Help for package metaplot

Type:

Package

Title:

Data-Driven Plot Design

Version:

0.8.4

Author:

Tim Bergsma

Maintainer:

Tim Bergsma <bergsmat@gmail.com>

Description:

Designs plots in terms of core structure. See 'example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' are honored, producing fully annotated plots with minimal effort. Attribute 'guide' is typically units, but may be encoded() to provide interpretations of categorical values (see '?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability.

Imports:

encode (≥ 0.3.6), lattice, magrittr, dplyr (≥ 0.7.1), tidyr, rlang, grid, gridExtra, gtable, ggplot2, scales

Suggests:

csv, nlme

Depends:

R (≥ 2.10)

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.2.3

NeedsCompilation:

Packaged:

2024-02-18 01:10:12 UTC; tim.bergsma

Repository:

CRAN

Date/Publication:

2024-02-18 05:30:10 UTC

Generic Axis Label

Description

Generic axis label, with method for 'data.frame'.

Usage

axislabel(x, ...)

Arguments

x

object

...

passed arguments

Axis Label for Data Frame

Description

Axis label for data.frame. Substitutes label attribute if present for column name, and puts units if present in parentheses, trailing. Puts 'log scale' in parentheses on a new line if log is TRUE.

Usage

## S3 method for class 'data.frame'
axislabel(x, var, log = FALSE, ...)

Arguments

x

data.frame

var

item of interest

log

whether this is for a log scale

...

passed arguments

Value

character

Boxplots

Description

Boxplots

Boxplot Method for Data Frame

Description

Boxplot for data.frame. Parses arguments and generates the call: fun(x, yvar, xvar, facets, ...).

Usage

## S3 method for class 'data.frame'
boxplot(
  x,
  ...,
  fun = metOption("box", "boxplot_data_frame"),
  verbose = metOption("verbose_boxplot", FALSE)
)

Arguments

x

data.frame

...

passed to fun

fun

function that does the actual plotting

verbose

generate messages describing process

Examples

library(dplyr)
library(magrittr)
Theoph %<>% mutate(site = ifelse(as.numeric(Subject) > 6, 'Site A','Site B'))
boxplot(Theoph,'Subject','conc')
boxplot(Theoph,Subject,conc)
boxplot(Theoph,Subject,conc, gg = T)
boxplot(Theoph,conc,Subject)
boxplot(Theoph,conc,Subject, gg = T)
boxplot(Theoph,conc,Subject,site)
boxplot(Theoph,conc,Subject,site, gg = T)
boxplot(Theoph,conc,Subject,site, gg = T, scales = 'free_x')
attr(Theoph,'title') <- 'Theophylline'
boxplot(Theoph, Subject, conc, main = function(x,...)attr(x,'title'))
boxplot(Theoph, Subject, conc, main = function(x,...)attr(x,'title'), gg = T)
boxplot(Theoph, Subject, conc, sub= function(x,...)attr(x,'title'))
boxplot(Theoph, Subject, conc, sub= function(x,...)attr(x,'title'), gg = T)
boxplot(Theoph %>% filter(conc > 0),Subject,conc, log = T)
boxplot(Theoph %>% filter(conc > 0),Subject,conc, log = T, gg = T)

Boxplot Function for Data Frame

Description

Boxplot for data.frame. Creates a boxplot using boxplot_panel by default.

Usage

boxplot_data_frame(
  x,
  yvar,
  xvar,
  facets = NULL,
  log = metOption("log_boxplot", FALSE),
  crit = metOption("crit_boxplot", 1.3),
  horizontal = metOption("horizontal_boxplot", NULL),
  scales = metOption("scales_boxplot", NULL),
  panel = metOption("panel_boxplot", "boxplot_panel"),
  ref = metOption("ref_boxplot", "metaplot_ref"),
  ref.col = metOption("ref.col_boxplot", "grey"),
  ref.lty = metOption("ref.lty_boxplot", "solid"),
  ref.lwd = metOption("ref.lwd_boxplot", 1),
  ref.alpha = metOption("ref.alpha_boxplot", 1),
  nobs = metOption("nobs_boxplot", FALSE),
  na.rm = metOption("na.rm_boxplot", TRUE),
  xlab = NULL,
  ylab = NULL,
  numlab = metOption("numlab_boxplot", "axislabel"),
  catlab = metOption("catlab_boxplot", "axislabel"),
  aspect = metOption("aspect_boxplot", 1),
  as.table = metOption("as.table_boxplot", TRUE),
  main = metOption("main_boxplot", NULL),
  sub = metOption("sub_boxplot", NULL),
  settings = metOption("settings_boxplot", NULL),
  padding = metOption("padding_boxplot", 1),
  reverse = metOption("reverse_boxplot", TRUE),
  pch = metOption("pch_boxplot", "|"),
  notch = metOption("notch_boxplot", FALSE),
  gg = metOption("gg_boxplot", FALSE),
  verbose = metOption("verbose_boxplot", FALSE),
  ...
)

Arguments

x

data.frame

yvar

y variable

xvar

x variable

facets

optional conditioning variables

log

whether to log transform numeric variable (auto-selected if NA)

crit

if log is NA, log-transform if mean/median ratio for non-missing values is greater than this value

horizontal

whether box/whisker axis should be horizontal (numeric x, categorical y); defaults TRUE if var[[2]] is numeric

scales

passed to xyplot (should be function(x = x, horizontal, log,...)) or facet_grid or facet_wrap

panel

panel function

ref

optional reference line(s) on numeric axis; can be function(x = x, var = con, ...) or NULL to suppress

ref.col

color for reference line(s); can be length one integer to auto-select that many colors

ref.lty

line type for reference line(s)

ref.lwd

line size for reference line(s)

ref.alpha

transparency for reference line(s)

nobs

whether to include the number of observations under the category label

na.rm

whether to remove data points with one or more missing coordinates

xlab

x axis label

ylab

y axis label

numlab

numeric axis label; can be function(x = x, var = numvar, log = ylog, ...)

catlab

categorical axis label; can be function(x = x, var = catvar, ...)

aspect

passed to bwplot or ggplot; use 'fill', NA, or NULL to calculate automatically

as.table

passed to xyplot

main

character, or a function of x, yvar, xvar, facets, and log

sub

character, or a function of x, yvar, xvar, facets, and log

settings

default parameter settings: a list from which matching elements are passed to lattice (as par.settings) or to ggplot theme() and facet_wrap() or facet_grid(). ncol and nrow are used as layout indices for lattice (for homology with facet_wrap).

padding

numeric (will be recycled to length 4) giving plot margins in default units: top, right, bottom, left (in multiples of 5.5 points for ggplot)

reverse

if y is categorical, present levels in reverse order (first at top)

pch

special character for box median: passed to panel.bwplot

notch

whether to draw notched boxes: passed to panel.bwplot

gg

logical: whether to generate ggplot instead of trellis

verbose

generate messages describing process

...

passed arguments

Examples

library(magrittr)
library(dplyr)
boxplot_data_frame(Theoph,'Subject','conc')
boxplot_data_frame(Theoph %>% filter(conc > 0),
'conc','Subject', log = TRUE, ref = c(2,5),horizontal = FALSE)

Panel Function for Metaplot Boxplot

Description

Panel function for metaplot boxplot. Calls, panel.bwplot and plots reference lines if ref has length.

Usage

boxplot_panel(
  ref = NULL,
  horizontal,
  pch = "|",
  notch = FALSE,
  ref.col,
  ref.lty,
  ref.lwd,
  ref.alpha,
  verbose = FALSE,
  ...
)

Arguments

ref

numeric

horizontal

passed to panel.bwplot

pch

passed to panel.bwplot

notch

passed to panel.bwplot

ref.col

passed to panel.abline as col

ref.lty

passed to panel.abline as lty

ref.lwd

passed to panel.abline as lwd

ref.alpha

passed to panel.abline as alpha

verbose

generate messages describing process

Categorical Plot

Description

Categorical Plot. Generic, with method for 'data.frame'.

Usage

categorical(x, ...)

Arguments

x

object of dispatch

...

passed arugments

Categorical Method for Data Frame

Description

Categorical method for 'data.frame'.

Usage

## S3 method for class 'data.frame'
categorical(
  x,
  ...,
  fun = metOption("categorical", "categorical_data_frame"),
  verbose = metOption("verbose_categorical_data_frame", FALSE)
)

Arguments

x

data.frame

...

other arguments

fun

function to draw the plot

verbose

generate messages describing process

Value

character

Categorical Function for Data Frame

Description

Categorical function for class 'data.frame'. Implements a simple mosaic plot.

Usage

categorical_data_frame(
  x,
  yvar = NULL,
  xvar,
  groups = NULL,
  facets = NULL,
  ylab = metOption("xlab_categorical", "axislabel"),
  xlab = metOption("ylab_categorical", "axislabel"),
  na.rm = metOption("na.rm_categorical", TRUE),
  aspect = metOption("aspect_categorical", 1),
  space = metOption("space_categorical", "right"),
  key = metOption("key_categorical", "metaplot_key"),
  as.table = metOption("as.table_categorical", TRUE),
  prepanel = metOption("prepanel_categorical", function(...) list(xlim = 0:1, ylim =
    0:1)),
  scales = metOption("scales_categorical", NULL),
  panel = metOption("panel_categorical", "categorical_panel"),
  colors = metOption("colors_categorical", NULL),
  fill = metOption("fill_categorical", 0.5),
  lines = metOption("lines_categorical", TRUE),
  main = metOption("main_categorical", NULL),
  sub = metOption("sub_categorical", NULL),
  tex = metOption("tex_categorical", 0.9),
  rot = metOption("rot_categorical", c(90, 0)),
  subscripts = metOption("subscripts_categorical", TRUE),
  settings = metOption("settings_categorical", NULL),
  padding = metOption("padding_categorical", 1),
  loc = metOption("loc_categorical", 5),
  msg = metOption("msg_categorical", "tilestats"),
  cex = metOption("cex_categorical", 1),
  gg = metOption("gg_categorical", FALSE),
  verbose = metOption("verbose_categorical", FALSE),
  ...
)

Arguments

x

data.frame

yvar

character: y variable (optional)

xvar

character: x variable

groups

optional grouping variable (can be missing)

facets

optional conditioning variables

ylab

y axis label; can be function(x = x, var = yvar, ..)

xlab

x axis label; can be function(x = x, var = xvar, ..)

na.rm

whether to remove data points with one or more missing coordinates

aspect

passed to bwplot or ggplot; use 'fill', NA, or NULL to calculate automatically

space

location of key (right, left, top, bottom)

key

list: passed to xyplot as auto.key or to theme; can be a function groups name, groups levels, fill, lines, space, gg, type ('categorical'), and ... . See metaplot_key.

