Maintainer:	Toby Dylan Hocking <toby.hocking@r-project.org>
Version:	2025.6.24
BugReports:	https://github.com/tdhock/directlabels/issues
License:	GPL-3
Title:	Direct Labels for Multicolor Plots
Description:	An extensible framework for automatically placing direct labels onto multicolor 'lattice' or 'ggplot2' plots. Label positions are described using Positioning Methods which can be re-used across several different plots. There are heuristics for examining "trellis" and "ggplot" objects and inferring an appropriate Positioning Method.
URL:	https://github.com/tdhock/directlabels
LazyData:	true
Suggests:	MASS, knitr, markdown, inlinedocs, RColorBrewer, ggplot2 (≥ 2.0), rlang, lattice, alphahull, nlme, lars, latticeExtra, dplyr, ggthemes, testthat
Imports:	grid (≥ 3.0.0), quadprog
Collate:	utility.function.R compare.R dotplot.R lineplot.R densityplot.R ggplot2.R positioning.functions.R doc.R lattice.R scatterplot.R contourplot.R
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-24 12:29:12 UTC; hoct2726
Author:	Toby Dylan Hocking [aut, cre]
Repository:	CRAN
Date/Publication:	2025-06-24 13:10:02 UTC

GeomDl

Description

ggproto object implementing direct labels.

Usage

"GeomDl"

ROC curve for LOPART algorithm and competitors

Description

For the LOPART paper we computed ROC curves for predictions of changepoint detection algorithms.

Usage

data("LOPART.ROC")

Format

A named list of two data frames: points has one row per model/algorithm, roc has one row per point on the ROC curve.

Source

Figure/paper describing LOPART algorithm and R package, https://github.com/tdhock/LOPART-paper/blob/master/figure-cv-BIC.R

Labeled Optimal Partitioning (LOPART) results

Description

Results of running LOPART algorithm (for changepoint detection in partially labeled data sequence) on a simulated data set of size 100.

Usage

data("LOPART100")

Format

Named list of data frames: signal has one row per data point, labels has one row per label, segments has one row per segment, cost has one row per feasible last changepoint for model up to t=100 data.

Source

Figure/paper describing LOPART algorithm and R package, https://github.com/tdhock/LOPART-paper/blob/master/figure-candidates.R

Cost of segmentation models

Description

20 segmentation models were fit to 2 simulated signals, and several different error measures were used to quantify the model fit.

Usage

data(SegCost)

Format

A data frame with 560 observations on the following 5 variables.

bases.per.probe

a factor with levels 374 7: the sampling density of the signal.

segments

numeric: the model complexity measured using number of segments.

cost

numeric: the cost value.

type

a factor with levels Signal Breakpoint Complete Incomplete Positive: how to judge model fit? Signal: log mean squared error between latent signal and estimated signal. Breakpoint: exact breakpoint error. Complete: annotation error with a complete set of annotations. Incomplete: annotation error with only half of those annotations. Positive: no negative annotations.

error

a factor with levels E FP FN I: what kind of error? FP = False Positive, FN = False Negative, I = Imprecision, E = Error (sum of the other terms).

Source

PhD thesis of Toby Dylan Hocking, chapter Optimal penalties for breakpoint detection using segmentation model selection.

ahull grid

Description

Label the closest point on the alpha hull of the data.

Usage

"ahull.grid"

ahull points

Description

Calculate the points on the ashape.

Usage

ahull.points(d, ..., 
    ahull = default.ahull(d))

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

angled boxes

Description

Draw a box with the label inside, at the point furthest away from the plot border and any other curve.

Usage

"angled.boxes"

angled endpoints

Description

Useful for labeling lines that all end at the top.

Usage

"angled.endpoints"

Apply a Positioning Method

Description

Run a Positioning Method list on a given data set. This function contains all the logic for parsing a Positioning Method and sequentially applying its elements to the input data to obtain the label positions.

Usage

apply.method(method, 
    d, columns.to.check = c("x", 
        "y", "groups", 
        "label"), ..., 
    debug = FALSE)

Arguments

Value

The final data frame returned after applying all of the items in the Positioning Method list, with x and y in units of cm.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

big boxes

Description

Calculate big boxes around the means of each cluster.

Usage

"big.boxes"

bottom pieces

Description

Positioning Method for the bottom of a group of points.

Usage

bottom.pieces(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

bottom points

Description

Positioning Method for the bottom of a group of points.

Usage

bottom.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

bottom polygons

Description

Draw a speech polygon to the bottom point.

Usage

"bottom.polygons"

bumpup

Description

Sequentially bump labels up, starting from the bottom, if they collide with the label underneath.

Usage

bumpup(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

calc borders

Description

Calculate bounding box based on newly calculated width and height.

Usage

calc.borders(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

calc boxes

Description

Calculate boxes around labels, for collision detection.

Usage

calc.boxes(d, debug = FALSE, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

check for columns

Description

Stop if a data.frame does not have some columns.

Usage

check.for.columns(d, 
    must.have)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

chull grid

Description

Label the closest point on the convex hull of the data.

Usage

"chull.grid"

chull points

Description

Calculate the points on the convex hull.

Usage

chull.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

default ahull

Description

Calculate the default alpha parameter for ashape based on the average size of label boxes.

Usage

default.ahull(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

default picker

Description

Look at options() for a user-defined default Positioning Method picker, and use that (or the hard-coded default picker), with the calling environment to figure out a good default.

Usage

default.picker(f)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

defaultpf ggplot

Description

Default method selection method for ggplot2 plots.

Usage

defaultpf.ggplot(geom, 
    p, L, colvar, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

defaultpf trellis

Description

If no Positioning Method specified, choose a default using this function. The idea is that this is called with all the variables in the environment of panel.superpose.dl, and this can be user-customizable by setting the directlabels.defaultpf.lattice option to a function like this.

Usage

defaultpf.trellis(lattice.fun.name, 
    groups, type, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Direct labels for color decoding

Description

Add direct labels to a plot, and hide the color legend. Modern plotting packages like lattice and ggplot2 show automatic legends based on the variable specified for color, but these legends can be confusing if there are too many colors. Direct labels are a useful and clear alternative to a confusing legend in many common plots.

