Help for package cassowaryr

Title:

Compute Scagnostics on Pairs of Numeric Variables in a Data Set

Version:

2.0.2

Description:

Computes a range of scatterplot diagnostics (scagnostics) on pairs of numerical variables in a data set. A range of scagnostics, including graph and association-based scagnostics described by Leland Wilkinson and Graham Wills (2008) <doi:10.1198/106186008X320465> and association-based scagnostics described by Katrin Grimm (2016,ISBN:978-3-8439-3092-5) can be computed. Summary and plotting functions are provided.

License:

GPL-3

Encoding:

UTF-8

LazyData:

true

URL:

https://github.com/numbats/cassowaryr

BugReports:

https://github.com/numbats/cassowaryr/issues

Depends:

R (≥ 4.0.0)

Imports:

igraph, alphahull (≥ 2.5), splancs, interp (≥ 1.1-6), energy, dplyr, ggplot2, magrittr, progress, tibble, stats, tidyselect

RoxygenNote:

7.3.2

Suggests:

rmarkdown, knitr, mgcv, GGally, tidyr, testthat (≥ 3.0.0), covr

VignetteBuilder:

knitr

Config/testthat/edition:

NeedsCompilation:

Packaged:

2024-09-13 06:48:35 UTC; hmas0003

Author:

Harriet Mason

[aut, cre], Stuart Lee

[aut], Ursula Laa

[aut], Dianne Cook

[aut]

Maintainer:

Harriet Mason <harriet.m.mason@gmail.com>

Repository:

CRAN

Date/Publication:

2024-09-13 07:20:02 UTC

Data from Anscombe's famous example in tidy format

Description

All variables and pairs of variables have same summary statistics but are very different data, as can be seen by visualisation.

Format

A tibble with 44 observations and 3 variables

set: label of the data set, each set has 11 observations
x: variable for horizontal axis
y: variable for vertical axis

Compute selected scagnostics on subsets

Description

Compute selected scagnostics on subsets

Usage

calc_scags(
  x,
  y,
  scags = c("outlying", "stringy", "striated", "striated2", "clumpy", "clumpy2",
    "sparse", "skewed", "convex", "skinny", "monotonic", "splines", "dcor"),
  out.rm = TRUE,
  euclid = FALSE
)

Arguments

x

numeric vector

y

numeric vector

scags

collection of strings matching names of scagnostics to calculate: outlying, stringy, striated, striated2, striped, clumpy, clumpy2, sparse, skewed, convex, skinny, monotonic, splines, dcor

out.rm

logical indicator to indicate if outliers should be removed before calculating non outlying measures

euclid

logical indicator to use Euclidean distance

Value

A data frame that gives the single plot's scagnostic score.

Examples

# Calculate selected scagnostics on a single pair
calc_scags(anscombe$x1, anscombe$y1, scags=c("monotonic", "outlying"))

# Compute on long form data, or subsets
# defined by a categorical variable
require(dplyr)
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(calc_scags(x,y, scags=c("monotonic", "outlying", "convex")))

Compute scagnostics on all possible scatter plots for the given data

Description

Compute scagnostics on all possible scatter plots for the given data

Usage

calc_scags_wide(
  all_data,
  scags = c("outlying", "stringy", "striated", "striated2", "clumpy", "clumpy2",
    "sparse", "skewed", "convex", "skinny", "monotonic", "splines", "dcor"),
  out.rm = TRUE,
  euclid = FALSE
)

Arguments

all_data

tibble of multivariate data on which to compute scagnostics

scags

collection of strings matching names of scagnostics to calculate: outlying, stringy, striated, striated2, striped, clumpy, clumpy2, sparse, skewed, convex, skinny, monotonic, splines, dcor

out.rm

logical indicator to indicate if outliers should be removed before calculating non outlying measures

euclid

logical indicator to use Euclidean distance

Value

A data frame that gives the data's scagnostic scores for each possible variable combination.

Examples

# Calculate selected scagnostics
data(pk)
calc_scags_wide(pk[,2:5], scags=c("outlying","monotonic"))

datasaurus_dozen data

Description

From the datasauRus package. A modern update of Anscombe. All plots have same x and y mean, variance and correlation, but look different visually.

All variables and pairs of variables have same summary statistics but are very different data, as can be seen by visualisation.

