fill

Character: Fill type for y2-axis: "none", "tozeroy", "tonexty".

fill_alpha

Numeric: Fill opacity for y-axis.

yline_width

Numeric: Line width for y-axis lines.

y2line_width

Numeric: Line width for y2-axis lines.

x_showspikes

Logical: If TRUE, show spikes on x-axis.

spike_dash

Character: Dash type for spikes: "solid", "dot", "dash", "longdash", "dashdot", "longdashdot".

spike_col

Color for spikes.

x_spike_thickness

Numeric: Thickness of spikes. -2 avoids drawing border around spikes.

tickfont_size

Numeric: Font size for tick labels.

x_tickmode

Character: "auto", "linear", "array".

x_tickvals

Numeric vector: Tick positions.

x_ticktext

Character vector: Tick labels.

x_tickangle

Numeric: Angle of tick labels.

legend_x

Numeric: X position of legend.

legend_y

Numeric: Y position of legend.

legend_xanchor

Character: "left", "center", "right".

legend_yanchor

Character: "top", "middle", "bottom".

legend_orientation

Character: "v" for vertical, "h" for horizontal.

margin

Named list with 4 numeric values: "l", "r", "t", "b" for left, right, top, bottom margins.

x_standoff

Numeric: Distance from x-axis to x-axis label.

y_standoff

Numeric: Distance from y-axis to y-axis label.

y2_standoff

Numeric: Distance from y2-axis to y2-axis label.

hovermode

Character: "closest", "x", "x unified".

displayModeBar

Logical: If TRUE, display plotly mode bar.

modeBar_file_format

Character: "png", "svg", "jpeg", "webp", "pdf": file format for mode bar image export.

scrollZoom

Logical: If TRUE, enable zooming by scrolling.

filename

Character: Path to save the plot image.

file_width

Numeric: Width of the saved plot image.

file_height

Numeric: Height of the saved plot image.

file_scale

Numeric: Scale of the saved plot image.

Value

plotly object.

Author(s)

EDG

Examples


datetime <- seq(
  as.POSIXct("2020-01-01 00:00"),
  as.POSIXct("2020-01-02 00:00"),
  by = "hour"
)
df <- data.frame(
  datetime = datetime,
  value1 = rnorm(length(datetime)),
  value2 = rnorm(length(datetime))
)
draw_xt(df, x = df[, 1], y = df[, 2:3])

Describe data.table

Description

Describe data.table

Usage

dt_describe(x, verbosity = 1L)

Arguments

x

data.table: Input data.table.

verbosity

Integer: If > 0, print output to console.

Value

List with three data.tables: Numeric, Categorical, and Date.

Author(s)

EDG

Examples

library(data.table)
origin <- as.POSIXct("2022-01-01 00:00:00", tz = "America/Los_Angeles")
x <- data.table(
  ID = paste0("ID", 1:10),
  V1 = rnorm(10),
  V2 = rnorm(10, 20, 3),
  V1_datetime = as.POSIXct(
    seq(
      1, 1e7,
      length.out = 10
    ),
    origin = origin
  ),
  V2_datetime = as.POSIXct(
    seq(
      1, 1e7,
      length.out = 10
    ),
    origin = origin
  ),
  C1 = sample(c("alpha", "beta", "gamma"), 10, TRUE),
  F1 = factor(sample(c("delta", "epsilon", "zeta"), 10, TRUE))
)

Inspect column types

Description

Will attempt to identify columns that should be numeric but are either character or factor by running inspect_type on each column.

Usage

dt_inspect_types(x, cols = NULL, verbosity = 1L)

Arguments

x

data.table: Input data.table.

cols

Character vector: columns to inspect.

verbosity

Integer: Verbosity level.

Value

Character vector.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  id = 8001:8006,
  a = c("3", "5", "undefined", "21", "4", NA),
  b = c("mango", "banana", "tangerine", NA, "apple", "kiwi"),
  c = c(1, 2, 3, 4, 5, 6)
)
dt_inspect_types(x)

Long to wide key-value reshaping

Description

Reshape a long format data.table using key-value pairs with data.table::dcast

Usage

dt_keybin_reshape(
  x,
  id_name,
  key_name,
  positive = 1,
  negative = 0,
  xname = NULL,
  verbosity = 1L
)

Arguments

x

data.table object.

id_name

Character: Name of column in x that defines the IDs identifying individual rows.

key_name

Character: Name of column in x that holds the key.

positive

Numeric or Character: Used to fill id ~ key combination present in the long format input x.

negative

Numeric or Character: Used to fill id ~ key combination NOT present in the long format input x.

xname

Character: Name of x to be used in messages.

verbosity

Integer: Verbosity level.

Value

data.table in wide format.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  ID = rep(1:3, each = 2),
  Dx = c("A", "C", "B", "C", "D", "A")
)
dt_keybin_reshape(x, id_name = "ID", key_name = "Dx")

Merge data.tables

Description

Merge data.tables

Usage

dt_merge(
  left,
  right,
  on = NULL,
  left_on = NULL,
  right_on = NULL,
  how = "left",
  left_name = NULL,
  right_name = NULL,
  left_suffix = NULL,
  right_suffix = NULL,
  verbosity = 1L,
  ...
)

Arguments

left

data.table

right

data.table

on

Character: Name of column to join on.

left_on

Character: Name of column on left table.

right_on

Character: Name of column on right table.

how

Character: Type of join: "inner", "left", "right", "outer".

left_name

Character: Name of left table.

right_name

Character: Name of right table.

left_suffix

Character: If provided, add this suffix to all left column names, excluding on/left_on.

right_suffix

Character: If provided, add this suffix to all right column names, excluding on/right_on.

verbosity

Integer: Verbosity level.

...

Additional arguments to be passed to data.table::merge.

Value

Merged data.table.

Author(s)

EDG

Examples

library(data.table)
xleft <- data.table(ID = 1:5, Alpha = letters[1:5])
xright <- data.table(ID = c(3, 4, 5, 6), Beta = LETTERS[3:6])
xlr_inner <- dt_merge(xleft, xright, on = "ID", how = "inner")

List column names by attribute

Description

List column names by attribute

Usage

dt_names_by_attr(x, attribute, exact = TRUE, sorted = TRUE)

Arguments

x

data.table: Input data.table.

attribute

Character: name of attribute.

exact

Logical: If TRUE, use exact matching.

sorted

Logical: If TRUE, sort the output.

Value

Character vector.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  id = 1:5,
  sbp = rnorm(5, 120, 15),
  dbp = rnorm(5, 80, 10),
  paO2 = rnorm(5, 90, 10),
  paCO2 = rnorm(5, 40, 5)
)
setattr(x[["id"]], "source", "demographics")
setattr(x[["sbp"]], "source", "outpatient")
setattr(x[["dbp"]], "source", "outpatient")
setattr(x[["paO2"]], "source", "icu")
setattr(x[["paCO2"]], "source", "icu")

dt_names_by_attr(x, "source", "outpatient")

Number of unique values per feature

Description

Number of unique values per feature

Usage

dt_nunique_perfeat(x, excludeNA = FALSE, limit = 20L, verbosity = 1L)

Arguments

x

data.table: Input data.table.

excludeNA

Logical: If TRUE, exclude NA values.

limit

Integer: Print up to this many features. Set to -1L to print all.

verbosity

Integer: If > 0, print output to console.

Value

Named integer vector of length NCOL(x) with number of unique values per column/feature, invisibly.

Author(s)

EDG

Examples

library(data.table)
ir <- as.data.table(iris)
dt_nunique_perfeat(ir)

Get N and percent match of values between two columns of two data.tables

Description

Get N and percent match of values between two columns of two data.tables

Usage

dt_pctmatch(x, y, on = NULL, left_on = NULL, right_on = NULL, verbosity = 1L)

Arguments

x

data.table: First input data.table.

y

data.table: Second input data.table.

on

Integer or character: column to read in x and y, if it is the same

left_on

Integer or character: column to read in x

right_on

Integer or character: column to read in y

verbosity

Integer: Verbosity level.

Value

list.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(ID = 1:5, Alpha = letters[1:5])
y <- data.table(ID = c(3, 4, 5, 6), Beta = LETTERS[3:6])
dt_pctmatch(x, y, on = "ID")

Get percent of missing values from every column

Description

Get percent of missing values from every column

Usage

dt_pctmissing(x, verbosity = 1L)

Arguments

x

data.frame or data.table

verbosity

Integer: Verbosity level.

Value

list

Author(s)

EDG

Examples

library(data.table)
x <- data.table(a = c(1, 2, NA, 4), b = c(NA, NA, 3, 4), c = c("A", "B", "C", NA))
dt_pctmissing(x)

Set column types automatically

Description

This function inspects a data.table and attempts to identify columns that should be numeric but have been read in as character, and fixes their type in-place. This can happen when one or more fields contain non-numeric characters, for example.

Usage

dt_set_autotypes(x, cols = NULL, verbosity = 1L)

Arguments

x

data.table: Input data.table. Will be modified in-place, if needed.

cols

Character vector: columns to work on. If not defined, will work on all columns

verbosity

Integer: Verbosity level.

Value

data.table, invisibly.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  id = 8001:8006,
  a = c("3", "5", "undefined", "21", "4", NA),
  b = c("mango", "banana", "tangerine", NA, "apple", "kiwi"),
  c = c(1, 2, 3, 4, 5, 6)
)
str(x)
# ***in-place*** operation means no assignment is needed
dt_set_autotypes(x)
str(x)

# Try excluding column 'a' from autotyping
x <- data.table(
  id = 8001:8006,
  a = c("3", "5", "undefined", "21", "4", NA),
  b = c("mango", "banana", "tangerine", NA, "apple", "kiwi"),
  c = c(1, 2, 3, 4, 5, 6)
)
str(x)
# exclude column 'a' from autotyping
dt_set_autotypes(x, cols = setdiff(names(x), "a"))
str(x)

Clean column names and factor levels in-place

Description

Clean column names and factor levels in-place

Usage

dt_set_clean_all(x, prefix_digits = NA)

Arguments

x

data.table: Input data.table. Will be modified in-place, if needed.

prefix_digits

Character: prefix to add to names beginning with a digit. Set to NA to skip

Value

Nothing, modifies x in-place.

Author(s)

EDG

Examples

library(data.table)
x <- as.data.table(iris)
levels(x[["Species"]]) <- c("setosa:iris", "versicolor$iris", "virginica iris")
names(x)
levels(x[["Species"]])
# ***in-place*** operation means no assignment is needed
dt_set_clean_all(x)
names(x)
levels(x[["Species"]])

Clean factor levels of data.table in-place

Description

Finds all factors in a data.table and cleans factor levels to include only underscore symbols

Usage

dt_set_cleanfactorlevels(x, prefix_digits = NA)

Arguments

x

data.table: Input data.table. Will be modified in-place.

prefix_digits

Character: If not NA, add this prefix to all factor levels that are numbers

Value

Nothing, modifies x in-place.

Author(s)

EDG

Examples

library(data.table)
x <- as.data.table(iris)
levels(x[["Species"]]) <- c("setosa:iris", "versicolor$iris", "virginica iris")
levels(x[["Species"]])
dt_set_cleanfactorlevels(x)
levels(x[["Species"]])

Convert data.table logical columns to factors

Description

Convert data.table logical columns to factors with custom labels in-place

Usage

dt_set_logical2factor(
  x,
  cols = NULL,
  labels = c("False", "True"),
  maintain_attributes = TRUE,
  fillNA = NULL
)

Arguments

x

data.table: Input data.table. Will be modified in-place.

cols

Optional Integer or character: columns to convert. If NULL, operates on all logical columns.

labels

Character: labels for factor levels.

maintain_attributes

Logical: If TRUE, maintain column attributes.

fillNA

Optional Character: If not NULL, fill NA values with this constant.

Value

data.table, invisibly.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(a = 1:5, b = c(TRUE, FALSE, FALSE, FALSE, TRUE))
x
dt_set_logical2factor(x)
x
z <- data.table(
  alpha = 1:5,
  beta = c(TRUE, FALSE, TRUE, NA, TRUE),
  gamma = c(FALSE, FALSE, TRUE, FALSE, NA)
)
# You can usee fillNA to fill NA values with a constant
dt_set_logical2factor(z, cols = "beta", labels = c("No", "Yes"), fillNA = "No")
z
w <- data.table(mango = 1:5, banana = c(FALSE, FALSE, TRUE, TRUE, FALSE))
w
dt_set_logical2factor(w, cols = 2, labels = c("Ugh", "Huh"))
w
# Column attributes are maintained by default:
z <- data.table(
  alpha = 1:5,
  beta = c(TRUE, FALSE, TRUE, NA, TRUE),
  gamma = c(FALSE, FALSE, TRUE, FALSE, NA)
)
for (i in seq_along(z)) setattr(z[[i]], "source", "Guava")
str(z)
dt_set_logical2factor(z, cols = "beta", labels = c("No", "Yes"))
str(z)

Convert data.table's factor to one-hot encoding in-place

Description

Convert data.table's factor to one-hot encoding in-place

Usage

dt_set_one_hot(x, xname = NULL, verbosity = 1L)

Arguments

x

data.table: Input data.table. Will be modified in-place.

xname

Character, optional: Dataset name.

verbosity

Integer: Verbosity level.

