Package 'ggtern'

Description

OLD FUNCTIONS new_panel','train_layout','train_position','train_ranges','map_position','map_layout','reset_scales','facet_render', xlabel','ylabel' expand_default', ## REMOVED

Usage

.getFunctions()

Description

Modified Aesthetic Mappings

Usage

aes(x, y, z, ...)

Arguments

Details

An extension to the base aes functin from ggplot2, this is modified to handle a default z mapping for application in ternary phase diagrams. Does not alter the standard behaviour.

See Also

Parent aes function.

Description

This function adds geoms to a plot. Unlike typical a geom function, the properties of the geoms are not mapped from variables of a data frame, but are instead passed in as vectors. This is useful for adding small annotations (such as text labels) or if you have your data in vectors, and for some reason don't want to put them in a data frame.

Usage

annotate(
  geom,
  x = NULL,
  y = NULL,
  z = NULL,
  xmin = NULL,
  xmax = NULL,
  ymin = NULL,
  ymax = NULL,
  zmin = NULL,
  zmax = NULL,
  xend = NULL,
  yend = NULL,
  zend = NULL,
  ...,
  na.rm = FALSE
)

Arguments

Details

Note that all position aesthetics are scaled (i.e. they will expand the limits of the plot so they are visible), but all other aesthetics are set. This means that layers created with this function will never affect the legend.

Author(s)

Nicholas Hamilton

See Also

annotate

Examples

ggtern() + 
annotate(geom  = 'text',
              x     = c(0.5,1/3,0.0),
              y     = c(0.5,1/3,0.0),
              z     = c(0.0,1/3,1.0),
              angle = c(0,30,60),
              vjust = c(1.5,0.5,-0.5),
              label = paste("Point",c("A","B","C")),
              color = c("green","red",'blue')) +
  theme_dark() + 
  theme_nomask()

Description

This is a special version of geom_raster optimised for static annotations that are the same in every panel. These annotations will not affect scales (i.e. the x and y axes will not grow to cover the range of the raster, and the raster must already have its own colours).

Usage

annotation_raster_tern(
  raster,
  xmin = 0,
  xmax = 1,
  ymin = 0,
  ymax = 1,
  interpolate = FALSE
)

Arguments

Details

Most useful for adding bitmap images.

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
data(FeldsparRaster)
ggtern(Feldspar,aes(Ab,An,Or)) + 
theme_rgbw() + 
annotation_raster_tern(FeldsparRaster,xmin=0,xmax=1,ymin=0,ymax=1) +
geom_mask() + 
geom_point(size=5,aes(shape=Feldspar,fill=Feldspar),color='black') +
scale_shape_manual(values=c(21,24)) +
labs(title="Demonstration of Raster Annotation")

Description

ggtern is a specialist extension to ggplot2 for rendering ternary diagrams, as such, many stats and geoms which come packaged with ggplot2 are either not relevant or will not work, as such, ggtern regulates during the plot construction process, which geoms and stats are able to be applied when using the coord_tern coordinate system. Attempting to apply non-approved geometries or stats (ie geometries / stats not in the below list), will result in the respective layers being stripped from the final plot.

Approved Geometries

The following geoms have been approved so far, including a combination of existing geoms and newly created geoms for the ggtern package APPROVED geoms in ggternare as follows:

• geom_point

• geom_path

• geom_line

• geom_label

• geom_text

• geom_jitter

• geom_Tline

• geom_Rline

• geom_Lline

• geom_polygon

• geom_segment

• geom_count

• geom_errorbarT

• geom_errorbarL

• geom_errorbarR

• geom_density_tern

• geom_confidence

• geom_curve

• geom_mask

• geom_smooth_tern

• geom_blank

• geom_jitter

• geom_Tisoprop

• geom_Lisoprop

• geom_Risoprop

• geom_interpolate_tern

• geom_crosshair_tern

• geom_Tmark

• geom_Lmark

• geom_Rmark

• geom_point_swap

• geom_rect

• geom_polygon_closed

• geom_hex_tern

• geom_tri_tern

• geom_mean_ellipse

• geom_text_viewport

• geom_label_viewport

Approved Stats

The following stats have been approved so far, including a combination of existing stats and newly created stats for the ggtern package APPROVED stats in ggternare as follows:

• stat_identity

• stat_confidence

• stat_density_tern

• stat_smooth_tern

• stat_sum

• stat_unique

• stat_interpolate_tern

• stat_mean_ellipse

• stat_hex_tern

• stat_tri_tern

Approved Positions

The following positions have been approved so far, including a combination of existing positions and newly created positions for the ggtern package APPROVED positions in ggternare as follows:

• position_identity

• position_nudge_tern

• position_jitter_tern

The balance of the available stats, geometries or positions within ggplot2 are either invalid or remain work in progress with regards to the ggtern package.

Author(s)

Nicholas Hamilton

Description

A very slight modification to the original function, removing the explicit direction to use the ggplotGrob function from the ggplot2 namespace

Usage

arrangeGrob(
  ...,
  grobs = list(...),
  layout_matrix,
  vp = NULL,
  name = "arrange",
  as.table = TRUE,
  respect = FALSE,
  clip = "off",
  nrow = NULL,
  ncol = NULL,
  widths = NULL,
  heights = NULL,
  top = NULL,
  bottom = NULL,
  left = NULL,
  right = NULL,
  padding = unit(0.5, "line")
)

grid.arrange(..., newpage = TRUE)

Arguments

Author(s)

Nicholas Hamilton

Description

Calculates the Breaks for Major or Minor Gridlines based on the input limits.

