Package 'bettr'

Title: A Better Way To Explore What Is Best
Description: bettr provides a set of interactive visualization methods to explore the results of a benchmarking study, where typically more than a single performance measures are computed. The user can weight the performance measures according to their preferences. Performance measures can also be grouped and aggregated according to additional annotations.
Authors: Federico Marini [aut] (ORCID: <https://orcid.org/0000-0003-3252-7758>), Charlotte Soneson [aut, cre] (ORCID: <https://orcid.org/0000-0003-3833-2169>), Daniel Incicau [aut] (ORCID: <https://orcid.org/0009-0001-1748-6145>)
Maintainer: Charlotte Soneson <[email protected]>
License: MIT + file LICENSE
Version: 1.9.0
Built: 2026-05-11 09:59:22 UTC
Source: https://github.com/bioc/bettr

Help Index

Assemble all bettr input into a SummarizedExperiment object

Description

Assemble all bettr input into a SummarizedExperiment object. This has the advantage of keeping all data together in a single object, and can be used as input to bettr or bettrGetReady, instead of providing the individual components.

Usage

assembleSE(
  df,
  idCol = "Method",
  metrics = setdiff(colnames(df), idCol),
  initialWeights = NULL,
  initialTransforms = list(),
  metricInfo = NULL,
  metricColors = NULL,
  idInfo = NULL,
  idColors = NULL
)

Arguments

df

A data.frame in wide format. Should contain one column with the IDs of the entities to be compared, and one column for each metric to use for the comparison.
idCol

Character scalar, indicating the name of the column of df and/or idInfo that contains IDs of the entities to be compared (e.g., methods).
metrics

Character vector, indicating which of the columns of df that correspond to metrics of interest. Only metrics included here will be displayed.
initialWeights

Named numeric vector providing initial weights for each metric to use for aggregating them into a final score. Must contain one entry per metric included in metrics.
initialTransforms

Named list with initial values of transformation parameters for each metric. Each list entry should correspond to one metric, and take the form of a list with up to four elements, named: 

* **flip**: Logical scalar; whether or not to flip the sign of the
    metric values. Defaults to `FALSE`.
* **offset**: Numeric scalar; offset to add to the (flipped)
    metric values. Defaults to `0`.
* **transform**: Character scalar; one of 'None', 'z-score',
    '\[0,1\]', '\[-1,1\]', 'Rank', 'Rank+\[0,1\]' or 'z-score+\[0,1\]',
    indicating which transform to apply to
    the metric values (after any flipping and/or adding the offset).
    Defaults to 'None'.
* **cuts**: Numeric vector or `NULL`; the cut points that will
    be used to bin the metric values (after the other transformations).
    Defaults to `NULL`.

Only values deviating from the defaults need to be explicitly specified,
the others will be initialized to their default values.
metricInfo

data.frame with annotations for metrics. Must have a column named 'Metric' identifying the respective metrics.
metricColors

Named list with colors used for columns of metricInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named 'Metric', which contains a named vector with colors to use for metrics.
idInfo

data.frame with annotations for entities. Must have a column named according to idCol identifying the respective entities.
idColors

Named list with colors used for columns of idInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named according to idCol, which contains a named vector with colors to use for entities.

Value

A SummarizedExperiment object with rows corresponding to methods and columns corresponding to metrics.

Author(s)

Charlotte Soneson

Examples

df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
bettrSE <- assembleSE(df = df, metricInfo = metricInfo, idInfo = idInfo)

Launch bettr app to explore and aggregate performance metrics

Description

Launch bettr app to explore and aggregate performance metrics

Usage

bettr(
  df = NULL,
  idCol = "Method",
  metrics = if (!is.null(df)) setdiff(colnames(df), idCol) else NULL,
  initialWeights = NULL,
  initialTransforms = list(),
  metricInfo = NULL,
  metricColors = NULL,
  idInfo = NULL,
  idColors = NULL,
  weightResolution = 0.05,
  bstheme = "darkly",
  appTitle = "bettr",
  bettrSE = NULL,
  addStopButton = TRUE,
  defaultWeight = 0.2,
  serverMode = FALSE,
  cacheVersion = NULL
)

Arguments

df

A data.frame in wide format. Should contain one column with the IDs of the entities to be compared, and one column for each metric to use for the comparison.
idCol