Usage

direct.label(p, method = NULL, 
    debug = FALSE)

Arguments

Value

A plot with direct labels and no color legend.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

if(require(ggplot2)){
  ## Add direct labels to a ggplot2 scatterplot, making sure that each
  ## label is close to its point cloud, and doesn't overlap points or
  ## other labels.
  scatter <- qplot(jitter(hwy),jitter(cty),data=mpg,colour=class,
                   main="Fuel efficiency depends on car size")
  direct.label(scatter)
}

## direct labels for lineplots that do not overlap and do not go off
## the plot.
if(require(nlme) && require(lattice)){
  oldopt <- lattice.options(panel.error=NULL)
  ratplot <-
    xyplot(weight~Time|Diet,BodyWeight,groups=Rat,type='l',layout=c(3,1))
  ## Using the default Positioning Method (maxvar.qp), the labels are
  ## placed on the side which is most spread out, so in multipanel
  ## plots they sometimes end up on different sides.
  print(direct.label(ratplot))
  ## To put them on the same side, just manually specify the
  ## Positioning Method.
  print(direct.label(ratplot,"last.qp"))
  lattice.options(oldopt)
}

direct label ggplot

Description

Direct label a ggplot2 grouped plot.

Usage

## S3 method for class 'ggplot'
direct.label(p, 
    method = NULL, debug = FALSE)

Arguments

Value

The ggplot object with direct labels added.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

direct label trellis

Description

Add direct labels to a grouped lattice plot. This works by parsing the trellis object returned by the high level plot function, and returning it with a new panel function that will plot direct labels using the specified method.

Usage

## S3 method for class 'trellis'
direct.label(p, 
    method = NULL, debug = FALSE)

Arguments

Value

The lattice plot.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Combine output of several methods

Description

Apply several Positioning methods to the original data frame.

Usage

dl.combine(...)

Arguments

Value

A Positioning Method that returns the combined data frame after applying each specified Positioning Method.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

## Simple example: label the start and endpoints
if(require(nlme) && require(lattice)){
  ratplot <- xyplot(
    weight~Time|Diet,BodyWeight,groups=Rat,type='l',layout=c(3,1))
  both <- dl.combine("first.points","last.points")
  rat.both <- direct.label(ratplot,"both")
  print(rat.both)
  ## same as repeated call to direct.label:
  rat.repeated <-
    direct.label(direct.label(ratplot,"last.points"),"first.points")
  print(rat.repeated)
}

## same with ggplot2:
if(require(nlme) && require(ggplot2)){
  rp2 <- qplot(
    Time,weight,data=BodyWeight,geom="line",facets=.~Diet,colour=Rat)
  print(direct.label(direct.label(rp2,"last.points"),"first.points"))
  print(direct.label(rp2,"both"))
}

## more complex example: first here is a function for computing the
## lasso path.
mylars <- function
## Least angle regression algorithm for calculating lasso solutions.
(x,
 ## Matrix of predictor variables.
 y,
 ## Vector of responses.
 epsilon=1e-6
 ## If correlation < epsilon, we are done.
 ){
  xscale <- scale(x) # need to work with standardized variables
  b <- rep(0,ncol(x))# coef vector starts at 0
  names(b) <- colnames(x)
  ycor <- apply(xscale,2,function(xj)sum(xj*y))
  j <- which.max(ycor) # variables in active set, starts with most correlated
  alpha.total <- 0
  out <- data.frame()
  while(1){## lar loop
    xak <- xscale[,j] # current variables
    r <- y-xscale%*%b # current residual
    ## direction of parameter evolution
    delta <- solve(t(xak)%*%xak)%*%t(xak)%*%r
    ## Current correlations (actually dot product)
    intercept <- apply(xscale,2,function(xk)sum(r*xk))
    ## current rate of change of correlations
    z <- xak%*%delta
    slope <- apply(xscale,2,function(xk)-sum(z*xk))
    ## store current values of parameters and correlation
    out <- rbind(out,data.frame(variable=colnames(x),
                                coef=b,
                                corr=abs(intercept),
                                alpha=alpha.total,
                                arclength=sum(abs(b)),
                                coef.unscaled=b/attr(xscale,"scaled:scale")))
    if(sum(abs(intercept)) < epsilon)#corr==0 so we are done
      return(transform(out,s=arclength/max(arclength)))
    ## If there are more variables we can enter into the regression,
    ## then see which one will cross the highest correlation line
    ## first, and record the alpha value of where the lines cross.
    d <- data.frame(slope,intercept)
    d[d$intercept<0,] <- d[d$intercept<0,]*-1
    d0 <- data.frame(d[j[1],])# highest correlation line
    d2 <- data.frame(rbind(d,-d),variable=names(slope))#reflected lines
    ## Calculation of alpha for where lines cross for each variable
    d2$alpha <- (d0$intercept-d2$intercept)/(d2$slope-d0$slope)
    subd <- d2[(!d2$variable%in%colnames(x)[j])&d2$alpha>epsilon,]
    subd <- subd[which.min(subd$alpha),]
    nextvar <- subd$variable
    alpha <- if(nrow(subd))subd$alpha else 1
    ## If one of the coefficients would hit 0 at a smaller alpha
    ## value, take it out of the regression and continue.
    hit0 <- xor(b[j]>0,delta>0)&b[j]!=0
    alpha0 <- -b[j][hit0]/delta[hit0]
    takeout <- length(alpha0)&&min(alpha0) < alpha
    if(takeout){
      i <- which.min(alpha0)
      alpha <- alpha0[i]
    }
    b[j] <- b[j]+alpha*delta ## evolve parameters
    alpha.total <- alpha.total+alpha
    ## add or remove a variable from the active set
    j <- if(takeout)j[j!=which(names(i)==colnames(x))]
         else c(j,which(nextvar==colnames(x)))
  }
}

## Calculate lasso path, plot labels at two points: (1) where the
## variable enters the path, and (2) at the end of the path.
if(require(lars) && require(lattice)){
  data(diabetes,envir=environment())
  dres <- with(diabetes,mylars(x,y))
  P <- xyplot(coef~arclength,dres,groups=variable,type="l")
  mylasso <- dl.combine("lasso.labels", "last.qp")
  plot(direct.label(P,"mylasso"))
}

dl env

Description

This environment holds an integer id that will be incremented to get a unique id for each dlgrob.

Usage

"dl.env"

dl jitter

Description

Jitter the label positions.

Usage

dl.jitter(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Manually move a direct label

Description

Sometimes there is 1 label that is placed oddly by another Positioning Function. This function can be used to manually place that label in a good spot.

Usage

dl.move(group, x, y, 
    ...)