Value

The input, invisibly, after it has been modified in-place.

Author(s)

EDG

Examples

ir <- data.table::as.data.table(iris)
# dt_set_one_hot operates ***in-place***; therefore no assignment is used:
dt_set_one_hot(ir)
ir

Exclude columns by character or numeric vector.

Description

Exclude columns by character or numeric vector.

Usage

exc(x, idx)

Arguments

x

tabular data.

idx

Character or numeric vector: Column names or indices to exclude.

Value

data.frame, tibble, or data.table.

Author(s)

EDG

Examples

exc(iris, "Species") |> head()
exc(iris, c(1, 3)) |> head()

Convert tabular data to feature matrix

Description

Convert a tabular dataset to a matrix, one-hot encoding factors, if present.

Usage

feature_matrix(x)

Arguments

x

tabular data: Input data to convert to a feature matrix.

Details

This is a convenience function that uses features(), preprocess(), as.matrix().

Value

Matrix with features. Factors are one-hot encoded, if present.

Author(s)

EDG

Examples

# reorder columns so that we have a categorical feature
x <- set_outcome(iris, "Sepal.Length")
feature_matrix(x) |> head()

Get feature names

Description

Returns all column names except the last one

Usage

feature_names(x)

Arguments

x

tabular data.

Details

This applied to tabular datasets used for supervised learning in rtemis, where, by convention, the last column is the outcome variable and all other columns are features.

Value

Character vector of feature names.

Author(s)

EDG

Examples

feature_names(iris)

Get features from tabular data

Description

Returns all columns except the last one.

Usage

features(x)

Arguments

x

tabular data: Input data to get features from.

Details

This can be applied to tabular datasets used for supervised learning in rtemis, where, by convention, the last column is the outcome variable and all other columns are features.

Value

Object of the same class as the input, after removing the last column.

Author(s)

EDG

Examples

features(iris) |> head()

Get factor names

Description

Get factor names

Usage

get_factor_names(x)

Arguments

x

tabular data.

Details

This applied to tabular datasets used for supervised learning in rtemis, where, by convention, the last column is the outcome variable and all other columns are features.

Value

Character vector of factor names.

Author(s)

EDG

Examples

get_factor_names(iris)

Get the mode of a factor or integer

Description

Returns the mode of a factor or integer

Usage

get_mode(x, na.rm = TRUE, getlast = TRUE, retain_class = TRUE)

Arguments

x

Vector, factor or integer: Input data.

na.rm

Logical: If TRUE, exclude NAs (using na.exclude(x)).

getlast

Logical: If TRUE, get the last value in case of ties.

retain_class

Logical: If TRUE, output is always same class as input.

Value

The mode of x

Author(s)

EDG

Examples

x <- c(9, 3, 4, 4, 0, 2, 2, NA)
get_mode(x)
x <- c(9, 3, 2, 2, 0, 4, 4, NA)
get_mode(x)
get_mode(x, getlast = FALSE)

Get Color Palette

Description

get_palette() returns a color palette (character vector of colors). Without arguments, prints names of available color palettes. Each palette is a named list of hexadecimal color definitions which can be used with any graphics function.

Usage

get_palette(palette = NULL, verbosity = 1L)

Arguments

palette

Character: Name of palette to return. Default = NULL: available palette names are printed and no palette is returned.

verbosity

Integer: Verbosity level.

Value

Character vector of colors for the specified palette, or invisibly returns list of available palettes if palette = NULL.

Author(s)

EDG

Examples

# Print available palettes
get_palette()
# Get the Imperial palette
get_palette("imperial")

Get names by string matching or class

Description

Get names by string matching or class

Usage

getnames(
  x,
  pattern = NULL,
  starts_with = NULL,
  ends_with = NULL,
  ignore_case = TRUE
)

getfactornames(x)

getnumericnames(x)

getlogicalnames(x)

getcharacternames(x)

getdatenames(x)

Arguments

x

object with names() method.

pattern

Character: pattern to match anywhere in names of x.

starts_with

Character: pattern to match in the beginning of names of x.

ends_with

Character: pattern to match at the end of names of x.

ignore_case

Logical: If TRUE, well, ignore case.

Details

For getnames() only: pattern, starts_with, and ends_with are applied sequentially. If more than one is provided, the result will be the intersection of all matches.

Value

Character vector of matched names.

Author(s)

EDG

Examples

getnames(iris, starts_with = "Sepal")
getnames(iris, ends_with = "Width")
getfactornames(iris)
getnumericnames(iris)

Get data.frame names and types

Description

Get data.frame names and types

Usage

getnamesandtypes(x)

Arguments

x

data.frame / data.table or similar

Value

character vector of column names with attribute "type" holding the class of each column

Author(s)

EDG

Examples

getnamesandtypes(iris)

Select (include) columns by character or numeric vector.

Description

Select (include) columns by character or numeric vector.

Usage

inc(x, idx)

Arguments

x

tabular data.

idx

Character or numeric vector: Column names or indices to include.

Value

data.frame, tibble, or data.table.

Author(s)

EDG

Examples

inc(iris, c(3, 4)) |> head()
inc(iris, c("Sepal.Length", "Species")) |> head()

Index columns by attribute name & value

Description

Index columns by attribute name & value

Usage

index_col_by_attr(x, name, value, exact = TRUE)

Arguments

x

tabular data.

name

Character: Name of attribute.

value

Character: Value of attribute.

exact

Logical: Passed to attr when retrieving attribute value. If TRUE, attribute name must match name exactly, otherwise, partial match is allowed.

Value

Integer vector.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  id = 1:5,
  sbp = rnorm(5, 120, 15),
  dbp = rnorm(5, 80, 10),
  paO2 = rnorm(5, 90, 10),
  paCO2 = rnorm(5, 40, 5)
)
setattr(x[["sbp"]], "source", "outpatient")
setattr(x[["dbp"]], "source", "outpatient")
setattr(x[["paO2"]], "source", "icu")
setattr(x[["paCO2"]], "source", "icu")
index_col_by_attr(x, "source", "icu")

Initialize Project Directory

Description

Initializes Directory Structure: "R", "Data", "Results"

Usage

init_project_dir(path, output_dir = "Out", verbosity = 1L)

Arguments

path

Character: Path to initialize project directory in.

output_dir

Character: Name of output directory to create.

verbosity

Integer: Verbosity level.

Value

Character: the path where the project directory was initialized, invisibly.

Author(s)

EDG

Examples

## Not run: 
# Will create "my_project" directory with
init_project_dir("my_project")

## End(Not run)

Inspect rtemis object

Description

Inspect rtemis object

Usage

inspect(x)

Arguments

x

R object to inspect.

Value

Called for side effect of printing information to console; returns character string invisibly.

Author(s)

EDG

Examples

inspect(iris)

Inspect character and factor vector

Description

Checks character or factor vector to determine whether it might be best to convert to numeric.

Usage

inspect_type(x, xname = NULL, verbosity = 1L, thresh = 0.5, na.omit = TRUE)

Arguments

x

Character or factor vector.

xname

Character: Name of input vector x.

verbosity

Integer: Verbosity level.

thresh

Numeric: Threshold for determining whether to convert to numeric.

na.omit

Logical: If TRUE, remove NA values before checking.

Details

All data can be represented as a character string. A numeric variable may be read as a character variable if there are non-numeric characters in the data. It is important to be able to automatically detect such variables and convert them, which would mean introducing NA values.

Value

Character.

Author(s)

EDG

Examples

x <- c("3", "5", "undefined", "21", "4", NA)
inspect_type(x)
z <- c("mango", "banana", "tangerine", NA)
inspect_type(z)

Check if vector is constant

Description

Check if vector is constant

Usage

is_constant(x, skip_missing = FALSE)

Arguments

x

Vector: Input

skip_missing

Logical: If TRUE, skip NA values before test

Value

Logical.

Author(s)

EDG

Examples

x <- rep(9, 1000000)
is_constant(x)
x[10] <- NA
is_constant(x)
is_constant(x, skip_missing = TRUE)

Format text for label printing

Description

Format text for label printing

Usage

labelify(
  x,
  underscores_to_spaces = TRUE,
  dotsToSpaces = TRUE,
  toLower = FALSE,
  toTitleCase = TRUE,
  capitalize_strings = c("id"),
  stringsToSpaces = c("\\$", "`")
)

Arguments

x

Character: Input

underscores_to_spaces

Logical: If TRUE, convert underscores to spaces.

dotsToSpaces

Logical: If TRUE, convert dots to spaces.

toLower

Logical: If TRUE, convert to lowercase (precedes toTitleCase). Default = FALSE (Good for getting all-caps words converted to title case, bad for abbreviations you want to keep all-caps)

toTitleCase

Logical: If TRUE, convert to Title Case. Default = TRUE (This does not change all-caps words, set toLower to TRUE if desired)

capitalize_strings

Character, vector: Always capitalize these strings, if present. Default = "id"

stringsToSpaces

Character, vector: Replace these strings with spaces. Escape as needed for gsub. Default = "\\$", which formats common input of the type data.frame$variable

Value

Character vector.

Author(s)

EDG

Examples

x <- c("county_name", "total.cost$", "age", "weight.kg")
labelify(x)

Mass-univariate GLM Analysis

Description

Mass-univariate GLM Analysis

Usage

massGLM(x, y, scale_y = NULL, center_y = NULL, verbosity = 1L)

Arguments

x

tabular data: Predictor variables. Usually a small number of covariates.

y

data.frame or similar: Each column is a different outcome. The function will train one GLM for each column of y. Usually a large number of features.

scale_y

Logical: If TRUE, scale each column of y to have mean 0 and sd 1. If NULL, defaults to TRUE if y is numeric, FALSE otherwise.

center_y

Logical: If TRUE, center each column of y to have mean 0. If NULL, defaults to TRUE if scale_y is TRUE, FALSE otherwise.

verbosity

Integer: Verbosity level.

Value

MassGLM object.

Author(s)

EDG

Examples

set.seed(2022)
y <- rnormmat(500, 40, return_df = TRUE)
x <- data.frame(
  x1 = y[[3]] - y[[5]] + y[[14]] + rnorm(500),
  x2 = y[[21]] + rnorm(500)
)
massmod <- massGLM(x, y)
# Print table of coefficients, p-values, etc. for all models
summary(massmod)

Match cases by covariates

Description

Find one or more cases from a pool data.frame that match cases in a target data.frame. Match exactly and/or by distance (sum of squared distances).

Usage

matchcases(
  target,
  pool,
  n_matches = 1,
  target_id = NULL,
  pool_id = NULL,
  exactmatch_factors = TRUE,
  exactmatch_cols = NULL,
  distmatch_cols = NULL,
  norepeats = TRUE,
  ignore_na = FALSE,
  verbosity = 1L
)

Arguments

target

data.frame you are matching against.

pool

data.frame you are looking for matches from.

n_matches

Integer: Number of matches to return.

target_id

Character: Column name in target that holds unique cases IDs. Default = NULL, in which case integer case numbers will be used.

pool_id

Character: Same as target_id for pool.

exactmatch_factors

Logical: If TRUE, selected cases will have to exactly match factors available in target.

exactmatch_cols

Character: Names of columns that should be matched exactly.

distmatch_cols

Character: Names of columns that should be distance-matched.

norepeats

Logical: If TRUE, cases in pool can only be chosen once.

ignore_na

Logical: If TRUE, ignore NA values during exact matching.

verbosity

Integer: Verbosity level.