Usage

breaks_tern(limits = c(0, 1), isMajor = TRUE, n = 5)

Arguments

Examples

breaks_tern()
 breaks_tern(limits = c(0,.5),FALSE,10)

Description

coord_tern is a function which creates a transformation mechanism between the ternary system, and, the cartesian system. It inherits from the fixed coordinate system, employing fixed ratio between x and y axes once transformed.

Usage

coord_tern(Tlim = NULL, Llim = NULL, Rlim = NULL, expand = TRUE)

Arguments

Value

coord_tern returns a CoordTern ggproto

Aesthetics (Required in Each Layer)

coord_ternunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• z

Abovementioned limitations include the types of geometries which can be used (ie approved geometries), or modifications to required aesthetic mappings. One such essential patch is, for approved geometries previously requiring x and y coordinates, now require an additional z coordinate, and, geom_segment goes one step further in that it requires both an additional z and zend coordinate mappings.

In essence, the required aesthetics are the product between what is required of each 'layer' and what is required of the 'coordinate system'.

Author(s)

Nicholas Hamilton

Description

Data relating to Elkins and Groves Feldspar Data, the following datasets include the experimental data and sample raster data from one of the images in the referenced paper. Feldspar - Experimental Data FeldsparRaster - Raster Data for Fig. 6.

Usage

#Experimental Data
data(Feldspar)

#Raster data
data(FeldsparRaster)

Format

Feldsdpar - One (1) row per Feldspar composition, FeldsdparRaster - Raster Matrix

Author(s)

Nicholas Hamilton

References

Elkins, L. T. & Grove, T. L. Ternary Feldspar Experiments and Thermodynamic Models American Mineralogist, Mineral Soc America, 1990, 75, 544-559

See Also

Data

Examples

#Summarize the Feldspar Data
data(Feldspar)
summary(Feldspar)

#Plot Felspar Data
ggtern(data=Feldspar,aes(x=An,y=Ab,z=Or)) + 
    geom_point()

# Plot Feldspar data and Underlying Raster Image
data(FeldsparRaster)
ggtern(Feldspar,aes(Ab,An,Or)) + 
    theme_rgbw() + 
    annotation_raster_tern(FeldsparRaster,xmin=0,xmax=1,ymin=0,ymax=1) +
    geom_point(size=5,aes(shape=Feldspar,fill=Feldspar),color='black') +
    scale_shape_manual(values=c(21,24)) +
    labs(title = "Demonstration of Raster Annotation")

Description

ABSTRACT: Chemical weathering influences the detrital composition of sand-size sediment derived from source areas subject to different amounts of precipitation in the Coweeta Basin, North Carolina. Of the grain types studied, rock fragments are most sensitive to chemical degradation; therefore, their abundance is the best indicator of cumulative weathering effects. Destruction of sand-size rock fragments by chemical weathering is a function of both the intensity and duration of chemical weathering experienced by grains in regoliths of the source area. In the Coweeta Basin, the intensity of chemical weathering is directly related to the climate via effective precipitation in individual subbasins, whereas the duration of chemical weathering is inversely related to the relief ratio of the watershe . Therefore, soils in watersheds with low-relief ratios and high discharge per unit area experience the most extensive chemical weathering, and sediments derived from these watersheds contain the lowest percentage of rock fragments. The effects of climate alone cannot explain the systematic variation of rock fragment abundance in sediments from the Coweeta Basin. The compositional imprint left on these sediments by chemical weathering is a function of both climate and topographic slope in the sediment source area.

Usage

data(Fragments)

Format

1row per point, Each point contains data on the following:

1. Watershed: By id: 2, 10, 34, 41, 13, 27, 32 or 37,

2. Position: By name: Tallulah or Coweeta,

3. CCWI: The Cumulative Chemical Weathering Index: numeric

4. Precipitation: Average Annual Precipitation, numeric

5. Discharge: Annual Average Discharge, numeric

6. Relief: Relief Ratio, numeric

7. GrainSize: Coarse Medium or Fine,

8. Sample: Field Sampling, A, B or C

9. Points: The number of points measured for each sample

10. Qm: Multicrystalline Quarts Amount, percentage

11. Qp: Polycrystalline Quarts Amount, percentage

12. Rf: Rock Fragments Amount, percentage

13. M: Mica Amount, percentage

Author(s)

Jeremy Hummon Grantham and Michael Anthony Velbel

References

Grantham, Jeremy Hummon, and Michael Anthony Velbel. "The influence of climate and topography on rock-fragment abundance in modern fluvial sands of the southern Blue Ridge Mountains, North Carolina." Journal of Sedimentary Research 58.2 (1988).

Examples

data(Fragments)
ggtern(Fragments,aes(Qm+Qp,Rf,M,colour=Sample)) +
  geom_density_tern(h=2,aes(fill=..level..),
  expand=0.75,alpha=0.5,bins=5) + 
  geom_point(aes(shape=Position,size=Relief)) + 
  theme_bw(base_size=8) + 
  theme_showarrows() + 
  custom_percent('%') + 
  labs(title = "Grantham and Valbel Rock Fragment Data",
       x = "Q_{m+p}", xarrow = "Quartz (Multi + Poly)",
       y = "R_f",     yarrow = "Rock Fragments",
       z = "M",       zarrow = "Mica") + 
  theme_latex() + 
  facet_wrap(~Sample,nrow=2)

Description

AFM compositions of 23 aphyric Skye lavas.