Arguments

Value

A Positioning Function that moves a label into a good spot.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

if(require(ggplot2) && require(lattice)){
  scatter <- xyplot(jitter(cty)~jitter(hwy),mpg,groups=class,aspect=1)
  dlcompare(list(scatter),
            list("extreme.grid",
                 `+dl.move`=list(extreme.grid,dl.move("suv",15,15))))
  p <- qplot(log10(gamma),rate,data=svmtrain,group=data,colour=data,
             geom="line",facets=replicate~nu)
  adjust.kif <- dl.move("KIF11",-0.9,hjust=1,vjust=1)
  dlcompare(list(p+xlim(-8,7)),
            list("last.points",
                 `+dl.move`=list(last.points,adjust.kif)))
}

dl summarize

Description

summarize which preserves important columns for direct labels.

Usage

dl.summarize(OLD, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Direct label data transform

Description

Make a function that transforms the data. This is for conveniently making a function that calls transform on the data frame, with the arguments provided. See examples.

Usage

dl.trans(...)

Arguments

Value

A Positioning Function.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

complicated <- list(dl.trans(x=x+10),
                    gapply.fun(d[-2,]),
                    rot=c(30,180))
if(require(lattice)){
  direct.label(dotplot(VADeaths,type="o"),complicated,TRUE)
}

Direct label comparison plot

Description

Compare several plots and/or label placement methods. This creates a custom grid graphics display based on lattice and/or ggplot2 output. Plots will be on the columns and positioning methods will be on the rows.

Usage

dlcompare(plots, pos.funs, 
    rects = TRUE, row.items = "plots", 
    debug = FALSE)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

## Compare two plots of the same data using lattice and ggplot2.
deaths.by.sex <- list(male=mdeaths, female=fdeaths)
deaths.list <- list()
for(sex in names(deaths.by.sex)){
  deaths.ts <- deaths.by.sex[[sex]]
  deaths.list[[sex]] <-
    data.frame(year=as.numeric(time(deaths.ts)),
               sex,
               deaths=as.integer(deaths.ts))
}
deaths <- do.call(rbind, deaths.list)
death.plot.list <- list()
if(require(lattice)){
  oldopt <- lattice.options(panel.error=NULL)
  death.plot.list[["lattice"]] <- xyplot(
    deaths~year,deaths,groups=sex,type="l")
}
if(require(ggplot2)){
  death.plot.list[["ggplot2"]] <- qplot(
    year,deaths,data=deaths,colour=sex,geom="line")
}
if(length(death.plot.list) && names(dev.cur())!="postscript"){##to avoid error on pkg check.
  ## Use some exotic labeling options with different rotation, font
  ## face, family, and alpha transparency.
  exotic <- list("last.points",
                 rot=c(0,180),
                 fontsize=c(10,20),
                 fontface=c("bold","italic"),
                 fontfamily=c("mono","serif"),
                 alpha=c(0.25,1))
  dlcompare(death.plot.list, list(exotic))
}
if(require(lattice))lattice.options(oldopt)
## Compare a legend with direct labels on the same plot.
if(require(ggplot2) && require(nlme)){
  ggrat <- qplot(Time,weight,data=BodyWeight,
                 colour=Rat,geom="line",facets=.~Diet)
  pfuns <- list("legend","direct labels"="last.qp")
  dlcompare(list(ggrat),pfuns,rects=FALSE,row.items="posfuns")
}

Make directlabels documentation

Description

Positioning Methods for direct labels are supposed to work with only certain plot types. Each Positioning Method is defined in R/file.R and plot examples are found in tests/doc/file/*.R so that we can automatically assemble a database of example plots from the code.

Usage

dldoc(pkgdir = "..")

Arguments

Value

Matrix of lists describing example plots and matching builtin Positioning Methods.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

dlgrob

Description

Make a grid grob that will draw direct labels.

Usage

dlgrob(data, method, 
    debug = FALSE, axes2native = identity, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

draw polygons

Description

Draw polygons around label positions.

Usage

draw.polygons(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

draw rects

Description

Positioning Function that draws boxes around label positions. Need to have previously called calc.boxes. Does not edit the data frame.

Usage

draw.rects(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

drawDetails dlgrob

Description

Process data points using the Positioning Method and draw the resulting direct labels. This is called for every panel with direct labels, every time the plot window is resized.

Usage

## S3 method for class 'dlgrob'
drawDetails(x, 
    recording)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

edges to outside

Description

Given a list of edges from the convex or alpha hull, and a list of cluster centers, calculate a point near to each cluster on the outside of the hull.

Usage

edges.to.outside(edges, 
    centers, debug = FALSE, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

empty grid

Description

Label placement method for scatterplots that ensures labels are placed in different places. A grid is drawn over the whole plot. Each cluster is considered in sequence and assigned to the point on this grid which is closest to the point given by the input data points. Makes use of attr(d,"orig.data").

Usage

empty.grid(d, debug = FALSE, 
    ...)

Arguments

Value

Data frame with columns groups x y, 1 line for each group, giving the positions on the grid closest to each cluster.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

enlarge box

Description

Make text bounding box larger by some amount.

Usage

enlarge.box(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Extract plot and definition for documentation

Description

Given an R code file, execute it, store the definition, and save the resulting plot in a variable.

Usage

extract.plot(f)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Extract Positioning Method for documentation

Description

Use inlinedocs to extract comments and definitions from code, then for each item found add the value and its name to the list.

Usage

extract.posfun(f)

Arguments

Value

List of lists, each of which describes one Positioning Method defined in f.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

extreme grid

Description

Label each point cloud near the extremities of the plot region.

Usage

"extreme.grid"

extreme points

Description

Label the points furthest from the middle for each group.

Usage

extreme.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

far from others borders

Description

Find the point on each curve which maximizes the distance to the plot border or to another curve.

Usage

far.from.others.borders(all.groups, 
    ..., debug = FALSE)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

filltemplate

Description

Fill in occurances of OBJ$item in the file template with the value in R of L$item.

Usage

filltemplate(L, template)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

first bumpup

Description

Label first points, bumping labels up if they collide.

Usage

"first.bumpup"

first points

Description

Positioning Method for the first of a group of points.

Usage

first.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

first polygons

Description

Draw a speech polygon to the first point.

Usage

"first.polygons"

first qp

Description

Label first points from QP solver that ensures labels do not collide.

Usage

"first.qp"

gapply

Description

apply a Positioning Method to every group. works like ddply from plyr package, but the grouping column is always called groups, and the Positioning Method is not necessarily a function (but can be).

Usage

gapply(d, method, ..., 
    groups = "groups")

Arguments

Value

data frame of results after applying FUN to each group in d.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Direct label groups independently

Description

Makes a function you can use to specify the location of each group independently.