Value

data.frame

Author(s)

EDG

Examples

set.seed(2021)
cases <- data.frame(
  PID = paste0("PID", seq(4)),
  Sex = factor(c(1, 1, 0, 0)),
  Handedness = factor(c(1, 1, 0, 1)),
  Age = c(21, 27, 39, 24),
  Var = c(.7, .8, .9, .6),
  Varx = rnorm(4)
)
controls <- data.frame(
  CID = paste0("CID", seq(50)),
  Sex = factor(sample(c(0, 1), 50, TRUE)),
  Handedness = factor(sample(c(0, 1), 50, TRUE, c(.1, .9))),
  Age = sample(16:42, 50, TRUE),
  Var = rnorm(50),
  Vary = rnorm(50)
)

mc <- matchcases(cases, controls, 2, "PID", "CID")

Get names by string matching multiple patterns

Description

Get names by string matching multiple patterns

Usage

mgetnames(
  x,
  pattern = NULL,
  starts_with = NULL,
  ends_with = NULL,
  ignore_case = TRUE,
  return_index = FALSE
)

Arguments

x

Character vector or object with names() method.

pattern

Character vector: pattern(s) to match anywhere in names of x.

starts_with

Character: pattern to match in the beginning of names of x.

ends_with

Character: pattern to match at the end of names of x.

ignore_case

Logical: If TRUE, well, ignore case.

return_index

Logical: If TRUE, return integer index of matches instead of names.

Details

pattern, starts_with, and ends_with are applied and the union of all matches is returned. pattern can be a character vector of multiple patterns to match.

Value

Character vector of matched names or integer index.

Author(s)

EDG

Examples

mgetnames(iris, pattern = c("Sepal", "Petal"))
mgetnames(iris, starts_with = "Sepal")
mgetnames(iris, ends_with = "Width")

List column names by class

Description

List column names by class

Usage

names_by_class(x, sorted = TRUE, item_format = highlight, maxlength = 24)

Arguments

x

tabular data.

sorted

Logical: If TRUE, sort the output

item_format

Function: Function to format each item

maxlength

Integer: Maximum number of items to print

Value

NULL, invisibly.

Author(s)

EDG

Examples

names_by_class(iris)

Convert one-hot encoded matrix to factor

Description

Convert one-hot encoded matrix to factor

Usage

one_hot2factor(x, labels = colnames(x))

Arguments

x

one-hot encoded matrix or data.frame.

labels

Character vector of level names.

Details

If input has a single column, it will be converted to factor and returned

Value

A factor.

Author(s)

EDG

Examples

x <- data.frame(matrix(FALSE, 10, 3))
colnames(x) <- c("Dx1", "Dx2", "Dx3")
x$Dx1[1:3] <- x$Dx2[4:6] <- x$Dx3[7:10] <- TRUE
one_hot2factor(x)

Get the outcome as a vector

Description

Returns the last column of x, which is by convention the outcome variable.

Usage

outcome(x)

Arguments

x

tabular data.

Details

This applied to tabular datasets used for supervised learning in rtemis, where, by convention, the last column is the outcome variable and all other columns are features.

Value

Vector containing the last column of x.

Author(s)

EDG

Examples

outcome(iris)

Get the name of the last column

Description

Get the name of the last column

Usage

outcome_name(x)

Arguments

x

tabular data.

Details

This applied to tabular datasets used for supervised learning in rtemis, where, by convention, the last column is the outcome variable and all other columns are features.

Value

Name of the last column.

Author(s)

EDG

Examples

outcome_name(iris)

Plot MassGLM using volcano plot

Description

Plot MassGLM using volcano plot

Usage

## S3 method for class 'MassGLM'
plot(
  x,
  coefname = NULL,
  p_adjust_method = "holm",
  p_transform = function(x) -log10(x),
  xlab = "Coefficient",
  ylab = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  verbosity = 1L,
  ...
)

Arguments

x

MassGLM object trained using massGLM.

coefname

Character: Name of coefficient to plot. If NULL, the first coefficient is used.

p_adjust_method

Character: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" - p-value adjustment method.

p_transform

Function to transform p-values for plotting. Default is function(x) -log10(x).

xlab

Character: x-axis label.

ylab

Character: y-axis label.

theme

Theme object. Create using one of the theme_ functions, e.g. theme_whitegrid().

verbosity

Integer: Verbosity level.

...

Additional arguments passed to draw_volcano.

Value

plotly object with volcano plot.

Author(s)

EDG

Examples


set.seed(2019)
y <- rnormmat(500, 500, return_df = TRUE)
x <- data.frame(x = y[, 3] + y[, 5] - y[, 9] + y[, 15] + rnorm(500))
mod <- massGLM(x, y)
plot(mod)

Manhattan plot

Description

Draw a Manhattan plot for MassGLM objects created with massGLM.

Usage

plot_manhattan(x, ...)

plot_manhattan.MassGLM(
  x,
  coefname = NULL,
  p_adjust_method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  p_transform = function(x) -log10(x),
  ylab = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  col_pos = "#43A4AC",
  col_neg = "#FA9860",
  alpha = 0.8,
  ...
)

Arguments

x

MassGLM object.

...

Additional arguments passed to draw_bar.

coefname

Character: Name of coefficient to plot. If NULL, the first coefficient is used.

p_adjust_method

Character: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" - p-value adjustment method.

p_transform

Function to transform p-values for plotting. Default is function(x) -log10(x).

ylab

Character: y-axis label.

theme

Theme object.

col_pos

Character: Color for positive significant coefficients.

col_neg

Character: Color for negative significant coefficients.

alpha

Numeric: Transparency level for the bars.

Value

plotly object.

Author(s)

EDG

Examples


# x: outcome of interest as first column, optional covariates in the other columns
# y: features whose association with x we want to study
set.seed(2022)
y <- data.table(rnormmat(500, 40))
x <- data.table(
  x1 = y[[3]] - y[[5]] + y[[14]] + rnorm(500),
  x2 = y[[21]] + rnorm(500)
)
massmod <- massGLM(x, y)
plot_manhattan(massmod)

Plot ROC curve

Description

This generic is used to plot the ROC curve for a model.

Usage

plot_roc(x, ...)

Arguments

x

Classification or ClassificationRes object.

...

Additional arguments passed to the plotting function.

Value

A plotly object containing the ROC curve.

Author(s)

EDG

Examples

ir <- iris[51:150, ]
ir[["Species"]] <- factor(ir[["Species"]])
species_glm <- train(ir, algorithm = "GLM")
plot_roc(species_glm)

Plot True vs. Predicted Values

Description

Plot True vs. Predicted Values for Supervised objects. For classification, it plots a confusion matrix. For regression, it plots a scatter plot of true vs. predicted values.

Usage

plot_true_pred(x, ...)

Arguments

x

Supervised or SupervisedRes object.

...

Additional arguments passed to methods.

Value

plotly object.

Author(s)

EDG

Examples

x <- set_outcome(iris, "Sepal.Length")
sepallength_glm <- train(x, algorithm = "GLM")
plot_true_pred(sepallength_glm)

Plot Variable Importance

Description

Plot Variable Importance for Supervised objects.

Usage

plot_varimp(x, ...)

Arguments

x

Supervised or SupervisedRes object.

...

Additional arguments passed to methods.

Details

This method calls draw_varimp internally. If you pass an integer to the plot_top argument, the method will plot this many top features. If you pass a number between 0 and 1 to the plot_top argument, the method will plot this fraction of top features.

Value

plotly object or invisible NULL if no variable importance is available.

Author(s)

EDG

See Also

draw_varimp, which is called by this method

Examples


ir <- set_outcome(iris, "Sepal.Length")
seplen_cart <- train(ir, algorithm = "CART")
plot_varimp(seplen_cart)
# Plot horizontally
plot_varimp(seplen_cart, orientation = "h")
plot_varimp(seplen_cart, orientation = "h", plot_top = 3L)
plot_varimp(seplen_cart, orientation = "h", plot_top = 0.5)

Preprocess Data

Description

Preprocess data for analysis and visualization.

Usage

preprocess(x, config, ...)

preprocess.class_tabular.PreprocessorConfig(
  x,
  config,
  dat_validation = NULL,
  dat_test = NULL,
  verbosity = 1L
)

preprocess.class_tabular.Preprocessor(x, config, verbosity = 1L)

Arguments

x

data.frame, data.table, tbl_df (tabular data): Data to be preprocessed.

config

PreprocessorConfig: Setup using setup_Preprocessor OR Preprocessor object: Output of previous run of preprocess. This allows, for example, applying preprocessing to a validation or test set using the same parameters as were used for the training set. In particular, the same scale centers and coefficients will be applied to the new data.

...

Not used.

dat_validation

tabular data: Validation set data.

dat_test

tabular data: Test set data.

verbosity

Integer: Verbosity level.

Details

Methods are provided for preprocessing training set data, which accepts a PreprocessorConfig object, and for preprocessing validation and test set data, which accept a Preprocessor object.

Value

Preprocessor object.

Author(s)

EDG

Examples

# Setup a `Preprocessor`: this outputs a `PreprocessorConfig` object.
prp <- setup_Preprocessor(remove_duplicates = TRUE, scale = TRUE, center = TRUE)

# Includes a long list of parameters
prp

# Resample iris to get train and test data
res <- resample(iris, setup_Resampler(seed = 2026))
iris_train <- iris[res[[1]], ]
iris_test <- iris[-res[[1]], ]

# Preprocess training data
iris_pre <- preprocess(iris_train, prp)

# Access preprocessd training data with `preprocessed()`
preprocessed(iris_pre)

# Apply the same preprocessing to test data
# In this case, the scale and center values from training data will be used.
# Note how `preprocess()` accepts either a `PreprocessorConfig` or `Preprocessor` object for
# this reason.
iris_test_pre <- preprocess(iris_test, iris_pre)

# Access preprocessed test data
preprocessed(iris_test_pre)

Get preprocessed data from Preprocessor.

Description

Returns the preprocessed data from a Preprocessor object.

Usage

preprocessed(x)

Arguments

x

Preprocessor: A Preprocessor object.

Value

data.frame: The preprocessed data.

Examples

prp <- preprocess(iris, setup_Preprocessor(scale = TRUE, center = TRUE))
preprocessed(prp)

Present rtemis object

Description

This generic is used to present an rtemis object by printing to console and drawing plots.

Usage

present(x, ...)

Arguments

x

Supervised or SupervisedRes object or list of such objects.

...

Additional arguments passed to the plotting function.

Value

A plotly object.

Author(s)

EDG

Examples


ir <- set_outcome(iris, "Sepal.Length")
seplen_lightrf <- train(ir, algorithm = "lightrf")
present(seplen_lightrf)

Present list of Supervised or SupervisedRes objects

Description

Plot training and testing performance boxplots of multiple Supervised or SupervisedRes objects

Usage

present.list(
  x,
  metric = NULL,
  model_names = NULL,
  ylim = NULL,
  theme = choose_theme(getOption("rtemis_theme")),
  boxpoints = "all",
  filename = NULL,
  file_width = 800,
  file_height = 600,
  file_scale = 1,
  verbosity = 1L
)

Arguments

x

List of Supervised or SupervisedRes objects.

metric

Character: Metric to plot.

model_names

Character: Names of models being plotted.

ylim

Numeric vector of length 2: y-axis limits for the boxplots.

theme

Theme object.

boxpoints

Character: "all", "outliers", or "suspectedoutliers". Determines how points are displayed in the boxplot.

filename

Character: Filename to save the plot to.

file_width

Numeric: Width of the exported image in pixels.

file_height

Numeric: Height of the exported image in pixels.

file_scale

Numeric: Scale factor for the exported image.

verbosity

Integer: Verbosity level.

Value

plotly object

Author(s)

EDG

Examples

## Not run: 
iris_lightrf <- train(
  iris,
  algorithm = "lightrf",
  outer_resampling_config = setup_Resampler(seed = 2026)
)
iris_rsvm <- train(
  iris,
  algorithm = "radialsvm",
  outer_resampling_config = setup_Resampler(seed = 2026)
)
present(list(iris_lightrf, iris_rsvm), metric = "Balanced_Accuracy")

## End(Not run)

Preview color

Description

Preview one or multiple colors using little rhombi with their little labels up top

Usage

previewcolor(
  x,
  main = NULL,
  bg = "#333333",
  main_col = "#b3b3b3",
  main_x = 0.7,
  main_y = 0.2,
  main_adj = 0,
  main_cex = 0.9,
  main_font = 2,
  width = NULL,
  xlim = NULL,
  ylim = c(0, 2.2),
  asp = 1,
  labels_y = 1.55,
  label_cex = NULL,
  mar = c(0, 0, 0, 1),
  filename = NULL,
  pdf_width = 8,
  pdf_height = 2.5
)

Arguments

x

Color, vector: One or more colors that R understands

main

Character: Title. Default = NULL, which results in deparse(substitute(x))

bg

Background color.

main_col

Color: Title color

main_x

Float: x coordinate for main.

main_y

Float: y coordinate for main.

main_adj

Float: adj argument to mtext for main.

main_cex

Float: character expansion factor for main.

main_font

Integer, 1 or 2: Weight of main 1: regular, 2: bold.

width

Float: Plot width. Default = NULL, i.e. set automatically

xlim

Vector, length 2: x-axis limits. Default = NULL, i.e. set automatically

ylim

Vector, length 2: y-axis limits.

asp

Float: Plot aspect ratio.

labels_y

Float: y coord for labels. Default = 1.55 (rhombi are fixed and range y .5 - 1.5)

label_cex

Float: Character expansion for labels. Default = NULL, and is calculated automatically based on length of x

mar

Numeric vector, length 4: margin size.

filename

Character: Path to save plot as PDF.

pdf_width

Numeric: Width of PDF in inches.

pdf_height

Numeric: Height of PDF in inches.