Format

1 row per point, 23 points in total, Each point contains data on the following:

1. No: ID, S1 to S23

2. A: Percent Na2O+K2O ,

3. F: Percent Fe2O3

4. F: Percent MgO

Author(s)

J. Aitchison

References

Aitchison, J. The statistical analysis of compositional data Chapman and Hall London, 1986, pp360

Examples

# Emulate & Enhance plot produced in Fig. 3, pg 7 of:
# Martin-Fernandez, J.; Chacon-Duran, J. & Mateu-Figueras, G.
# Updating on the kernel density estimation for compositional data 
# Proceedings of 17th Conference IASC-ERSS, Compstat, Roma,(Italy), 2006, 713-720

data(SkyeLava)
breaks  = c(.01,.05,.10,.25,.5,.75,.9,.95,.99)
ggtern(SkyeLava,aes(F,A,M)) + 
theme_bw() + 
theme_showarrows() + 
theme_latex() + 
theme(tern.panel.grid.minor = element_blank(),
      tern.panel.grid.major = element_line(linetype='dotted',color='darkgray'),
      tern.axis.text        = element_text(size=8)) + 
      geom_density_tern() + 
      geom_point() +
      limit_tern(breaks = breaks,
                 labels = sprintf("%.2f",breaks)) +
labs(title    = "Aphyric Skye Lavas",
     subtitle = "AFM Compositions of 23 samples",
     Tarrow = "A = Na_2O + K_2O",
     Larrow = "F = Fe_20_3",
     Rarrow = "M = MgO")

Description

This dataset was issued by the United States Department of Agriculture (USDA) in the form of a ternary diagram, this original ternary diagram has been converted to numerical data and included here.

Usage

data(USDA)

Format

1row per point, many points per classification representing the extremes of the area.

Author(s)

United States Department of Agriculture (USDA)

Nicholas Hamilton

Source

Soil Mechanics Level 1, Module 3, USDA Textural Classification Study Guide

See Also

ggtern datasets

Examples

#Load the Libraries
library(ggtern)
library(plyr)

#Load the Data.
data(USDA)

#Put tile labels at the midpoint of each tile.
USDA.LAB <- ddply(USDA,"Label",function(df){
  apply(df[,1:3],2,mean)
})

#Tweak
USDA.LAB$Angle = sapply(as.character(USDA.LAB$Label),function(x){
    switch(x,"Loamy Sand"=-35,0)
})

#Construct the plot.
ggtern(data=USDA,aes(Sand,Clay,Silt,color=Label,fill=Label)) +
  geom_polygon(alpha=0.75,size=0.5,color="black") +
  geom_mask() +  
  geom_text(data=USDA.LAB,aes(label=Label,angle=Angle),color="black",size=3.5) +
  theme_rgbw() + 
  theme_showsecondary() +
  theme_showarrows() +
  weight_percent() + 
  guides(fill='none') + 
  theme_legend_position("topleft") + 
  labs(title = "USDA Textural Classification Chart",
       fill  = "Textural Class",
       color = "Textural Class")

Description

White-cell compositions of 30 blood cells by two different methods

Format

1 row per point, 60 points in total, 2 experiments x 30 points each, Each point contains data on the following:

1. No: ID, S1 to S30

2. Experiment: MicroscopicInspection or ImageAnalysis

3. G: Fraction Granulocytes

4. L: Fraction Lymphocytes

5. M: Fraction Monocytes

Author(s)

J. Aitchison

References

Aitchison, J. The statistical analysis of compositional data Chapman and Hall London, 1986, pp366

Examples

data(WhiteCells)
   ggtern(WhiteCells,aes(G,L,M)) + 
   geom_density_tern(aes(color=Experiment)) +
   geom_point(aes(shape=Experiment)) +
   facet_wrap(~Experiment,nrow=2)

Description

Each Geom has an associated function that draws the key when the geom needs to be displayed in a legend. These are the options built into ggplot2.

Usage

draw_key_crosshair_tern(data, params, size)

draw_key_Tmark(data, params, size)

draw_key_Lmark(data, params, size)

draw_key_Rmark(data, params, size)

draw_key_Tline(data, params, size)

draw_key_Lline(data, params, size)

draw_key_Rline(data, params, size)

draw_key_Tiso(data, params, size)

draw_key_Liso(data, params, size)

draw_key_Riso(data, params, size)

draw_key_point_swap(data, params, size)

Arguments

Value

A grid grob.

Author(s)

Nicholas Hamilton

Description

Calculates the confidence intervals, via the Mahalnobis Distance and use of the Log-Ratio Transformation

Statistic

Usage

geom_confidence_tern(
  mapping = NULL,
  data = NULL,
  stat = "ConfidenceTern",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_confidence_tern(
  mapping = NULL,
  data = NULL,
  geom = "ConfidenceTern",
  position = "identity",
  ...,
  contour = TRUE,
  n = 100,
  h = NULL,
  na.rm = FALSE,
  breaks = c(0.5, 0.9, 0.95),
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_ConfidenceTernunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• linetype

• size

Computed variables

Same as stat_contour

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
  ggtern(data=Feldspar,aes(An,Ab,Or)) + 
    geom_point() + 
    geom_confidence_tern()

Description

A new geometry, geom_crosshair_tern is one that that marks on the respective axes, the values of each data point. We also include additional geometries geom_Tmark, geom_Rmark and geom_Lmark – to render only the respective axis component of the abovementioned crosshair.