as.table

passed to xyplot

prepanel

passed to xyplot (guessed if NULL)

scales

passed to xyplot or facet_grid or facet_wrap (guessed if NULL)

panel

name or definition of panel function for lattice

colors

replacements for default colors in group order; can be length one integer to auto-select that many colors

fill

whether to fill rectangles for each group: logical, or alpha values between 0 and 1

lines

whether to plot borders for each group: logical, or alpha values between 0 and 1

main

character, or a function of x, yvar, xvar, groups, facets

sub

character, or a function of x, yvar, xvar, groups, facets

tex

tile expansion: scale factor for reducing each tile size relative to full size (<= 1)

rot

rotation for axis labels; can be length 2 for y and x axes, respectively

subscripts

passed to xyplot

settings

padding

numeric (will be recycled to length 4) giving plot margins in default units: top, right, bottom, left (in multiples of 5.5 points for ggplot)

loc

where to print statistics in a tile

msg

a function of x and y to print text in a tile

cex

expansion for msg text

gg

logical: whether to generate ggplot instead of trellis

verbose

generate messages describing process

...

passed to region

Examples


library(magrittr)
library(dplyr)
library(csv)
x <- as.csv(system.file(package = 'metaplot', 'extdata/theoph.csv'))
x %<>% pack
x %>% metaplot(site)
x %>% metaplot(site, gg = T)
x %>% metaplot(arm, site)
x %>% metaplot(arm, site, gg = T)
x %>% metaplot(arm, site, cohort)
x %>% metaplot(arm, site, cohort, gg = T)
x %>% metaplot(arm, site, cohort, space = 'top')
x %>% metaplot(arm, site, , cohort)
x %>% metaplot(arm, site, , cohort, gg = T)

x %>% metaplot(arm, site, , cohort, rot = c(0,90))
x %>% metaplot(arm, site, , cohort, rot = c(0,90), gg = T)
x %>% metaplot(arm, site, , cohort, rot = c(45, 45))
x %>% metaplot(subject,cohort,arm, site, lines = F, rot = c(45,45))
x %>% metaplot(subject,cohort,arm, site, lines = F, rot = c(45,45), gg=T)
# panel-specific axis not well-supported for gg version
x %>% metaplot(subject,cohort,,arm, site)
x %>% metaplot(subject,cohort,,arm, site, gg=T)

Panel Function for Metaplot Categorical Plot

Description

Default panel function for categorical_data_frame. Implements a simple mosaic plot. Global options are supported but typically are supplied by the calling function and may therefore be unreachable.

Usage

categorical_panel(
  x,
  y,
  groups,
  bivariate = TRUE,
  loc = metOption("loc_categorical_panel", 5),
  msg = metOption("msg_categorical_panel", "tilestats"),
  tex = metOption("tex_categorical_panel", 0.9),
  cex = metOption("cex_categorical_panel", 1),
  rot = metOption("rot_categorical_panel", c(90, 0)),
  subscripts,
  verbose = metOption("verbose_categorical_panel", FALSE),
  ...
)

Arguments

x

x values

y

y values

groups

optional grouping item

bivariate

whether to create y axis

loc

where to print statistics in a tile

msg

a function of x and y to print text in a tile

tex

tile expansion: scale factor for reducing each tile size relative to full size (<= 1)

cex

expansion for msg text

rot

rotation for axis labels; can be length 2 for y and x axes, respectively

subscripts

subscripts of the original data for this panel

verbose

generate messages describing process

...

passed to panel.superpose

Calculate Categorical Axis Labels and Positions

Description

Calculates axis labels and positions for categorical values.

Usage

cax(x, ...)

Arguments

x

x values

...

other arguments

Value

data.frame

Correlated Splom

Description

Scatterplot matrix with correlations.

Usage

corsplom(x, ...)

Arguments

x

object

...

passed arguments

Correlated Scatterplot Matrix Method for Data Frame

Description

Creates a scatterplot matrix. Parses arguments and generates the call: fun(x, xvar, ...).

Usage

## S3 method for class 'data.frame'
corsplom(
  x,
  ...,
  fun = metOption("corsplom", "corsplom_data_frame"),
  verbose = metOption("verbose_corsplom_data_frame", FALSE)
)

Arguments

x

data.frame

...

passed to fun

fun

function to do the actual plotting

verbose

generate messages describing process

Correlated Scatterplot Matrix Function for Data Frame

Description

Creates a scatterplot matrix with correlations in lower panel, by default.

Usage

corsplom_data_frame(
  x,
  xvar = names(x),
  upper.panel = metOption("upper.panel_corsplom", if (gg) "corsplom_gg_scatter" else
    "corsplom_panel_scatter"),
  lower.panel = metOption("lower.panel_corsplom", if (gg) "corsplom_gg_correlation" else
    "corsplom_panel_correlation"),
  diag.panel = metOption("diag.panel_corsplom", if (gg) "corsplom_gg_diagonal" else
    "corsplom_panel_diagonal"),
  pscales = metOption("pscales_corsplom", 0),
  xlab = metOption("xlab_corsplom", NULL),
  varname.cex = metOption("varname.cex_corsplom", 1),
  main = metOption("main_corsplom", NULL),
  sub = metOption("sub_corsplom", NULL),
  col = metOption("col_corsplom", "blue"),
  smooth.col = metOption("smooth.col_corsplom", NULL),
  smooth.lty = metOption("smooth.lty_corsplom", "solid"),
  smooth.lwd = metOption("smooth.lwd_corsplom", 1),
  smooth.alpha = metOption("smooth.alpha_corsplom", 1),
  density = metOption("density_corsplom", TRUE),
  diag.label = metOption("diag.label_corsplom", "diag_label"),
  pin = metOption("pin_corsplom", "diag_pin"),
  pin.col = metOption("pin.col_corsplom", "darkgrey"),
  pin.alpha = metOption("pin.alpha_corsplom", 1),
  dens.col = metOption("dens.col_corsplom", "grey"),
  dens.scale = metOption("dens.scale_corsplom", 0.2),
  dens.alpha = metOption("dens.alpha_corsplom", 0.5),
  settings = metOption("settings.corsplom", NULL),
  padding = metOption("padding_corsplom", 1),
  as.table = metOption("as.table_corsplom", FALSE),
  dens.up = metOption("dens.up_corsplom", TRUE),
  gg = metOption("gg_corsplom", FALSE),
  verbose = metOption("verbose_corsplom", FALSE),
  ...
)

Arguments

x

data.frame

xvar

variables to plot

upper.panel

passed to splom or ggplot

lower.panel

passed to splom or ggplot

diag.panel

passed to splom or ggplot

pscales

passed to splom

xlab

can be function(x = x, var = xvar, ...)

varname.cex

text size multiplier

main

character, or a function of x, xvar

sub

character, or a function of x, xvar

col

point color

smooth.col

smooth color, defaults to col

smooth.lty

smooth line type

smooth.lwd

smooth line size

smooth.alpha

smooth alpha

density

whether to plot density polygons

diag.label

label for the diagonal: a function of x, varname, .data

pin

location for a pin (reference line) in the density region; can be function(x, varname, .data) or NULL to suppress

pin.col

color of pin, if any

pin.alpha

alpha transparency of pin

dens.col

color for density region

dens.scale

inflation factor for height of density smooth

dens.alpha

alpha transparency for density region

settings

default parameter settings: a list from which matching elements are passed to lattice (as par.settings) or to ggplot theme()

padding

numeric (will be recycled to length 4) giving plot margins in default units: top, right, bottom, left (in multiples of 5.5 points for ggplot)

as.table

diagonal arranged top-left to bottom-right

dens.up

whether density plots in diagonal should face the upper triangle vs. lower

gg

logical: whether to generate ggplot instead of trellis

verbose

generate messages describing process

...

extra arguments passed to splom and ggplot

Value

trellis or grob

Examples


library(magrittr)
library(dplyr)
library(csv)
x <- as.csv(system.file(package = 'metaplot', 'extdata/theoph.csv'))
x %<>% pack
# setOption(gg = TRUE)
x %>% metaplot(lKe, lKa, lCl)
x %>% metaplot(
  lKe, lKa, lCl,
  col = 'black',smooth.col = 'red', pin.col = 'red',
  dens.col='blue',dens.alpha = 0.1
)

Correlation GG Function for GG Metaplot Corsplom

Description

Default lower panel function for corsplom_data_frame() with gg = TRUE. Plots Pearson correlation coefficient. corsplom_data_frame() typically supplies smooth aesthetics in case smooth is desired; smooth arguments are defined to suppress corresponding warnings.

Usage

corsplom_gg_correlation(
  data,
  mapping,
  col = metOption("col_corsplom_gg", "blue"),
  smooth.col,
  smooth.lty,
  smooth.lwd,
  smooth.alpha,
  use = "pairwise.complete.obs",
  verbose = FALSE,
  ...
)

Arguments

data

data

mapping

mapping

col

text color

smooth.col

ignored

smooth.lty

ignored

smooth.lwd

ignored

smooth.alpha

ignored

use

passed to cor

verbose

generate messages describing process

...

ignored

Diagonal GG Function for Metaplot Corsplom

Description

Default diagonal panel function for corsplom_data_frame with gg = TRUE. Plots a density smooth against the corresponding axis from within the diagonal panel. Plots a grey pin at each axis zero. Formats and displays the variable name.

Usage

corsplom_gg_diagonal(
  data,
  mapping,
  .data,
  density = TRUE,
  diag.label = metOption("diag_label_corsplom_gg", "diag_label"),
  pin = metOption("pin_loc_corsplom_gg", diag_pin),
  pin.col = metOption("pin.col_corsplom_gg", "darkgrey"),
  pin.alpha = metOption("pin.alpha_corsplom_gg", 1),
  dens.col = metOption("dens.col_corsplom_gg", "grey"),
  dens.scale = metOption("dens.scale_corsplom_gg", 0.2),
  dens.alpha = metOption("dens.alpha_corsplom_gg", 0.5),
  varname.cex = metOption("varname.cex_corsplom_gg", 1),
  as.table = metOption("as.table_corsplom_gg", TRUE),
  dens.up = metOption("dens.up_corsplom_gg", TRUE),
  verbose = metOption("verbose_corsplom_gg", FALSE),
  ...
)

Arguments

data

data

mapping

mapping

.data

copy of original dataset

density

whether to plot density polygons; can be a length 4 vector for top, right, bottom, left

diag.label

label for the diagonal: a function of x, varname, .data

pin

location for a pin (reference line) in the density region; can be a function of x, varname, .data

pin.col

color of pin, if any

pin.alpha

alpha transparency of pin

dens.col

color for density region

dens.scale

inflation factor for height of density smooth

dens.alpha

alpha transparency for density region

varname.cex

text size multiplier

as.table

diagonal arranged top-left to bottom-right

dens.up

whether density plots should face the upper triangle (or lower, if FALSE)

verbose

generate messages describing process

...

passed arguments

Scatter GG Function for Metaplot Corsplom

Description

Default upper panel function for corsplom_data_frame with gg = TRUE. Plots data with loess smooth.

Usage

corsplom_gg_scatter(
  data,
  mapping,
  method = "loess",
  se = F,
  col = metOption("col_corsplom_gg", "blue"),
  smooth.col = metOption("smooth.col_corsplom_gg", "blue"),
  smooth.alpha = metOption("smooth.alpha_corsplom_gg", 1),
  smooth.lty = metOption("smooth.lty_corsplom_gg", "solid"),
  smooth.lwd = metOption("smooth.lwd_corsplom_gg", 1),
  verbose = metOption("verbose_corsplom_gg", FALSE),
  ...
)

Arguments

data

data

mapping

mapping

col

point color

smooth.col

smooth color

smooth.alpha

smooth alpha

smooth.lty

smooth line type

smooth.lwd

smooth line size

verbose

generate messages describing process

...

passed arguments

Correlation Panel Function for Metaplot Corsplom

Description

Default lower panel function for corsplom_data_frame. Plots Pearson correlation coefficient.

