| Type: | Package |
| Title: | Parallel Coordinate Plots in the 'ggplot2' Framework |
| Version: | 0.2.0 |
| Date: | 2022-11-22 |
| Maintainer: | Heike Hofmann <hofmann@iastate.edu> |
| Description: | Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation also incorporates categorical variables into the plots in a principled manner. By separating the data managing part from the visual rendering, we give full access to the users while keeping the number of parameters manageably low. |
| License: | GPL-3 |
| Imports: | assertthat (≥ 0.2.1), dplyr (≥ 1.0.7), ggplot2 (≥ 3.3.5),rlang (≥ 0.4.11), tibble (≥ 3.1.4), tidyselect (≥ 1.1.1),tidyr (≥ 1.1.3) |
| Depends: | R (≥ 4.1.0) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.2 |
| Suggests: | knitr, rmarkdown, purrr, testthat, GGally |
| URL: | https://github.com/heike/ggpcp |
| BugReports: | https://github.com/heike/ggpcp/issues |
| NeedsCompilation: | no |
| Packaged: | 2022-11-25 16:11:33 UTC; hofmann |
| Author: | Heike Hofmann [aut, cre], Susan VanderPlas [aut], Yawei Ge [aut] |
| Repository: | CRAN |
| Date/Publication: | 2022-11-28 09:30:08 UTC |
ggpcp: Parallel Coordinate Plots in the 'ggplot2' Framework
Description
Modern Parallel Coordinate Plots have been introduced in the 1980s as a way to visualize arbitrarily many numeric variables. This Grammar of Graphics implementation also incorporates categorical variables into the plots in a principled manner. By separating the data managing part from the visual rendering, we give full access to the users while keeping the number of parameters manageably low.
Author(s)
Maintainer: Heike Hofmannhofmann@iastate.edu (ORCID)
Authors:
Susan VanderPlassusan.vanderplas@unl.edu (ORCID)
Yawei Geyaweige@iastate.edu
See Also
Useful links:
Data set: Assessment of Carcinoma slides
Description
A differently formatted data is set available ascarcinoma in packagepoLCA.Here, pathologists' ratings are recorded
Usage
CarcinomaFormat
A data frame with 118 rows and 9 variables:
Overall structure
- No
slide number 1 through 126 (data for slides 14, 20, 21, 50, 75, 97, 109, and 125 are missing)
- Average
average rating of all eight pathologists.
Pathologist ratings
- A
scores 1 to 5 of pathologist's A evaluation (1) Negative; (2) Atypical Squamous Hyperplasia; (3) Carcinoma in Situ; (4) Squamous Carcinoma with Early Stromal Invasion; (5) Invasive Carcinoma.
- B
scores by pathologist B.
- C
scores by pathologist C.
- D
scores by pathologist D.
- E
scores by pathologist E.
- F
scores by pathologist F.
- G
scores by pathologist G.
Source
Data published as Table 1 in Landis, J. Richard, and Koch, Gary G. "An Application of Hierarchical Kappa-type Statistics in the Assessment of Majority Agreement among Multiple Observers." Biometrics 33.2 (1977): 363-74,doi:10.2307/2529786.
Study and Design in Holmquist, Nelson D., McMahan C.A., Williams O. Dale. Variability in classification of carcinoma in situ of the uterine cervix. Arch Pathol. 1967 Oct;84(4):334-45. PMID: 6045443,doi:10.1097/00006254-196806000-00023.
Examples
library(ggplot2)Carcinoma |> pcp_select(F, D, C, A, G, E, B, Average) |> pcp_scale(method="uniminmax") |> pcp_arrange() |> ggplot(aes_pcp()) + geom_pcp_axes() + geom_pcp(aes(colour = Average > 2)) + geom_pcp_boxes(colour="black", alpha=0) + geom_pcp_labels(aes(label = pcp_level), fill="white", alpha = 1) + theme_bw() + scale_x_discrete(expand = expansion(add=0.25)) + xlab("Pathologist") + ylab("Carcinoma score 1 (Negative) to 5 (Invasive Carcinoma)") + theme(axis.text.y=element_blank(), axis.ticks.y=element_blank(), legend.position="none")Proto version of the pcp geoms
Description
These functions are only exported so that they are visible tothe ggplot2 internal functions.User-relevant documentation can be found instead ingeom_pcp().