Usage

geom_crosshair_tern(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_Tmark(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_Lmark(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_Rmark(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Aesthetics

geom_crosshair_ternunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• z

• alpha

• colour

• linetype

• size

Author(s)

Nicholas Hamilton

Examples

set.seed(1)
df = data.frame(x=runif(10),y=runif(10),z=runif(10))
base = ggtern(df,aes(x,y,z)) + geom_point()
base + geom_crosshair_tern()
base + geom_Tmark()
base + geom_Rmark()
base + geom_Lmark()

Description

Perform a 2D kernel density estimatation using kde2d and display the results with contours. This can be useful for dealing with overplotting. Additional weight aesthetic (see aesthetic section below) permits better weighting if desired

Usage

geom_density_tern(
  mapping = NULL,
  data = NULL,
  stat = "DensityTern",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_density_tern(
  mapping = NULL,
  data = NULL,
  geom = "density_tern",
  position = "identity",
  ...,
  contour = TRUE,
  n = 100,
  h = NULL,
  bdl = 0,
  bdl.val = NA,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  weight = 1,
  base = "ilr",
  expand = c(0.5, 0.5)
)

Arguments

Aesthetics

geom_density_ternunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• linetype

• size

• weight

Author(s)

Nicholas Hamilton

Examples

#Plot Density Estimate, on isometric log ratio transformation of original data
data('Feldspar')
ggtern(Feldspar,aes(Ab,An,Or)) + 
 geom_density_tern(aes(color=..level..),bins=5) +
 geom_point()

#Plot Density Estimate w/ Polygon Geometry
data('Feldspar')
ggtern(data=Feldspar,aes(Ab,An,Or)) + 
    stat_density_tern(
        geom='polygon',
        aes(fill=..level..),
        bins=5,
        color='grey') +
    geom_point()

Description

geom_errorbarT, geom_errorbarL and geom_errorbarR are geometries to render error bars for the top, left and right apex species respectively, analogous to geom_errorbar and/or geom_errorbarh as provided in the base ggplot2 package.

Usage

geom_errorbarT(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_errorbarL(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_errorbarR(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

Aesthetics (geom_errorbarT)

geom_errorbartunderstands the following aesthetics (required aesthetics are in bold):

• Tmax

• Tmin

• x

• y

• z

• alpha

• colour

• linetype

• linewidth

Aesthetics (geom_errorbarL)

geom_errorbarlunderstands the following aesthetics (required aesthetics are in bold):

• Lmax

• Lmin

• x

• y

• z

• alpha

• colour

• linetype

• linewidth

Aesthetics (geom_errorbarR)

geom_errorbarrunderstands the following aesthetics (required aesthetics are in bold):

• Rmax

• Rmin

• x

• y

• z

• alpha

• colour

• linetype

• linewidth

Author(s)

Nicholas Hamilton

Examples

#Example with Dummy Data.
tmp <- data.frame(x=1/3,
y=1/3,
z=1/3,
Min=1/3-1/6,
Max=1/3+1/6)
ggtern(data=tmp,aes(x,y,z)) + 
  geom_point() + 
  geom_errorbarT(aes(Tmin=Min,Tmax=Max),colour='red')+
  geom_errorbarL(aes(Lmin=Min,Lmax=Max),colour='green')+
  geom_errorbarR(aes(Rmin=Min,Rmax=Max),colour='blue')

Description

Divides the plane into regular hexagons, counts the number of cases in each hexagon, and then (by default) maps the number of cases to the hexagon fill. Hexagon bins avoid the visual artefacts sometimes generated by the very regular alignment of [geom_bin2d()].

Usage

geom_hex_tern(
  mapping = NULL,
  data = NULL,
  stat = "hex_tern",
  position = "identity",
  ...,
  fun = sum,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_hex_tern(
  mapping = NULL,
  data = NULL,
  geom = "hex_tern",
  position = "identity",
  ...,
  bins = 30,
  fun = sum,
  binwidth = NULL,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Details

This geometry is loosely based on the base ggplot2 geom_hex, with a few subtle (but advantageous differences). The user can control the border thickness of the hexagonal polygons using the size aesthetic. The user can also control the particular statistic to use, by defining the fun argument (sum by default), which by default is applied over a value of 1 per point, however, this can also be mapped to a data variable via the 'value' mapping.

Aesthetics

@section Aesthetics: geom_hex()understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• fill

• group

• linetype

• linewidth

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Examples

set.seed(1)
n  = 1000
df = data.frame(x  = runif(n),
                y  = runif(n),
                z  = runif(n),
                wt = runif(n))
                
#Equivalent of Hexbin
ggtern(df,aes(x,y,z)) + 
    geom_hex_tern(binwidth=0.1)
    
#Calculate Mean of variable wt
ggtern(df,aes(x,y,z)) + 
     geom_hex_tern(binwidth=0.05,
                   aes(value=wt),
                   fun=mean)

#Custom functions, for ex. discrete output...
myfun = function(x) sample(LETTERS,1)
ggtern(df,aes(x,y,z)) + 
     geom_hex_tern(binwidth=0.05,
                   fun=myfun)

Description

This is the heavily requested geometry for interpolating between ternary values, results being rendered using contours on a ternary mesh.

Usage

geom_interpolate_tern(
  mapping = NULL,
  data = NULL,
  stat = "InterpolateTern",
  position = "identity",
  ...,
  method = "auto",
  formula = value ~ poly(x, y, degree = 1),
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_interpolate_tern(
  mapping = NULL,
  data = NULL,
  geom = "interpolate_tern",
  position = "identity",
  ...,
  method = "auto",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  n = 80,
  formula = value ~ poly(x, y, degree = 1),
  base = "ilr"
)

Arguments

Aesthetics

geom_InterpolateTernunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• linetype

• size

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
ggtern(Feldspar,aes(Ab,An,Or,value=T.C)) + 
stat_interpolate_tern(geom="polygon",
                     formula=value~x+y,
                     method=lm,n=100,
                     breaks=seq(0,1000,by=100),
                     aes(fill=..level..),expand=1) +
                     geom_point()

Description

Since it is sometimes counter intuitive for working with ternary or other non-cartesian coordinates in the event that the the user wishes to place a label-geometry based on visual inspection, this geometry positions such text item at a fraction from x=[0,1] and y=[0,1] of the viewport in x and y cartesian coordinates.