Usage

corsplom_panel_correlation(
  x,
  y,
  use = "pairwise.complete.obs",
  verbose = FALSE,
  ...
)

Arguments

x

x values

y

y values

use

passed to cor

verbose

generate messages describing process

...

passed arguments

Diagonal Panel Function for Metaplot Corsplom

Description

Default diagonal panel function for corsplom_data_frame. Plots a density smooth against the corresponding axis from within the diagonal panel. Plots a grey pin at each axis zero.

Usage

corsplom_panel_diagonal(
  x,
  varname,
  .data,
  density = TRUE,
  diag.label = metOption("diag_label_corsplom_panel", diag_label),
  pin = metOption("pin_loc_corsplom_panel", diag_pin),
  pin.col = metOption("pin_col_corsplom_panel", "darkgrey"),
  pin.alpha = metOption("pin_alpha_corsplom_panel", 1),
  dens.col = metOption("dens_col_corsplom_panel", "grey"),
  dens.scale = metOption("dens_scale_corsplom_panel", 0.2),
  dens.alpha = metOption("dens_alpha_corsplom_panel", 0.5),
  as.table = metOption("as.table_corsplom_panel", FALSE),
  dens.up = metOption("densup_corsplom_panel", TRUE),
  verbose = metOption("verbose_corsplom_panel", FALSE),
  ...
)

Arguments

x

numeric

varname

variable name

.data

copy of original dataset

density

whether to plot density polygons

diag.label

label for the diagonal; can be a function of x, varname, .data

pin

location for a pin (reference line) in the density region; can be a function of x, varname, .data

pin.col

color of pin, if any

pin.alpha

alpha transparency of pin

dens.col

color for density region

dens.scale

inflation factor for height of density smooth

dens.alpha

alpha transparency for density region

as.table

diagonal arranged top-left to bottom-right

dens.up

whether density plots should face the upper triangle (or lower, if FALSE)

verbose

generate messages describing process

...

passed arguments

Scatter Panel Function for Metaplot Corsplom

Description

Default upper panel function for corsplom_data_frame. Plots data with loess smooth.

Usage

corsplom_panel_scatter(
  x,
  y,
  col = metOption("point_col_corsplom_panel", "#0080ff"),
  smooth.col = metOption("smooth_col_corsplom_panel", col),
  smooth.lty = metOption("smooth_lty_corspom_panel", "solid"),
  smooth.lwd = metOption("smooth_lwd_corspom_panel", 1),
  smooth.alpha = metOption("smooth_alpha_corsplom_panel", 1),
  verbose = metOption("verbose_corsplom_panel"),
  ...
)

Arguments

x

x values

y

y values

col

point color

smooth.col

smooth color

smooth.lty

smooth line type

smooth.lwd

smooth line size

smooth.alpha

smooth alpha

verbose

generate messages describing process

...

passed arguments

Panel Function for Metaplot Density Plot

Description

Default panel function for dens_data_frame. Calls panel.densityplot, and plots reference lines if ref has length.

Usage

dens_panel(
  ref = NULL,
  ref.col,
  ref.lty,
  ref.lwd,
  ref.alpha,
  verbose = FALSE,
  ...
)

Arguments

ref

numeric

ref.col

passed to panel.abline as col

ref.lty

passed to panel.abline as lty

ref.lwd

passed to panel.abline as lwd

ref.alpha

passed to panel.abline as alpha

verbose

generate messages describing process

Density Plot

Description

Creates a density plot.

Usage

densplot(x, ...)

Arguments

x

object

...

passed arguments

Densplot Method for Data Frame

Description

Plot density for object of class 'data.frame'. Parses arguments and generates the call: fun(x, xvar, groups, facets,...).

Usage

## S3 method for class 'data.frame'
densplot(
  x,
  ...,
  fun = metOption("densplot", "densplot_data_frame"),
  verbose = metOption("verbose_densplot_data_frame", FALSE)
)

Arguments

x

data.frame

...

passed to fun

fun

plotting function

verbose

generate messages describing process

Examples

densplot(Theoph, conc, grid = TRUE )
densplot(Theoph, conc, grid = TRUE, gg = TRUE )
densplot(Theoph, conc, Subject )
densplot(Theoph, conc, , Subject )
densplot(Theoph, conc, , Subject, gg = TRUE, scales = 'free_y' )
attr(Theoph,'title') <- 'Theophylline'
densplot(Theoph, conc, main= function(x,...)attr(x,'title'))
densplot(Theoph, conc, sub= function(x,...)attr(x,'title'))

Density Function for Data Frame

Description

Plot density for object of class 'data.frame' using dens_panel by default.

Usage

densplot_data_frame(
  x,
  xvar,
  groups = NULL,
  facets = NULL,
  xlab = metOption("xlab_dens", "axislabel"),
  ref = metOption("ref_x_dens", "metaplot_ref"),
  ref.col = metOption("ref_col_dens", "grey"),
  ref.lty = metOption("ref_lty_dens", "solid"),
  ref.lwd = metOption("ref_lwd_dens", 1),
  ref.alpha = metOption("ref_alpha_dens", 1),
  log = metOption("log_dens", FALSE),
  crit = metOption("crit_dens", 1.3),
  aspect = metOption("aspect_dens", 1),
  scales = metOption("scales_dens", NULL),
  panel = metOption("panel_dens", "dens_panel"),
  points = metOption("points_dens", TRUE),
  colors = metOption("colors_dens", NULL),
  symbols = metOption("symbols_dens", NULL),
  sizes = metOption("sizes_dens", 1),
  lines = metOption("lines_dens", TRUE),
  types = metOption("types_dens", "solid"),
  widths = metOption("widths_dens", 1),
  fill = metOption("fill_dens", FALSE),
  space = metOption("space_dens", "right"),
  key = metOption("key_dens", "metaplot_key"),
  as.table = metOption("as.table_dens", TRUE),
  main = metOption("main_dens", NULL),
  sub = metOption("sub_dens", NULL),
  settings = metOption("settings_dens", NULL),
  padding = metOption("padding_dens", 1),
  gg = metOption("gg_dens", FALSE),
  verbose = metOption("verbose_dens", FALSE),
  ...
)

Arguments

x

data.frame

xvar

variable to plot

groups

optional grouping variable

facets

optional conditioning variables

xlab

x axis label; can be function(x = x, var = xvar, log = log, ...)

ref

reference line; can be function(x = x, var = xvar, ...) or NULL to suppress

ref.col

color for reference line(s); can be length one integer to auto-select that many colors

ref.lty

type for reference line(s)

ref.lwd

size for reference line(s)

ref.alpha

transparency for reference line(s)

log

whether to log-transform x axis (auto-selected if NA)

crit

if log is NA, log-transform if mean/median ratio for non-missing x is greater than this value (and no negative values)

aspect

passed to bwplot or ggplot; use 'fill', NA, or NULL to calculate automatically

scales

passed to xyplot or facet_grid or facet_wrap (guessed if NULL)

panel

passed to densityplot

points

whether to plot points: logical or alpha, same length as groups

colors

replacements for default colors in group order; can be length one integer to auto-select that many colors

symbols

replacements for default symbols in group order

sizes

replacements for default symbol sizes in group order

lines

whether to plot lines: logical or alpha, same length as groups

types

replacements for default line types in group order

widths

replacements for default line widths in group order

fill

whether to fill curves: logical or alpha, same length as groups (symbol fill color is same as point color)

space

location of key (right, left, top, bottom)

key

list: passed to xyplot as auto.key or to theme; can be a function groups name, groups levels, points, lines, space, gg, and ... . See metaplot_key.

as.table

passed to xyplot

main

character, or a function of x, xvar, groups, facets, and log

sub

character, or a function of x, xvar, groups, facets, and log

settings

padding

numeric (will be recycled to length 4) giving plot margins in default units: top, right, bottom, left (in multiples of 5.5 points for ggplot)

gg

logical: whether to generate ggplot instead of trellis

verbose

generate messages describing process

...

passed to densityplot

Examples

densplot_data_frame(Theoph, 'conc', grid = TRUE)
densplot_data_frame(Theoph, 'conc', 'Subject')
densplot_data_frame(Theoph, 'conc', 'Subject',
space = 'top', columns = 4, legend.direction = 'horizontal')
densplot_data_frame(Theoph, 'conc', 'Subject',
space = 'top', columns = 4, legend.direction = 'horizontal', gg = TRUE)
densplot_data_frame(Theoph, 'conc', , 'Subject')

Format a Diagonal Label

Description

Formats a diagonal label. Can return a simple column name, a column label (if attribute defined), a fractured column label (split on spaces), or a processed symbol (over-rides label).

Usage

diag_label(
  varname,
  .data,
  diag_label_simple = metOption("diag_label_simple", FALSE),
  diag_label_split = metOption("diag_label_split", TRUE),
  diag_symbol_format = metOption("diag_symbol_format", "wikisym2plotmath"),
  verbose = metOption("verbose_diag_label", FALSE),
  ...
)

Arguments

varname

character

.data

data.frame

diag_label_simple

logical: just return varname?

diag_label_split

whether to substitute line breaks for spaces

diag_symbol_format

function to process symbol attribute, if present

verbose

generate messages describing process

...

ignored

Value

character

Calculate Pin Placement

Description

Calculates pin placement in the density region, inside margin of diagonal panels.

Usage

diag_pin(x, varname, .data, ...)

Arguments

x

vector of data

varname

name of vector in .data

.data

original dataset, possibly with column attributes such as 'reference'

...

passed arguments

Value

numeric

Prepanel Function for Isometric Axes

Description

Prepanel function for isometric axes. Returns join minimum and maximum for limits on both axes.

Usage

iso_prepanel(x, y, ...)

Arguments

x

numeric

y

numeric

Value

list

Merge Two Lists

Description

Merges two lists. Every named element in the second argument is added recursively at the corresponding position in the first argument by name, over-writing existing values as necessary. Every un-named argument is added if there is no named argument to over-write.

Usage

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

Arguments

x

a list (coerced if not)

y

a list (coerced if not)

...

ignored

Examples

foo <- list(
  a = list(         # substituted by name
    col = 'red',    # substituted by name
    lty = 'dashed', # substituted by name
    alpha = 1,      # preserved
    8,              # preserved, since element 4 in replacement matches by name
    9               # substituted by position
  ),
  letters[8:10],    # preserved, siince elment 2 in replacement matches by name
  b = 3
)

bar <- list(
  letters[11:13],  # ignored: position conflict with named element
  b = 2,           # substituted by name
  a = list(        # substituted by name
    'blue',        # ignored: position conflict with named element
    col = 'green', # substituted by name
    lty = 'solid', # substituted by name
    lwd = 2,       # added by name
    3,             # substituted by postion
    4,             # added by postion
    hue = 5        # added by name
  ),
  'baz'            # added by postion
)

foo
bar
merge(foo,bar)
merge(list(1), list(2,foo = 3)) # 3 is assigned and named
merge(list(1), list(foo = 2,3)) # 3 is ignored since position 2 has been named by time of evaluation
merge(list(foo = 1), list(2,foo = 3)) # 2 ignored since pos. matches named argument; 3 overwrites
merge(list(foo = 1), list(2,3)) # 2 is ignored since position matches a named argument; 3 added

Get Metaplot Option with Partial Matching

Description

Gets a metaplot option value from the named list getOption('metaplot'). If an exact match is not found, trailing elements of x, separated by underscore, are removed one at a time in search of a partial match. Thus 'ref.col' will match for 'ref.col_dens' and 'ref.col_scatter' if neither of these is set (allowing selective override). However, global' will never match 'global.col'.