Value

Nothing, prints plot.

Author(s)

EDG

Examples

previewcolor(get_palette("rtms"))

Read tabular data from a variety of formats

Description

Read data and optionally clean column names, keep unique rows, and convert characters to factors

Usage

read(
  filename,
  datadir = NULL,
  make_unique = FALSE,
  character2factor = FALSE,
  clean_colnames = TRUE,
  delim_reader = c("data.table", "vroom", "duckdb", "arrow"),
  xlsx_sheet = 1,
  sep = NULL,
  quote = "\"",
  na_strings = c(""),
  output = c("data.table", "tibble", "data.frame"),
  attr = NULL,
  value = NULL,
  verbosity = 1L,
  fread_verbosity = 0L,
  timed = verbosity > 0L,
  ...
)

Arguments

filename

Character: filename or full path if datadir = NULL.

datadir

Character: Optional path to directory where filename is located. If not specified, filename must be the full path.

make_unique

Logical: If TRUE, keep unique rows only.

character2factor

Logical: If TRUE, convert character variables to factors.

clean_colnames

Logical: If TRUE, clean columns names using clean_colnames.

delim_reader

Character: package to use for reading delimited data.

xlsx_sheet

Integer or character: Name or number of XLSX sheet to read.

sep

Single character: field separator. If delim_reader = "fread" and sep = NULL, this defaults to "auto", otherwise defaults to ",".

quote

Single character: quote character.

na_strings

Character vector: Strings to be interpreted as NA values. For delim_reader = "duckdb", this must be a single string.

output

Character: "default" or "data.table", If default, return the delim_reader's default data structure, otherwise convert to data.table.

attr

Character: Attribute to set (Optional).

value

Character: Value to set (if attr is not NULL).

verbosity

Integer: Verbosity level.

fread_verbosity

Integer: Verbosity level. Passed to data.table::fread

timed

Logical: If TRUE, time the process and print to console

...

Additional arguments to pass to data.table::fread, arrow::read_delim_arrow(), vroom::vroom(), or readxl::read_excel().

Details

read is a convenience function to read:

Value

data.frame, data.table, or tibble.

Author(s)

EDG

Examples

## Not run: 
# Replace with your own data directory and filename
datadir <- "/Data"
dat <- read("iris.csv", datadir)

## End(Not run)

Read SuperConfig from TOML file

Description

Read SuperConfig object from TOML file that was written with write_toml().

Usage

read_config(file)

Arguments

file

Character: Path to input TOML file.

Value

SuperConfig object.

Author(s)

EDG

Examples

# Create a SuperConfig object
x <- setup_SuperConfig(
  dat_training_path = "~/Data/iris.csv",
  algorithm = "LightRF",
  hyperparameters = setup_LightRF()
)
# Write TOML file
tmpdir <- tempdir()
tmpfile <- file.path(tmpdir, "rtemis_test.toml")
write_toml(x, tmpfile)
# Read config from TOML file
x_read <- read_config(tmpfile)

Regression Metrics

Description

Regression Metrics

Usage

regression_metrics(true, predicted, na.rm = TRUE, sample = NULL)

Arguments

true

Numeric vector: True values.

predicted

Numeric vector: Predicted values.

na.rm

Logical: If TRUE, remove NA values before computation.

sample

Character: Sample name (e.g. "training", "test").

Value

RegressionMetrics object.

Author(s)

EDG

Examples

true <- rnorm(100)
predicted <- true + rnorm(100, sd = 0.5)
regression_metrics(true, predicted)

Resample data

Description

Create resamples of your data, e.g. for model building or validation. "KFold" creates stratified folds, , "StratSub" creates stratified subsamples, "Bootstrap" gives the standard bootstrap, i.e. random sampling with replacement, while "StratBoot" uses StratSub and then randomly duplicates some of the training cases to reach original length of input (default) or length defined by target_length.

Usage

resample(x, config = setup_Resampler(), verbosity = 1L)

Arguments

x

Vector or data.frame: Usually the outcome; NROW(x) defines the sample size.

config

Resampler object created by setup_Resampler.

verbosity

Integer: Verbosity level.

Details

Note that option 'KFold' may result in resamples of slightly different length. Avoid all operations which rely on equal-length vectors. For example, you can't place resamples in a data.frame, but must use a list instead.

Value

Resampler object.

Author(s)

EDG

Examples

y <- rnorm(200)
# 10-fold (stratified)
y_10fold <- resample(y, setup_Resampler(10L, "kfold"))
y_10fold
# 25 stratified subsamples
y_25strat <- resample(y, setup_Resampler(25L, "stratsub"))
y_25strat
# 100 stratified bootstraps
y_100strat <- resample(y, setup_Resampler(100L, "stratboot"))
y_100strat
# LOOCV
y_loocv <- resample(y, setup_Resampler(type = "LOOCV"))
y_loocv

Random Normal Matrix

Description

Create a matrix or data frame of defined dimensions, whose columns are random normal vectors

Usage

rnormmat(
  nrow = 10,
  ncol = 10,
  mean = 0,
  sd = 1,
  return_df = FALSE,
  seed = NULL
)

Arguments

nrow

Integer: Number of rows.

ncol

Integer: Number of columns.

mean

Float: Mean.

sd

Float: Standard deviation.

return_df

Logical: If TRUE, return data.frame, otherwise matrix.

seed

Integer: Set seed for rnorm.

Value

matrix or data.frame.

Author(s)

EDG

Examples

x <- rnormmat(20, 5, mean = 12, sd = 6, return_df = TRUE, seed = 2026)
x

rtemis Color System

Description

A named list of colors used consistently across all packages in the rtemis ecosystem.

Usage

rtemis_colors

Format

A named list with the following elements:

red

"kaimana red"

blue

"kaimana light blue"

green

"kaimana medium green"

orange

"coastside orange"

teal

"rtemis teal"

purple

"rtemis purple"

magenta

"rtemis magenta"

highlight_col

"highlight color"

object

"rtemis teal"

info

"lmd burgundy"

outer

"kaimana red"

tuner

"coastside orange"

Details

Colors are provided as hex strings.

Author(s)

EDG

Examples

rtemis_colors[["orange"]]

rtemis_colors[["teal"]]

Get rtemis version and system info

Description

Get rtemis version and system info

Usage

rtversion()

Value

List: rtemis version and system info, invisibly.

Author(s)

EDG

Examples

rtversion()

Random Uniform Matrix

Description

Create a matrix or data frame of defined dimensions, whose columns are random uniform vectors

Usage

runifmat(
  nrow = 10,
  ncol = 10,
  min = 0,
  max = 1,
  return_df = FALSE,
  seed = NULL
)

Arguments

nrow

Integer: Number of rows.

ncol

Integer: Number of columns.

min

Float: Min.

max

Float: Max.

return_df

Logical: If TRUE, return data.frame, otherwise matrix.

seed

Integer: Set seed for rnorm.

Value

matrix or data.frame.

Author(s)

EDG

Examples

x <- runifmat(20, 5, min = 12, max = 18, return_df = TRUE, seed = 2026)
x

Move outcome to last column

Description

Move outcome to last column

Usage

set_outcome(dat, outcome_column)

Arguments

dat

data.frame or similar.

outcome_column

Character: Name of outcome column.

Value

object of same class as data

Author(s)

EDG

Examples

ir <- set_outcome(iris, "Sepal.Length")
head(ir)

Symmetric Set Difference

Description

Symmetric Set Difference

Usage

setdiffsym(x, y)

Arguments

x

vector

y

vector of same type as x

Value

Vector.

Author(s)

EDG

Examples

setdiff(1:10, 1:5)
setdiff(1:5, 1:10)
setdiffsym(1:10, 1:5)
setdiffsym(1:5, 1:10)

Setup CART Hyperparameters

Description

Setup hyperparameters for CART training.

Usage

setup_CART(
  cp = 0.01,
  maxdepth = 20L,
  minsplit = 2L,
  minbucket = 1L,
  prune_cp = NULL,
  method = "auto",
  model = TRUE,
  maxcompete = 4L,
  maxsurrogate = 5L,
  usesurrogate = 2L,
  surrogatestyle = 0L,
  xval = 0L,
  cost = NULL,
  ifw = FALSE
)

Arguments

cp

(Tunable) Numeric: Complexity parameter.

maxdepth

(Tunable) Integer: Maximum depth of tree.

minsplit

(Tunable) Integer: Minimum number of observations in a node to split.

minbucket

(Tunable) Integer: Minimum number of observations in a terminal node.

prune_cp

(Tunable) Numeric: Complexity for cost-complexity pruning after tree is built

method

String: Splitting method.

model

Logical: If TRUE, return a model.

maxcompete

Integer: Maximum number of competitive splits.

maxsurrogate

Integer: Maximum number of surrogate splits.

usesurrogate

Integer: Number of surrogate splits to use.

surrogatestyle

Integer: Type of surrogate splits.

xval

Integer: Number of cross-validation folds.

cost

Numeric (>=0): One for each feature.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from rpart::rpart and rpart::rpart.control.

Value

CARTHyperparameters object.

Author(s)

EDG

Examples

cart_hyperparams <- setup_CART(cp = 0.01, maxdepth = 10L, ifw = TRUE)
cart_hyperparams

Setup CMeansConfig

Description

Setup CMeansConfig

Usage

setup_CMeans(
  k = 2L,
  max_iter = 100L,
  dist = c("euclidean", "manhattan"),
  method = c("cmeans", "ufcl"),
  m = 2,
  rate_par = NULL,
  weights = 1,
  control = list()
)

Arguments

k

Integer: Number of clusters.

max_iter

Integer: Maximum number of iterations.

dist

Character: Distance measure to use: 'euclidean' or 'manhattan'.

method

Character: "cmeans" - fuzzy c-means clustering; "ufcl": on-line update.

m

Float (>1): Degree of fuzzification.

rate_par

Float (0, 1): Learning rate for the online variant.

weights

Float (>0): Case weights.

control

List: Control config for clustering algorithm.

Value

CMeansConfig object.

Author(s)

EDG

Examples

cmeans_config <- setup_CMeans(k = 4L, dist = "euclidean")
cmeans_config

Setup DBSCANConfig

Description

Setup DBSCANConfig

Usage

setup_DBSCAN(
  eps = 0.5,
  min_points = 5L,
  weights = NULL,
  border_points = TRUE,
  search = c("kdtree", "linear", "dist"),
  bucket_size = 100L,
  split_rule = c("SUGGEST", "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR"),
  approx = FALSE
)

Arguments

eps

Float: Radius of neighborhood.

min_points

Integer: Minimum number of points in a neighborhood to form a cluster.

weights

Numeric vector: Weights for data points.

border_points

Logical: If TRUE, assign border points to clusters.

search

Character: Nearest neighbor search strategy: "kdtree", "linear", or "dist".

bucket_size

Integer: Size of buckets for k-dtree search.

split_rule

Character: Rule for splitting clusters: "SUGGEST", "STD", "MIDPT", "FAIR", "SL_MIDPT", "SL_FAIR".

approx

Logical: If TRUE, use approximate nearest neighbor search.

Value

DBSCANConfig object.

Author(s)

EDG

Examples

dbscan_config <- setup_DBSCAN(eps = 0.5, min_points = 5L)
dbscan_config

Setup Execution Configuration

Description

Setup Execution Configuration

Usage

setup_ExecutionConfig(
  backend = c("future", "mirai", "none"),
  n_workers = NULL,
  future_plan = NULL
)

Arguments

backend

Character: Execution backend: "future", "mirai", or "none".

n_workers

Integer: Number of workers for parallel execution. Only used if ⁠backend is "future"⁠ or "mirai". Do not rely on the default value, set to an appropriate number depending on your system.

future_plan

Character: Future plan to use if backend is "future".