Usage

geom_label_viewport(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  hjust = "inward",
  vjust = "inward",
  parse = FALSE,
  label.padding = unit(0.25, "lines"),
  label.r = unit(0.15, "lines"),
  label.size = 0.25,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_Labelunderstands the following aesthetics (required aesthetics are in bold):

• label

• x

• y

• alpha

• angle

• colour

• family

• fill

• fontface

• hjust

• lineheight

• size

• vjust

Author(s)

Nicholas Hamilton

See Also

geom_label

Examples

library(ggplot2)
data(Feldspar)
base = ggtern(data=Feldspar,aes(Ab,An,Or)) + 
  geom_mask() + 
  geom_point() + 
  geom_label_viewport(x=0.5,y=0.5,label="Middle",color='red') + 
  geom_label_viewport(x=1.0,y=1.0,label="Top Right",color='blue') + 
  geom_label_viewport(x=0.0,y=0.0,label="Bottom Left",color='green') +
  geom_label_viewport(x=0.0,y=1.0,label="Top Left",color='orange') + 
  geom_label_viewport(x=1.0,y=0.0,label="Bottom Right",color='magenta')
base

base + 
  geom_label_viewport(x=0.9,y=0.5,label="Clipping Turned Off",color='purple',hjust=0,clip='on') 

base + 
  geom_label_viewport(x=0.9,y=0.5,label="Clipping Turned Off",color='purple',hjust=0,clip='off')

Description

This function creates a manual clipping mask, which in turn suppresses the standard clipping mask that would otherwise be rendered in the foregound rendering procedure, giving the user control over the exact placement with respect to other layers. For example, the user may wish to have the clipping mask placed after the geom_point(...) layer, but before the geom_label(...) layer, this situation has been demonstrated in the example below. In the event that the user wishes to suppress the mask altogether, then a convenience function has been provided, theme_nomask().

Usage

geom_mask()

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
x = ggtern(Feldspar,aes(Ab,An,Or,label=Experiment)) + geom_point()

#Default Behaviour
x + geom_label()

#Insert manual mask before the labels, to prevent them being truncated
x + geom_point(size=6) + geom_mask() + geom_label()

Description

Produce ellipses from a mean and a variance of ternary compositional data, based off the function included in the compositions package.

Usage

geom_mean_ellipse(
  mapping = NULL,
  data = NULL,
  stat = "MeanEllipse",
  position = "identity",
  ...,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_mean_ellipse(
  mapping = NULL,
  data = NULL,
  geom = "MeanEllipse",
  position = "identity",
  ...,
  steps = 72,
  r = 1,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_MeanEllipseunderstands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• linetype

• size

Computed variables

Same as stat_contour

Author(s)

Nicholas Hamilton & Ashton Drew

Examples

data(Feldspar)
  ggtern(data=Feldspar,aes(An,Ab,Or)) + 
    geom_point() + 
    geom_mean_ellipse()
data(Feldspar)
ggtern(data=Feldspar,aes(Ab,An,Or)) +
  theme_bw() + 
  stat_mean_ellipse(geom='polygon',steps=500,fill='red',color='black') +
  geom_point()

Description

The geom_point_swap geometry is used to create scatterplots, however, this version swaps the colour and the fill mappings. Useful if the fill mapping is already occupied (say with existing polygon geometry), this geometry will allow points of shape 21-25 to use colour mapping for the center colour, and fill mapping for the border.

Usage

geom_point_swap(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
ggtern(Feldspar,aes(Ab,An,Or)) + 
stat_confidence_tern(geom='polygon',aes(fill=..level..),color='white') + 
geom_mask() + 
geom_point_swap(aes(colour=T.C,shape=Feldspar),fill='black',size=5) +
scale_shape_manual(values=c(21,24)) +
scale_color_gradient(low='green',high='red') +
labs(title="Feldspar",color="Temperature",fill='Confidence')

Description

A little like geom_area, in the sense that polygons are either upper or lower closed based on the starting and finishing points index.

Usage

geom_polygon_closed(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  closure = "none"
)

Arguments

Author(s)

Nicholas Hamilton

Description

Aids the eye in seeing patterns in the presence of overplotting. geom_smooth_tern and stat_smooth_tern are effectively aliases: they both use the same arguments. Use geom_smooth_tern unless you want to display the results with a non-standard geom.

Usage

geom_smooth_tern(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  method = "auto",
  formula = y ~ x,
  se = TRUE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  expand = c(0.5, 0.5)
)

stat_smooth_tern(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  method = "auto",
  formula = y ~ x,
  se = TRUE,
  n = 80,
  span = 0.75,
  fullrange = FALSE,
  level = 0.95,
  method.args = list(),
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  expand = c(0.5, 0.5)
)

Arguments

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
ggtern(data=Feldspar,aes(Ab,An,Or,group=Feldspar)) +  
  geom_smooth_tern(method=lm,fullrange=TRUE,colour='red') + 
  geom_point() +
  labs(title="Example Smoothing")

Description

Since it is sometimes counter intuitive for working with ternary or other non-cartesian coordinates in the event that the the user wishes to place a text-geometry based on visual inspection, this geometry positions such text item at a fraction from x=[0,1] and y=[0,1] of the viewport in x and y cartesian coordinates.