Usage

metOption(x, default = NULL)

Arguments

x

a character string holding an option name

default

the value returned if option is not set

Details

If x is missing a list of all metaplot options is returned.

Examples


library(magrittr)
library(dplyr)
library(csv)
x <- as.csv(system.file(package = 'metaplot', 'extdata/theoph.csv'))
x %<>% pack

multiplot(
x %>% metaplot(conc, gg = F),
x %>% metaplot(conc, time, gg = F),
x %>% metaplot(conc, arm, gg = F),
x %>% metaplot(conc, arm,  gg = T)
)

# Add a reference line at 9 mg/L
x$conc %<>% structure(reference = 9)

# Make the reference line green universally.
setOption(ref_col = 'green')

# Make the reference line orange for density plots
setOption(ref_col_dens = 'orange')

multiplot(
x %>% metaplot(conc, gg = F),
x %>% metaplot(conc, time, gg = F),
x %>% metaplot(conc, arm, gg = F),
x %>% metaplot(conc, arm,  gg = T)
)

# Restore defaults
# setOption() # clears all metaplot options
setOption(ref_col = NULL)
setOption(ref_col_dens = NULL)

Metaplot

Description

Metaplot creates univariate, bivariate, or multivariate plots depending on the number and types of variables represented by the anonymous arguments. Types are either numeric (NUM, e.g. real, integer) or categorical (CAT, e.g. factor, character). A variable stored as numeric that nonetheless has an encoded guide attribute will be treated as categorical. Mnemonic: x %>% metaplot(yvars, xvar, groupvar, facets) where arguments are unquoted column names, and only xvar is required. Column attributes label, guide, reference, and symbol modify the behavior of the default handlers.

Usage

metaplot(x, ...)

Arguments

x

object

...

passed arguments

Details

Design your plot by specifying y variables (optional), the x variable, the groups variable (optional) and the conditioning variables (i.e., facets, optional).

The single groups variable, if any, is the first categorical in the third position or later. An earlier categorical gives a "mixed" bivariate plot or mosaic plot, depending on the type of the remaining variable.

The x variable is the last variable before groups, if present.

The y variables are those before x. If none, the result is univariate. If one, the result is typically a boxplot or scatterplot, depending on x. Several numeric y followed by a numeric x are treated as multivariate (scatterplot matrix). But if all y have the same guide attribute and it is different from that for x, the result is bivariate (i.e, an overlay scatterplot).

A single categorical variable results in a simple mosaic plot (see link[graphics]{mosaicplot} and vcd for more sophisticated treatment). Mosaic plots support only a single y variable; thus, whenever the first two variables are categorical, a two-way mosaic plot results, with remaining variables understood as groups and facets.

Wherever a groups argument is meaningful, it may be missing. This allows specification of facets in the absence of groups, e.g., (metaplot(y, x, , facet1, facet2)). For multiple y (overlay), the sources of y are the implied groups: any trailing categorical arguments are treated as facets.

Template designs follow; substitute behaviors by setting global options (see argument list).

NUM:: univariate (densityplot)
CAT:: categorical (one-way mosaic plot)
CAT, CAT:: categorical (two-way mosaic plot)
CAT, CAT, CAT:: grouped mosaic
CAT, CAT, CAT, CAT:: grouped mosaic with one facet
CAT, CAT, CAT,, CAT:: non-grouped mosaic with one facet
NUM, CAT:: mixedvariate (vertical boxplot)
CAT, NUM:: mixedvariate (horizontal boxplot)
CAT, NUM, CAT:: mixedvariate with one facet
NUM, NUM:: bivariate (scatterplot)
NUM, NUM, CAT:: grouped bivariate (grouped scatterplot)
NUM, NUM,, CAT:: non-grouped bivariate with one facet
NUM, NUM, CAT, CAT:: grouped bivariate with one facet
NUM, NUM, CAT, CAT, CAT:: grouped bivariate with two facets
NUM, NUM, NUM:: multivariate, or grouped bivariate for overlay
NUM, NUM, NUM, CAT: multivariate, or faceted bivariate for overlay
NUM, NUM, NUM, CAT, CAT: multivariate, or bivariate with two facets for overlay

Variable attributes may be supplied by conventional means; pack and unpack support storing and retrieving scalar column attributes. The following scalar attributes are currently supported.

label:: A variable descriptor. If present, panel functions will use label to create informative axis labels. See axislabel.
guide:: Units for a numeric variable, or an encoding (scalar string giving codes and possibly decodes) for a categorical item. If present, units will be used to inform the corresponding axis label (axislabel). If present, codes will be used to impose sort order on categorical variables. If present, decodes will be used as substitutes for stored values when presenting categorical labels, legends, and facet names. For more on encodings, see encode.
reference:: Some variables have values to which they can be compared. For example, residual error is often expected to be centered at zero. Default panel functions plot corresponding reference lines if this attribute is present. See for example dens_panel.
symbol:: Variable names are useful for programming, and variable labels are useful as axis labels. A symbol can be more formal than a variable name and more compact than a label. For example, diag_label will use variable names as labels for the diagonal panels of a scatterplot matrix; but it will prefer labels, if available; and will prefer symbols most of all. Markup rules for symbols are given in wikisym2plotmath_.

Examples


library(magrittr)
library(dplyr)
library(csv)
x <- as.csv(system.file(package = 'metaplot', 'extdata/theoph.csv'))
x %<>% pack
# setOption(gg = TRUE)
# setOption(verbose = TRUE)  # all messages; equiv. to metaplot(verbose = T,...)
# setOption(verbose_densplot = TRUE) # densplot messages
# sample plots
x %>% metaplot(sres)
x %>% metaplot(site)
x %>% metaplot(conc, arm)
x %>% densplot(conc, arm)
x %>% metaplot(arm, conc)
x %>% metaplot(conc, arm, site)
x %>% metaplot(conc, site, arm)
x %>% metaplot(conc, time)
x %>% metaplot(arm, site)
x %>% metaplot(arm, site, cohort)
x %>% metaplot(arm, site, cohort, space = 'top')
x %>% metaplot(arm, site, , cohort)
x %>% metaplot(conc, time, subject)
x %>% metaplot(conc, time, , subject)
x %>% metaplot(conc, time, subject, site)
x %>% metaplot(conc, time, subject, site, arm)
x %>% metaplot(lKe, lKa, lCl)


x %>% metaplot(
  lKe, lKa, lCl,
  col = 'black',smooth.col = 'red', pin.col = 'red',
  dens.col='blue',dens.alpha = 0.1
)
x %>% metaplot(conc, pred, ipred, time, space = 'top')
x %>% metaplot(conc, pred, ipred, time, subject, space = 'top')
x %>% metaplot(conc, pred, ipred, time, subject,
  colors = c('black','blue','orange'),
  points = c(0.9,0, 0.4),
  lines = c(F,T,T),
  types = c('blank','dashed','solid'),
  space = 'top'
)

x %>% metaplot(conc, ipred, time, site, arm, space = 'top')
x %>% metaplot(res, conc, yref = 0, ysmooth = T, conf = T, grid = T, loc = 1)
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T )
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T, global = T, ref.col = 'red')
x %>% metaplot(subject,conc)

# manage metadata
attr(x$arm, 'guide') # //1/Arm A//2/Arm B//

x %>% metaplot(conc, arm) # default

x %>% mutate(arm = arm %>%
  structure(guide = '//2/Arm B//1/Arm A//')) %>%
  metaplot(conc, arm) # different presentation order

x %>% mutate(arm = arm %>%
  structure(guide = '//1/Both Arms//2/Both Arms//')) %>%
  metaplot(conc, arm) # collapse cases

Create Metaplot for Data Frame.

Description

Creates a metaplot for class 'data.frame'. Implements a rule to decided whether to make a density plot, a boxplot, a scatter plot, or a scatterplot matrix, given the supplied column names.

Usage

## S3 method for class 'data.frame'
metaplot(
  x,
  ...,
  univariate = metOption("univariate", "densplot"),
  mixedvariate = metOption("mixedvariate", "boxplot"),
  bivariate = metOption("bivariate", "scatter"),
  multivariate = metOption("multivariate", "corsplom"),
  categorical = metOption("categorical", "categorical"),
  verbose = metOption("verbose", FALSE)
)

Arguments

x

object

...

passed arguments

univariate

function for univariate arguments

mixedvariate

function for bivariate combinations of numeric and categoral arguments

bivariate

function for arguments that resolve to two numerics (see rules)

multivariate

function for more than two numeric arguments

categorical

function for categorical arguments

verbose

generate messages describing process; passed to called functions if explicitly supplied

Examples

## Not run: 
library(magrittr)
library(dplyr)
library(csv)
library(nlme)
x <- Theoph

# mixed effects model
m1 <- nlme(
  conc ~ SSfol(Dose, Time, lKe, lKa, lCl),
  data = x,
  fixed = lKe + lKa + lCl ~ 1,
  random = lKe + lKa + lCl ~ 1
)