Character scalar, indicating the name of the column of df and/or idInfo that contains IDs of the entities to be compared (e.g., methods).
metrics

Character vector, indicating which of the columns of df that correspond to metrics of interest. Only metrics included here will be displayed.
initialWeights

Named numeric vector providing initial weights for each metric to use for aggregating them into a final score. Must contain one entry per metric included in metrics.
initialTransforms

Named list with initial values of transformation parameters for each metric. Each list entry should correspond to one metric, and take the form of a list with up to four elements, named: 

* **flip**: Logical scalar; whether or not to flip the sign of the
    metric values. Defaults to `FALSE`.
* **offset**: Numeric scalar; offset to add to the (flipped)
    metric values. Defaults to `0`.
* **transform**: Character scalar; one of 'None', 'z-score',
    '\[0,1\]', '\[-1,1\]', 'Rank', 'Rank+\[0,1\]' or 'z-score+\[0,1\]',
    indicating which transform to apply to
    the metric values (after any flipping and/or adding the offset).
    Defaults to 'None'.
* **cuts**: Numeric vector or `NULL`; the cut points that will
    be used to bin the metric values (after the other transformations).
    Defaults to `NULL`.

Only values deviating from the defaults need to be explicitly specified,
the others will be initialized to their default values.
metricInfo

data.frame with annotations for metrics. Must have a column named 'Metric' identifying the respective metrics.
metricColors

Named list with colors used for columns of metricInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named 'Metric', which contains a named vector with colors to use for metrics.
idInfo

data.frame with annotations for entities. Must have a column named according to idCol identifying the respective entities.
idColors

Named list with colors used for columns of idInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named according to idCol, which contains a named vector with colors to use for entities.
weightResolution

Numeric scalar in (0,1), giving the resolution at which weights can be specified using the sliders in the interface.
bstheme

Character scalar giving the bootswatch theme for the app (see https://bootswatch.com/). Default 'darkly'.
appTitle

Character scalar giving the title that will be used for the app. Defaults to 'bettr'.
bettrSE

A SummarizedExperiment generated by assembleSE. If this is not NULL, df, metrics, initialWeights, initialTransforms, metricInfo, metricColors, idInfo and idColors arguments will be ignored and the information will be extracted from the SummarizedExperiment object.
addStopButton

Logical scalar. If TRUE (default), will add a button to stop the app (by calling shiny::stopApp).
defaultWeight

Numeric scalar between 0 and 1, giving the default weight to assign to each metric.
serverMode

Logical scalar. If TRUE, launches the app in server mode where users can upload JSON files (in bettr format). If FALSE (default), requires data to be provided via the df or bettrSE parameter.
cacheVersion

Character string or NULL (default). A version identifier for the browser cache. When specified, cached data from previous versions will be automatically invalidated. Useful for forcing cache refresh when deploying updates. Examples: "v1.0", "2024-01-15", or any string.

Value

A shiny application

Author(s)

Charlotte Soneson, Daniel Incicau

Examples

df <- data.frame(Method = c("M1", "M2", "M3"), metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2), metric3 = factor(c("a", "a", "b")))
initialTransforms <- list(metric1 = list(flip = TRUE, offset = 4))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
metricColors <- list(Group = c(G1 = "red", G2 = "blue"))
if (interactive()) {
    bettr(df = df, idCol = "Method",
    metrics = c("metric1", "metric2", "metric3"),
    initialTransforms = initialTransforms,
    metricInfo = metricInfo, metricColors = metricColors,
    idInfo = idInfo)
}

Import bettr data from JSON format

Description

Import bettr data from a standardized JSON format and create a SummarizedExperiment object using assembleSE.

Usage

bettrFromJSON(file = NULL, json = NULL)

Arguments

file

Character scalar, path to the JSON file to import.
json

Character scalar, JSON string to parse. If provided, file is ignored.

Value

A SummarizedExperiment object that can be passed to bettr.

Author(s)

Daniel Incicau

Examples

df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
bettrSE <- assembleSE(df = df)
json_file <- tempfile(fileext = ".json")
bettrToJSON(bettrSE, file = json_file)
bettrSE_reload <- bettrFromJSON(file = json_file)

Prepare data for plotting with bettr

Description

Prepare input data for plotting with bettr. This function replicates the steps that are performed in the shiny app.