Usage

geom_text_viewport(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  hjust = "inward",
  vjust = "inward",
  parse = FALSE,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_Textunderstands the following aesthetics (required aesthetics are in bold):

• label

• x

• y

• alpha

• angle

• colour

• family

• fontface

• hjust

• lineheight

• size

• vjust

Author(s)

Nicholas Hamilton

See Also

geom_text

Examples

library(ggplot2)
data(Feldspar)
base = ggtern(data=Feldspar,aes(Ab,An,Or)) + 
  geom_mask() + 
  geom_point() + 
  geom_text_viewport(x=0.5,y=0.5,label="Middle",color='red') + 
  geom_text_viewport(x=1.0,y=1.0,label="Top Right",color='blue') + 
  geom_text_viewport(x=0.0,y=0.0,label="Bottom Left",color='green') +
  geom_text_viewport(x=0.0,y=1.0,label="Top Left",color='orange') + 
  geom_text_viewport(x=1.0,y=0.0,label="Bottom Right",color='magenta')
base

base + 
  geom_text_viewport(x=0.9,y=0.5,label="Clipping Turned Off",color='purple',hjust=0,clip='on') 

base + 
  geom_text_viewport(x=0.9,y=0.5,label="Clipping Turned Off",color='purple',hjust=0,clip='off')

Description

Divides the plane into regular triangles, counts the number of cases in each triangles, and then (by default) maps the number of cases to the triangle fill.

Usage

geom_tri_tern(
  mapping = NULL,
  data = NULL,
  stat = "tri_tern",
  position = "identity",
  ...,
  fun = sum,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

stat_tri_tern(
  mapping = NULL,
  data = NULL,
  geom = "tri_tern",
  position = "identity",
  ...,
  bins = 30,
  fun = sum,
  centroid = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

@section Aesthetics: geom_hex()understands the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• fill

• group

• linetype

• linewidth

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Examples

set.seed(1)
n  = 1000
df = data.frame(x  = runif(n),
                y  = runif(n),
                z  = runif(n),
                wt = runif(n))
#Equivalent of Hexbin
ggtern(df,aes(x,y,z)) + 
   geom_tri_tern(bins=10,aes(fill=..count..)) + 
   geom_point(size=0.25)

#Custom Function, Mean
ggtern(df,aes(x,y,z)) + 
   geom_tri_tern(bins=5,aes(fill=..stat..,value=wt),fun=mean) + 
   geom_point(size=0.25)

Description

Create fixed isoproportion lines for each of the ternary axes, geom_Xisoprop(...), (X = T, L, R) will draw an isoproportion line projecting from the T, L and R apex respectively.

Usage

geom_Tisoprop(
  mapping = NULL,
  data = NULL,
  ...,
  value,
  na.rm = FALSE,
  show.legend = NA
)

geom_Lisoprop(
  mapping = NULL,
  data = NULL,
  ...,
  value,
  na.rm = FALSE,
  show.legend = NA
)

geom_Risoprop(
  mapping = NULL,
  data = NULL,
  ...,
  value,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

Aesthetics

geom_Tisopropunderstands the following aesthetics (required aesthetics are in bold):

• value

• alpha

• arrow

• colour

• linetype

• size

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
ggtern(data=Feldspar,aes(Ab,An,Or)) +
geom_Tisoprop(value=0.5) +
geom_Lisoprop(value=0.5) +
geom_Risoprop(value=0.5) +
geom_point()

Description

Plot fixed value lines, for the top, left and right axis, analagous to the geom_hline and geom_vline geometries in ggplot2

Usage

geom_Tline(
  mapping = NULL,
  data = NULL,
  ...,
  Tintercept,
  na.rm = FALSE,
  show.legend = NA
)

Tline(
  mapping = NULL,
  data = NULL,
  ...,
  Tintercept,
  na.rm = FALSE,
  show.legend = NA
)

tline(
  mapping = NULL,
  data = NULL,
  ...,
  Tintercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_Lline(
  mapping = NULL,
  data = NULL,
  ...,
  Lintercept,
  na.rm = FALSE,
  show.legend = NA
)

Lline(
  mapping = NULL,
  data = NULL,
  ...,
  Lintercept,
  na.rm = FALSE,
  show.legend = NA
)

lline(
  mapping = NULL,
  data = NULL,
  ...,
  Lintercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_Rline(
  mapping = NULL,
  data = NULL,
  ...,
  Rintercept,
  na.rm = FALSE,
  show.legend = NA
)

Rline(
  mapping = NULL,
  data = NULL,
  ...,
  Rintercept,
  na.rm = FALSE,
  show.legend = NA
)

rline(
  mapping = NULL,
  data = NULL,
  ...,
  Rintercept,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

Author(s)

Nicholas Hamilton

Examples

ggtern() + 
geom_Tline(Tintercept=.5,arrow=arrow(), colour='red') + 
geom_Lline(Lintercept=.2, colour='green') + 
geom_Rline(Rintercept=.1, colour='blue')

Description

ggplot() initializes a ggplot object. It can be used to declare the input data frame for a graphic and to specify the set of plot aesthetics intended to be common throughout all subsequent layers unless specifically overridden.