# some numeric and categorical properties
names(x) <- tolower(names(x))
x %<>% mutate(arm = ifelse(as.numeric(as.character(subject)) %% 2 == 0, 1, 2))
x %<>% mutate(site = ifelse(as.numeric(as.character(subject)) < 6, 1, 2))
x %<>% mutate(cohort = ifelse(as.numeric(as.character(subject)) %in% c(1:2,6:8), 1,2))
x %<>% mutate(pred = predict(m1,level = 0) %>% signif(4))
x %<>% mutate(ipred = predict(m1) %>% signif(4))
x %<>% mutate(res = residuals(m1) %>% signif(4))
x %<>% mutate(sres = residuals(m1, type = 'pearson') %>% signif(4))
r <- ranef(m1) %>% signif(4)
r$subject <- rownames(r)
x %<>% left_join(r)
# metadata
attr(x$subject,'label') <- 'subject identifier'
attr(x$wt,'label') <- 'subject weight'
attr(x$dose,'label') <- 'theophylline dose'
attr(x$time,'label') <- 'time since dose administration'
attr(x$conc,'label') <- 'theophylline concentration'
attr(x$arm,'label') <- 'trial arm'
attr(x$site,'label') <- 'investigational site'
attr(x$cohort,'label') <- 'recruitment cohort'
attr(x$pred,'label') <- 'population-predicted concentration'
attr(x$ipred,'label') <- 'individual-predicted concentration'
attr(x$res,'label') <- 'residuals'
attr(x$sres,'label') <- 'standardized residuals'
attr(x$lKe,'label') <- 'natural log of elimination rate constant'
attr(x$lKa,'label') <- 'natural log of absorption rate constant'
attr(x$lCl,'label') <- 'natural log of clearance'
attr(x$subject,'guide') <- '....'
attr(x$wt,'guide') <- 'kg'
attr(x$dose,'guide') <- 'mg/kg'
attr(x$time,'guide') <- 'h'
attr(x$conc,'guide') <- 'mg/L'
attr(x$arm,'guide') <- '//1/Arm A//2/Arm B//'
attr(x$site,'guide') <- '//1/Site 1//2/Site 2//'
attr(x$cohort,'guide') <- '//1/Cohort 1//2/Cohort 2//'
attr(x$pred,'guide') <- 'mg/L'
attr(x$ipred,'guide') <- 'mg/L'

attr(x$lKe,'reference') <- 0
attr(x$lKa,'reference') <- 0
attr(x$lCl,'reference') <- 0
attr(x$res,'reference') <- 0
attr(x$sres,'reference') <- '//-1.96//1.96//'

attr(x$subject,'symbol') <- 'ID_i'
attr(x$wt,'symbol') <- 'W_i'
attr(x$dose,'symbol') <- 'A_i'
attr(x$time,'symbol') <- 't_i,j'
attr(x$conc,'symbol') <- 'C_i,j'
attr(x$arm,'symbol') <- 'Arm_i'
attr(x$site,'symbol') <- 'Site_i'
attr(x$cohort,'symbol') <- 'Cohort_i'
attr(x$pred,'symbol') <- 'C_pred_p'
attr(x$ipred,'symbol') <- 'C_pred_i'
attr(x$res,'symbol') <- '\\epsilon'
attr(x$sres,'symbol') <- '\\epsilon_st'
attr(x$lKe,'symbol') <- 'ln(K_e.)'
attr(x$lKa,'symbol') <- 'ln(K_a.)'
attr(x$lCl,'symbol') <- 'ln(Cl_c./F)'


x %>% unpack %>% as.csv('theoph.csv')

## End(Not run)

Default Key

Description

Default key function for constructing scatterplot legends.

Usage

metaplot_key(
  groups,
  levels,
  points = rep(FALSE, length.out = length(levels)),
  lines = rep(FALSE, length.out = length(levels)),
  fill = rep(FALSE, length.out = length(levels)),
  space = "right",
  gg = FALSE,
  type = "scatter",
  verbose = FALSE,
  ...
)

Arguments

groups

name of the grouping variable

levels

the (unique) levels of the grouping variable

points

logical or alpha, same length as groups

lines

logical or alpha, same length as groups

fill

logical or alpha, same length as groups

space

character: left, right, top, or bottom

gg

logical: whether to to return a list of arguments for theme instead of for auto.key as in xyplot

type

typically one of 'categorical','density', or 'scatter'

verbose

generate messages describing process

...

ignored

Value

list, or possibly logical if gg is FALSE

Calculate Reference Values

Description

Calculates reference values for x and y axes. Coerces column attribute 'reference' to numeric: a single value or an encoding giving multiple numeric values (decodes are ignored).

Usage

metaplot_ref(x, var, ...)

Arguments

x

data.frame

var

name of vector in x

...

ignored

Value

numeric

Format GLM Statistics

Description

Formats GLM statistics. Uses a gaussian family by default, or binomial family if all y are 0 or 1, to fit a general linear model. Formats number of observations, p-value, and Pearson correlation coefficient into a string for printing.

Usage

metastats(
  x,
  y,
  family = if (all(y %in% 0:1, na.rm = TRUE)) "binomial" else "gaussian",
  ...
)

Arguments

x

x values

y

y values

family

regression family

...

other arguments

Value

character

Execute Linear Model

Description

Executes a linear model, automatically choosing binomial family as necessary.

Usage

model(
  x,
  y,
  family = if (all(y %in% 0:1, na.rm = TRUE)) "binomial" else "gaussian",
  ...
)

Arguments

x

x values

y

y values

family

gaussian by default, or binomial for all y either zero or 1

...

passed to glm

Value

glm

Arrange Multiple Trellis or GG Plots in a Grid

Description

Arranges mutiple trellis plots or ggplots in a grid, automatically choosing number of rows and columns. By default, number of rows is one less than or equal to the number of columns.

Usage

multiplot(..., nrow = NULL, ncol = NULL)

Arguments

...

trellis or ggplot objects

nrow

number of rows of plots

ncol

number of columns of plots

Value

metaplot_gtable

Examples

library(lattice)
a <- xyplot(
conc ~ Time,
xlab=NULL,
ylab = NULL,
Theoph,
aspect = 1,
scales=list(draw=FALSE)
)
multiplot(a,a,a,a,a,a)
multiplot(a,a,a,a,a,a,a)
multiplot(a,a,a,a,a,a,a,a)
multiplot(a,a,a,a,a,a,a,a,a)
multiplot(a,a,a,a,a,a,a,a,a,a)
multiplot(a,a,a,a,a,a,a,a, nrow = 2)
multiplot(a,a,a,a,a,a,a,a, ncol = 4)
multiplot(a,a,a,a,a,a,a,a, ncol = 2)
multiplot(a,a,a,a,a,a,a,a, ncol = 4, nrow = 3)
multiplot(multiplot(a,a), a)

Pack Something

Description

Pack Something. Generic, with method for data.frame.

Usage

pack(x, ...)

Arguments

x

object

...

other arguments

Capture Scalar Column Metadata as Column Attributes

Description

Captures scalar column metadata (row values) as column attributes. Excises rows with non-missing values of meta, converting column values to column attributes. Afterward, column classes are re-optimized using default behavior of read.table. It is an error if meta is not in names(x).

Usage

## S3 method for class 'data.frame'
pack(
  x,
  meta = getOption("meta", "meta"),
  as.is = TRUE,
  attributes = TRUE,
  na.rm = TRUE,
  ...
)

Arguments

x

data.frame

meta

column in x giving names of attributes

as.is

passed to type.convert

attributes

preserve non-standard attributes (ignores names, row.names, class)

na.rm

if TRUE, NA values (presumably structural) will not be assigned as column attributes.

...

ignored arguments

Value

data.frame

Examples

foo <- data.frame(head(Theoph))
attr(foo$Subject, 'label') <-  'subject identifier'
attr(foo$Wt, 'label') <-  'weight'
attr(foo$Dose, 'label') <-  'dose'
attr(foo$Time, 'label') <-  'time'
attr(foo$conc, 'label') <-  'concentration'
attr(foo$Subject, 'guide') <-  '////'
attr(foo$Wt, 'guide') <-  'kg'
attr(foo$Dose, 'guide') <-  'mg/kg'
attr(foo$Time, 'guide') <-  'h'
attr(foo$conc, 'guide') <-  'mg/L'
unpack(foo, pos = 1)
unpack(foo, pos = 2)
unpack(foo, pos = 3)
unpack(foo, pos = 4)
bar <- unpack(foo)
pack(bar)
attributes(pack(bar)$Subject)

Panel Function for Metaplot Density

Description

Variant of panel.densityplot that supports filled area and alpha points.

Usage

panel.meta_densityplot(
  x,
  darg = list(n = 512),
  plot.points = "jitter",
  ref = FALSE,
  groups = NULL,
  weights = NULL,
  jitter.amount = 0.01 * diff(current.panel.limits()$ylim),
  type = "p",
  verbose = FALSE,
  ...,
  identifier = "density",
  group.number
)

Arguments

x

see link[lattice]{panel.densityplot}

darg

see link[lattice]{panel.densityplot}

plot.points

see link[lattice]{panel.densityplot}

ref

see link[lattice]{panel.densityplot}

groups

see link[lattice]{panel.densityplot}

weights

see link[lattice]{panel.densityplot}

jitter.amount

see link[lattice]{panel.densityplot}

type

see link[lattice]{panel.densityplot}

verbose

generate messages describing process

...

see link[lattice]{panel.densityplot}

identifier

see link[lattice]{panel.densityplot}

Draw a Tile

Description

Draws a tile in a mosaic.

Usage

panel_tile(
  x,
  y,
  subscripts,
  group.number,
  group.value,
  col,
  alpha,
  border,
  loc,
  msg,
  .src,
  cex,
  verbose,
  ...
)

Arguments

x

x values

y

y values

subscripts

subscripts

group.number

group number

group.value

group value

col

fill color

alpha

alpha transparency for fill

border

border color

loc

location for output of msg

msg

ignored

.src

data source for which subscripts give x, y, msg, and tile limits

cex

expansion for msg text; passed to msg

verbose

generate messages describing process

...

passed arguments

Plot a Metaplot_gtable

Description

Plots a metaplot_gtable. Underlying object type is 'gtable'. Defaults for link{corsplom_data_frame} and link{multiplot} prepend class 'metaplot_gtable' to modify plot and print functionality.

Usage

## S3 method for class 'metaplot_gtable'
plot(x, ...)

Arguments

x

metaplot_gtable

...

ignored

Value

(invisible) gtable

Print a Metaplot_gtable

Description

Prints a metaplot_gtable. Underlying object type is 'gtable'. Defaults for link{corsplom_data_frame} and link{multiplot} prepend class 'metaplot_gtable' to modify plot and print functionality. Print method for 'gtable' summarizes the object. This method invokes the plot method to give interactive behavior more like splom().

Usage

## S3 method for class 'metaplot_gtable'
print(x, ...)

Arguments

x

metaplot_gtable

...

ignored

Value

gtable

Calculate a Confidence Region

Description

Calculates a confidence region. se.fit from predict.glm is multiplied by z and added or subtracted from fits to give hi and lo columns in return value. z is normal quantile for the one-tailed probablitity corresponding to conf, e.g. ~ 1.96 for conf = 0.95. If non-missing y is only 0 or 1, the model family is binomial and resulting confidence intervals are back-transformed using plogis.

Usage

region(
  x,
  y,
  family = if (all(y %in% 0:1, na.rm = TRUE)) "binomial" else "gaussian",
  length.out = 1000,
  conf = 0.95,
  ...
)

Arguments

x

x values

y

y values

family

gaussian by default, or binomial for all y either zero or 1

length.out

number of prediction points

conf

width of confidence interval; logical TRUE defaults to 0.95

...

passed to model

Value

data.frame with x, y, hi, lo at 1000 points

Scatterplot

Description

Scatterplot.

Usage

scatter(x, ...)

Arguments

x

object

...

passed arguments

Scatterplot Method for Data Frame

Description

Scatterplot method for class 'data.frame'. Parses arguments and generates the call: fun(x, yvar, xvar, groups, facets, ...).