Value

ExecutionConfig object.

Author(s)

EDG

Examples

setup_ExecutionConfig(backend = "future", n_workers = 4L, future_plan = "multisession")

Setup GAM Hyperparameters

Description

Setup hyperparameters for GAM training.

Usage

setup_GAM(k = 5L, ifw = FALSE)

Arguments

k

(Tunable) Integer: Number of knots.

ifw

(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from mgcv::gam.

Value

GAMHyperparameters object.

Author(s)

EDG

Examples

gam_hyperparams <- setup_GAM(k = 5L, ifw = FALSE)
gam_hyperparams

Setup GLM Hyperparameters

Description

Setup hyperparameters for GLM training.

Usage

setup_GLM(ifw = FALSE)

Arguments

ifw

(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Value

GLMHyperparameters object.

Author(s)

EDG

Examples

glm_hyperparams <- setup_GLM(ifw = TRUE)
glm_hyperparams

Setup GLMNET Hyperparameters

Description

Setup hyperparameters for GLMNET training.

Usage

setup_GLMNET(
  alpha = 1,
  family = NULL,
  offset = NULL,
  which_lambda_cv = "lambda.1se",
  nlambda = 100L,
  lambda = NULL,
  penalty_factor = NULL,
  standardize = TRUE,
  intercept = TRUE,
  ifw = TRUE
)

Arguments

alpha

(Tunable) Numeric: Mixing parameter.

family

Character: Family for GLMNET.

offset

Numeric: Offset for GLMNET.

which_lambda_cv

Character: Which lambda to use for prediction: "lambda.1se" or "lambda.min"

nlambda

Positive integer: Number of lambda values.

lambda

Numeric: Lambda values.

penalty_factor

Numeric: Penalty factor for each feature.

standardize

Logical: If TRUE, standardize features.

intercept

Logical: If TRUE, include intercept.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from glmnet::glmnet.

Value

GLMNETHyperparameters object.

Author(s)

EDG

Examples

glm_hyperparams <- setup_GLMNET(alpha = 1, ifw = TRUE)
glm_hyperparams

Setup Grid Search Config

Description

Create a GridSearchConfig object that can be passed to train.

Usage

setup_GridSearch(
  resampler_config = setup_Resampler(n_resamples = 5L, type = "KFold"),
  search_type = "exhaustive",
  randomize_p = NULL,
  metrics_aggregate_fn = "mean",
  metric = NULL,
  maximize = NULL
)

Arguments

resampler_config

ResamplerConfig set by setup_Resampler.

search_type

Character: "exhaustive" or "randomized". Type of grid search to use. Exhaustive search will try all combinations of config. Randomized will try a random sample of size randomize_p * ⁠N of total combinations⁠

randomize_p

Float (0, 1): For search_type == "randomized", randomly test this proportion of combinations.

metrics_aggregate_fn

Character: Name of function to use to aggregate error metrics.

metric

Character: Metric to minimize or maximize.

maximize

Logical: If TRUE, maximize metric, otherwise minimize it.

Value

A GridSearchConfig object.

Author(s)

EDG

Examples

gridsearch_config <- setup_GridSearch(
  resampler_config = setup_Resampler(n_resamples = 5L, type = "KFold"),
  search_type = "exhaustive"
)
gridsearch_config

Setup HardCLConfig

Description

Setup HardCLConfig

Usage

setup_HardCL(k = 3L, dist = c("euclidean", "manhattan"))

Arguments

k

Number of clusters.

dist

Character: Distance measure to use: 'euclidean' or 'manhattan'.

Value

HardCLConfig object.

Author(s)

EDG

Examples

hardcl_config <- setup_HardCL(k = 4L, dist = "euclidean")
hardcl_config

setup_ICA

Description

Setup ICA config.

Usage

setup_ICA(
  k = 3L,
  type = c("parallel", "deflation"),
  fun = c("logcosh", "exp"),
  alpha = 1,
  row_norm = TRUE,
  maxit = 100L,
  tol = 1e-04
)

Arguments

k

Integer: Number of components.

type

Character: Type of ICA: "parallel" or "deflation".

fun

Character: ICA function: "logcosh", "exp".

alpha

Numeric [1, 2]: Used in approximation to neg-entropy with fun = "logcosh".

row_norm

Logical: If TRUE, normalize rows of x before ICA.

maxit

Integer: Maximum number of iterations.

tol

Numeric: Tolerance.

Value

ICAConfig object.

Author(s)

EDG

Examples

ica_config <- setup_ICA(k = 3L)
ica_config

Setup Isomap config.

Description

Setup Isomap config.

Usage

setup_Isomap(
  k = 2L,
  dist_method = c("euclidean", "manhattan"),
  nsd = 0L,
  path = c("shortest", "extended")
)

Arguments

k

Integer: Number of components.

dist_method

Character: Distance method.

nsd

Integer: Number of shortest dissimilarities retained.

path

Character: Path argument for vegan::isomap.

Value

IsomapConfig object.

Author(s)

EDG

Examples

isomap_config <- setup_Isomap(k = 3L)
isomap_config

Setup Isotonic Hyperparameters

Description

Setup hyperparameters for Isotonic Regression.

Usage

setup_Isotonic(ifw = FALSE)

Arguments

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

There are not hyperparameters for this algorithm at this moment.

Value

IsotonicHyperparameters object.

Author(s)

EDG

Examples

isotonic_hyperparams <- setup_Isotonic(ifw = TRUE)
isotonic_hyperparams

Setup KMeansConfig

Description

Setup KMeansConfig

Usage

setup_KMeans(k = 3L, dist = c("euclidean", "manhattan"))

Arguments

k

Number of clusters.

dist

Character: Distance measure to use: 'euclidean' or 'manhattan'.

Value

KMeansConfig object.

Author(s)

EDG

Examples

kmeans_config <- setup_KMeans(k = 4L, dist = "euclidean")
kmeans_config

Setup LightCART Hyperparameters

Description

Setup hyperparameters for LightCART training.

Usage

setup_LightCART(
  num_leaves = 32L,
  max_depth = -1L,
  lambda_l1 = 0,
  lambda_l2 = 0,
  min_data_in_leaf = 20L,
  max_cat_threshold = 32L,
  min_data_per_group = 100L,
  linear_tree = FALSE,
  objective = NULL,
  ifw = FALSE
)

Arguments

num_leaves

(Tunable) Positive integer: Maximum number of leaves in one tree.

max_depth

(Tunable) Integer: Maximum depth of trees.

lambda_l1

(Tunable) Numeric: L1 regularization.

lambda_l2

(Tunable) Numeric: L2 regularization.

min_data_in_leaf

(Tunable) Positive integer: Minimum number of data in a leaf.

max_cat_threshold

(Tunable) Positive integer: Maximum number of categories for categorical features.

min_data_per_group

(Tunable) Positive integer: Minimum number of observations per categorical group.

linear_tree

(Tunable) Logical: If TRUE, use linear trees.

objective

Character: Objective function.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from lightgbm::lgb.train.

Value

LightCARTHyperparameters object.

Author(s)

EDG

Examples

lightcart_hyperparams <- setup_LightCART(num_leaves = 32L, ifw = FALSE)
lightcart_hyperparams

Setup LightGBM Hyperparameters

Description

Setup hyperparameters for LightGBM training.

Usage

setup_LightGBM(
  max_nrounds = 1000L,
  force_nrounds = NULL,
  early_stopping_rounds = 10L,
  num_leaves = 8L,
  max_depth = -1L,
  learning_rate = 0.01,
  feature_fraction = 1,
  subsample = 1,
  subsample_freq = 1L,
  lambda_l1 = 0,
  lambda_l2 = 0,
  max_cat_threshold = 32L,
  min_data_per_group = 32L,
  linear_tree = FALSE,
  ifw = FALSE,
  objective = NULL,
  device_type = "cpu",
  tree_learner = "serial",
  force_col_wise = TRUE
)

Arguments

max_nrounds

Positive integer: Maximum number of boosting rounds.

force_nrounds

Positive integer: Use this many boosting rounds. Disable search for nrounds.

early_stopping_rounds

Positive integer: Number of rounds without improvement to stop training.

num_leaves

(Tunable) Positive integer: Maximum number of leaves in one tree.

max_depth

(Tunable) Integer: Maximum depth of trees.

learning_rate

(Tunable) Numeric: Learning rate.

feature_fraction

(Tunable) Numeric: Fraction of features to use.

subsample

(Tunable) Numeric: Fraction of data to use.

subsample_freq

(Tunable) Positive integer: Frequency of subsample.

lambda_l1

(Tunable) Numeric: L1 regularization.

lambda_l2

(Tunable) Numeric: L2 regularization.

max_cat_threshold

(Tunable) Positive integer: Maximum number of categories for categorical features.

min_data_per_group

(Tunable) Positive integer: Minimum number of observations per categorical group.

linear_tree

Logical: If TRUE, use linear trees.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

objective

Character: Objective function.

device_type

Character: "cpu" or "gpu".

tree_learner

Character: "serial", "feature", "data", or "voting".

force_col_wise

Logical: Use only with CPU - If TRUE, force col-wise histogram building.

Details

Get more information from lightgbm::lgb.train.

Value

LightGBMHyperparameters object.

Author(s)

EDG

Examples

lightgbm_hyperparams <- setup_LightGBM(
  max_nrounds = 500L,
  learning_rate = c(0.001, 0.01, 0.05), ifw = TRUE
)
lightgbm_hyperparams

Setup LightRF Hyperparameters

Description

Setup hyperparameters for LightRF training.

Usage

setup_LightRF(
  nrounds = 500L,
  num_leaves = 4096L,
  max_depth = -1L,
  feature_fraction = 0.7,
  subsample = 0.623,
  lambda_l1 = 0,
  lambda_l2 = 0,
  max_cat_threshold = 32L,
  min_data_per_group = 32L,
  linear_tree = FALSE,
  ifw = FALSE,
  objective = NULL,
  device_type = "cpu",
  tree_learner = "serial",
  force_col_wise = TRUE
)

Arguments

nrounds

(Tunable) Positive integer: Number of boosting rounds.

num_leaves

(Tunable) Positive integer: Maximum number of leaves in one tree.

max_depth

(Tunable) Integer: Maximum depth of trees.

feature_fraction

(Tunable) Numeric: Fraction of features to use.

subsample

(Tunable) Numeric: Fraction of data to use.

lambda_l1

(Tunable) Numeric: L1 regularization.

lambda_l2

(Tunable) Numeric: L2 regularization.

max_cat_threshold

(Tunable) Positive integer: Maximum number of categories for categorical features.

min_data_per_group

(Tunable) Positive integer: Minimum number of observations per categorical group.

linear_tree

Logical: If TRUE, use linear trees.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

objective

Character: Objective function.

device_type

Character: "cpu" or "gpu".

tree_learner

Character: "serial", "feature", "data", or "voting".

force_col_wise

Logical: Use only with CPU - If TRUE, force col-wise histogram building.

Details

Get more information from lightgbm::lgb.train. Note that hyperparameters subsample_freq and early_stopping_rounds are fixed, and cannot be set because they are what makes lightgbm train a random forest. These can all be set when training gradient boosting with LightGBM.

Value

LightRFHyperparameters object.

Author(s)

EDG

Examples

lightrf_hyperparams <- setup_LightRF(nrounds = 1000L, ifw = FALSE)
lightrf_hyperparams

Setup LightRuleFit Hyperparameters

Description

Setup hyperparameters for LightRuleFit training.

Usage

setup_LightRuleFit(
  nrounds = 200L,
  num_leaves = 32L,
  max_depth = 4L,
  learning_rate = 0.1,
  subsample = 0.666,
  subsample_freq = 1L,
  lambda_l1 = 0,
  lambda_l2 = 0,
  objective = NULL,
  ifw_lightgbm = FALSE,
  alpha = 1,
  lambda = NULL,
  ifw_glmnet = FALSE,
  ifw = FALSE
)

Arguments

nrounds

(Tunable) Positive integer: Number of boosting rounds.

num_leaves

(Tunable) Positive integer: Maximum number of leaves in one tree.

max_depth

(Tunable) Integer: Maximum depth of trees.

learning_rate

(Tunable) Numeric: Learning rate.

subsample

(Tunable) Numeric: Fraction of data to use.

subsample_freq

(Tunable) Positive integer: Frequency of subsample.

lambda_l1

(Tunable) Numeric: L1 regularization.

lambda_l2

(Tunable) Numeric: L2 regularization.

objective

Character: Objective function.

ifw_lightgbm

(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the LightGBM step.

alpha

(Tunable) Numeric: Alpha for GLMNET.

lambda

Numeric: Lambda for GLMNET.

ifw_glmnet

(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the GLMNET step.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification. This applies IFW to both LightGBM and GLMNET.