Usage

gapply.fun(expr)

Arguments

Value

A Positioning Function.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

complicated <- list(dl.trans(x=x+10),
                    gapply.fun(d[-2,]),
                    rot=c(30,180))
if(require(lattice)){
  direct.label(dotplot(VADeaths,type="o"),complicated,TRUE)
}

geom dl

Description

Geom that will plot direct labels.

Usage

geom_dl(mapping = NULL, 
    data = NULL, ..., 
    method = stop("must specify method= argument"), 
    debug = FALSE, stat = "identity", 
    position = "identity", 
    inherit.aes = TRUE)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

if(require(ggplot2)){
  vad <- as.data.frame.table(VADeaths)
  names(vad) <- c("age","demographic","deaths")
  ## color + legend
  leg <- ggplot(vad,aes(deaths,age,colour=demographic))+
    geom_line(aes(group=demographic))+
    xlim(8,80)
  print(direct.label(leg,list("last.points",rot=30)))
  ## this is what direct.label is doing internally:
  labeled <- leg+
    geom_dl(aes(label=demographic), method=list("last.points",rot=30))+
    scale_colour_discrete(guide="none")
  print(labeled)
  ## no color, just direct labels!
  p <- ggplot(vad,aes(deaths,age))+
    geom_line(aes(group=demographic))+
    geom_dl(aes(label=demographic),method="top.qp")
  print(p)
  ## add color:
  p.color <- p+aes(colour=demographic)+
    scale_colour_discrete(guide="none")
  print(p.color)
  ## add linetype:
  p.linetype <- p+aes(linetype=demographic)+
    scale_linetype(guide="none")
  print(p.linetype)
  ## no color, just direct labels
  if(require(nlme)){
    bwbase <- ggplot(BodyWeight,aes(Time,weight,label=Rat))+
      geom_line(aes(group=Rat))+
      facet_grid(.~Diet)
    bw <- bwbase+geom_dl(method="last.qp")
    print(bw)
    ## add some more direct labels
    bw2 <- bw+geom_dl(method="first.qp")
    print(bw2)
    ## add color
    colored <- bw2+aes(colour=Rat)+
      scale_colour_discrete(guide="none")
    print(colored)
    ## or just use direct.label if you use color:
    print(direct.label(bwbase+aes(colour=Rat),dl.combine("first.qp","last.qp")))
  }
  ## iris data example
  giris <- ggplot(iris,aes(Petal.Length,Sepal.Length))+
    geom_point(aes(shape=Species))
  giris.labeled <- giris+
    geom_dl(aes(label=Species),method="smart.grid")+
    scale_shape_manual(values=c(setosa=1,virginica=6,versicolor=3),
                       guide="none")
  ##png("~/R/directlabels/www/scatter-bw-ggplot2.png",h=503,w=503)
  print(giris.labeled)
  ##dev.off()
}

get means

Description

Positioning Function for the mean of each cluster of points.

Usage

get.means(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

getLegendVariables

Description

get the aes which are variable in one legend.

Usage

getLegendVariables(mb)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

ignore na

Description

Remove rows for which either x or y is NA

Usage

ignore.na(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

in1box

Description

Calculate how many points fall in a box.

Usage

in1box(p, box)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

in1which

Description

Calculate which points fall in a box.

Usage

in1which(p, box)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

inside

Description

Calculate for each box how many points are inside.

Usage

inside(boxes, points)

Arguments

Value

Vector of point counts for each box.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Clustering of the iris data with the l1 clusterpath

Description

The l1 clustering algorithm from the clusterpath package was applied to the iris dataset and the breakpoints in the solution path are stored in this data frame.

Usage

data(iris.l1.cluster)

Format

A data frame with 9643 observations on the following 8 variables.

row

a numeric vector: row of the original iris data matrix

Species

a factor with levels setosa versicolor virginica: Species from corresponding row

alpha

a numeric vector: the value of the optimal solution.

lambda

a numeric vector: the regularization parameter (ie point in the path).

col

a factor with levels Sepal.Length Sepal.Width Petal.Length Petal.Width: column from the original iris data.

gamma

a factor with levels 0: parameter from clustering.

norm

a factor with levels 1 parameter from clustering.

solver

a factor with levels path algorithm used for clustering.

Source

clusterpath package

References

clusterpath article

Examples

data(iris.l1.cluster,package="directlabels")
iris.l1.cluster$y <- iris.l1.cluster$alpha
if(require(ggplot2)){
  p <- ggplot(iris.l1.cluster,aes(lambda,y,group=row,colour=Species))+
    geom_line(alpha=1/4)+
    facet_grid(col~.)
  p2 <- p+xlim(-0.0025,max(iris.l1.cluster$lambda))
  print(direct.label(p2,list(first.points,get.means)))
}

label endpoints

Description

Make a Positioning Method that labels a certain x value.

Usage

label.endpoints(FUN, 
    HJUST)

Arguments

Value

A Positioning Method like first.points or last.points.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

label pieces

Description

Make a Positioning Method that will, for every piece, select points and assign a vjust value.

Usage

label.pieces(FUN, VJUST)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

lasso labels

Description

Label points at the zero before the first nonzero y value.

Usage

"lasso.labels"

last bumpup

Description

Label last points, bumping labels up if they collide.

Usage

"last.bumpup"

last points

Description

Positioning Method for the last of a group of points.

Usage

last.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

last polygons

Description

Draw a speech polygon to the last point.

Usage

"last.polygons"

last qp

Description

Label last points from QP solver that ensures labels do not collide.

Usage

"last.qp"

lattice translators

Description

Some lattice plot functions do some magic in the background to translate the data you give them into the data points that are plotted onscreen. We have to replicate this magic in native coordinate space before applying the Positioning Method in cm space. These functions accomplish this translation.

Usage

"lattice.translators"

left points

Description

Positioning Method for the first of a group of points.

Usage

left.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

left polygons

Description

Draw a speech polygon to the first point.

Usage

"left.polygons"

legends2hide

Description

Extract guides to hide from a ggplot.

Usage

legends2hide(p)

Arguments

Value

NULL if no legends with colour or fill to hide.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

lines2

Description

Positioning Method for 2 groups of longitudinal data. One curve is on top of the other one (on average), so we label the top one at its maximal point, and the bottom one at its minimal point. Vertical justification is chosen to minimize collisions with the other line. This may not work so well for data with high variability, but then again lineplots may not be the best for these data either.

Usage

lines2(d, offset = 0.3, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

make tiebreaker

Description

Make a tiebreaker function that can be used with qp.labels.