Usage

## S3 method for class 'data.frame'
scatter(
  x,
  ...,
  fun = metOption("scatter", "scatter_data_frame"),
  verbose = metOption("verbose_scatter", FALSE)
)

Arguments

x

data.frame

...

passed to fun

fun

function to draw the plot

verbose

generate messages describing process

Examples

library(magrittr)
library(dplyr)
attr(Theoph$conc,'label') <- 'theophylline concentration'
attr(Theoph$conc,'guide') <- 'mg/L'
attr(Theoph$Time,'label') <- 'time'
attr(Theoph$Time,'guide') <- 'h'
attr(Theoph$Subject,'guide') <- '////'
# setOption(gg = T)
scatter(Theoph,conc, Time)
scatter(Theoph, conc, Time, Subject) # Subject as groups
scatter(Theoph, conc, Time, , Subject) # Subject as facet
scatter(Theoph, conc, Time, , Subject, gg = TRUE, scales = 'free_y' )
scatter(Theoph %>% filter(conc > 0), conc, Time, Subject, ylog = TRUE, yref = 5)
scatter(Theoph, conc, Time, Subject, ysmooth = TRUE)
scatter(Theoph, conc, Time, conf = TRUE, loc = 3, yref = 6)
scatter(Theoph, conc, Time, conf = TRUE, loc = 3, yref = 6, global = TRUE)
## Not run: 
\dontshow{
attr(Theoph,'title') <- 'Theophylline'
scatter(Theoph, conc, Time, main = function(x,...)attr(x,'title'))
scatter(Theoph, conc, Time, sub= function(x,...)attr(x,'title'))
setOption(main = function(x,...)attr(x,'title'))
scatter(Theoph, conc, Time)
}

## End(Not run)

Scatterplot Function for Data Frame

Description

Scatterplot function for class 'data.frame'.

Usage

scatter_data_frame(
  x,
  yvar,
  xvar,
  groups = NULL,
  facets = NULL,
  log = metOption("log_scatter", FALSE),
  ylog = metOption("ylog_scatter", log),
  xlog = metOption("xlog_scatter", log),
  crit = metOption("crit_scatter", 1.3),
  yref = metOption("yref_scatter", "metaplot_ref"),
  xref = metOption("xref_scatter", "metaplot_ref"),
  ylab = metOption("ylab_scatter", "axislabel"),
  xlab = metOption("xlab_scatter", "axislabel"),
  ysmooth = metOption("ysmooth_scatter", FALSE),
  xsmooth = metOption("xsmooth_scatter", FALSE),
  iso = metOption("iso_scatter", FALSE),
  na.rm = metOption("na.rm_scatter", TRUE),
  aspect = metOption("aspect_scatter", 1),
  space = metOption("space_scatter", "right"),
  key = metOption("key_scatter", "metaplot_key"),
  as.table = metOption("as.table_scatter", TRUE),
  prepanel = metOption("prepanel_scatter", NULL),
  isoprepanel = metOption("isoprepanel_scatter", "iso_prepanel"),
  scales = metOption("scales_scatter", NULL),
  panel = metOption("panel_scatter", "scatter_panel"),
  points = metOption("points_scatter", TRUE),
  colors = metOption("colors_scatter", NULL),
  fill = metOption("fill_scatter", NULL),
  symbols = metOption("symbols_scatter", NULL),
  sizes = metOption("sizes_scatter", 1),
  types = metOption("types_scatter", "solid"),
  widths = metOption("widths_scatter", 1),
  lines = metOption("lines_scatter", FALSE),
  main = metOption("main_scatter", NULL),
  sub = metOption("sub_scatter", NULL),
  subscripts = metOption("subscripts_scatter", TRUE),
  settings = metOption("settings_scatter", NULL),
  padding = metOption("padding_scatter", 1),
  ref.col = metOption("ref.col_scatter", "grey"),
  ref.lty = metOption("ref.lty_scatter", "solid"),
  ref.lwd = metOption("ref.lwd_scatter", 1),
  ref.alpha = metOption("ref.alpha_scatter", 1),
  xref.col = metOption("xref.col_scatter", NULL),
  xref.lty = metOption("xref.lty_scatter", NULL),
  xref.lwd = metOption("xref.lwd_scatter", NULL),
  xref.alpha = metOption("xref.alpha_scatter", NULL),
  yref.col = metOption("yref.col_scatter", NULL),
  yref.lty = metOption("yref.lty_scatter", NULL),
  yref.lwd = metOption("yref.lwd_scatter", NULL),
  yref.alpha = metOption("yref.alpha_scatter", NULL),
  smooth.lty = metOption("smooth.lty_scatter", "dashed"),
  smooth.lwd = metOption("smooth.lwd_scatter", 1),
  smooth.alpha = metOption("smooth.alpha_scatter", 1),
  fit = metOption("fit_scatter", conf),
  fit.lty = metOption("fit.lty_scatter", "solid"),
  fit.lwd = metOption("fit.lwd_scatter", 1),
  fit.alpha = metOption("fit.alpha_scatter", 1),
  conf = metOption("conf_scatter", FALSE),
  conf.alpha = metOption("conf.alpha_scatter", 0.3),
  loc = metOption("loc_scatter", 0),
  global = metOption("global_scatter", FALSE),
  global.col = metOption("global.col_scatter", "grey"),
  global.fill = metOption("global.fill_scatter", "grey"),
  msg = metOption("msg_scatter", "metastats"),
  gg = metOption("gg_scatter", FALSE),
  verbose = metOption("verbose", FALSE),
  ...
)

Arguments

x

data.frame

yvar

character: y variable(s)

xvar

character: x variable

groups

optional grouping variable; ignored if more than one yvar

facets

optional conditioning variables

log

a default shared by ylog and xlog

ylog

log transform y axis (auto-selected if NA)

xlog

log transform x axis (auto-selected if NA)

crit

if ylog or xlog missing, log transform if mean/median ratio for non-missing values is greater than crit

yref

reference line from y axis; can be function(x = x, var = yvar, ...) or NULL to suppress

xref

reference line from x axis; can be function(x = x, var = xvar, ...) or NULL to suppress

ylab

y axis label; can be function(x = x, var = yvar, log = ylog, ..)

xlab

x axis label; can be function(x = x, var = xvar, log = xlog, ..)

ysmooth

supply loess smooth of y on x

xsmooth

supply loess smmoth of x on y

iso

logical: plot line of unity (auto-selected if NA); can be a (partial) list of aesthetics (col, lty, lwd, alpha)

na.rm

whether to remove data points with one or more missing coordinates

aspect

passed to bwplot or ggplot; use 'fill', NA, or NULL to calculate automatically

space

location of key (right, left, top, bottom)

key

list: passed to xyplot as auto.key or to theme; can be a function groups name, groups levels, points, lines, space, gg, and ... . See metaplot_key.

as.table

passed to xyplot

prepanel

passed to xyplot (guessed if NULL)

isoprepanel

passed to xyplot if iso is TRUE

scales

passed to xyplot or facet_grid or facet_wrap (guessed if NULL)

panel

name or definition of panel function

points

whether to plot points and fill for each group: logical, or alpha values between 0 and 1

colors

replacements for default colors in group order; can be length one integer to auto-select that many colors

fill

replacements for default fill colors in group order (means something different for densplot_data_frame and categorical_data_frame). Used for confidence regions and for filling symbols (pch 21:25).

symbols

replacements for default symbols in group order (i.e. values of pch)

sizes

replacements for default symbol sizes in group order

types

replacements for default line types in group order

widths

replacements for default line widths in group order

lines

whether to plot lines for each group: logical, or alpha values between 0 and 1. Points are connected in the order in which they appear in the data.

main

character, or a function of x, yvar, xvar, groups, facets, and log

sub

character, or a function of x, yvar, xvar, groups, facets, and log

subscripts

passed to xyplot

settings

padding

numeric (will be recycled to length 4) giving plot margins in default units: top, right, bottom, left (in multiples of 5.5 points for ggplot)

ref.col

default shared by xref.col and yref.col; can be length one integer to auto-select that many colors

ref.lty

default shared by xref.lty and yref.lty

ref.lwd

default shared by xref.lwd and yref.lwd

ref.alpha

default shared by xref.alpha and yref.alpha

xref.col

x reference line color (recycled)

xref.lty

x reference line type (recycled)

xref.lwd

x reference line size (recycled)

xref.alpha

x reference line alpha (recycled)

yref.col

y reference line color (recycled)

yref.lty

y reference line type (recycled)

yref.lwd

y reference line size (recycled)

yref.alpha

y reference line alpha (recycled)

smooth.lty

smooth line type

smooth.lwd

smooth line size

smooth.alpha

smooth alpha

fit

draw a linear fit of y ~ x

fit.lty

fit line type

fit.lwd

fit line size

fit.alpha

fit alpha

conf

logical, or width for a confidence region around a linear fit; passed to region; TRUE defaults to 95 percent confidence interval; may not make sense if xlog is TRUE

conf.alpha

alpha transparency for confidence region

loc

where to print statistics on a panel; suppressed for grouped plots an facetted ggplots

global

if TRUE, xsmooth, ysmooth, fit, and conf are applied to all data rather than groupwise

global.col

color for global aesthetics

global.fill

fill color for global aesthetics

msg

a function to print text on a panel: called with x values, y values, and ....

gg

logical: whether to generate ggplot instead of trellis

verbose

generate messages describing process

...

passed to called functions e.g., region

Examples

library(magrittr)
library(dplyr)
attr(Theoph$conc,'label') <- 'theophylline concentration'
attr(Theoph$conc,'guide') <- 'mg/L'
attr(Theoph$Time,'label') <- 'time'
attr(Theoph$Time,'guide') <- 'h'
attr(Theoph$Subject,'guide') <- '////'
scatter_data_frame(Theoph, 'conc', 'Time')
scatter_data_frame(Theoph, 'conc','Time', 'Subject')
scatter_data_frame(Theoph, 'conc','Time', facets = 'Subject')
scatter_data_frame(Theoph %>% filter(conc > 0), 'conc','Time', 'Subject',ylog = TRUE, yref = 5)
scatter_data_frame(Theoph, 'conc','Time', 'Subject',ylog = TRUE, yref = 5)
scatter_data_frame(Theoph, 'conc','Time', 'Subject',ysmooth = TRUE)
scatter_data_frame(Theoph, 'conc','Time', 'Subject',ysmooth = TRUE,global = TRUE)
scatter_data_frame(Theoph, 'conc','Time', conf = TRUE, loc = 3, yref = 6)
scatter_data_frame(Theoph, 'conc','Time', conf = TRUE, loc = 3, yref = 6)

Panel Function for Metaplot Scatterplot

Description

Default panel function for scatter_data_frame. Calls panel.xyplot and optionally plots linear fit, confidence region, reference lines, and statistics. Note that, although global options are supported, typically these are unreachable since the calling function supplies appropriate values.