Wrapper for aes defaults
Description
The function provides a mapping fromggpcp internal variable names to thevariables' functional purpose in the grammar of graphics framework. Any ofthe defaults can be overwritten by the user or flexibly expanded by otheraesthetic mappings in the usual manner.
Usage
aes_pcp( x = pcp_x, y = pcp_y, yend = pcp_yend, class = pcp_class, group = pcp_id, level = pcp_level, label = pcp_level, ...)Arguments
x | x axis |
y | y axis |
yend | end point of line segment |
class | specifying type of the variable |
group | identifier |
level | character string of factor levels |
label | label for factors |
... | other aesthetics are directly passed on to |
Value
a list of default mappings for all required aesthetics
See Also
Examples
library(ggplot2)iris |> pcp_select(tidyselect::everything()) |> pcp_scale() |> pcp_arrange() |> ggplot(aes_pcp(colour = Species)) + geom_pcp() + theme_pcp()Generalized Parallel Coordinate plots
Description
Theggpcp package for generalized parallel coordinate plots is implemented as aggplot2 extension.In particular, this implementation makes use ofggplot2's layer framework,allowing for a lot of flexibility in the choice and order of showing graphical elements.
| command | graphical element |
geom_pcp | line segments |
geom_pcp_axes | vertical lines to represent all axes |
geom_pcp_box | boxes for levels on categorical axes |
geom_pcp_labels | labels for levels on categorical axes |
Theseggpcp specific layers can be mixed withggplot2's regular geoms,such as e.g.ggplot2::geom_point(),ggplot2::geom_boxplot(),ggdensity::geom_hdr(), etc.
Usage
geom_pcp( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, axiswidth = c(0, 0.1), overplot = "small-on-top", show.legend = NA, inherit.aes = TRUE, ...)Arguments
mapping | Set of aesthetic mappings created by |
data | The data to be displayed in this layer. There are threeoptions: If A A |
stat | The statistical transformation to use on the data for thislayer, either as a |
position | Position adjustment, either as a string naming the adjustment(e.g. |
na.rm | If |
axiswidth | vector of two values indicating the space numeric and categorical axes are supposed to take. Minimum of 0, maximum of 1.Defaults to 0 for a numeric axis and 0.1 for a categorical axis. |
overplot | character value indicating which method should be used to mitigate overplotting of lines. Defaults to 'small-on-top'. The overplotting strategy 'small-on-top' identifies the number observations for each combination of levels between two categorical variables and plots the lines from highest frequency to smallest (effectively plotting small groups on top). The strategy 'none' gives most flexibility to the user - the plotting order is preserved by the order in which observations are included in the original data. |
show.legend | logical. Should this layer be included in the legends? |
inherit.aes | If |
... | other arguments passed on to |
Value
a list consisting of aggplot2::layer() object and its associated scales.
About Parallel Coordinate Plots
Parallel coordinate plots are a multivariate visualization that allows several aspects of anobserved entity to be shown in a single plot. Each aspect is represented by a verticalaxis (giving the plot its name), values are marked on each of these axes. Values corresponding to the same entity are connectedby line segments between adjacent axes. This type of visualization was firstused by d’Ocagne (1985). Modern re-inventions go back to Inselberg (1985) andWegman (1990).This implementation takes a more general approach in that it is also able to dealwith categorical in the same principled way that allows a tracking of individualobservations across multiple dimensions.
Data wrangling
The data pipeline feedinggeom_pcp is implemented in a three-step modularizedform rather than in astat_pcp function more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
References
M. d’Ocagne. (1885)Coordonnées parallèles et axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallèles. Gauthier-Villars, page 112,https://archive.org/details/coordonnesparal00ocaggoog/page/n10.
Al Inselberg. (1985)The plane with parallel coordinates. The Visual Computer, 1(2):69–91,doi:10.1007/BF01898350.
Ed J. Wegman. (1990)Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675,doi:10.2307/2290001.