Details

Get more information from lightgbm::lgb.train.

Value

LightRuleFitHyperparameters object.

Author(s)

EDG

Examples

lightrulefit_hyperparams <- setup_LightRuleFit(nrounds = 300L, max_depth = 3L)
lightrulefit_hyperparams

Setup LinearSVM Hyperparameters

Description

Setup hyperparameters for LinearSVM training.

Usage

setup_LinearSVM(cost = 1, ifw = FALSE)

Arguments

cost

(Tunable) Numeric: Cost of constraints violation.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from e1071::svm.

Value

LinearSVMHyperparameters object.

Author(s)

EDG

Examples

linear_svm_hyperparams <- setup_LinearSVM(cost = 0.5, ifw = TRUE)
linear_svm_hyperparams

Setup NMF config.

Description

Setup NMF config.

Usage

setup_NMF(k = 2L, method = "brunet", nrun = if (length(k) > 1L) 30L else 1L)

Arguments

k

Integer: Number of components.

method

Character: NMF method. See NMF::nmf.

nrun

Integer: Number of runs to perform.

Value

NMFConfig object.

Author(s)

EDG

Examples

nmf_config <- setup_NMF(k = 3L)
nmf_config

Setup NeuralGasConfig

Description

Setup NeuralGasConfig

Usage

setup_NeuralGas(k = 3L, dist = c("euclidean", "manhattan"))

Arguments

k

Number of clusters.

dist

Character: Distance measure to use: 'euclidean' or 'manhattan'.

Value

NeuralGasConfig object.

Author(s)

EDG

Examples

neuralgas_config <- setup_NeuralGas(k = 4L, dist = "euclidean")
neuralgas_config

Setup PCA config.

Description

Setup PCA config.

Usage

setup_PCA(k = 3L, center = TRUE, scale = TRUE, tol = NULL)

Arguments

k

Integer: Number of components. (passed to prcomp rank.)

center

Logical: If TRUE, center the data.

scale

Logical: If TRUE, scale the data.

tol

Numeric: Tolerance.

Value

PCAConfig object.

Author(s)

EDG

Examples

pca_config <- setup_PCA(k = 3L)
pca_config

Setup Preprocessor

Description

Creates a PreprocessorConfig object, which can be used in preprocess.

Usage

setup_Preprocessor(
  complete_cases = FALSE,
  remove_features_thres = NULL,
  remove_cases_thres = NULL,
  missingness = FALSE,
  impute = FALSE,
  impute_type = c("missRanger", "micePMM", "meanMode"),
  impute_missRanger_params = list(pmm.k = 3, maxiter = 10, num.trees = 500),
  impute_discrete = "get_mode",
  impute_continuous = "mean",
  integer2factor = FALSE,
  integer2numeric = FALSE,
  logical2factor = FALSE,
  logical2numeric = FALSE,
  numeric2factor = FALSE,
  numeric2factor_levels = NULL,
  numeric_cut_n = 0,
  numeric_cut_labels = FALSE,
  numeric_quant_n = 0,
  numeric_quant_NAonly = FALSE,
  unique_len2factor = 0,
  character2factor = FALSE,
  factorNA2missing = FALSE,
  factorNA2missing_level = "missing",
  factor2integer = FALSE,
  factor2integer_startat0 = TRUE,
  scale = FALSE,
  center = scale,
  scale_centers = NULL,
  scale_coefficients = NULL,
  remove_constants = FALSE,
  remove_constants_skip_missing = TRUE,
  remove_features = NULL,
  remove_duplicates = FALSE,
  one_hot = FALSE,
  one_hot_levels = NULL,
  add_date_features = FALSE,
  date_features = c("weekday", "month", "year"),
  add_holidays = FALSE,
  exclude = NULL
)

Arguments

complete_cases

Logical: If TRUE, only retain complete cases (no missing data).

remove_features_thres

Float (0, 1): Remove features with missing values in >= to this fraction of cases.

remove_cases_thres

Float (0, 1): Remove cases with >= to this fraction of missing features.

missingness

Logical: If TRUE, generate new boolean columns for each feature with missing values, indicating which cases were missing data.

impute

Logical: If TRUE, impute missing cases. See impute_discrete and impute_continuous.

impute_type

Character: Package to use for imputation.

impute_missRanger_params

Named list with elements "pmm.k" and "maxiter", which are passed to missRanger::missRanger. pmm.k greater than 0 results in predictive mean matching. Default pmm.k = 3 maxiter = 10 num.trees = 500. Reduce num.trees for faster imputation especially in large datasets. Set pmm.k = 0 to disable predictive mean matching.

impute_discrete

Character: Name of function that returns single value: How to impute discrete variables for impute_type = "meanMode".

impute_continuous

Character: Name of function that returns single value: How to impute continuous variables for impute_type = "meanMode".

integer2factor

Logical: If TRUE, convert all integers to factors. This includes bit64::integer64 columns.

integer2numeric

Logical: If TRUE, convert all integers to numeric (will only work if integer2factor = FALSE). This includes bit64::integer64 columns.

logical2factor

Logical: If TRUE, convert all logical variables to factors.

logical2numeric

Logical: If TRUE, convert all logical variables to numeric.

numeric2factor

Logical: If TRUE, convert all numeric variables to factors.

numeric2factor_levels

Character vector: Optional - will be passed to levels arg of factor() if numeric2factor = TRUE. For advanced/ specific use cases; need to know unique values of numeric vector(s) and given all numeric vars have same unique values.

numeric_cut_n

Integer: If > 0, convert all numeric variables to factors by binning using base::cut with breaks equal to this number.

numeric_cut_labels

Logical: The labels argument of base::cut.

numeric_quant_n

Integer: If > 0, convert all numeric variables to factors by binning using base::cut with breaks equal to this number of quantiles. produced using stats::quantile.

numeric_quant_NAonly

Logical: If TRUE, only bin numeric variables with missing values.

unique_len2factor

Integer (>=2): Convert all variables with less than or equal to this number of unique values to factors. For example, if binary variables are encoded with 1, 2, you could use unique_len2factor = 2 to convert them to factors.

character2factor

Logical: If TRUE, convert all character variables to factors.

factorNA2missing

Logical: If TRUE, make NA values in factors be of level factorNA2missing_level. In many cases this is the preferred way to handle missing data in categorical variables. Note that since this step is performed before imputation, you can use this option to handle missing data in categorical variables and impute numeric variables in the same preprocess call.

factorNA2missing_level

Character: Name of level if factorNA2missing = TRUE.

factor2integer

Logical: If TRUE, convert all factors to integers.

factor2integer_startat0

Logical: If TRUE, start integer coding at 0.

scale

Logical: If TRUE, scale columns of x.

center

Logical: If TRUE, center columns of x. Note that by default it is the same as scale.

scale_centers

Named vector: Centering values for each feature.

scale_coefficients

Named vector: Scaling values for each feature.

remove_constants

Logical: If TRUE, remove constant columns.

remove_constants_skip_missing

Logical: If TRUE, skip missing values, before checking if feature is constant.

remove_features

Character vector: Features to remove.

remove_duplicates

Logical: If TRUE, remove duplicate cases.

one_hot

Logical: If TRUE, convert all factors using one-hot encoding.

one_hot_levels

List: Named list of the form "feature_name" = "levels". Used when applying one-hot encoding to validation or test data using Preprocessor.

add_date_features

Logical: If TRUE, extract date features from date columns.

date_features

Character vector: Features to extract from dates.

add_holidays

Logical: If TRUE, extract holidays from date columns.

exclude

Integer, vector: Exclude these columns from preprocessing.

Value

PreprocessorConfig object.

Order of Operations

Author(s)

EDG

Examples

preproc_config <- setup_Preprocessor(factorNA2missing = TRUE)
preproc_config

Setup RadialSVM Hyperparameters

Description

Setup hyperparameters for RadialSVM training.

Usage

setup_RadialSVM(cost = 1, gamma = 0.01, ifw = FALSE)

Arguments

cost

(Tunable) Numeric: Cost of constraints violation.

gamma

(Tunable) Numeric: Kernel coefficient.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Get more information from e1071::svm.

Value

RadialSVMHyperparameters object.

Author(s)

EDG

Examples

radial_svm_hyperparams <- setup_RadialSVM(cost = 10, gamma = 0.1, ifw = TRUE)
radial_svm_hyperparams

Setup Ranger Hyperparameters

Description

Setup hyperparameters for Ranger Random Forest training.

Usage

setup_Ranger(
  num_trees = 500,
  mtry = NULL,
  importance = "impurity",
  write_forest = TRUE,
  probability = FALSE,
  min_node_size = NULL,
  min_bucket = NULL,
  max_depth = NULL,
  replace = TRUE,
  sample_fraction = ifelse(replace, 1, 0.632),
  case_weights = NULL,
  class_weights = NULL,
  splitrule = NULL,
  num_random_splits = 1,
  alpha = 0.5,
  minprop = 0.1,
  poisson_tau = 1,
  split_select_weights = NULL,
  always_split_variables = NULL,
  respect_unordered_factors = NULL,
  scale_permutation_importance = FALSE,
  local_importance = FALSE,
  regularization_factor = 1,
  regularization_usedepth = FALSE,
  keep_inbag = FALSE,
  inbag = NULL,
  holdout = FALSE,
  quantreg = FALSE,
  time_interest = NULL,
  oob_error = TRUE,
  save_memory = FALSE,
  verbose = TRUE,
  node_stats = FALSE,
  seed = NULL,
  na_action = "na.learn",
  ifw = FALSE
)

Arguments

num_trees

(Tunable) Positive integer: Number of trees.

mtry

(Tunable) Positive integer: Number of features to consider at each split.

importance

Character: Variable importance mode. "none", "impurity", "impurity_corrected", "permutation". The "impurity" measure is the Gini index for classification, the variance of the responses for regression.

write_forest

Logical: Save ranger.forest object, required for prediction. Set to FALSE to reduce memory usage if no prediction intended.

probability

Logical: Grow a probability forest as in Malley et al. (2012). For classification only.

min_node_size

(Tunable) Positive integer: Minimal node size. Default 1 for classification, 5 for regression, 3 for survival, and 10 for probability.

min_bucket

Positive integer: Minimal number of samples in a terminal node. Only for survival. Deprecated in favor of min_node_size.

max_depth

(Tunable) Positive integer: Maximal tree depth. A value of NULL or 0 (the default) corresponds to unlimited depth, 1 to tree stumps (1 split per tree).

replace

Logical: Sample with replacement.

sample_fraction

(Tunable) Numeric: Fraction of observations to sample. Default is 1 for sampling with replacement and 0.632 for sampling without replacement.

case_weights

Numeric vector: Weights for sampling of training observations. Observations with larger weights will be selected with higher probability in the bootstrap (or subsampled) samples for the trees.

class_weights

Numeric vector: Weights for the outcome classes for classification. Vector of the same length as the number of classes, with names corresponding to the class labels.

splitrule

(Tunable) Character: Splitting rule. For classification: "gini", "extratrees", "hellinger". For regression: "variance", "extratrees", "maxstat", "beta". For survival: "logrank", "extratrees", "C", "maxstat".

num_random_splits

(Tunable) Positive integer: For "extratrees" splitrule: Number of random splits to consider for each candidate splitting variable.

alpha

(Tunable) Numeric: For "maxstat" splitrule: significance threshold to allow splitting.

minprop

(Tunable) Numeric: For "maxstat" splitrule: lower quantile of covariate distribution to be considered for splitting.

poisson_tau

Numeric: For "poisson" regression splitrule: tau parameter for Poisson regression.

split_select_weights

Numeric vector: Numeric vector with weights between 0 and 1, representing the probability to select variables for splitting. Alternatively, a list of size num_trees, with one weight vector per tree.

always_split_variables

Character vector: Character vector with variable names to be always selected in addition to the mtry variables tried for splitting.

respect_unordered_factors

Character or logical: Handling of unordered factor covariates. For "partition" all 2^(k-1)-1 possible partitions are considered for splitting, where k is the number of factor levels. For "ignore", all factor levels are ordered by their first occurrence in the data. For "order", all factor levels are ordered by their average response. TRUE corresponds to "partition" for the randomForest package compatibility.

scale_permutation_importance

Logical: Scale permutation importance by standard error as in (Breiman 2001). Only applicable if permutation variable importance mode selected.

local_importance

Logical: For permutation variable importance, use local importance as in Breiman (2001) and Liaw & Wiener (2002).

regularization_factor

(Tunable) Numeric: Regularization factor. Penalize variables with many split points. Requires splitrule = "variance".

regularization_usedepth

Logical: Use regularization factor with node depth. Requires regularization_factor.

keep_inbag

Logical: Save how often observations are in-bag in each tree. These will be used for (local) variable importance if inbag.counts in predict() is NULL.

inbag

List: Manually set observations per tree. List of size num_trees, containing inbag counts for each observation. Can be used for stratified sampling.

holdout

Logical: Hold-out mode. Hold-out all samples with case weight 0 and use these for variable importance and prediction error.

quantreg

Logical: Prepare quantile prediction as in quantile regression forests (Meinshausen 2006). For regression only. Set keep_inbag = TRUE to prepare out-of-bag quantile prediction.

time_interest

Numeric: For GWAS data: SNP with this number will be used as time variable. Only for survival. Deprecated, use time.var in formula instead.

oob_error

Logical: Compute OOB prediction error. Set to FALSE to save computation time if only the forest is needed.

save_memory

Logical: Use memory saving (but slower) splitting mode. No effect for survival and GWAS data. Warning: This option slows down the tree growing, use only if you encounter memory problems.

verbose

Logical: Show computation status and estimated runtime.

node_stats

Logical: Save additional node statistics. Only terminal nodes for now.

seed

Positive integer: Random seed. Default is NULL, which generates the seed from R. Set to 0 to ignore the R seed.

na_action

Character: Action to take if the data contains missing values. "na.learn" uses observations with missing values in splitting, treating missing values as a separate category.

ifw

Logical: Inverse Frequency Weighting for classification. If TRUE, class weights are set inversely proportional to the class frequencies.