Usage

scatter_panel(
  x,
  y,
  groups,
  xref = metOption("xref_scatter_panel", scatter_panel_ref),
  yref = metOption("yref_scatter_panel", scatter_panel_ref),
  ref.col = metOption("ref.col_scatter_panel", "grey"),
  ref.lty = metOption("ref.lty_scatter_panel", "solid"),
  ref.lwd = metOption("ref.lwd_scatter_panel", 1),
  ref.alpha = metOption("ref.alpha_scatter_panel", 1),
  xref.col = metOption("xref.col_scatter_panel", NULL),
  xref.lty = metOption("xref.lty_scatter_panel", NULL),
  xref.lwd = metOption("xref_lwd_scatter_panel", NULL),
  xref.alpha = metOption("xref_alpha_scatter_panel", NULL),
  yref.col = metOption("yref_col_scatter_panel", NULL),
  yref.lty = metOption("yref_lty_scatter_panel", NULL),
  yref.lwd = metOption("yref_lwd_scatter_panel", NULL),
  yref.alpha = metOption("yref_alpha_scatter_panel", NULL),
  ysmooth = metOption("ysmooth_scatter_panel", FALSE),
  xsmooth = metOption("xsmooth_scatter_panel", FALSE),
  smooth.lty = metOption("smooth.lty_scatter_panel", "dashed"),
  smooth.lwd = metOption("smooth.lwd_scatter_panel", 1),
  smooth.alpha = metOption("smooth.alpha_scatter_panel", 1),
  fit = metOption("fit_scatter_panel", NULL),
  fit.lty = metOption("fit.lty_scatter_panel", "solid"),
  fit.lwd = metOption("fit.lwd_scatter_panel", 1),
  fit.alpha = metOption("fit.alpha_scatter_panel", 1),
  conf = metOption("conf_scatter_panel", FALSE),
  conf.alpha = metOption("conf.alpha_scatter_panel", 0.3),
  loc = metOption("loc_scatter_panel", 0),
  iso = metOption("iso_scatter_panel", FALSE),
  global = metOption("global_scatter_panel", FALSE),
  global.col = metOption("global.col_scatter_panel", "grey"),
  global.fill = metOption("global.fill_scatter_panel", "grey"),
  msg = metOption("msg_scatter_panel", "metastats"),
  type,
  verbose = metOption("verbose_scatter_panel", FALSE),
  ...
)

Arguments

x

x values

y

y values

groups

optional grouping item

xref

reference line from x axis; can be function(x, y, ...)

yref

reference line from y axis; can be function(y, x, ...)

ref.col

default shared by xref.col and yref.col

ref.lty

default shared by xref.lty and yref.lty

ref.lwd

default shared by xref.lwd and yref.lwd

ref.alpha

default shared by xref.alpha and yref.alpha

xref.col

x reference line color (recycled)

xref.lty

x reference line type (recycled)

xref.lwd

x reference line size (recycled)

xref.alpha

x reference line alpha (recycled)

yref.col

y reference line color (recycled)

yref.lty

y reference line type (recycled)

yref.lwd

y reference line size (recycled)

yref.alpha

y reference line alpha (recycled)

ysmooth

supply loess smooth of y on x

xsmooth

supply loess smmoth of x on y

smooth.lty

smooth line type

smooth.lwd

smooth line size

smooth.alpha

smooth alpha

fit

draw a linear fit of y ~ x; defaults to as.logical(conf)

fit.lty

fit line type

fit.lwd

fit line size

fit.alpha

fit alpha

conf

logical, or width for a confidence region around a linear fit; passed to region; TRUE defaults to 95 percent confidence interval; may not make sense if xlog is TRUE

conf.alpha

alpha transparency for confidence region

loc

where to print statistics on a panel; suppressed for grouped plots

iso

logical: use isometric axes with line of unity (auto-selected if NA); can be a (partial) list of aesthetics (col, lty, lwd, alpha)

global

if TRUE, xsmooth, ysmooth, fit, and conf are applied to all data rather than groupwise

global.col

color for global aesthetics

global.fill

fill color for global aesthetics

msg

a function to print text on a panel: called with x values, y values, and ....

type

overridden by scatter_panel

verbose

generate messages describing process

...

passed to panel.superpose, panel.xyplot, panel.polygon, region, panel.text

Calculate Panel Reference Values

Description

Calculates reference values for x and y axes at the panel level.

Usage

scatter_panel_ref(a, b, ...)

Arguments

a

vector of interest

b

vector for other axis

...

ignored

Value

numeric

Set or Reset Metaplot Options

Description

Sets an option value in the list getOption('metaplot'). If invoked without named arguments, option 'metaplot' is set to NULL. Setting an existing option moves it to the end of the list (breaks ties in metOption).

Usage

setOption(...)

Arguments

...

any metaplot options can be defined, using name = value.

Value

(invisible) character vector of option names that were set or unset

Examples

example(metOption)

Test Metaplot Variants

Description

Tests metaplot variants by example. Returns null. Use example(test_metaplot).

Usage

test_metaplot()

Examples


library(magrittr)
library(dplyr)
library(csv)
x <- as.csv(system.file(package = 'metaplot', 'extdata/theoph.csv'))
x %<>% pack

multiplot(
x %>% metaplot(sres, gg = F),
x %>% metaplot(sres, gg = T, padding = 3.5)
)
multiplot(
x %>% metaplot(site, gg = F),
x %>% metaplot(site, gg = T, padding = 3.5)
)
multiplot(
x %>% metaplot(conc, arm, gg = F),
x %>% metaplot(conc, arm, gg = T, padding = 4)
)
multiplot(
x %>% densplot(conc, arm, gg = F),
x %>% densplot(conc, arm, gg = T, padding = 8)
)
multiplot(
x %>% densplot(
  conc, arm, gg = F, space = 'top',
  columns = 2,
  legend.direction = 'horizontal' # ignored
),
x %>% densplot(conc, arm, gg = T, space = 'top',
  columns = 2, # ignored
  legend.direction = 'horizontal' , padding = 3
))
multiplot(
x %>% metaplot(arm, conc, gg = F),
x %>% metaplot(arm, conc, gg = T, padding = 3.5)
)
multiplot(
x %>% metaplot(conc, arm, site, gg = F),
x %>% metaplot(conc, arm, site, gg = T, padding = 5)
)
multiplot(
x %>% metaplot(conc, site, arm, gg = F),
x %>% metaplot(conc, site, arm, gg = T, padding = 5)
)
multiplot(
x %>% metaplot(conc, time, gg = F),
x %>% metaplot(conc, time, gg = T, padding = 5)
)
multiplot(
x %>% metaplot(arm, site, gg = F),
x %>% metaplot(arm, site, gg = T, padding = 3)
)
multiplot(
x %>% metaplot(arm, site, cohort, gg = F),
x %>% metaplot(arm, site, cohort, gg = T, padding = 5)
)
multiplot(
x %>% metaplot(arm, site, cohort, gg = F, space = 'top',
  columns = 2, padding = c(5,1,1,1)),
x %>% metaplot(arm, site, cohort, gg = T, space = 'top',
  legend.direction = 'horizontal', padding = 2)
)
multiplot(
x %>% metaplot(arm, site, , cohort, gg = F),
x %>% metaplot(arm, site, , cohort, gg = T, padding = 4)
)
multiplot(
x %>% metaplot(conc, time, subject, gg = F),
x %>% metaplot(conc, time, subject, gg = T, padding = 3)
)
multiplot(
x %>% metaplot(conc, time, , subject, gg = F),
x %>% metaplot(conc, time, , subject, gg = T, padding = 5)
)
multiplot( ncol = 2,
x %>% metaplot(conc, time, subject, site, gg = F),
x %>% metaplot(conc, time, subject, site, gg = T, padding = 4)
)
multiplot(
x %>% metaplot(conc, time, subject, site, arm, gg = F, padding = 2),
x %>% metaplot(conc, time, subject, site, arm, gg = T)
)
multiplot(
x %>% metaplot(lKe, lKa, lCl, gg = F),
x %>% metaplot(lKe, lKa, lCl, gg = T, padding = 2)
)
multiplot(
x %>% metaplot(
  lKe, lKa, lCl,
  col = 'black',smooth.col = 'red', pin.col = 'red',
  dens.col = 'blue', dens.alpha = 0.1, gg = F
),
x %>% metaplot(
  lKe, lKa, lCl,
  col = 'black',smooth.col = 'red', pin.col = 'red',
  dens.col='blue',dens.alpha = 0.1, gg = T, padding = 2)
)
multiplot(
x %>% metaplot(conc, pred, ipred, time, space = 'top', gg = F),
x %>% metaplot(conc, pred, ipred, time, space = 'top', gg = T, padding = 3)
)
multiplot(
x %>% metaplot(conc, pred, ipred, time, subject, space = 'top', gg = F),
x %>% metaplot(conc, pred, ipred, time, subject, space = 'top', gg = T, padding = 5)
)
multiplot(
x %>% metaplot(
  conc, pred, ipred, time, subject,
  colors = c('black','blue','orange'),
  points = c(0.9,0, 0.4),
  lines = c(F,T,T),
  space = 'top', gg = F
),
x %>% metaplot(
  conc, pred, ipred, time, subject,
  colors = c('black','blue','orange'),
  points = c(0.9,0, 0.4),
  lines = c(F,T,T),
  space = 'top', gg = T, padding = 4
))
multiplot(
x %>% metaplot(conc, ipred, time, site, arm, space = 'top', gg = F),
x %>% metaplot(conc, ipred, time, site, arm, space = 'top', gg = T)
)
multiplot(
x %>% metaplot(res, conc, yref = 0, ysmooth = T, conf = T, grid = T, loc = 1, gg = F),
x %>% metaplot(res, conc, yref = 0, ysmooth = T, conf = T, grid = T, loc = 1, gg = T, padding = 3.5)
)
multiplot(
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T , gg = F),
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T , gg = T, padding = 3.5)
)
# Fill color can differ from point color but is the same for points and regions.
# 'points' controls alpha of point and point fill independently of conf.fill.
multiplot(
x %>% metaplot(res, conc, arm, conf = T , gg = F, yref = NULL, points = 0.3,
  symbols = 21:22, colors = c('blue','black'), fill = c('green','red')
),
x %>% metaplot(res, conc, arm, conf = T , gg = T, yref = NULL, points = 0.3, padding = 3.5,
  symbols = 21:22, colors = c('blue','black'), fill = c('green','red')
))
multiplot(
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T, global = T,
  ref.col = 'red', gg = F),
x %>% metaplot(res, conc, arm, ysmooth = T, conf = T, global = T,
  ref.col = 'red', gg = T, padding = 3.5)
)
multiplot(
x %>% metaplot(subject,conc, gg = F),
x %>% metaplot(subject,conc, gg = T, padding = 3.5)
)