Usage

bettrGetReady(
  df,
  idCol = "Method",
  metrics = setdiff(colnames(df), idCol),
  initialWeights = NULL,
  initialTransforms = list(),
  metricInfo = NULL,
  metricColors = NULL,
  idInfo = NULL,
  idColors = NULL,
  scoreMethod = "weighted mean",
  idOrdering = "high-to-low",
  showOnlyTopIds = FALSE,
  nbrTopIds = 10L,
  idTopNGrouping = NULL,
  keepIds = NULL,
  metricGrouping = NULL,
  metricCollapseGroup = FALSE,
  metricCollapseMethod = "mean",
  defaultWeight = 0.2,
  bettrSE = NULL
)

Arguments

df

A data.frame in wide format. Should contain one column with the IDs of the entities to be compared, and one column for each metric to use for the comparison.
idCol

Character scalar, indicating the name of the column of df and/or idInfo that contains IDs of the entities to be compared (e.g., methods).
metrics

Character vector, indicating which of the columns of df that correspond to metrics of interest. Only metrics included here will be displayed.
initialWeights

Named numeric vector providing initial weights for each metric to use for aggregating them into a final score. Must contain one entry per metric included in metrics.
initialTransforms

Named list with initial values of transformation parameters for each metric. Each list entry should correspond to one metric, and take the form of a list with up to four elements, named: 

* **flip**: Logical scalar; whether or not to flip the sign of the
    metric values. Defaults to `FALSE`.
* **offset**: Numeric scalar; offset to add to the (flipped)
    metric values. Defaults to `0`.
* **transform**: Character scalar; one of 'None', 'z-score',
    '\[0,1\]', '\[-1,1\]', 'Rank', 'Rank+\[0,1\]' or 'z-score+\[0,1\]',
    indicating which transform to apply to
    the metric values (after any flipping and/or adding the offset).
    Defaults to 'None'.
* **cuts**: Numeric vector or `NULL`; the cut points that will
    be used to bin the metric values (after the other transformations).
    Defaults to `NULL`.

Only values deviating from the defaults need to be explicitly specified,
the others will be initialized to their default values.
metricInfo

data.frame with annotations for metrics. Must have a column named 'Metric' identifying the respective metrics.
metricColors

Named list with colors used for columns of metricInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named 'Metric', which contains a named vector with colors to use for metrics.
idInfo

data.frame with annotations for entities. Must have a column named according to idCol identifying the respective entities.
idColors

Named list with colors used for columns of idInfo. Should follow the format required for ComplexHeatmap heatmap annotations. The list can include an entry named according to idCol, which contains a named vector with colors to use for entities.
scoreMethod

Character scalar specifying the scoring method, that is, how to aggregate scores across metrics. Should be one of "weighted mean", "weighted median", "weighted fraction highest" or "weighted fraction lowest".
idOrdering

Character scalar indicating whether methods should be ranked with highest aggregated scores on top ("high-to-low") or opposite ("low-to-high").
showOnlyTopIds

Logical scalar indicating whether to only retain the top N methods (ranked by the aggregated score).
nbrTopIds

If showOnlyTopIds is TRUE, the number of top-ranked methods to retain.
idTopNGrouping

If showOnlyTopIds is TRUE, a character scalar providing the name of a column in idInfo that groups the methods. If specified, he top nbrTopIds within each group will be retained.
keepIds

Character vector indicating which methods (a subset of the values in df[[idCol]]) that should be considered. If NULL, all methods are considered.
metricGrouping

A character scalar providing the name of a column in metricInfo by which metrics should be grouped. If NULL, no grouping is performed.
metricCollapseGroup

A logical scalar indicating whether metric values should be collapsed within each group defined by metricGrouping.
metricCollapseMethod

If metricCollapseGroup is TRUE, the way in which metric values are collapsed within a group. Should be one of "mean", "max" or "min".
defaultWeight

Numeric scalar between 0 and 1, giving the default weight to assign to each metric.
bettrSE

A SummarizedExperiment generated by assembleSE. If this is not NULL, df, metrics, initialWeights, initialTransforms, metricInfo, metricColors, idInfo and idColors arguments will be ignored and the information will be extracted from the SummarizedExperiment object.