Examples
library(ggplot2)data(mtcars)mtcars_pcp <- mtcars |> dplyr::mutate( cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear), carb = factor(carb) ) |> pcp_select(1:11) |> # select everything pcp_scale() |> pcp_arrange() base <- mtcars_pcp |> ggplot(aes_pcp()) # Just the base plot: base + geom_pcp() # with the pcp theme base + geom_pcp() + theme_pcp() # with boxplots: base + geom_pcp(aes(colour = cyl)) + geom_boxplot(aes(x = pcp_x, y = pcp_y), inherit.aes=FALSE, data = dplyr::filter(mtcars_pcp, pcp_class!="factor")) + theme_pcp()# base plot with boxes and labels base + geom_pcp(aes(colour = cyl)) + geom_pcp_boxes() + geom_pcp_labels() + theme_pcp()Generalized Parallel Coordinate plots
Description
Theggpcp package for generalized parallel coordinate plots is implemented as aggplot2 extension.In particular, this implementation makes use ofggplot2's layer framework,allowing for a lot of flexibility in the choice and order of showing graphical elements.
| command | graphical element |
geom_pcp | line segments |
geom_pcp_axes | vertical lines to represent all axes |
geom_pcp_box | boxes for levels on categorical axes |
geom_pcp_labels | labels for levels on categorical axes |
Theseggpcp specific layers can be mixed withggplot2's regular geoms,such as e.g.ggplot2::geom_point(),ggplot2::geom_boxplot(),ggdensity::geom_hdr(), etc.
Usage
geom_pcp_axes( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)Arguments
mapping | Set of aesthetic mappings created by |
data | The data to be displayed in this layer. There are threeoptions: If A A |
stat | The statistical transformation to use on the data for thislayer, either as a |
position | Position adjustment, either as a string naming the adjustment(e.g. |
na.rm | If |
show.legend | logical. Should this layer be included in the legends? |
inherit.aes | If |
... | other arguments passed on to |
Value
a list consisting of aggplot2::layer() object and its associated scales.
About Parallel Coordinate Plots
Parallel coordinate plots are a multivariate visualization that allows several aspects of anobserved entity to be shown in a single plot. Each aspect is represented by a verticalaxis (giving the plot its name), values are marked on each of these axes. Values corresponding to the same entity are connectedby line segments between adjacent axes. This type of visualization was firstused by d’Ocagne (1985). Modern re-inventions go back to Inselberg (1985) andWegman (1990).This implementation takes a more general approach in that it is also able to dealwith categorical in the same principled way that allows a tracking of individualobservations across multiple dimensions.
Data wrangling
The data pipeline feedinggeom_pcp is implemented in a three-step modularizedform rather than in astat_pcp function more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
References
M. d’Ocagne. (1885)Coordonnées parallèles et axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallèles. Gauthier-Villars, page 112,https://archive.org/details/coordonnesparal00ocaggoog/page/n10.
Al Inselberg. (1985)The plane with parallel coordinates. The Visual Computer, 1(2):69–91,doi:10.1007/BF01898350.
Ed J. Wegman. (1990)Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675,doi:10.2307/2290001.
Examples
library(ggplot2)data(mtcars)mtcars_pcp <- mtcars |> dplyr::mutate( cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear), carb = factor(carb) ) |> pcp_select(1:11) |> # select everything pcp_scale() |> pcp_arrange() base <- mtcars_pcp |> ggplot(aes_pcp()) # Just the base plot: base + geom_pcp() # with the pcp theme base + geom_pcp() + theme_pcp() # with boxplots: base + geom_pcp(aes(colour = cyl)) + geom_boxplot(aes(x = pcp_x, y = pcp_y), inherit.aes=FALSE, data = dplyr::filter(mtcars_pcp, pcp_class!="factor")) + theme_pcp()# base plot with boxes and labels base + geom_pcp(aes(colour = cyl)) + geom_pcp_boxes() + geom_pcp_labels() + theme_pcp()Generalized Parallel Coordinate plots
Description
Theggpcp package for generalized parallel coordinate plots is implemented as aggplot2 extension.In particular, this implementation makes use ofggplot2's layer framework,allowing for a lot of flexibility in the choice and order of showing graphical elements.
| command | graphical element |
geom_pcp | line segments |
geom_pcp_axes | vertical lines to represent all axes |
geom_pcp_box | boxes for levels on categorical axes |
geom_pcp_labels | labels for levels on categorical axes |
Theseggpcp specific layers can be mixed withggplot2's regular geoms,such as e.g.ggplot2::geom_point(),ggplot2::geom_boxplot(),ggdensity::geom_hdr(), etc.