# manage metadata
attr(x$arm, 'guide') # //1/Arm A//2/Arm B//
multiplot(
x %>% metaplot(conc, arm, gg = F),
x %>% metaplot(conc, arm, gg = T, padding = 4)
) # default

multiplot(
x %>% mutate(arm = arm %>%
  structure(guide = '//2/Arm B//1/Arm A//')) %>%
  metaplot(conc, arm, gg = F),
x %>% mutate(arm = arm %>%
  structure(guide = '//2/Arm B//1/Arm A//')) %>%
  metaplot(conc, arm, gg = T, padding = 4) # different presentation order
)

multiplot(
x %>% mutate(arm = arm %>%
  structure(guide = '//1/Both Arms//2/Both Arms//')) %>%
  metaplot(conc, arm, gg = F),
x %>% mutate(arm = arm %>%
  structure(guide = '//1/Both Arms//2/Both Arms//')) %>%
  metaplot(conc, arm, gg = T, padding = 4) # collapse cases
)

x %>% densplot(
 main = 'Density Plot',
 sub = 'using lattice',
 gg = F,
 sres, subject,
 ref.col = 'red', ref.alpha = 0.5,
 ref.lty = 'dashed', ref.lwd = 2,
 log = F,
 aspect = NULL,
 colors = c('red','blue','darkgreen'),
 symbols = c(21, 22, 23),
 points = 0.3,
 lines = .5,
 fill = 0.1,
 space = 'left',
 padding = c(1,2,3,4),
 other = 'none'
)
x %>% densplot(
 main = 'Density Plot',
 sub = 'using ggplot',
 gg = T,
 sres, subject,
 ref.col = 'red', ref.alpha = 0.5,
 ref.lty = 'dashed', ref.lwd = 2,
 log = F,
 aspect = NULL,
 colors = c('red','blue','darkgreen'),
 symbols = c(21, 22, 23),
 points = 0.3,
 lines = 0.5,
 fill = 0.1,
 space = 'left',
 padding = 1:4,
 other = 'none'
)
x %>% filter(conc > 0) %>% metaplot(
 main = 'Box Plot',
 sub = 'using lattice',
 gg = F,
 arm, conc,
 log = T,
 ref = 4, ref.col = 'red',
 ref.lty = 'dashed', ref.lwd = 2,
 nobs = T,
 padding = 1:4,
 reverse = FALSE,
 pch = 20,
 notch = TRUE,
 aspect = NA,
 other = 'none'
)

x %>% filter(conc > 0) %>% metaplot(
 main = 'Box Plot',
 sub = 'using ggplot',
 gg = T,
 arm, conc,
 log = T,
 ref = 4, ref.col = 'red',
 ref.lty = 'dashed', ref.lwd = 2,
 nobs = T,
 padding = 1:4,
 reverse = FALSE,
 pch = 20,
 notch = TRUE,
 aspect = NA,
 other = 'none'
)
x %>% metaplot(
 main = 'Categorical Plot',
 sub = 'using lattice',
 gg = F,
 arm, site, cohort,
 aspect = 'fill', space = 'top',
 as.table = FALSE,
 colors = c('red','blue','green'),
 fill = c(0.3, 0.5, 0.7),
 lines = c(0.7, 0.5, 0.3),
 tex = 0.8, rot = 45,
 padding = 1:4, loc = 1,
 cex = .5,
 other = 'none'
)

x %>% metaplot(
 main = 'Categorical Plot',
 sub = 'using ggplot2',
 gg = T,
 arm, site, cohort,
 aspect = 'fill', space = 'top',
 as.table = FALSE,
 colors = c('red','blue','green'),
 fill = c(0.3, 0.5, 0.7),
 lines = c(0.7, 0.5, 0.3),
 tex = 0.8, rot = 45,
 padding = 1:4, loc = 1,
 cex = .5,
 other = 'none'
)
x %>% metaplot(
  main = 'Correlation Splom',
  sub = 'using lattice',
  gg = F,
  lKe, lKa, lCl,
  varname.cex = 2,
  col = 'purple',
  smooth.col = 'orange', smooth.alpha = 0.9,
  smooth.lty = 'dashed', smooth.lwd = 2,
  pin.col = 'orange', pin.alpha = 0.9,
  dens.col = 'purple',dens.alpha = 0.2, dens.scale = 0.1,
  padding = 1:4,
  other = 'none',
  xlab = 'parameters'
)
x %>% metaplot(
  main = 'Correlation Splom',
  sub = 'using ggplot',
  gg = T,
  lKe, lKa, lCl,
  varname.cex = 2,
  col = 'purple',
  smooth.col = 'orange', smooth.alpha = 0.9,
  smooth.lty = 'dashed', smooth.lwd = 2,
  pin.col = 'orange', pin.alpha = 0.9,
  dens.col = 'purple',dens.alpha = 0.2, dens.scale = 0.1,
  padding = 1:4,
  other = 'none',
  xlab = 'parameters'
)
x %>% metaplot(
  main = 'Scatterplot',
  sub = 'using lattice',
  gg = F,
  res, conc,
  yref = 0, ysmooth = T,
  smooth.lty = 'dotted', smooth.lwd = 2,
  smooth.alpha = 1,
  aspect = 0.8,
  space = 'bottom',
  colors = c('purple','darkgreen','peach'),
  symbols = 21:23,
  points = c(0.3, 0.5, 0.7),
  lines = F,
  padding = 1:4,
  ref.col = 'blue',
  ref.lty = 'dashed', ref.lwd = 2,
  ref.alpha = 0.5,
  conf = .99999,
  fit.lty = 'dashed', fit.lwd = 2,
  fit.alpha = 0.5,
  conf.alpha = 0.2,
  global = T,
  global.col = 'darkgreen',
  grid = T, loc = 1,
  other = 'none'
 )
x %>% metaplot(
  main = 'Scatterplot',
  sub = 'using ggplot',
  gg = T,
  res, conc,
  yref = 0, ysmooth = T,
  smooth.lty = 'dotted', smooth.lwd = 2,
  smooth.alpha = 1,
  aspect = 0.8,
  space = 'bottom',
  colors = c('purple','darkgreen','peach'),
  symbols = 21:23,
  points = c(0.3, 0.5, 0.7),
  lines = F,
  padding = 1:4,
  ref.col = 'blue',
  ref.lty = 'dashed', ref.lwd = 2,
  ref.alpha = 0.5,
  conf = .99999,
  fit.lty = 'dashed', fit.lwd = 2,
  fit.alpha = 0.5,
  conf.alpha = 0.2,
  global = T,
  global.col = 'darkgreen',
  grid = T, loc = 1,
  other = 'none'
 )

 # vectorized reference aesthetics
multiplot(
  x %>% metaplot(
   sres, gg = F,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted')
  ),
  x %>% metaplot(
   sres, gg = T,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted'),
   padding = 3.5
  )
)
multiplot(
  x %>% densplot(
   sres, arm, gg = F,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted')
  ),
  x %>% densplot(
   sres, arm, gg = T,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted'),
   padding = 3.5
  )
)
multiplot(
  x %>% densplot(
   sres,, arm, gg = F,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted')
  ),
  x %>% densplot(
   sres,, arm, gg = T,
   ref.col = c('blue','red'),
   ref.lty = c('dashed','dotted'),
   padding = 3.5
 )
)
multiplot(
  x %>% metaplot(
   sres, time,, arm, gg = F,
   yref = c(-4,0,4),
   xref = c(5, 10, 15),
   yref.col = c('blue','red'),
   yref.lty = c('dashed','dotted'),
   xref.col = c('green','orange')
  ),
  x %>% metaplot(
   sres, time,, arm, gg = T,
   yref = c(-4,0,4),
   xref = c(5, 10, 15),
   yref.col = c('blue','red'),
   yref.lty = c('dashed','dotted'),
   xref.col = c('green','orange'),
   padding = 3.5
 )
)
# use of settings
multiplot(
 x %>% metaplot(conc, ,subject, settings = list(ncol = 4, nrow = 3), gg = F),
 x %>% metaplot(conc, ,subject, settings = list(ncol = 4), padding = 4, gg = T)
)
multiplot(
 x %>% metaplot(conc, time,, subject, settings = list(ncol = 4, nrow = 3), gg = F),
 x %>% metaplot(conc, time,, subject, settings = list(ncol = 4), padding = 4,  gg = T)
)
multiplot(
 x %>% metaplot(conc, arm, site, settings = list(ncol = 1, nrow = 2), gg = F),
 x %>% metaplot(conc, arm, site, settings = list(ncol = 1), padding = 4,  gg = T)
)

#iso aesthetics
multiplot(
 x %>% metaplot(conc, ipred, iso = NA, gg = F),
 x %>% metaplot(conc, ipred, iso = NA, gg = T, padding = 4)
)
multiplot(
 x %>% metaplot(conc, ipred, iso = list(lty = 'dashed'), gg = F),
 x %>% metaplot(conc, ipred, iso = list(lty = 'dashed'), gg = T, padding = 4)
)

Calculate Tile Limits

Description

Calculates limits for mosaic tiles

Usage

tiles(x, ..., tex = 0.9, msg = "tilestats", verbose = FALSE)

Arguments

x

a data.frame with at least columns x, y, and g, possibly f1 and f2 (facets)

...

other arguments

tex

tile shrinkage <= 1

msg

a function of x and y to create a tile message

verbose

generate messages describing process

Value

data.frame

Format Tile Statistics

Description

Formats statistics for a mosaic tile.

Usage

tilestats(x, y, ...)

Arguments

x

x values

y

y values

...

other arguments

Value

character

Unpack Something

Description

Unpack Something. Generic, with method for data.frame.

Usage

unpack(x, ...)

Arguments

x

object

...

other arguments

Express Scalar Column Attributes as Column Metadata

Description

Expresses scalar column attributes as column metadata (row values). Column with name meta is created to hold names of attributes, if any. A transposed table (sorted by attribute name) of scalar column attribute values (coerced to character) is bound to the existing data.frame (the attributes themselves are removed from columns). Bind position is controlled by position such that the intersection of new rows and column occurs in the corresponding corner, numbered clockwise from top-left. Resulting column classes are character. It is an error if meta is already in names(x).

Usage

## S3 method for class 'data.frame'
unpack(
  x,
  meta = getOption("meta", "meta"),
  position = 1L,
  ignore = c("class", "levels"),
  ...
)

Arguments

x

data.frame

meta

column in result giving names of attributes

position

1 (top-left), 2 (top-right), 3 (bottom-right), or 4 (bottom-left)

ignore

character: attributes to ignore

...

ignored arguments

Value

data.frame

data.frame with all columns of class character

Convert Wiki Symbol to Plotmath

Description

Converts wiki symbol to plotmath. Vectorized version of wikisym2plotmath_.

Usage

wikisym2plotmath(x, ...)

Arguments

x

character

...

ignored

Value

expression

Convert One Wiki Symbol to Plotmath

Description

Converts one wiki symbol to plotmath. A Wiki symbol is simple text with arbitrarily nested subscript (_) and superscript (^) groupings. Use dot (.) to explicitly terminate a grouping, and use backslash-dot (\.) for a literal dot. Examples: V_c./F. Trailing dots need not be supplied. Leading/trailing whitespace is removed. Tab character not allowed.

Usage

wikisym2plotmath_(x, ...)

Arguments

x

character

...

ignored

Value

expression

Examples

wikisym2plotmath_('V_c./F')
wikisym2plotmath_('AUC_ss')
wikisym2plotmath_('C_max_ss')
wikisym2plotmath_('var^eta_j')

Generic Axis Label

Description

Usage

Arguments

See Also

Axis Label for Data Frame

Description

Usage

Arguments

Value

See Also

Boxplots

Description

Boxplot Method for Data Frame

Description

Usage

Arguments

See Also

Examples

Boxplot Function for Data Frame

Description

Usage

Arguments

See Also

Examples

Panel Function for Metaplot Boxplot

Description

Usage

Arguments

See Also

Categorical Plot

Description

Usage

Arguments

See Also

Categorical Method for Data Frame

Description

Usage

Arguments

Value

See Also

Categorical Function for Data Frame

Description

Usage

Arguments

See Also

Examples

Panel Function for Metaplot Categorical Plot

Description

Usage

Arguments

See Also

Calculate Categorical Axis Labels and Positions

Description

Usage

Arguments

Value

See Also

Correlated Splom

Description

Usage

Arguments

See Also

Correlated Scatterplot Matrix Method for Data Frame

Description

Usage

Arguments

See Also

Correlated Scatterplot Matrix Function for Data Frame

Description

Usage

Arguments

Value

See Also

Examples

Correlation GG Function for GG Metaplot Corsplom

Description

Usage

Arguments

See Also