Package 'metaplot'

Title: Data-Driven Plot Design
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.
Authors: Tim Bergsma
Maintainer: Tim Bergsma <[email protected]>
License: GPL-3
Version: 0.8.4
Built: 2025-02-13 02:56:07 UTC
Source: https://github.com/bergsmat/metaplot

Help Index

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

See Also

Other mixedvariate plots: boxplot.data.frame(), boxplot_panel()

Other boxplot: boxplot.data.frame()

Other metaplot: categorical_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot_key(), metaplot(), scatter_data_frame(), test_metaplot()

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)

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

See Also

Other mixedvariate plots: boxplot_data_frame(), boxplot_panel()

Other boxplot: boxplot_data_frame()

Other methods: axislabel.data.frame(), categorical.data.frame(), corsplom.data.frame(), densplot.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.data.frame()

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)

Categorical Plot

Description

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

Usage

categorical(x, ...)

Arguments

x

object of dispatch
...

passed arugments

See Also

Other generic functions: axislabel(), corsplom(), densplot(), metaplot(), pack(), scatter(), test_metaplot(), unpack()

Other categorical: categorical.data.frame(), categorical_data_frame(), categorical_panel(), panel_tile()

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

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)
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

See Also

categorical_panel

Other categorical: categorical.data.frame(), categorical_panel(), categorical(), panel_tile()

Other metaplot: boxplot_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot_key(), metaplot(), scatter_data_frame(), test_metaplot()

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

See Also

tilestats

categorical.data.frame

Other panel functions: boxplot_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref(), scatter_panel()

Other categorical: categorical.data.frame(), categorical_data_frame(), categorical(), panel_tile()

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

See Also

Other categorical: categorical_data_frame(), categorical_panel(), categorical(), panel_tile()

Other methods: axislabel.data.frame(), boxplot.data.frame(), corsplom.data.frame(), densplot.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.data.frame()

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

See Also

categorical_panel

Other categorical family: tilestats(), tiles()

Correlated Splom

Description

Scatterplot matrix with correlations.

Usage

corsplom(x, ...)

Arguments

x

object
...

passed arguments

See Also

Other generic functions: axislabel(), categorical(), densplot(), metaplot(), pack(), scatter(), test_metaplot(), unpack()

Other corsplom: corsplom.data.frame(), corsplom_data_frame(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_scatter(), plot.metaplot_gtable(), print.metaplot_gtable()

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

See Also

Other multivariate plots: corsplom.data.frame(), metaplot.data.frame()

Other corsplom: corsplom.data.frame(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_scatter(), corsplom(), plot.metaplot_gtable(), print.metaplot_gtable()

Other metaplot: boxplot_data_frame(), categorical_data_frame(), densplot_data_frame(), metaplot_key(), metaplot(), scatter_data_frame(), test_metaplot()

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
)

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

See Also

Other multivariate plots: corsplom_data_frame(), metaplot.data.frame()

Other corsplom: corsplom_data_frame(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_scatter(), corsplom(), plot.metaplot_gtable(), print.metaplot_gtable()

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), densplot.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.data.frame()

Density Plot

Description

Creates a density plot.

Usage

densplot(x, ...)

Arguments

x

object
...

passed arguments

See Also

Other generic functions: axislabel(), categorical(), corsplom(), metaplot(), pack(), scatter(), test_metaplot(), unpack()

Other univariate plots: dens_panel(), densplot.data.frame(), densplot_data_frame(), metaplot.data.frame(), panel.meta_densityplot()

Other densplot: densplot.data.frame(), densplot_data_frame()

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

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)
gg

logical: whether to generate ggplot instead of trellis
verbose

generate messages describing process
...

passed to densityplot

See Also

Other univariate plots: dens_panel(), densplot.data.frame(), densplot(), metaplot.data.frame(), panel.meta_densityplot()

Other densplot: densplot.data.frame(), densplot()

Other metaplot: boxplot_data_frame(), categorical_data_frame(), corsplom_data_frame(), metaplot_key(), metaplot(), scatter_data_frame(), test_metaplot()

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')

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

See Also

Other univariate plots: dens_panel(), densplot_data_frame(), densplot(), metaplot.data.frame(), panel.meta_densityplot()

Other densplot: densplot_data_frame(), densplot()

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), corsplom.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.data.frame()

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'))

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

See Also

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_pin(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref(), scatter_panel()

Other formatters: wikisym2plotmath_(), wikisym2plotmath()

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

See Also

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref(), scatter_panel()

Other reference lines: metaplot_ref(), scatter_panel_ref()

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_.