Usage
geom_pcp_boxes( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, boxwidth = 0.2, ...)Arguments
mapping | Set of aesthetic mappings created by |
data | The data to be displayed in this layer. There are threeoptions: If A A |
stat | The statistical transformation to use on the data for thislayer, either as a |
position | Position adjustment, either as a string naming the adjustment(e.g. |
na.rm | If |
show.legend | logical. Should this layer be included in the legends? |
inherit.aes | If |
boxwidth | width of the box for a level on a categorical axis, defaults to 0.2. |
... | other arguments passed on to |
Value
a list consisting of aggplot2::layer() object and its associated scales.
About Parallel Coordinate Plots
Parallel coordinate plots are a multivariate visualization that allows several aspects of anobserved entity to be shown in a single plot. Each aspect is represented by a verticalaxis (giving the plot its name), values are marked on each of these axes. Values corresponding to the same entity are connectedby line segments between adjacent axes. This type of visualization was firstused by d’Ocagne (1985). Modern re-inventions go back to Inselberg (1985) andWegman (1990).This implementation takes a more general approach in that it is also able to dealwith categorical in the same principled way that allows a tracking of individualobservations across multiple dimensions.
Data wrangling
The data pipeline feedinggeom_pcp is implemented in a three-step modularizedform rather than in astat_pcp function more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
References
M. d’Ocagne. (1885)Coordonnées parallèles et axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallèles. Gauthier-Villars, page 112,https://archive.org/details/coordonnesparal00ocaggoog/page/n10.
Al Inselberg. (1985)The plane with parallel coordinates. The Visual Computer, 1(2):69–91,doi:10.1007/BF01898350.
Ed J. Wegman. (1990)Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675,doi:10.2307/2290001.
Examples
library(ggplot2)data(mtcars)mtcars_pcp <- mtcars |> dplyr::mutate( cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear), carb = factor(carb) ) |> pcp_select(1:11) |> # select everything pcp_scale() |> pcp_arrange() base <- mtcars_pcp |> ggplot(aes_pcp()) # Just the base plot: base + geom_pcp() # with the pcp theme base + geom_pcp() + theme_pcp() # with boxplots: base + geom_pcp(aes(colour = cyl)) + geom_boxplot(aes(x = pcp_x, y = pcp_y), inherit.aes=FALSE, data = dplyr::filter(mtcars_pcp, pcp_class!="factor")) + theme_pcp()# base plot with boxes and labels base + geom_pcp(aes(colour = cyl)) + geom_pcp_boxes() + geom_pcp_labels() + theme_pcp()Generalized Parallel Coordinate plots
Description
Theggpcp package for generalized parallel coordinate plots is implemented as aggplot2 extension.In particular, this implementation makes use ofggplot2's layer framework,allowing for a lot of flexibility in the choice and order of showing graphical elements.
| command | graphical element |
geom_pcp | line segments |
geom_pcp_axes | vertical lines to represent all axes |
geom_pcp_box | boxes for levels on categorical axes |
geom_pcp_labels | labels for levels on categorical axes |
Theseggpcp specific layers can be mixed withggplot2's regular geoms,such as e.g.ggplot2::geom_point(),ggplot2::geom_boxplot(),ggdensity::geom_hdr(), etc.
Usage
geom_pcp_labels( mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)Arguments
mapping | Set of aesthetic mappings created by |
data | The data to be displayed in this layer. There are threeoptions: If A A |
stat | The statistical transformation to use on the data for thislayer, either as a |
position | Position adjustment, either as a string naming the adjustment(e.g. |
na.rm | If |
show.legend | logical. Should this layer be included in the legends? |
inherit.aes | If |
... | other arguments passed on to |
Value
a list consisting of aggplot2::layer() object and its associated scales.
About Parallel Coordinate Plots
Parallel coordinate plots are a multivariate visualization that allows several aspects of anobserved entity to be shown in a single plot. Each aspect is represented by a verticalaxis (giving the plot its name), values are marked on each of these axes. Values corresponding to the same entity are connectedby line segments between adjacent axes. This type of visualization was firstused by d’Ocagne (1985). Modern re-inventions go back to Inselberg (1985) andWegman (1990).This implementation takes a more general approach in that it is also able to dealwith categorical in the same principled way that allows a tracking of individualobservations across multiple dimensions.