Value

A list of objects, which can be directly used as inputs for the bettr plotting functions. See the man page for the respective plotting function for more details.

Author(s)

Charlotte Soneson

Examples

## Generate example data
df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo)
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo,
                          metricGrouping = "Group",
                          metricCollapseGroup = TRUE)

Export SummarizedExperiment to bettr JSON format

Description

Export a bettr SummarizedExperiment object to a standardized JSON format that can be re-imported using bettrSEFromJSON.

Usage

bettrToJSON(bettrSE, file = NULL, pretty = TRUE)

Arguments

bettrSE

A SummarizedExperiment object created by assembleSE.
file

Character scalar, path to the output JSON file. If NULL, returns the JSON string without writing to file.
pretty

Logical scalar, whether to format the JSON output with indentation for readability (default: TRUE).

Value

If file is NULL, returns a JSON string. Otherwise, writes to file and returns the file path invisibly.

Author(s)

Daniel Incicau

Examples

df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
bettrSE <- assembleSE(df = df)
json_str <- bettrToJSON(bettrSE)

Create a bar/polar plot

Description

Create a bar/polar plot. The input arguments for this functions are typically generated using bettrGetReady, which ensures that all required columns are available.

Usage

makeBarPolarPlot(
  bettrList = NULL,
  plotdata,
  scoredata,
  idCol,
  metricCol = "Metric",
  valueCol = "ScaledValue",
  weightCol = "Weight",
  scoreCol = "Score",
  metricGroupCol = "metricGroup",
  metricColors,
  metricCollapseGroup = FALSE,
  metricGrouping = "---",
  methods = NULL,
  labelSize = 10,
  showComposition = FALSE,
  scaleFactorPolars = 1
)

Arguments

bettrList

A list, the output object from prepData. If bettrList is provided, arguments plotdata, scoredata, idCol, metricCol, valueCol, weightCol, scoreCol, metricGroupCol, metricInfo, metricColors, idInfo, idColors, metricCollapseGroup, metricGrouping and methods will be ignored and the corresponding values will be extracted from bettrList. This is the recommended way of calling the plotting functions, as it ensures compatibility of all components.
plotdata

A data.frame with columns representing methods, metrics, scores, and weights. Typically obtained as prepData$plotdata, where prepData is the output from bettrGetReady.
scoredata

A data.frame with columns representing methods, aggregated scores, and any other method annotations. Typically obtained as prepData$scoredata, where prepData is the output from bettrGetReady.
idCol

Character scalar indicating which column of plotdata and scoredata contains the method IDs.
metricCol

Character scalar indicating which column of plotdata contains the metric IDs. Typically, "Metric".
valueCol

Character scalar indicating which column of plotdata contains the metric values. Typically, "ScaledValue".
weightCol

Character scalar indicating which column of plotdata contains the weight values. Typically, "Weight".
scoreCol

Character scalar indicating which column of scoredata contains the aggregated score values. Typically, "Score".
metricGroupCol

Character scalar indicating which column of plotdata contains the information about the metric group. Typically, "metricGroup".
metricColors

Named list with colors used for the metrics and any other metric annotations. Typically obtained as prepData$metricColors, where prepData is the output from bettrGetReady.
metricCollapseGroup

Logical scalar indicating whether metrics should be collapsed by the group variable provided by metricGrouping. Typically obtained as prepData$metricCollapseGroup, where prepData is the output from bettrGetReady.
metricGrouping

Character scalar indicating the column of metricInfo that was used to group metrics. Typically obtained as prepData$metricGrouping, where prepData is the output from bettrGetReady.
methods

Character vector containing the methods for which to make polar plots. If NULL (default), all methods will be used.
labelSize

Numeric scalar providing the size of the labels in the plot.
showComposition

Logical scalar indicating whether to show the composition of the score in the bar plots. This is only interpretable if the scores are obtained via a weighted mean approach.
scaleFactorPolars

Numeric scalar giving the scale factor determining the size of the polar plots.

Value

A ggplot object.

Author(s)

Charlotte Soneson

Examples

## Generate example data
df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo)
makeBarPolarPlot(bettrList = prepData, showComposition = TRUE)

Create a summary heatmap

Description

Create a summary heatmap. The input arguments for this functions are typically generated using bettrGetReady, which ensures that all required columns are available.