See Also

Other generic functions: axislabel(), categorical(), corsplom(), densplot(), pack(), scatter(), test_metaplot(), unpack()

Other metaplot: boxplot_data_frame(), categorical_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot_key(), scatter_data_frame(), test_metaplot()

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

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

See Also

Other metaplot: boxplot_data_frame(), categorical_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot(), scatter_data_frame(), test_metaplot()

Other scatter: scatter.data.frame(), scatter_data_frame(), scatter_panel(), scatter()

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref(), scatter_panel()

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

See Also

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_key(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref(), scatter_panel()

Other reference lines: diag_pin(), scatter_panel_ref()

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

See Also

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), corsplom.data.frame(), densplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.data.frame()

Other univariate plots: dens_panel(), densplot.data.frame(), densplot_data_frame(), densplot(), panel.meta_densityplot()

Other bivariate plots: iso_prepanel(), scatter.data.frame(), scatter_data_frame(), scatter()

Other multivariate plots: corsplom.data.frame(), corsplom_data_frame()

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)

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

See Also

scatter_panel

Other regression functions: model(), region()

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.

See Also

getOption setOption

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)

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

See Also

arrangeGrob

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

See Also

Other generic functions: axislabel(), categorical(), corsplom(), densplot(), metaplot(), scatter(), test_metaplot(), unpack()

Other pack: pack.data.frame(), unpack.data.frame(), unpack()

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

See Also

Other pack: pack(), unpack.data.frame(), unpack()

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), corsplom.data.frame(), densplot.data.frame(), metaplot.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame(), unpack.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)

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

See Also

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), scatter_panel_ref(), scatter_panel()

Other categorical: categorical.data.frame(), categorical_data_frame(), categorical_panel(), categorical()

Scatterplot

Description

Scatterplot.

Usage

scatter(x, ...)

Arguments

x

object
...

passed arguments

See Also

Other generic functions: axislabel(), categorical(), corsplom(), densplot(), metaplot(), pack(), test_metaplot(), unpack()

Other scatter: metaplot_key(), scatter.data.frame(), scatter_data_frame(), scatter_panel()

Other bivariate plots: iso_prepanel(), metaplot.data.frame(), scatter.data.frame(), scatter_data_frame()

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

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). Also merged with ....
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

See Also

scatter_panel

Other bivariate plots: iso_prepanel(), metaplot.data.frame(), scatter.data.frame(), scatter()

Other metaplot: boxplot_data_frame(), categorical_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot_key(), metaplot(), test_metaplot()

Other scatter: metaplot_key(), scatter.data.frame(), scatter_panel(), scatter()

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

See Also

metastats

scatter.data.frame

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel_ref()

Other scatter: metaplot_key(), scatter.data.frame(), scatter_data_frame(), scatter()

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

See Also

Other panel functions: boxplot_panel(), categorical_panel(), corsplom_gg_correlation(), corsplom_gg_diagonal(), corsplom_gg_scatter(), corsplom_panel_correlation(), corsplom_panel_diagonal(), corsplom_panel_scatter(), dens_panel(), diag_label(), diag_pin(), iso_prepanel(), metaplot_key(), metaplot_ref(), panel.meta_densityplot(), panel_tile(), scatter_panel()

Other reference lines: diag_pin(), metaplot_ref()

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

See Also

scatter_data_frame

Other bivariate plots: iso_prepanel(), metaplot.data.frame(), scatter_data_frame(), scatter()

Other scatter: metaplot_key(), scatter_data_frame(), scatter_panel(), scatter()

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), corsplom.data.frame(), densplot.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), unpack.data.frame()

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)

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

See Also

metOptionoptions

Examples

example(metOption)

Test Metaplot Variants

Description

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

Usage

test_metaplot()

See Also

Other generic functions: axislabel(), categorical(), corsplom(), densplot(), metaplot(), pack(), scatter(), unpack()

Other metaplot: boxplot_data_frame(), categorical_data_frame(), corsplom_data_frame(), densplot_data_frame(), metaplot_key(), metaplot(), scatter_data_frame()

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

See Also

categorical_panel

Other categorical family: cax(), tilestats()

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

See Also

categorical_panel

Other categorical family: cax(), tiles()

Unpack Something

Description

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

Usage

unpack(x, ...)

Arguments

x

object
...

other arguments

See Also

Other pack: pack.data.frame(), pack(), unpack.data.frame()

Other generic functions: axislabel(), categorical(), corsplom(), densplot(), metaplot(), pack(), scatter(), test_metaplot()

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

See Also

Other pack: pack.data.frame(), pack(), unpack()

Other methods: axislabel.data.frame(), boxplot.data.frame(), categorical.data.frame(), corsplom.data.frame(), densplot.data.frame(), metaplot.data.frame(), pack.data.frame(), plot.metaplot_gtable(), print.metaplot_gtable(), scatter.data.frame()

Convert Wiki Symbol to Plotmath

Description

Converts wiki symbol to plotmath. Vectorized version of wikisym2plotmath_.

Usage

wikisym2plotmath(x, ...)

Arguments

x

character
...

ignored

Value

expression

See Also

Other formatters: diag_label(), wikisym2plotmath_()

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

See Also

Other formatters: diag_label(), wikisym2plotmath()

Examples

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