Format

A tibble with 1,846 observations and 3 variables

dataset: label of data set
x: variable for horizontal axis
y: variable for vertical axis

A tibble with 142 observations and 26 variables

away_x, away_y: x and y variables for away data
bullseye_x, bullseye_y: x and y variables for bullseye data
circle_x, circle_y: x and y variables for circle data
dino_x, dino_y: x and y variables for dino data
dots_x, dots_y: x and y variables for dots data
h_lines_x, h_lines_y: x and y variables for h_lines data
high_lines_x, high_lines_y: x and y variables for high_lines data
slant_down_x, slant_down_y: x and y variables for slant_down data
slant_up_x, slant_up_y: x and y variables for slant_up data
star_x, star_y: x and y variables for star data
v_lines_x, v_lines_y: x and y variables for v_lines data
wide_lines_x, wide_lines_y: x and y variables for wide_lines data
star_x, star_y: x and y variables for star data
x_shape_x, x_shape_y: x and y variables for x_shape data

Drawing the alphahull

Description

This function will draw the alphahull for a scatterplot.

Usage

draw_alphahull(x, y, alpha = 0.5, clr = "black", fill = FALSE, out.rm = TRUE)

Arguments

x

numeric vector

y

numeric vector

alpha

transparency value of points

clr

optional colour of points and lines, default black

fill

Fill the polygon

out.rm

option to return the outlier removed alphahull

Value

A alphahull::ahull(del, alpha = alpha) "gg" object that draws the plot's alpha hull.

Examples

require(dplyr)
require(ggplot2)
require(alphahull)
data("features")
nl <- features %>% filter(feature == "clusters")
draw_alphahull(nl$x, nl$y)

Drawing the Convex Hull

Description

This function will draw the Convex Hull for a scatterplot.

Usage

draw_convexhull(x, y, alpha = 0.5, clr = "black", fill = FALSE, out.rm = TRUE)

Arguments

x

numeric vector

y

numeric vector

alpha

transparency value of points

clr

optional colour of points and lines, default black

fill

Fill the polygon

out.rm

option to return the outlier removed convex hull

Value

A "gg" object that draws the plot's convex hull.

Examples

require(dplyr)
require(ggplot2)
data("features")
nl <- features %>% filter(feature == "clusters")
draw_convexhull(nl$x, nl$y, fill=TRUE, out.rm=FALSE)

Drawing the MST

Description

This function will draw the MST for a scatterplot.

Usage

draw_mst(x, y, alpha = 0.5, out.rm = TRUE)

Arguments

x

numeric vector

y

numeric vector

alpha

The alpha value used to build the graph object. Larger values allow points further apart to be connected.

out.rm

option to return the outlier removed MST

Value

A "gg" object that draws the plot's MST.

Examples

require(dplyr)
require(ggplot2)
data("features")
nl <- features %>% filter(feature == "nonlinear2")
draw_mst(nl$x, nl$y)

Simulated data with special features

Description

Simulated data with common features that might be seen in 2D data. Variable are feature, x, y.

Format

A tibble with 1,013 observations and 3 variables, and 15 different patterns

feature: label of data set
x: variable for horizontal axis
y: variable for vertical axis

A toy data set with a numbat shape hidden among noise variables

Description

There are 7 variables (x1-x7) and 2,100 observations. Variables 4 and 7 have the numbat. The rest are noise. Group A has the numbat, and group B is all noise.

Parkinsons data from UCI machine learning archive

Description

Biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD.

Format

A tibble with 1,013 observations and 3 variables

name: ASCII subject name and recording number
MDVP:Fo(Hz): Average vocal fundamental frequency
MDVP:Fhi(Hz): Maximum vocal fundamental frequency
MDVP:Flo(Hz): Minimum vocal fundamental frequency
MDVP:Jitter,MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP: Several measures of variation in fundamental frequency
MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA: Several measures of variation in amplitude
NHR,HNR: Two measures of ratio of noise to tonal components in the voice
status: Health status of the subject (one) - Parkinson's, (zero) - healthy
RPDE,D2: Two nonlinear dynamical complexity measures
DFA: Signal fractal scaling exponent
spread1,spread2,PPE: Three nonlinear measures of fundamental frequency variation

Details

The data is available at The UCI Machine Learning Repository in ASCII CSV format. The rows of the CSV file contain an instance corresponding to one voice recording. There are around six recordings per patient, the name of the patient is identified in the first column.