Usage

ggplot(data = NULL, mapping = aes(), ..., environment = parent.frame())

## S3 method for class 'ggplot'
print(x, newpage = is.null(vp), vp = NULL, ...)

## S3 method for class 'ggplot'
plot(x, newpage = is.null(vp), vp = NULL, ...)

Arguments

Details

ggplot() is typically used to construct a plot incrementally, using the + operator to add layers to the existing ggplot object. This is advantageous in that the code is explicit about which layers are added and the order in which they are added. For complex graphics with multiple layers, initialization with ggplot is recommended.

There are three common ways to invoke ggplot:

• ggplot(df, aes(x, y, <other aesthetics>))

• ggplot(df)

• ggplot()

The first method is recommended if all layers use the same data and the same set of aesthetics, although this method can also be used to add a layer using data from another data frame. See the first example below. The second method specifies the default data frame to use for the plot, but no aesthetics are defined up front. This is useful when one data frame is used predominantly as layers are added, but the aesthetics may vary from one layer to another. The third method initializes a skeleton ggplot object which is fleshed out as layers are added. This method is useful when multiple data frames are used to produce different layers, as is often the case in complex graphics.

Value

Invisibly returns the result of ggplot_build, which is a list with components that contain the plot itself, the data, information about the scales, panels etc.

Author(s)

Nicholas Hamilton

Description

ggsave() is a convenient function for saving a plot. It defaults to saving the last plot that you displayed, using the size of the current graphics device. It also guesses the type of graphics device from the extension.

Usage

ggsave(
  filename,
  plot = last_plot(),
  device = NULL,
  path = NULL,
  scale = 1,
  width = NA,
  height = NA,
  units = c("in", "cm", "mm"),
  dpi = 300,
  limitsize = TRUE,
  ...
)

Arguments

Author(s)

Nicholas Hamilton

Examples

## Not run: 
data(Feldspar)
base = ggtern(Feldspar,aes(Ab,An,Or)) + geom_point()
ggsave("./output.pdf",base,width=10,height=10)

## End(Not run)

Description

Plots in ggtern are instigated via the default constructor: ggtern(...), which is essentially a convenience wrapper for the following: ggplot{...} + coord_tern(), indeed, if one wishes to use ggplot{...} + coord_tern() then this is quite satisfactory.

Usage

ggtern(data = NULL, mapping = aes(), ..., environment = parent.frame())

Arguments

Value

ggtern(...) returns an object of class ggplot.

Author(s)

Nicholas Hamilton

See Also

For an introduction to the ggtern package, (including many examples), click HERE.

Examples

ggtern(data=data.frame(x=1,y=1,z=1),aes(x,y,z)) + geom_point()

Description

New label modification functions, equivalent to the original functions in ggplot2 (xlab and ylab) however for the new axes used in the ggtern package

Usage

Tlab(label, labelarrow = label)

Llab(label, labelarrow = label)

Rlab(label, labelarrow = label)

Wlab(label)

zlab(label)

Tarrowlab(label)

Larrowlab(label)

Rarrowlab(label)

Arguments

Details

Tlab and xlab are equivalent (when T='x' in the coord_tern definition), as is Llab and ylab (when L='y') , and Rlab and zlab (when R='z'), for other assignments when coord_tern is defined, the equivalence is not the case, however, if T='XXX', then Tlab will be the same as XXXlab (where XXX can be substituted for 'x', 'y' or 'z', and likewise for Llab and Rlab).

zlab is new to ggtern, but is intended to be an analogous to xlab and ylab as per the definitions in ggplot2.

Arrow Label

Tarrowlab, Larrowlab and Rarrowlab permits setting a different label to the apex labels.

Arrow Label Suffix

Wlab changes the ternary arrow suffix (ie atomic percent, weight percent etc) when the ternary arrows are enabled (see theme_showarrows and weight_percent)

Precedence

AAAlab takes precedence over BBBlab (where AAA represents T, L or R and BBB represents x, y or z)

Use of Expressions

Expressions can be used in the labels, in the event that the user wishes to render formula, subscripts or superscripts, see the last example below.

Creation of Aliasses

Aliasses exist for Tlab, Llab, Rlab and Wlab, which are tlab, llab, rlab and wlab. These aliasses produce an identical result, and are there for convenience (as opposed to having an error thrown) in the event that the user forgets to use an upper-case letter.

Arguments for these functions can be provided as a character or expression, although other values can be inputed (such as, for example, scalar numeric or logical). ggtern also imports the latex2exp package, and these formats can be parsed too.

Author(s)

Nicholas Hamilton

See Also

ggplot2 labs

Examples

data(Feldspar)
plot <- ggtern(data=Feldspar,aes(Ab,An,Or)) +  geom_point() + 
        xlab("ABC") + ylab("DEF") + zlab("GHI")

#Alternatives, and Arrow Label
plot + Tlab("TOP") + Llab("LHS") + Rlab("RHS") + 
  Tarrowlab("Top Arrow Label") + Larrowlab("Left Arrow Label") + Rarrowlab("Right Arrow Label") +
  theme_showarrows() + Wlab("WEIGHT")

#Demonstrate the use of the latex2exp integration, and seperate arrow labels.  
ggtern(data=Feldspar,aes(x=Ab,y=An,z=Or)) + 
labs( x       = "NaAlSi_3O_8",
      xarrow  = "Albite, NaAlSi_3O_8",
      y       = "(Na,K)AlSi_3O_8",
      yarrow  = "Anorthite (Na,K)AlSi_3O_8",
      z       = "KAlSi_3O_8",
      zarrow  = "Orthoclase KAlSi_3O_8") + 
theme_latex(TRUE) + 
geom_point() + 
theme_showarrows() + 
theme_clockwise() + 
weight_percent()

Description

By default there are no suffixes behind the arrow label marker (the arrow up next to the ternary axes), and these functions appends to the set of arrow labels, a value to indicate the nature of the scale.

percent_weight adds 'Wt. %' to the arrow marker label as a suffix

weight_percent is an alias for percent_weight()

percent_atomic adds 'At. %' to the arrow marker label as a suffix

atomic_percent is an alias for percent_atomic()

percent_custom adds a custom suffix to the arrow label marker.

custom_percent is an alias for percent_custom()

Usage

percent_weight()

weight_percent()

percent_atomic()

atomic_percent()

percent_custom(x)

custom_percent(x)

Arguments

Details

These are convenience wrappers to labs(W="XYZ").