Usage

makeHeatmap(
  bettrList = NULL,
  plotdata,
  scoredata,
  idCol,
  metricCol = "Metric",
  valueCol = "ScaledValue",
  weightCol = "Weight",
  scoreCol = "Score",
  metricGroupCol = "metricGroup",
  metricInfo,
  metricColors,
  idInfo,
  idColors,
  metricCollapseGroup = FALSE,
  metricGrouping = "---",
  labelSize = 10,
  showRowNames = TRUE,
  plotType = "Heatmap",
  rownamewidth_cm = 6,
  colnameheight_cm = 6
)

Arguments

bettrList

A list, the output object from prepData. If bettrList is provided, arguments plotdata, scoredata, idCol, metricCol, valueCol, weightCol, scoreCol, metricGroupCol, metricInfo, metricColors, idInfo, idColors, metricCollapseGroup, metricGrouping and methods will be ignored and the corresponding values will be extracted from bettrList. This is the recommended way of calling the plotting functions, as it ensures compatibility of all components.
plotdata

A data.frame with columns representing methods, metrics, scores, and weights. Typically obtained as prepData$plotdata, where prepData is the output from bettrGetReady.
scoredata

A data.frame with columns representing methods, aggregated scores, and any other method annotations. Typically obtained as prepData$scoredata, where prepData is the output from bettrGetReady.
idCol

Character scalar indicating which column of plotdata and scoredata contains the method IDs.
metricCol

Character scalar indicating which column of plotdata contains the metric IDs. Typically, "Metric".
valueCol

Character scalar indicating which column of plotdata contains the metric values. Typically, "ScaledValue".
weightCol

Character scalar indicating which column of plotdata contains the weight values. Typically, "Weight".
scoreCol

Character scalar indicating which column of scoredata contains the aggregated score values. Typically, "Score".
metricGroupCol

Character scalar indicating which column of plotdata contains the information about the metric group. Typically, "metricGroup".
metricInfo

data.frame with annotations for metrics. Typically obtained as prepData$metricInfo, where prepData is the output from bettrGetReady.
metricColors

Named list with colors used for the metrics and any other metric annotations. Typically obtained as prepData$metricColors, where prepData is the output from bettrGetReady.
idInfo

data.frame with annotations for entities. Typically obtained as prepData$idInfo, where prepData is the output from bettrGetReady.
idColors

Named list with colors used for methods and any other method annotations. Typically obtained as prepData$idColors, where prepData is the output from bettrGetReady.
metricCollapseGroup

Logical scalar indicating whether metrics should be collapsed by the group variable provided by metricGrouping. Typically obtained as prepData$metricCollapseGroup, where prepData is the output from bettrGetReady.
metricGrouping

Character scalar indicating the column of metricInfo that was used to group metrics. Typically obtained as prepData$metricGrouping, where prepData is the output from bettrGetReady.
labelSize

Numeric scalar providing the size of the labels in the plot.
showRowNames

Logical scalar indicating whether to show row (method) names in the heatmap.
plotType

Either "Heatmap" or "Dot plot" indicating the type of plot to construct.
rownamewidth_cm, colnameheight_cm

Numeric scalars defining the width of row names and height of column names, in cm.

Value

A HeatmapList object.

Author(s)

Charlotte Soneson

Examples

## Generate example data
df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo)
makeHeatmap(bettrList = prepData, plotType = "Heatmap")
makeHeatmap(bettrList = prepData, plotType = "Dot plot")

Create a parallel coordinates plot

Description

Create a parallel coordinates plot. The input arguments for this functions are typically generated using bettrGetReady, which ensures that all required columns are available.