Data wrangling
The data pipeline feedinggeom_pcp is implemented in a three-step modularizedform rather than in astat_pcp function more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
References
M. d’Ocagne. (1885)Coordonnées parallèles et axiales: Méthode de transformation géométrique et procédé nouveau de calcul graphique déduits de la considération des coordonnées parallèles. Gauthier-Villars, page 112,https://archive.org/details/coordonnesparal00ocaggoog/page/n10.
Al Inselberg. (1985)The plane with parallel coordinates. The Visual Computer, 1(2):69–91,doi:10.1007/BF01898350.
Ed J. Wegman. (1990)Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675,doi:10.2307/2290001.
Examples
library(ggplot2)data(mtcars)mtcars_pcp <- mtcars |> dplyr::mutate( cyl = factor(cyl), vs = factor(vs), am = factor(am), gear = factor(gear), carb = factor(carb) ) |> pcp_select(1:11) |> # select everything pcp_scale() |> pcp_arrange() base <- mtcars_pcp |> ggplot(aes_pcp()) # Just the base plot: base + geom_pcp() # with the pcp theme base + geom_pcp() + theme_pcp() # with boxplots: base + geom_pcp(aes(colour = cyl)) + geom_boxplot(aes(x = pcp_x, y = pcp_y), inherit.aes=FALSE, data = dplyr::filter(mtcars_pcp, pcp_class!="factor")) + theme_pcp()# base plot with boxes and labels base + geom_pcp(aes(colour = cyl)) + geom_pcp_boxes() + geom_pcp_labels() + theme_pcp()Data set: NASA - Data Expo 2006
Description
The data are geographic and atmospheric measures on a very coarse24 by 24 grid covering Central America. This data was provided bythe NASA Langley Research Center Atmospheric Sciences Data Centeras part of the ASA Data Expo in 2006. Monthly averages of a set ofatmospheric measurements are provided for Jan 1995 to Dec 2000.A subset of this data is available from theGGally package.
Usage
nasaFormat
A data frame with 41472 (= 24 x 24 x 72) rows and 15 variables:
Structural variables
- time
time index for each month from 1 (= Jan 1995) to 72 (= Dec 2000)
- id
identifier for each grid point 1-1 to 24-24
- lat, long
geographic latitude and longitude
- elevation
altitude of the location in meters above sea level
- month, year, date
year/month of each measurement
Measured variables
- cloudlow, cloudmid, cloudhigh
Cloud cover (in percent) at low, middle, and high levels.
- ozone
mean ozone abundance (in dobson)
- pressure
mean surface pressure (in millibars)
- surftemp, temperature
mean surface/near surface air temperature (in Kelvin)
Source
https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2006
Examples
data(nasa)library(ggplot2)nasa |> dplyr::filter(id == "1-10") |> pcp_select(starts_with("cloud"), ozone, temperature) |> pcp_scale() |> ggplot(aes_pcp()) + geom_pcp(aes(colour=month))Data wrangling for GPCPs: Step 3 order observations in factor variables
Description
Break ties for levels in factor variables, space cases out equally and set an order.Note that only ties infactor variables are addressed this way.
Usage
pcp_arrange(data, method = "from-right", space = 0.05, .by_group = TRUE)Arguments
data | data frame - preferably processed using |
method | method for breaking ties, one of "from-right", "from-left" or "from-both". |
space | number between 0 and 1, indicating the proportion of space used for separating multiple levels. |
.by_group | logical value. If TRUE, scaling will respect any previous grouping variables. Applies to grouped data frames only. |
Details
The data pipeline feeding any of the geom layers in theggpcp package is implemented in a three-step modularizedform rather than as the stat functions more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
Value
data frame of the same size as the input data; values ofpcp_y andpcp_yend are adjusted forpcp_class == "factor"
See Also
Examples
library(ggplot2)data(Carcinoma)# select scorespcp_data <- Carcinoma |> pcp_select(A:G) |> pcp_scale()# y values are on five different valuestable(pcp_data$pcp_y)# spread out y valuespcp_data |> pcp_arrange() |> ggplot(aes(x = pcp_y)) + geom_histogram(binwidth=0.05)Data wrangling for GPCPs: Step 2 scale values
Description
The functionpcp_scale provides access to a set of transformations to usein parallel coordinate plots. All transformations other thanraw tend toproduce y values in the interval from 0 and 1.