Usage

make.tiebreaker(x.var, 
    tiebreak.var)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

maxvar points

Description

Do first or last, whichever has points most spread out.

Usage

maxvar.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

maxvar qp

Description

Label first or last points, whichever are more spread out, and use a QP solver to make sure the labels do not collide.

Usage

"maxvar.qp"

merge recurse

Description

Copied from reshape.

Usage

merge_recurse(dfs, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

midrange

Description

Point halfway between the min and max

Usage

midrange(x)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Clustering of some normal data in 2d with the l2 clusterpath

Description

The l2 clustering algorithm from the clusterpath package was applied to some randomly generated data in 2 dimensions, and the solutions found using the descent algorithm are stored in this data frame.

Usage

data(normal.l2.cluster)

Format

The format is: List of 2 $ pts :'data.frame': 320 obs. of 3 variables: ..$ class: Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ... ..$ x : num [1:320] -2.73 -3.63 -2.13 -1.27 -2.98 ... ..$ y : num [1:320] -3.89 -3.43 -3.42 -3.17 -2.75 ... $ path:Classes 'l2', 'clusterpath' and 'data.frame': 21760 obs. of 7 variables: ..$ x : num [1:21760] -2.73 -3.63 -2.13 -1.27 -2.98 ... ..$ y : num [1:21760] -3.89 -3.43 -3.42 -3.17 -2.75 ... ..$ lambda: num [1:21760] 0 0 0 0 0 0 0 0 0 0 ... ..$ row : Factor w/ 320 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ... ..$ gamma : Factor w/ 1 level "0.1": 1 1 1 1 1 1 1 1 1 1 ... ..$ norm : Factor w/ 1 level "2": 1 1 1 1 1 1 1 1 1 1 ... ..$ solver: Factor w/ 1 level "descent.nocheck": 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*, "data")= num [1:320, 1:2] -2.73 -3.63 -2.13 -1.27 -2.98 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "x" "y" ..- attr(*, "alphacolnames")= chr [1:2] "x" "y" ..- attr(*, "weight.pts")= num [1:320, 1:2] -2.73 -3.63 -2.13 -1.27 -2.98 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "x" "y"

Source

clusterpath package

References

clusterpath article

Examples

data(normal.l2.cluster)
if(require(ggplot2)){
  p <- ggplot(normal.l2.cluster$path,aes(x,y))+
    geom_path(aes(group=row),colour="grey")+
    geom_point(aes(size=lambda),colour="grey")+
    geom_point(aes(colour=class),data=normal.l2.cluster$pts)+
    coord_equal()
  print(direct.label(p))
}

Odd timings

Description

These timings data made strange output labels with the "right.polygons" method.

Usage

data("odd_timings")

Format

A data frame with 116 observations on the following 4 variables. Plot median.seconds versus N.col using a different line for each fun and a different panel for each captures.

N.col

a numeric vector

fun

a character vector

captures

a numeric vector

median.seconds

a numeric vector

Source

https://github.com/tdhock/nc-article

only unique vals

Description

Create a 1-row data.frame consisting of only the columns for which there is only 1 unique value.

Usage

only.unique.vals(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

outside ahull

Description

Calculate closest point on the alpha hull with size of the boxes, and put it outside that point.

Usage

outside.ahull(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

outside chull

Description

Calculate closest point on the convex hull and put it outside that point. Assume d is the center for each point cloud and then use orig.data to calculate hull.

Usage

outside.chull(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

panel superpose dl

Description

Call panel.superpose for the data points and then for the direct labels. This is a proper lattice panel function that behaves much like panel.superpose.

Usage

panel.superpose.dl(x, 
    y = NULL, subscripts, 
    groups, panel.groups, 
    method = NULL, .panel.superpose = lattice::panel.superpose, 
    type = "p", debug = FALSE, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

loci <- data.frame(
  ppp=c(rbeta(800,10,10),rbeta(100,0.15,1),rbeta(100,1,0.15)),
  type=factor(c(rep("NEU",800),rep("POS",100),rep("BAL",100))))
## 3 equivalent ways to make the same plot:
if(require(lattice)){
  print(direct.label( ## most user-friendly
    densityplot(~ppp,loci,groups=type,n=500)
  ))
  print(direct.label( ## exactly the same as above but with specific panel fns
    densityplot(
      ~ppp,loci,groups=type,n=500,
      panel=lattice::panel.superpose,
      panel.groups="panel.densityplot")
  ))
  ## using panel.superpose.dl as the panel function automatically adds
  ## direct labels
  print(densityplot(
    ~ppp,loci,groups=type,n=500,
    panel=panel.superpose.dl,panel.groups="panel.densityplot"
  ))
  ## Exploring custom panel and panel.groups functions
  if(require(nlme)){
    ## Say we want to use a simple linear model to explain rat body weight:
    fit <- lm(weight~Time+Diet+Rat,BodyWeight)
    bw <- BodyWeight
    bw$.fitted <- predict(fit,BodyWeight)
    ## lots of examples to come, all with these arguments:
    ratxy <- function(...){
      xyplot(weight~Time|Diet,bw,groups=Rat,type="l",layout=c(3,1),...)
    }
    ## No custom panel functions:
    ##regular <- ratxy(par.settings=simpleTheme(col=c("red","black")))
    regular <- ratxy()
    print(regular) ## normal lattice plot
    print(direct.label(regular)) ## with direct labels
    ## The direct label panel function panel.superpose.dl can be used to
    ## display direct labels as well:
    print(ratxy(panel=panel.superpose.dl,panel.groups="panel.xyplot"))
    print(ratxy(panel=function(...)
      panel.superpose.dl(panel.groups="panel.xyplot",...)))
    ## Not very user-friendly, since default label placement is
    ## impossible, but these should work:
    print(ratxy(
      panel=panel.superpose.dl,panel.groups=panel.xyplot,
      method=first.points))
    print(ratxy(panel=function(...)
      panel.superpose.dl(panel.groups=panel.xyplot,...),
      method=first.points))
    ## Custom panel.groups functions:
    ## This panel.groups function will display the model fits:
    panel.model <- function(x,subscripts,col.line,...){
      panel.xyplot(x=x,subscripts=subscripts,col.line=col.line,...)
      llines(x,bw[subscripts,".fitted"],col=col.line,lty=2)
    }
    pg <- ratxy(panel=lattice::panel.superpose,panel.groups=panel.model)
    print(pg)
    ## If you use panel.superpose.dl with a custom panel.groups function,
    ## you need to manually specify the Positioning Method, since the
    ## name of panel.groups is used to infer a default:
    print(direct.label(pg,method="first.qp"))
    print(ratxy(
      panel=panel.superpose.dl,panel.groups="panel.model",
      method="first.qp"))
    ## Custom panel function that draws a box around values:
    panel.line1 <- function(ps=lattice::panel.superpose){
      function(y,...){
        panel.abline(h=range(y))
        ps(y=y,...)
      }
    }
    custom <- ratxy(panel=panel.line1())
    print(custom)
    print(direct.label(custom))
    ## Alternate method, producing the same results, but using
    ## panel.superpose.dl in the panel function. This is useful for direct
    ## label plots where you use several datasets.
    print(ratxy(panel=panel.line1(panel.superpose.dl),panel.groups="panel.xyplot"))
    ## Lattice plot with custom panel and panel.groups functions:
    both <- ratxy(panel=panel.line1(),panel.groups="panel.model")
    print(both)
    print(direct.label(both,method="first.qp"))
    print(ratxy(panel=panel.line1(panel.superpose.dl),
                panel.groups=panel.model,method="first.qp"))
  }
}