Details

Get more information from ranger::ranger.

Value

RangerHyperparameters object.

Author(s)

EDG

Examples

ranger_hyperparams <- setup_Ranger(num_trees = 1000L, ifw = FALSE)
ranger_hyperparams

Setup Resampler

Description

Setup Resampler

Usage

setup_Resampler(
  n_resamples = 10L,
  type = c("KFold", "StratSub", "StratBoot", "Bootstrap", "LOOCV"),
  stratify_var = NULL,
  train_p = 0.75,
  strat_n_bins = 4L,
  target_length = NULL,
  id_strat = NULL,
  seed = NULL,
  verbosity = 1L
)

Arguments

n_resamples

Integer: Number of resamples to make.

type

Character: Type of resampler: "KFold", "StratSub", "StratBoot", "Bootstrap", "LOOCV"

stratify_var

Character: Variable to stratify by.

train_p

Float: Training set percentage.

strat_n_bins

Integer: Number of bins to stratify by.

target_length

Integer: Target length for stratified bootstraps.

id_strat

Integer: Vector of indices to stratify by. These may be, for example, case IDs if your dataset contains repeated measurements. By specifying this vector, you can ensure that each case can only be present in the training or test set, but not both.

seed

Integer: Random seed.

verbosity

Integer: Verbosity level.

Value

ResamplerConfig object.

Author(s)

EDG

Examples

tenfold_resampler <- setup_Resampler(n_resamples = 10L, type = "KFold", seed = 2026L)
tenfold_resampler

Setup SuperConfig

Description

Setup SuperConfig object.

Usage

setup_SuperConfig(
  dat_training_path,
  dat_validation_path = NULL,
  dat_test_path = NULL,
  weights = NULL,
  preprocessor_config = NULL,
  algorithm = NULL,
  hyperparameters = NULL,
  tuner_config = NULL,
  outer_resampling_config = NULL,
  execution_config = setup_ExecutionConfig(),
  question = NULL,
  outdir = "results/",
  verbosity = 1L
)

Arguments

dat_training_path

Character: Path to training data file.

dat_validation_path

Character: Path to validation data file.

dat_test_path

Character: Path to test data file.

weights

Optional Character: Column name in training data to use as observation weights. If NULL, no weights are used.

preprocessor_config

PreprocessorConfig object: Configuration for data preprocessing.

algorithm

Character: Algorithm to use for training.

hyperparameters

Hyperparameters object: Configuration for model hyperparameters.

tuner_config

TunerConfig object: Configuration for hyperparameter tuning.

outer_resampling_config

ResamplerConfig object: Configuration for outer res resampling during model training.

execution_config

ExecutionConfig object: Configuration for execution settings. Setup with setup_ExecutionConfig.

question

Character: Question to answer with the supervised learning analysis.

outdir

Character: Output directory for results.

verbosity

Integer: Verbosity level.

Value

SuperConfig object.

Author(s)

EDG

Examples

sc <- setup_SuperConfig(
  dat_training_path = "train.csv",
  preprocessor_config = setup_Preprocessor(remove_duplicates = TRUE),
  algorithm = "LightRF",
  hyperparameters = setup_LightRF(),
  tuner_config = setup_GridSearch(),
  outer_resampling_config = setup_Resampler(),
  execution_config = setup_ExecutionConfig(),
  question = "Can we tell iris species apart given their measurements?",
  outdir = "models/"
)

Setup TabNet Hyperparameters

Description

Setup hyperparameters for TabNet training.

Usage

setup_TabNet(
  batch_size = 1024^2,
  penalty = 0.001,
  clip_value = NULL,
  loss = "auto",
  epochs = 50L,
  drop_last = FALSE,
  decision_width = NULL,
  attention_width = NULL,
  num_steps = 3L,
  feature_reusage = 1.3,
  mask_type = "sparsemax",
  virtual_batch_size = 256^2,
  valid_split = 0,
  learn_rate = 0.02,
  optimizer = "adam",
  lr_scheduler = NULL,
  lr_decay = 0.1,
  step_size = 30,
  checkpoint_epochs = 10L,
  cat_emb_dim = 1L,
  num_independent = 2L,
  num_shared = 2L,
  num_independent_decoder = 1L,
  num_shared_decoder = 1L,
  momentum = 0.02,
  pretraining_ratio = 0.5,
  device = "auto",
  importance_sample_size = NULL,
  early_stopping_monitor = "auto",
  early_stopping_tolerance = 0,
  early_stopping_patience = 0,
  num_workers = 0L,
  skip_importance = FALSE,
  ifw = FALSE
)

Arguments

batch_size

(Tunable) Positive integer: Batch size.

penalty

(Tunable) Numeric: Regularization penalty.

clip_value

Numeric: Clip value.

loss

Character: Loss function.

epochs

(Tunable) Positive integer: Number of epochs.

drop_last

Logical: If TRUE, drop last batch.

decision_width

(Tunable) Positive integer: Decision width.

attention_width

(Tunable) Positive integer: Attention width.

num_steps

(Tunable) Positive integer: Number of steps.

feature_reusage

(Tunable) Numeric: Feature reusage.

mask_type

Character: Mask type.

virtual_batch_size

(Tunable) Positive integer: Virtual batch size.

valid_split

Numeric: Validation split.

learn_rate

(Tunable) Numeric: Learning rate.

optimizer

Character or torch function: Optimizer.

lr_scheduler

Character or torch function: "step", "reduce_on_plateau".

lr_decay

Numeric: Learning rate decay.

step_size

Positive integer: Step size.

checkpoint_epochs

(Tunable) Positive integer: Checkpoint epochs.

cat_emb_dim

(Tunable) Positive integer: Categorical embedding dimension.

num_independent

(Tunable) Positive integer: Number of independent Gated Linear Units (GLU) at each step of the encoder.

num_shared

(Tunable) Positive integer: Number of shared Gated Linear Units (GLU) at each step of the encoder.

num_independent_decoder

(Tunable) Positive integer: Number of independent GLU layers for pretraining.

num_shared_decoder

(Tunable) Positive integer: Number of shared GLU layers for pretraining.

momentum

(Tunable) Numeric: Momentum.

pretraining_ratio

(Tunable) Numeric: Pretraining ratio.

device

Character: Device "cpu" or "cuda".

importance_sample_size

Positive integer: Importance sample size.

early_stopping_monitor

Character: Early stopping monitor. "valid_loss", "train_loss", "auto".

early_stopping_tolerance

Numeric: Minimum relative improvement to reset the patience counter.

early_stopping_patience

Positive integer: Number of epochs without improving before stopping.

num_workers

Positive integer: Number of subprocesses for data loacding.

skip_importance

Logical: If TRUE, skip importance calculation.

ifw

Logical: If TRUE, use Inverse Frequency Weighting in classification.

Value

TabNetHyperparameters object.

Author(s)

EDG

Examples

tabnet_hyperparams <- setup_TabNet(epochs = 100L, learn_rate = 0.01)
tabnet_hyperparams

Setup UMAP config.

Description

Setup UMAP config.

Usage

setup_UMAP(
  k = 2L,
  n_neighbors = 15L,
  init = "spectral",
  metric = c("euclidean", "cosine", "manhattan", "hamming", "categorical"),
  n_epochs = NULL,
  learning_rate = 1,
  scale = TRUE
)

Arguments

k

Integer: Number of components.

n_neighbors

Integer: Number of keighbors.

init

Character: Initialization type. See ⁠uwot::umap "init"⁠.

metric

Character: Distance metric to use: "euclidean", "cosine", "manhattan", "hamming", "categorical".

n_epochs

Integer: Number of epochs.

learning_rate

Float: Learning rate.

scale

Logical: If TRUE, scale input data before doing UMAP.

Details

A high n_neighbors value may give error in some systems: "Error in irlba::irlba(L, nv = n, nu = 0, maxit = iters) : function 'as_cholmod_sparse' not provided by package 'Matrix'"

Value

UMAPConfig object.

Author(s)

EDG

Examples

umap_config <- setup_UMAP(k = 3L)
umap_config

Setup tSNE config.

Description

Setup tSNE config.

Usage

setup_tSNE(
  k = 2L,
  initial_dims = 50L,
  perplexity = 30,
  theta = 0.5,
  check_duplicates = TRUE,
  pca = TRUE,
  partial_pca = FALSE,
  max_iter = 1000L,
  verbose = getOption("verbose", FALSE),
  is_distance = FALSE,
  Y_init = NULL,
  pca_center = TRUE,
  pca_scale = FALSE,
  normalize = TRUE,
  stop_lying_iter = ifelse(is.null(Y_init), 250L, 0L),
  mom_switch_iter = ifelse(is.null(Y_init), 250L, 0L),
  momentum = 0.5,
  final_momentum = 0.8,
  eta = 200,
  exaggeration_factor = 12,
  num_threads = 1L
)

Arguments

k

Integer: Number of components.

initial_dims

Integer: Initial dimensions.

perplexity

Integer: Perplexity.

theta

Float: Theta.

check_duplicates

Logical: If TRUE, check for duplicates.

pca

Logical: If TRUE, perform PCA.

partial_pca

Logical: If TRUE, perform partial PCA.

max_iter

Integer: Maximum number of iterations.

verbose

Logical: If TRUE, print messages.

is_distance

Logical: If TRUE, x is a distance matrix.

Y_init

Matrix: Initial Y matrix.

pca_center

Logical: If TRUE, center PCA.

pca_scale

Logical: If TRUE, scale PCA.

normalize

Logical: If TRUE, normalize.

stop_lying_iter

Integer: Stop lying iterations.

mom_switch_iter

Integer: Momentum switch iterations.

momentum

Float: Momentum.

final_momentum

Float: Final momentum.

eta

Float: Eta.

exaggeration_factor

Float: Exaggeration factor.

num_threads

Integer: Number of threads.

Details

Get more information on the config by running ?Rtsne::Rtsne.

Value

tSNEConfig object.

Author(s)

EDG

Examples

tSNE_config <- setup_tSNE(k = 3L)
tSNE_config

Size of object

Description

Returns the size of an object

Usage

size(x, verbosity = 1L)

Arguments

x

any object with length() or dim().

verbosity

Integer: Verbosity level. If > 0, print size to console

Details

If dim(x) is NULL, returns length(x).

Value

Integer vector with length equal to the number of dimensions of x, invisibly.

Author(s)

EDG

Examples

x <- rnorm(20)
size(x)
# 20
x <- matrix(rnorm(100), 20, 5)
size(x)
# 20  5

Tabulate column attributes

Description

Tabulate column attributes

Usage

table_column_attr(x, attr = "source", useNA = "always")

Arguments

x

tabular data: Input data set.

attr

Character: Attribute to get

useNA

Character: Passed to table

Value

table.