The data are originally analysed in: Max A. Little, Patrick E. McSharry, Eric J. Hunter, Lorraine O. Ramig (2008), 'Suitability of dysphonia measurements for telemonitoring of Parkinson's disease', IEEE Transactions on Biomedical Engineering.

Compute clumpy scagnostic measure using MST

Description

Compute clumpy scagnostic measure using MST

Usage

sc_clumpy(x, y)

## Default S3 method:
sc_clumpy(x, y)

## S3 method for class 'scree'
sc_clumpy(x, y = NULL)

## S3 method for class 'igraph'
sc_clumpy(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's clumpy score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(clumpy = sc_clumpy(x,y))
  sc_clumpy(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Compute adjusted clumpy measure using MST

Description

Compute adjusted clumpy measure using MST

Usage

sc_clumpy2(x, y)

## Default S3 method:
sc_clumpy2(x, y)

## S3 method for class 'scree'
sc_clumpy2(x, y = NULL)

## S3 method for class 'igraph'
sc_clumpy2(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's clumpy2 score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(clumpy = sc_clumpy2(x,y))
  sc_clumpy2(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Compute robust clumpy scagnostic measure using MST

Description

Compute robust clumpy scagnostic measure using MST

Usage

sc_clumpy_r(x, y)

## Default S3 method:
sc_clumpy_r(x, y)

## S3 method for class 'scree'
sc_clumpy_r(x, y = NULL)

## S3 method for class 'igraph'
sc_clumpy_r(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's robust clumpy score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(clumpy = sc_clumpy_r(x,y))
  sc_clumpy_r(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Compute convex scagnostic measure

Description

Compute convex scagnostic measure

Usage

sc_convex(x, y)

## Default S3 method:
sc_convex(x, y)

## S3 method for class 'scree'
sc_convex(x, y = NULL)

## S3 method for class 'list'
sc_convex(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's convex score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(convex = sc_convex(x,y))
  sc_convex(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Distance correlation index.

Description

(Taken from tourr package) Computes the distance correlation based index on 2D projections of the data.

Usage

sc_dcor(x, y)

Arguments

x

numeric vector

y

numeric vector

Value

A "numeric" object that gives the plot's dcor score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe)
  anscombe_tidy <- anscombe %>%
  pivot_longer(cols = everything(),
    names_to = c(".value", "set"),
    names_pattern = "(.)(.)")
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_dcor(anscombe$x1, anscombe$y1)
  sc_dcor(anscombe$x2, anscombe$y2)
  sc_dcor(anscombe$x3, anscombe$y3)
  sc_dcor(anscombe$x4, anscombe$y4)

Measure of Spearman Correlation

Description

Measure of Spearman Correlation

Usage

sc_monotonic(x, y)

Arguments

x

numeric vector

y

numeric vector

Value

A "numeric" object that gives the plot's monotonic score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe)
  anscombe_tidy <- anscombe %>%
  pivot_longer(cols = everything(),
    names_to = c(".value", "set"),
    names_pattern = "(.)(.)")
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_monotonic(anscombe$x1, anscombe$y1)
  sc_monotonic(anscombe$x2, anscombe$y2)
  sc_monotonic(anscombe$x3, anscombe$y3)
  sc_monotonic(anscombe$x4, anscombe$y4)

Compute outlying scagnostic measure using MST

Description

Compute outlying scagnostic measure using MST

Usage

sc_outlying(x, y)

## Default S3 method:
sc_outlying(x, y)

## S3 method for class 'scree'
sc_outlying(x, y = NULL)

## S3 method for class 'igraph'
sc_outlying(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's outlying score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  ggplot(datasaurus_dozen, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~dataset, ncol=3, scales = "free")
  sc_outlying(datasaurus_dozen_wide$dino_x, datasaurus_dozen_wide$dino_y)
  sc_outlying(datasaurus_dozen_wide$dots_x, datasaurus_dozen_wide$dots_y)
  sc_outlying(datasaurus_dozen_wide$h_lines_x, datasaurus_dozen_wide$h_lines_y)

Compute skewed scagnostic measure using MST

Description

Compute skewed scagnostic measure using MST

Usage

sc_skewed(x, y)