Usage

makeParCoordPlot(
  bettrList = NULL,
  plotdata,
  idCol,
  metricCol = "Metric",
  valueCol = "ScaledValue",
  metricGroupCol = "metricGroup",
  metricColors,
  idColors,
  methods = NULL,
  metricGrouping = "---",
  highlightMethod = NULL,
  labelSize = 10
)

Arguments

bettrList

A list, the output object from prepData. If bettrList is provided, arguments plotdata, scoredata, idCol, metricCol, valueCol, weightCol, scoreCol, metricGroupCol, metricInfo, metricColors, idInfo, idColors, metricCollapseGroup, metricGrouping and methods will be ignored and the corresponding values will be extracted from bettrList. This is the recommended way of calling the plotting functions, as it ensures compatibility of all components.
plotdata

A data.frame with columns representing methods, metrics, scores, and weights. Typically obtained as prepData$plotdata, where prepData is the output from bettrGetReady.
idCol

Character scalar indicating which column of plotdata and scoredata contains the method IDs.
metricCol

Character scalar indicating which column of plotdata contains the metric IDs. Typically, "Metric".
valueCol

Character scalar indicating which column of plotdata contains the metric values. Typically, "ScaledValue".
metricGroupCol

Character scalar indicating which column of plotdata contains the information about the metric group. Typically, "metricGroup".
metricColors

Named list with colors used for the metrics and any other metric annotations. Typically obtained as prepData$metricColors, where prepData is the output from bettrGetReady.
idColors

Named list with colors used for methods and any other method annotations. Typically obtained as prepData$idColors, where prepData is the output from bettrGetReady.
methods

Character vector containing the methods to include. If NULL (default), all methods will be used.
metricGrouping

Character scalar indicating the column of metricInfo that was used to group metrics. Typically obtained as prepData$metricGrouping, where prepData is the output from bettrGetReady.
highlightMethod

Character vector indicating one or more methods that should be highlighted in the plot.
labelSize

Numeric scalar providing the size of the labels in the plot.

Value

A ggplot object.

Author(s)

Charlotte Soneson

Examples

## Generate example data
df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo)
makeParCoordPlot(bettrList = prepData, highlightMethod = "M2")

Create a polar plot

Description

Create a polar plot. The input arguments for this functions are typically generated using bettrGetReady, which ensures that all required columns are available.

Usage

makePolarPlot(
  bettrList = NULL,
  plotdata,
  idCol,
  metricCol = "Metric",
  valueCol = "ScaledValue",
  metricGroupCol = "metricGroup",
  metricColors,
  metricCollapseGroup = FALSE,
  metricGrouping = "---",
  labelSize = 10
)

Arguments

bettrList

A list, the output object from prepData. If bettrList is provided, arguments plotdata, scoredata, idCol, metricCol, valueCol, weightCol, scoreCol, metricGroupCol, metricInfo, metricColors, idInfo, idColors, metricCollapseGroup, metricGrouping and methods will be ignored and the corresponding values will be extracted from bettrList. This is the recommended way of calling the plotting functions, as it ensures compatibility of all components.
plotdata

A data.frame with columns representing methods, metrics, scores, and weights. Typically obtained as prepData$plotdata, where prepData is the output from bettrGetReady.
idCol

Character scalar indicating which column of plotdata and scoredata contains the method IDs.
metricCol

Character scalar indicating which column of plotdata contains the metric IDs. Typically, "Metric".
valueCol

Character scalar indicating which column of plotdata contains the metric values. Typically, "ScaledValue".
metricGroupCol

Character scalar indicating which column of plotdata contains the information about the metric group. Typically, "metricGroup".
metricColors

Named list with colors used for the metrics and any other metric annotations. Typically obtained as prepData$metricColors, where prepData is the output from bettrGetReady.
metricCollapseGroup

Logical scalar indicating whether metrics should be collapsed by the group variable provided by metricGrouping. Typically obtained as prepData$metricCollapseGroup, where prepData is the output from bettrGetReady.
metricGrouping

Character scalar indicating the column of metricInfo that was used to group metrics. Typically obtained as prepData$metricGrouping, where prepData is the output from bettrGetReady.
labelSize

Numeric scalar providing the size of the labels in the plot.

Value

A ggplot object.

Author(s)

Charlotte Soneson

Examples

## Generate example data
df <- data.frame(Method = c("M1", "M2", "M3"),
                 metric1 = c(1, 2, 3),
                 metric2 = c(3, 1, 2))
metricInfo <- data.frame(Metric = c("metric1", "metric2", "metric3"),
                         Group = c("G1", "G2", "G2"))
idInfo <- data.frame(Method = c("M1", "M2", "M3"),
                     Type = c("T1", "T1", "T2"))
prepData <- bettrGetReady(df = df, idCol = "Method",
                          metricInfo = metricInfo, idInfo = idInfo)
makePolarPlot(bettrList = prepData)