Author(s)

EDG

Examples

library(data.table)
x <- data.table(
  id = 1:5,
  sbp = rnorm(5, 120, 15),
  dbp = rnorm(5, 80, 10),
  paO2 = rnorm(5, 90, 10),
  paCO2 = rnorm(5, 40, 5)
)
setattr(x[["sbp"]], "source", "outpatient")
setattr(x[["dbp"]], "source", "outpatient")
setattr(x[["paO2"]], "source", "icu")
setattr(x[["paCO2"]], "source", "icu")
table_column_attr(x, "source")

Themes for ⁠draw_*⁠ functions

Description

Themes for ⁠draw_*⁠ functions

Usage

theme_black(
  bg = "#000000",
  plot_bg = "transparent",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = FALSE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = fg,
  grid_alpha = 0.2,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = fg,
  tick_alpha = 0.5,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_blackgrid(
  bg = "#000000",
  plot_bg = "transparent",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = fg,
  grid_alpha = 0.2,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = fg,
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_blackigrid(
  bg = "#000000",
  plot_bg = "#1A1A1A",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = bg,
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = fg,
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_darkgray(
  bg = "#121212",
  plot_bg = "transparent",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = FALSE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = fg,
  grid_alpha = 0.2,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = fg,
  tick_alpha = 0.5,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_darkgraygrid(
  bg = "#121212",
  plot_bg = "transparent",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = "#404040",
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "#00000000",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_darkgrayigrid(
  bg = "#121212",
  plot_bg = "#202020",
  fg = "#ffffff",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = bg,
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "transparent",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_white(
  bg = "#ffffff",
  plot_bg = "transparent",
  fg = "#000000",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = FALSE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = fg,
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = fg,
  tick_alpha = 0.5,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_whitegrid(
  bg = "#ffffff",
  plot_bg = "transparent",
  fg = "#000000",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = "#c0c0c0",
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "#00000000",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_whiteigrid(
  bg = "#ffffff",
  plot_bg = "#E6E6E6",
  fg = "#000000",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = bg,
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "transparent",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_lightgraygrid(
  bg = "#dfdfdf",
  plot_bg = "transparent",
  fg = "#000000",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = "#c0c0c0",
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "#00000000",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

theme_mediumgraygrid(
  bg = "#b3b3b3",
  plot_bg = "transparent",
  fg = "#000000",
  pch = 16,
  cex = 1,
  lwd = 2,
  bty = "n",
  box_col = fg,
  box_alpha = 1,
  box_lty = 1,
  box_lwd = 0.5,
  grid = TRUE,
  grid_nx = NULL,
  grid_ny = NULL,
  grid_col = "#d0d0d0",
  grid_alpha = 1,
  grid_lty = 1,
  grid_lwd = 1,
  axes_visible = TRUE,
  axes_col = "transparent",
  tick_col = "#00000000",
  tick_alpha = 1,
  tick_labels_col = fg,
  tck = -0.01,
  tcl = NA,
  x_axis_side = 1,
  y_axis_side = 2,
  labs_col = fg,
  x_axis_line = 0,
  x_axis_las = 0,
  x_axis_padj = -1.1,
  x_axis_hadj = 0.5,
  y_axis_line = 0,
  y_axis_las = 1,
  y_axis_padj = 0.5,
  y_axis_hadj = 0.5,
  xlab_line = 1.4,
  ylab_line = 2,
  zerolines = TRUE,
  zerolines_col = fg,
  zerolines_alpha = 0.5,
  zerolines_lty = 1,
  zerolines_lwd = 1,
  main_line = 0.25,
  main_adj = 0,
  main_font = 2,
  main_col = fg,
  font_family = getOption("rtemis_font", "Helvetica")
)

Arguments

bg

Color: Figure background.

plot_bg

Color: Plot region background.

fg

Color: Foreground color used as default for multiple elements like axes and labels, which can be defined separately.

pch

Integer: Point character.

cex

Float: Character expansion factor.

lwd

Float: Line width.

bty

Character: Box type: "o", "l", "7", "c", "u", or "]", or "n".

box_col

Box color if bty != "n".

box_alpha

Float: Box alpha.

box_lty

Integer: Box line type.

box_lwd

Float: Box line width.

grid

Logical: If TRUE, draw grid in plot regions.

grid_nx

Integer: N of vertical grid lines.

grid_ny

Integer: N of horizontal grid lines.

grid_col

Grid color.

grid_alpha

Float: Grid alpha.

grid_lty

Integer: Grid line type.

grid_lwd

Float: Grid line width.

axes_visible

Logical: If TRUE, draw axes.

axes_col

Axes colors.

tick_col

Tick color.

tick_alpha

Float: Tick alpha.

tick_labels_col

Tick labels' color.

tck

graphics::parr's tck argument: Tick length, can be negative.

tcl

graphics::parr's tcl argument.

x_axis_side

Integer: Side to place x-axis.

y_axis_side

Integer: Side to place y-axis.

labs_col

Labels' color.

x_axis_line

Numeric: graphics::axis's line argument for the x-axis.

x_axis_las

Numeric: graphics::axis's las argument for the x-axis.

x_axis_padj

Numeric: x-axis' padj: Adjustment for the x-axis tick labels' position.

x_axis_hadj

Numeric: x-axis' hadj.

y_axis_line

Numeric: graphics::axis's line argument for the y-axis.

y_axis_las

Numeric: graphics::axis's las argument for the y-axis.

y_axis_padj

Numeric: y-axis' padj.

y_axis_hadj

Numeric: y-axis' hadj.

xlab_line

Numeric: Line to place xlab.

ylab_line

Numeric: Line to place ylab.

zerolines

Logical: If TRUE, draw lines on x = 0, y = 0, if within plot limits.

zerolines_col

Zerolines color.

zerolines_alpha

Float: Zerolines alpha.

zerolines_lty

Integer: Zerolines line type.

zerolines_lwd

Float: Zerolines line width.

main_line

Float: How many lines away from the plot region to draw title.

main_adj

Float: How to align title.

main_font

Integer: 1: Regular, 2: Bold.

main_col

Title color.

font_family

Character: Font to be used throughout plot.

Value

Theme object.

Examples

theme <- theme_black(font_family = "Geist")
theme

Train Supervised Learning Models

Description

Preprocess, tune, train, and test supervised learning models using nested resampling in a single call.

Usage

train(
  x,
  dat_validation = NULL,
  dat_test = NULL,
  weights = NULL,
  algorithm = NULL,
  preprocessor_config = NULL,
  hyperparameters = NULL,
  tuner_config = NULL,
  outer_resampling_config = NULL,
  execution_config = setup_ExecutionConfig(),
  question = NULL,
  outdir = NULL,
  verbosity = 1L,
  ...
)

Arguments

x

Tabular data, i.e. data.frame, data.table, or tbl_df (tibble): Training set data.

dat_validation

Tabular data: Validation set data.

dat_test

Tabular data: Test set data.

weights

Optional vector of case weights.

algorithm

Character: Algorithm to use. Can be left NULL, if hyperparameters is defined.

preprocessor_config

Optional PreprocessorConfig object: Setup using setup_Preprocessor.

hyperparameters

Hyperparameters object: Setup using one of ⁠setup_*⁠ functions.

tuner_config

TunerConfig object: Setup using setup_GridSearch.

outer_resampling_config

Optional ResamplerConfig object: Setup using setup_Resampler. This defines the outer resampling method, i.e. the splitting into training and test sets for the purpose of assessing model performance. If NULL, no outer resampling is performed, in which case you might want to use a dat_test dataset to assess model performance on a single test set.

execution_config

ExecutionConfig object: Setup using setup_ExecutionConfig. This allows you to set backend ("future", "mirai", or "none"), number of workers, and future plan if using backend = "future".

question

Optional character string defining the question that the model is trying to answer.

outdir

Character, optional: String defining the output directory.

verbosity

Integer: Verbosity level.

...

Not used.

Details

Online book & documentation

See docs.rtemis.org/r for detailed documentation.

Preprocessing

There are many different stages at which preprocessing could be applied, when running a supervised learning pipeline with nested resampling. Some operations are best done before passing data to train():

User-defined preprocessing through preprocessor_config is applied on training set data, the learned parameters are stored in the returned Supervised or SupervisedRes object, and the preprocessing is applied on validation and test data.

Binary Classification

For binary classification, the outcome should be a factor where the 2nd level corresponds to the positive class.

Resampling

Note that you should not use an outer resampling method with replacement if you will also be using an inner resampling (for tuning). The duplicated cases from the outer resampling may appear both in the training and test sets of the inner resamples, leading to underestimated test error.

Reproducibility

If using outer resampling, you can set a seed when defining outer_resampling_config, e.g. 

outer_resampling_config = setup_Resampler(n_resamples = 10L, type = "KFold", seed = 2026L)

If using tuning with inner resampling, you can set a seed when defining tuner_config, e.g. 

tuner_config = setup_GridSearch(
  resampler_config = setup_Resampler(n_resamples = 5L, type = "KFold", seed = 2027L)
)

Parallelization

There are three levels of parallelization that may be used during training:

  1. Algorithm training (e.g. a parallelized learner like LightGBM)

  2. Tuning (inner resampling, where multiple resamples can be processed in parallel)

  3. Outer resampling (where multiple outer resamples can be processed in parallel)

The train() function will automatically manage parallelization depending on:

Value

Object of class Regression(Supervised), RegressionRes(SupervisedRes), Classification(Supervised), or ClassificationRes(SupervisedRes).

Author(s)

EDG

Examples


iris_c_lightRF <- train(
   iris,
   algorithm = "LightRF",
   outer_resampling_config = setup_Resampler(),
)

Get protein sequence from UniProt

Description

Get protein sequence from UniProt

Usage

uniprot_get(
  accession,
  baseURL = "https://rest.uniprot.org/uniprotkb",
  verbosity = 1
)

Arguments

accession

Character: UniProt Accession number - e.g. "Q9UMX9"

baseURL

Character: UniProt rest API base URL. Default = "https://rest.uniprot.org/uniprotkb"

verbosity

Integer: Verbosity level.

Value

List with three elements: Identifier, Annotation, and Sequence.

Author(s)

E.D. Gennatas

Examples

## Not run: 
# This gets the sequence from uniprot.org by default
mapt <- uniprot_get("Q9UMX9")

## End(Not run)

Write to TOML file

Description

Write to TOML file

Usage

write_toml(x, file, overwrite = FALSE, verbosity = 1L)

Arguments

x

SuperConfig object.

file

Character: Path to output TOML file.

overwrite

Logical: If TRUE, overwrite existing file.

verbosity

Integer: Verbosity level.

Value

SuperConfig object, invisibly.

Author(s)

EDG

Examples

x <- setup_SuperConfig(
  dat_training_path = "~/Data/iris.csv",
  dat_validation_path = NULL,
  dat_test_path = NULL,
  weights = NULL,
  preprocessor_config = setup_Preprocessor(remove_duplicates = TRUE),
  algorithm = "LightRF",
  hyperparameters = setup_LightRF(),
  tuner_config = setup_GridSearch(),
  outer_resampling_config = setup_Resampler(),
  execution_config = setup_ExecutionConfig(),
  question = "Can we tell iris species apart given their measurements?",
  outdir = "models/",
  verbosity = 1L
)
tmpdir <- tempdir()
write_toml(x, file.path(tmpdir, "rtemis.toml"))

Example longitudinal dataset

Description

A small synthetic dataset demonstrating various participation patterns in longitudinal data, suitable for examples with xtdescribe.

Usage

xt_example

Format

A data frame with 30 rows and 4 variables:

patient_id

Integer: Patient identifier (1-10).

year

Integer: Year of measurement (2020-2024).

blood_pressure

Numeric: Systolic blood pressure measurement.

treatment

Character: Treatment group ("A" or "B").

Details

This dataset includes 10 patients measured at up to 5 time points (years 2020-2024). The dataset demonstrates various participation patterns typical in longitudinal studies:

Examples

data(xt_example)
head(xt_example)
summary(xt_example)

Describe longitudinal dataset

Description

This function emulates the xtdescribe function in Stata.

Usage

xtdescribe(x, id_col = 1, time_col = 2, n_patterns = 9)

Arguments

x

data.frame: Longitudinal data with ID and time variables.

id_col

Integer: The column position of the ID variable.

time_col

Integer: The column position of the time variable.

n_patterns

Integer: The number of patterns to display.

Value

data.frame: Summary of participation patterns, returned invisibly.

Author(s)

EDG

Examples

# Load example longitudinal dataset
data(xt_example)

# Describe the longitudinal structure
xtdescribe(xt_example)