## Default S3 method:
sc_skewed(x, y)

## S3 method for class 'scree'
sc_skewed(x, y = NULL)

## S3 method for class 'igraph'
sc_skewed(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's skewed score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe_tidy)
  ggplot(datasaurus_dozen, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~dataset, ncol=3, scales = "free")
  sc_skewed(datasaurus_dozen_wide$dots_x, datasaurus_dozen_wide$dots_y)
  sc_skewed(datasaurus_dozen_wide$h_lines_x, datasaurus_dozen_wide$h_lines_y)
  sc_skewed(datasaurus_dozen_wide$x_shape_x, datasaurus_dozen_wide$x_shape_y)

Compute skinny scagnostic measure

Description

Compute skinny scagnostic measure

Usage

sc_skinny(x, y)

## Default S3 method:
sc_skinny(x, y)

## S3 method for class 'scree'
sc_skinny(x, y = NULL)

## S3 method for class 'list'
sc_skinny(x, y = NULL)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's skinny score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(skinny = sc_skinny(x,y))
  sc_skinny(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Compute sparse scagnostic measure using MST

Description

Compute sparse scagnostic measure using MST

Usage

sc_sparse(x, y)

## Default S3 method:
sc_sparse(x, y)

## S3 method for class 'scree'
sc_sparse(x, y = NULL)

## S3 method for class 'igraph'
sc_sparse(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's sparse score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  ggplot(datasaurus_dozen, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~dataset, ncol=3, scales = "free")
  sc_sparse(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)
  sc_sparse(datasaurus_dozen_wide$circle_x, datasaurus_dozen_wide$circle_y)
  sc_sparse(datasaurus_dozen_wide$dino_x, datasaurus_dozen_wide$dino_y)

Compute adjusted sparse measure using the alpha hull

Description

Compute adjusted sparse measure using the alpha hull

Usage

sc_sparse2(x, y)

## Default S3 method:
sc_sparse2(x, y)

## S3 method for class 'scree'
sc_sparse2(x, y = NULL)

## S3 method for class 'list'
sc_sparse2(x, y = NULL)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's sparse2 score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe_tidy)
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_sparse2(anscombe$x1, anscombe$y1)

Spline based index.

Description

(Taken from tourr git repo) Compares the variance in residuals of a fitted spline model to the overall variance to find functional dependence in 2D projections of the data.

Usage

sc_splines(x, y)

Arguments

x

numeric vector

y

numeric vector

Value

A "numeric" object that gives the plot's spines score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe)
  anscombe_tidy <- anscombe %>%
  pivot_longer(cols = everything(),
    names_to = c(".value", "set"),
    names_pattern = "(.)(.)")
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_splines(anscombe$x1, anscombe$y1)
  sc_splines(anscombe$x2, anscombe$y2)
  sc_splines(anscombe$x3, anscombe$y3)

Compute striated scagnostic measure using MST

Description

Compute striated scagnostic measure using MST

Usage

sc_striated(x, y)

## Default S3 method:
sc_striated(x, y)

## S3 method for class 'scree'
sc_striated(x, y = NULL)

## S3 method for class 'igraph'
sc_striated(x, y)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's striated score.

Examples

  require(ggplot2)
  require(dplyr)
  data(anscombe_tidy)
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_striated(anscombe$x1, anscombe$y1)
  sc_striated(anscombe$x2, anscombe$y2)

Compute angle adjusted striated measure using MST

Description

Compute angle adjusted striated measure using MST

Usage

sc_striated2(x, y)

## Default S3 method:
sc_striated2(x, y)

## S3 method for class 'scree'
sc_striated2(x, y = NULL)

## S3 method for class 'igraph'
sc_striated2(x, y)

Arguments

x

numeric vector of x values, or an MST object

y

numeric vector of y values, or a scree object

Value

A "numeric" object that gives the plot's striated2 score.

Examples

  require(ggplot2)
  require(dplyr)
  ggplot(features, aes(x=x, y=y)) +
     geom_point() +
     facet_wrap(~feature, ncol = 5, scales = "free")
  features %>% group_by(feature) %>% summarise(striated = sc_striated2(x,y))
  sc_striated2(datasaurus_dozen_wide$away_x, datasaurus_dozen_wide$away_y)

Compute stringy scagnostic measure using MST

Description

Compute stringy scagnostic measure using MST

Usage

sc_stringy(x, y)

## Default S3 method:
sc_stringy(x, y)

## S3 method for class 'scree'
sc_stringy(x, y = NULL)

## S3 method for class 'igraph'
sc_stringy(x, y = NULL)

Arguments

x

numeric vector of x values

y

numeric vector of y values

Value

A "numeric" object that gives the plot's stringy score.