pkgFun

Description

https://github.com/tdhock/directlabels/issues/2 CRAN won't complain about this version of :::

Usage

pkgFun(fun, pkg = "ggplot2")

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

polygon method

Description

Make a Positioning Method that places non-overlapping speech polygons at the first or last points.

Usage

polygon.method(top.bottom.left.right, 
    offset.cm = 0.1, 
    padding.cm = 0.05, 
    custom.colors = NULL)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Built-in Positioning Methods for direct label placement

Description

When adding direct labels to a grouped plot, label placement can be specified using a Positioning Method (or a list of them), of the form function(d,...), where d is a data frame of the points to plot, with columns x y groups. The job of the Positioning Method(s) is to return the position of each direct label you want to plot as a data frame, with 1 row for each label. Thus normally a Positioning Method will return 1 row for each group. Several built-in Positioning Methods are discussed below, but you can also create your own, either from scratch or by using dl.indep and dl.trans.

Author(s)

Toby Dylan Hocking <toby.hocking@inria.fr>

Examples

## Not run: 
### contourplot Positioning Methods
for(p in list({
## Example from help(contourplot)
require(stats)
require(lattice)
attach(environmental)
ozo.m <- loess((ozone^(1/3)) ~ wind * temperature * radiation,
               parametric = c("radiation", "wind"), span = 1, degree = 2)
w.marginal <- seq(min(wind), max(wind), length.out = 50)
t.marginal <- seq(min(temperature), max(temperature), length.out = 50)
r.marginal <- seq(min(radiation), max(radiation), length.out = 4)
wtr.marginal <- list(wind = w.marginal, temperature = t.marginal,
                     radiation = r.marginal)
grid <- expand.grid(wtr.marginal)
grid[, "fit"] <- c(predict(ozo.m, grid))
detach(environmental)
library(ggplot2)
p <- ggplot(grid,aes(wind,temperature,z=fit))+
  stat_contour(aes(colour=..level..))+
  facet_wrap(~radiation)

},
{
## example from help(stat_contour)
library(reshape2)
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
library(ggplot2)
p <- ggplot(volcano3d, aes(x, y, z = z))+
  stat_contour(aes(colour = ..level..))
})){
  print(direct.label(p,"bottom.pieces"))
  print(direct.label(p,"top.pieces"))
}

### densityplot Positioning Methods
for(p in list({
data(Chem97,package="mlmRev")
library(lattice)
p <- densityplot(~gcsescore|gender,Chem97,
            groups=factor(score),layout=c(1,2),
            n=500,plot.points=FALSE)
},
{
library(reshape2)
iris2 <- melt(iris,id="Species")
library(lattice)
p <- densityplot(~value|variable,iris2,groups=Species,scales="free")
},
{
loci <- data.frame(ppp=c(rbeta(800,10,10),rbeta(100,0.15,1),rbeta(100,1,0.15)),
                   type=factor(c(rep("NEU",800),rep("POS",100),rep("BAL",100))))
library(ggplot2)
p <- qplot(ppp,data=loci,colour=type,geom="density")
})){
  print(direct.label(p,"top.bumptwice"))
  print(direct.label(p,"top.bumpup"))
  print(direct.label(p,"top.points"))
}

### dotplot Positioning Methods
for(p in list({
library(lattice)
p <- dotplot(VADeaths,xlim=c(8,85),type="o")
},
{
vad <- as.data.frame.table(VADeaths)
names(vad) <- c("age","demographic","deaths")
library(ggplot2)
p <- qplot(deaths,age,data=vad,group=demographic,geom="line",colour=demographic)+
  xlim(8,80)
})){
  print(direct.label(p,"angled.endpoints"))
  print(direct.label(p,"top.qp"))
}