Usage
pcp_scale(data, method = "uniminmax", .by_group = TRUE)Arguments
data | data frame as returned by |
method | string specifying the method that should be used for scaling the valuesin a parallel coordinate plot (see Details). |
.by_group | logical value. If TRUE, scaling will respect any previous grouping variables. Applies to grouped data frames only. |
Details
The data pipeline feeding any of the geom layers in theggpcp package isimplemented in a three-step modularized form rather than as the statfunctions more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
method is a character string that denotes how to scale the variablesin the parallel coordinate plot. Options are named in the same way as the options inGGally::ggparcoord():
raw: raw data used, no scaling will be done.std: univariately, subtract mean and divide by standard deviation. To get values into a unit interval we use a linear transformation of f(y) = y/4+0.5.robust: univariately, subtract median and divide by median absolute deviation. To get values into an expected interval of unit interval we use a linear transformation of f(y) = y/4+0.5.uniminmax: univariately, scale so the minimum of the variable is zero, and the maximum is one.globalminmax: global scaling; the global maximum is mapped to 1,global minimum across the variables is mapped to 0.
Value
data frame of the same size as the input data; values ofpcp_y andpcp_yend are scaled according to the specified method.
See Also
Examples
data(Carcinoma)dim(Carcinoma)# select all variablespcp_data <- Carcinoma |> pcp_select(1:9)summary(pcp_data)pcp_data |> pcp_scale() |> summary()# scaling gets values of pcp_y and pcp_yend between 0 and 1Data wrangling for GPCPs: Step 1 variable selection
Description
Thepcp_select function allows a selection of variables from a data set.These variables are transformed into an embellished long form of the data.
Usage
pcp_select(data, ...)Arguments
data | a dataframe or tibble |
... | choose the columns to be used in the parallel coordinate plot.Variables can be selected by position, name or any of the |
Details
The data pipeline feeding any of the geom layers in theggpcp package is implemented in a three-step modularizedform rather than as the stat functions more typical forggplot2 extensions.The three steps of data pre-processing are:
| command | data processing step |
pcp_select | variable selection (and horizontal ordering) |
pcp_scale | (vertical) scaling of values |
pcp_arrange | dealing with tie-breaks on categorical axes |
Note that these data processing steps are executed before the call toggplot2and the identity function is used by default in all of theggpcp specific layers.Besides the speed-up by only executing the processing steps once for all layers,the separation has the additional benefit, that it provides the users with thepossibility to make specific choices at each step in the process. Additionally,separation allows for a cleaner user interface: parameters affecting the datapreparation process can be moved to the relevant (set of) function(s) only, therebyreducing the number of arguments without any loss of functionality.
Value
dataframe of a long form of the selected variables with extra columns:
| variable | functionality |
pcp_x,pcp_y | values for the mappings to x and y axes |
pcp_yend | vertical endpoint of a line segment |
pcp_class | type of each of the input variables |
pcp_level | preserves order of levels in categorical variables |
pcp_id | identifier for each observation |
The dimensions of the returned data set are: 6 + the number of input variables for itscolumns. The number of rows is given as the multiple of the number of selectedvariables and the number of rows in the original data.
See Also
Examples
data(Carcinoma)dim(Carcinoma)# select all variablespcp_data <- Carcinoma |> pcp_select(1:9)dim(pcp_data) # 6 more columns, 9 times as many observationshead(pcp_data)Theme for parallel coordinate plots
Description
The functiontheme_pcp provides a wrapper for thematicchoices suitable for parallel coordinate plots. In particular,the labeling of axes in parallel coordinate plot is quite un-informative.In the default theme axes labels are based on variable names derived during thedata wrangling step.
Usage
theme_pcp(base_size = 11, base_family = "")Arguments
base_size | base font size, given in pts. |
base_family | base font family |
Value
Aggplot2 theme object based onggplot2::theme_bw() without y axis and x axes labels.
See Also
Examples
library(ggplot2)gg <- iris |> pcp_select(tidyselect::everything()) |> pcp_scale() |> pcp_arrange() |> ggplot(aes_pcp(colour = Species)) + geom_pcp()# plot with the default ggplot2 themegg# better:gg + theme_pcp()