Examples

  require(ggplot2)
  require(tidyr)
  require(dplyr)
  data(anscombe_tidy)
  ggplot(anscombe_tidy, aes(x=x, y=y)) +
    geom_point() +
    facet_wrap(~set, ncol=2, scales = "free")
  sc_stringy(anscombe$x1, anscombe$y1)
  sc_stringy(anscombe$x2, anscombe$y2)
  sc_stringy(anscombe$x3, anscombe$y3)
  sc_stringy(anscombe$x4, anscombe$y4)

Measure of Discreteness

Description

This metric computes the 1-(ratio between the number of unique values to total data values) on number of rotations of the data, and returns the smallest value. If this value is large it means that there are only a few unique data values, and hence the distribution is discrete

Usage

sc_striped(x, y)

Arguments

x

numeric vector

y

numeric vector

Value

double

Examples

data("datasaurus_dozen_wide")
sc_striped(datasaurus_dozen_wide$v_lines_x,
           datasaurus_dozen_wide$v_lines_y)
sc_striped(datasaurus_dozen_wide$dino_x,
           datasaurus_dozen_wide$dino_y)

Pre-processing to generate scagnostic measures

Description

Pre-processing to generate scagnostic measures

Usage

scree(x, y, binner = NULL, ...)

Arguments

x, y

numeric vectors

binner

an optional function that bins the x and y vectors prior to triangulation

...

other args

Value

An object of class "scree" that consists of three elements:

del: the Delauney-Voronoi tesselation from alphahull::delvor()
weights: the lengths of each edge in the Delauney triangulation
alpha: the radius or alpha value that will be used to generate the alphahull

Examples


x <- runif(100)
y <- runif(100)
scree(x,y)

Calculate the top scagnostic for each pair of variables

Description

Calculate the top scagnostic for each pair of variables

Usage

top_pairs(scags_data)

Arguments

scags_data

A dataset of scagnostic values that was returned by calc_scags or calc_scags_wide

Value

A data frame where each row is a each scatter plot, its highest valued scagnostic, and its respective value

Examples

#an example using calc_scags
require(dplyr)
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(calc_scags(x,y, scags=c("monotonic", "outlying", "convex"))) %>%
  top_pairs()
 #an example using calc_scags_wide
 data(pk)
 scags_data <- calc_scags_wide(pk[,2:5], scags=c("outlying","clumpy","monotonic"))
 top_pairs(scags_data)

Calculate the top pair of variables or group for each scagnostic

Description

Calculate the top pair of variables or group for each scagnostic

Usage

top_scags(scags_data)

Arguments

scags_data

A dataset of scagnostic values that was returned by calc_scags or calc_scags_wide

Value

A data frame where each row is a scagnostic with its highest pair and the associated value

Examples

#an example using calc_scags
require(dplyr)
datasaurus_dozen %>%
  group_by(dataset) %>%
  summarise(calc_scags(x,y, scags=c("monotonic", "outlying", "convex"))) %>%
  top_scags()
 #an example using calc_scags_wide
 data(pk)
 scags_data <- calc_scags_wide(pk[,2:5], scags=c("outlying","clumpy","monotonic"))
 top_scags(scags_data)

Data from Anscombe's famous example in tidy format

Description

Format

Compute selected scagnostics on subsets

Description

Usage

Arguments

Value

See Also

Examples

Compute scagnostics on all possible scatter plots for the given data

Description

Usage

Arguments

Value

See Also

Examples

datasaurus_dozen data

Description

Format

Drawing the alphahull

Description

Usage

Arguments

Value

Examples

Drawing the Convex Hull

Description

Usage

Arguments

Value

Examples

Drawing the MST

Description

Usage

Arguments

Value

Examples

Simulated data with special features

Description

Format

A toy data set with a numbat shape hidden among noise variables

Description

Parkinsons data from UCI machine learning archive

Description

Format

Details

Compute clumpy scagnostic measure using MST

Description

Usage

Arguments

Value

Examples

Compute adjusted clumpy measure using MST

Description

Usage

Arguments

Value

Examples

Compute robust clumpy scagnostic measure using MST

Description

Usage

Arguments

Value

Examples

Compute convex scagnostic measure

Description

Usage

Arguments

Value

Examples

Distance correlation index.

Description

Usage

Arguments

Value

Examples

Measure of Spearman Correlation

Description

Usage