### lineplot Positioning Methods
for(p in list({
data(BodyWeight,package="nlme")
library(lattice)
p <- xyplot(weight~Time|Diet,BodyWeight,groups=Rat,type='l',
       layout=c(3,1),xlim=c(-10,75))
},
{
data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore|gender,Chem97,groups=factor(score),
       type=c('l','g'),f.value=ppoints(100))
},
{
data(Chem97,package="mlmRev")
library(lattice)
p <- qqmath(~gcsescore,Chem97,groups=gender,
       type=c("l","g"),f.value=ppoints(100))
},
{
data(prostate,package="ElemStatLearn")
pros <- subset(prostate,select=-train,train==TRUE)
ycol <- which(names(pros)=="lpsa")
x <- as.matrix(pros[-ycol])
y <- pros[[ycol]]
library(lars)
fit <- lars(x,y,type="lasso")
beta <- scale(coef(fit),FALSE,1/fit$normx)
arclength <- rowSums(abs(beta))
library(reshape2)
path <- data.frame(melt(beta),arclength)
names(path)[1:3] <- c("step","variable","standardized.coef")
library(ggplot2)
p <- ggplot(path,aes(arclength,standardized.coef,colour=variable))+
  geom_line(aes(group=variable))+
  ggtitle("LASSO path for prostate cancer data calculated using the LARS")+
  xlim(0,20)
},
{
data(projectionSeconds, package="directlabels")
p <- ggplot(projectionSeconds, aes(vector.length/1e6))+
  geom_ribbon(aes(ymin=min, ymax=max,
                  fill=method, group=method), alpha=1/2)+
  geom_line(aes(y=mean, group=method, colour=method))+
  ggtitle("Projection Time against Vector Length (Sparsity = 10
  guides(fill="none")+
  ylab("Runtime (s)")
},
{
## complicated ridge regression lineplot ex. fig 3.8 from Elements of
## Statistical Learning, Hastie et al.
myridge <- function(f,data,lambda=c(exp(-seq(-15,15,l=200)),0)){
  require(MASS)
  require(reshape2)
  fit <- lm.ridge(f,data,lambda=lambda)
  X <- data[-which(names(data)==as.character(f[[2]]))]
  Xs <- svd(scale(X)) ## my d's should come from the scaled matrix
  dsq <- Xs$d^2
  ## make the x axis degrees of freedom
  df <- sapply(lambda,function(l)sum(dsq/(dsq+l)))
  D <- data.frame(t(fit$coef),lambda,df) # scaled coefs
  molt <- melt(D,id=c("lambda","df"))
  ## add in the points for df=0
  limpts <- transform(subset(molt,lambda==0),lambda=Inf,df=0,value=0)
  rbind(limpts,molt)
}
data(prostate,package="ElemStatLearn")
pros <- subset(prostate,train==TRUE,select=-train)
m <- myridge(lpsa~.,pros)
library(lattice)
p <- xyplot(value~df,m,groups=variable,type="o",pch="+",
       panel=function(...){
         panel.xyplot(...)
         panel.abline(h=0)
         panel.abline(v=5,col="grey")
       },
       xlim=c(-1,9),
       main="Ridge regression shrinks least squares coefficients",
       ylab="scaled coefficients",
       sub="grey line shows coefficients chosen by cross-validation",
       xlab=expression(df(lambda)))
},
{
library(ggplot2)
tx <- time(mdeaths)
Time <- ISOdate(floor(tx),round(tx
uk.lung <- rbind(data.frame(Time,sex="male",deaths=as.integer(mdeaths)),
                 data.frame(Time,sex="female",deaths=as.integer(fdeaths)))
p <- qplot(Time,deaths,data=uk.lung,colour=sex,geom="line")+
  xlim(ISOdate(1973,9,1),ISOdate(1980,4,1))
})){
  print(direct.label(p,"angled.boxes"))
  print(direct.label(p,"first.bumpup"))
  print(direct.label(p,"first.points"))
  print(direct.label(p,"first.polygons"))
  print(direct.label(p,"first.qp"))
  print(direct.label(p,"lasso.labels"))
  print(direct.label(p,"last.bumpup"))
  print(direct.label(p,"last.points"))
  print(direct.label(p,"last.polygons"))
  print(direct.label(p,"last.qp"))
  print(direct.label(p,"lines2"))
  print(direct.label(p,"maxvar.points"))
  print(direct.label(p,"maxvar.qp"))
}

### scatterplot Positioning Methods
for(p in list({
data(mpg,package="ggplot2")
m <- lm(cty~displ,data=mpg)
mpgf <- fortify(m,mpg)
library(lattice)
library(latticeExtra)
p <- xyplot(cty~hwy|manufacturer,mpgf,groups=class,aspect="iso",
       main="City and highway fuel efficiency by car class and manufacturer")+
  layer_(panel.abline(0,1,col="grey90"))
},
{
data(mpg,package="ggplot2")
m <- lm(cty~displ,data=mpg)
mpgf <- fortify(m,mpg)
library(lattice)
p <- xyplot(jitter(.resid)~jitter(.fitted),mpgf,groups=factor(cyl))
},
{
library(lattice)
p <- xyplot(jitter(Sepal.Length)~jitter(Petal.Length),iris,groups=Species)
},
{
data(mpg,package="ggplot2")
library(lattice)
p <- xyplot(jitter(cty)~jitter(hwy),mpg,groups=class,
       main="Fuel efficiency depends on car size")
},
{
library(ggplot2)
data(mpg,package="ggplot2")
p <- qplot(jitter(hwy),jitter(cty),data=mpg,colour=class,
      main="Fuel efficiency depends on car size")
},
{
data(normal.l2.cluster,package="directlabels")
library(ggplot2)
p <- ggplot(normal.l2.cluster$path,aes(x,y))+
  geom_path(aes(group=row),colour="grey")+
  geom_point(aes(size=lambda),colour="grey")+
  geom_point(aes(colour=class),data=normal.l2.cluster$pts,pch=21,fill="white")+
  coord_equal()
})){
  print(direct.label(p,"ahull.grid"))
  print(direct.label(p,"chull.grid"))
  print(direct.label(p,"extreme.grid"))
  print(direct.label(p,"smart.grid"))
}


## End(Not run)

project onto segments

Description

Given a point and a set of line segments representing a convex or alpha hull, calculate the closest point on the segments.

Usage

project.onto.segments(m, 
    h, debug = FALSE, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Timings of projection algorithms

Description

Timings of seconds for 3 projection algorithms.

Usage

data(projectionSeconds)

Format

A data frame with 603 observations on the following 6 variables.

vector.length

a numeric vector

method

a factor with levels Heap Random Sort

mean

a numeric vector

sd

a numeric vector

min

a numeric vector

max

a numeric vector

Source

Mark Schmidt's prettyPlot code for MATLAB http://www.di.ens.fr/~mschmidt/Software/prettyPlot.html

Make a Positioning Method for non-overlapping lineplot labels

Description

Use a QP solver to find the best places to put the points on a line, subject to the constraint that they should not overlap.

Usage

qp.labels(target.var, 
    lower.var, upper.var, 
    order.labels = function(d) order(d[, 
        target.var]), 
    limits = NULL)

Arguments

Value

Positioning Method that adjusts target.var so there is no overlap of the label bounding boxes, as specified by upper.var and lower.var.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

SegCost$error <- factor(SegCost$error,c("FP","FN","E","I"))
if(require(ggplot2)){
  fp.fn.colors <- c(FP="skyblue",FN="#E41A1C",I="black",E="black")
  fp.fn.sizes <- c(FP=2.5,FN=2.5,I=1,E=1)
  fp.fn.linetypes <- c(FP="solid",FN="solid",I="dashed",E="solid")
  err.df <- subset(SegCost,type!="Signal")

  kplot <- ggplot(err.df,aes(segments,cost))+
    geom_line(aes(colour=error,size=error,linetype=error))+
    facet_grid(type~bases.per.probe)+
    scale_linetype_manual(values=fp.fn.linetypes)+
    scale_colour_manual(values=fp.fn.colors)+
    scale_size_manual(values=fp.fn.sizes)+
    scale_x_continuous(limits=c(0,20),breaks=c(1,7,20),minor_breaks=NULL)+
    theme_bw()+theme(panel.margin=grid::unit(0,"lines"))

  ## The usual ggplot without direct labels.
  print(kplot)

  ## Get rid of legend for direct labels.
  no.leg <- kplot+guides(colour="none",linetype="none",size="none")

  ## Default direct labels.
  direct.label(no.leg)

  ## Explore several options for tiebreaking and limits. First let's
  ## make a qp.labels Positioning Method that does not tiebreak.
  no.tiebreak <- list("first.points",
                      "calc.boxes",
                      qp.labels("y","bottom","top"))
  direct.label(no.leg, no.tiebreak)

  ## Look at the weird labels in the upper left panel. The E curve is
  ## above the FN curve, but the labels are the opposite! This is
  ## because they have the same y value on the first points, which are
  ## the targets for qp.labels. We need to tiebreak.
  qp.break <- qp.labels("y","bottom","top",make.tiebreaker("x","y"))
  tiebreak <- list("first.points",
                   "calc.boxes",
                   "qp.break")
  direct.label(no.leg, tiebreak)

  ## Enlarge the text size and spacing.
  tiebreak.big <- list("first.points",
                       cex=2,
                       "calc.boxes",
                       dl.trans(h=1.25*h),
                       "calc.borders",
                       "qp.break")
  direct.label(no.leg, tiebreak.big)

  ## Even on my big monitor, the FP runs off the bottom of the screen
  ## in the top panels. To avoid that you can specify a limits
  ## function.

  ## Below, the ylimits function uses the limits of each panel, so
  ## labels appear inside the plot region. Also, if you resize your
  ## window so that it is small, you can see that the text size of the
  ## labels is decreased until they all fit in the plotting region.
  qp.limited <-  qp.labels("y","bottom","top",make.tiebreaker("x","y"),ylimits)
  tiebreak.lim <- list("first.points",
                       cex=2,
                       "calc.boxes",
                       dl.trans(h=1.25*h),
                       "calc.borders",
                       "qp.limited")
  direct.label(no.leg, tiebreak.lim)
}

reduce cex

Description

If edges of the text are going out of the plotting region, then decrease cex until it fits. We call calc.boxes inside, so you should set cex before using this.

Usage

reduce.cex(sides)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

Examples

if(require(lars) && require(ggplot2)){
  data(diabetes,package="lars",envir=environment())
  X <- diabetes$x
  colnames(X) <- paste(colnames(X), colnames(X))
  fit <- lars(X,diabetes$y,type="lasso")
  beta <- scale(coef(fit),FALSE,1/fit$normx)
  arclength <- rowSums(abs(beta))
  path.list <- list()
  for(variable in colnames(beta)){
    standardized.coef <- beta[, variable]
    path.list[[variable]] <-
      data.frame(step=seq_along(standardized.coef),
                 arclength,
                 variable,
                 standardized.coef)
  }
  path <- do.call(rbind, path.list)
  p <- ggplot(path,aes(arclength,standardized.coef,colour=variable))+
    geom_line(aes(group=variable))
  ## the legend isn't very helpful.
  print(p)
  ## add direct labels at the end of the lines.
  direct.label(p, "last.points")
  ## on my screen, some of the labels go off the end, so we can use
  ## this Positioning Method to reduce the text size until the labels
  ## are on the plot.
  direct.label(p, list("last.points",reduce.cex("lr")))
  ## the default direct labels for lineplots are similar.
  direct.label(p)
}

reduce cex lr

Description

If edges of the text are going left or right out of the plotting region, then decrease cex until it fits.

Usage

reduce.cex.lr(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

reduce cex tb

Description

If edges of the text are going over the top or bottom of the plotting region, then decrease cex until it fits.

Usage

reduce.cex.tb(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

rhtmlescape

Description

for standards compliance we should escape <>&

Usage

rhtmlescape(code)

Arguments

Value

Standards compliant HTML to display.

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

right points

Description

Positioning Method for the last of a group of points.

Usage

right.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

right polygons

Description

Draw a speech polygon to the last point.

Usage

"right.polygons"

smart grid

Description

Search the plot region for a label position near the center of each point cloud.

Usage

"smart.grid"

static labels

Description

to hard-code label positions...

Usage

static.labels(x, y, groups, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

False positive rates from several 1-SVM models

Description

Support Vector Machine density estimation (1-SVM) was applied to a set of negative control samples, and then used to test on a positive control.

Usage

data(svmtrain)

Format

A data frame with 378 observations on the following 5 variables.

replicate

a factor with levels 1 2 3, the experimental replicate. We fit 1-SVM models to each replicate separately.

rate

a numeric vector, the percent of observations that were outside the trained model.

data

a factor with levels KIF11 test train, which set of observations did we measure. test and train are each 50% random splits of the negative controls in the experiment, and KIF11 is the positive control in the experiment.

gamma

a numeric vector, the tuning parameter of the radial basis function kernel.

nu

a numeric vector, the regularization parameter of the 1-SVM.

top bumptwice

Description

Label the tops, bump labels up to avoid other labels, then to the side to avoid collisions with points.

Usage

top.bumptwice(d, debug = FALSE, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

top bumpup

Description

Label the tops, but bump labels up to avoid collisions.

Usage

"top.bumpup"

top pieces

Description

Positioning Method for the top of a group of points.

Usage

top.pieces(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

top points

Description

Positioning Method for the top of a group of points.

Usage

top.points(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

top polygons

Description

Draw a speech polygon to the top point.

Usage

"top.polygons"

top qp

Description

Label points at the top, making sure they don't collide.

Usage

"top.qp"

uselegend ggplot

Description

Show the ggplot2 legend, for comparison.

Usage

uselegend.ggplot(p, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

uselegend trellis

Description

Add a legend to a trellis plot, for comparison.

Usage

uselegend.trellis(p, 
    ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

vertical qp

Description

Make a Positioning Function from a set of points on a vertical line that will be spaced out using qp.labels.

Usage

vertical.qp(M)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

visualcenter

Description

Point in the middle of the min and max for each group.

Usage

visualcenter(d, ...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

xlimits

Description

Return the positions of the plot horizontal limits in cm, for use as the limit argument to qp.labels.

Usage

xlimits(...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)

ylimits

Description

Return the positions of the plot vertical limits in cm, for use as the limit argument to qp.labels.

Usage

ylimits(...)

Arguments

Author(s)

Toby Dylan Hocking <toby.hocking@r-project.org> [aut, cre] (ORCID: <https://orcid.org/0000-0002-3146-0865>)