Author(s)

Nicholas Hamilton

See Also

Convenience functions for T, L, R, W labels

Description

Ternary diagrams are used frequently in a number of disciplines to graph compositional features for mixtures of three different elements or compounds. It is possible to represent a coordinate system having three (3) degrees of freedom, in 2D space, since the third dimention is linear and depends only on the other two.

The ggtern package is based on (extends) the very popular ggplot2 package, which is an implementation of Wilkinsons "The Grammar of Graphics", and, makes provision for a highly methodical construction process for the development of meaningful (graphical) data representations. Of course, the above book by Wilkinson outlines the theory, whilst Hadley Wickhams ggplot2 implementation is where much of the magic happens, and, an ideal base-platform for the ggtern package.

In this document, some of the main features are highlighted, however, current examples (and corresponding outputs) can be viewed at http://ggtern.com

ggtern Constructor

Plots in ggtern are instigated via the default constructor: ggtern(...), for additional information, click HERE:

ggtern Ternary Coordinate System

The foundation of this package, is the ternary coordinate system, which can be produced with the coord_tern(...) command and added to an existing ggplot object. The ggtern(...) constructor adds the coord_tern(...) coordinate system by default. For further information on the coord_tern(...) coordinate system, click HERE.

ggtern Valid Geometries

ggplot2, using the grid and proto architectures, makes provision for a many number of geometries to be added progressively in 'layers' to a given base plot. Due to the nature of the ternary coordinate system, some of the geometries which are available in ggplot2, are not relevant (or won't function) with ternary plots and as such, a limited number of 'approved' geometries can be used. Click HERE for the full list of approved geometries.

Notably, ggtern includes novel geometries not available to ggplot2 which include:

1. Confidence Intervals via the Mahalnobis Distance

2. Ternary Errorbars

3. Ternary Constant-Lines

ggtern Handling Non-Approved Geometries

If a geometric layer is added that is NOT contained in the approved list, IT WILL BE STRIPPED / IGNORED from the ternary diagram when rendering takes place (notifying the user to such effect). The reason for this is that subtle 'patches' have been applied, which are mainly to do with the transformation procedures when incorporating a 'third' dimention. NB: In the future, others may be made available once patched.

ggtern New Theme Elements and Heirarchies

ggtern implements many new theme elements and heirarchies which can be tailored on a case-by-case basis. The full list of new elements can is provided HERE.

ggtern Theme Element Convenience Functions

ggtern has made available a number of convenience functions, for rapid tweaking of common theme elements, for a comprehensive list, see HERE.

ggtern Modification to Required Aesthetics

Each geometry has a pre-determined set of required aesthetics. These have been modifid such that where x and y were previously required, now an additional z aesthetic is required (geom_segment now requires z and zend). This is made possible without affecting the standard ggplot2 behaviour because ggtern distinuishes between ggplot2 and ggtern objects, distinguished by the presence of the coord_tern(...) coordinate system.

ggtern Provided Datasets

ggtern ships with a number of datasets, including:

1. Elkin and Groves Feldspar Data

2. USDA Textural Classification Data

3. Grantham and Valbel Rock Fragment Data

Author(s)

Nicholas Hamilton

References

To cite this package, please use the following:

Hamilton NE and Ferry M (2018). "ggtern: Ternary Diagrams Using ggplot2." Journal of Statistical Software, Code Snippets, 87(3), pp. 1-17. doi: 10.18637/jss.v087.c03 (URL:http://doi.org/10.18637/jss.v087.c03)

A bibtex entry can be obtained by executing the following command: citation('ggtern')

Examples

##-----------------------------------------------
## Basic Usage
##-----------------------------------------------
df = data.frame(x = runif(50),
                y = runif(50),
                z = runif(50),
                Value = runif(50,1,10),
                Group = as.factor(round(runif(50,1,2))))
ggtern(data=df,aes(x,y,z,color=Group)) + 
  theme_rgbw() + 
  geom_point() + geom_path() + 
  labs(x="X",y="Y",z="Z",title="Title")

Description

Themes set the general aspect of the plot such as the colour of the background, gridlines, the size and colour of fonts.

Usage

theme_ggtern(base_size = 11, base_family = "")

theme_gray(base_size = 11, base_family = "")

theme_bw(base_size = 12, base_family = "")

theme_linedraw(base_size = 12, base_family = "")

theme_light(base_size = 12, base_family = "")

theme_minimal(base_size = 12, base_family = "")

theme_classic(base_size = 12, base_family = "")

theme_dark(base_size = 12, base_family = "")

theme_void(base_size = 12, base_family = "")

theme_darker(base_size = 12, base_family = "")

theme_custom(
  base_size = 12,
  base_family = "",
  tern.plot.background = NULL,
  tern.panel.background = NULL,
  col.T = "black",
  col.L = "black",
  col.R = "black",
  col.grid.minor = "white"
)

theme_rgbw(base_size = 12, base_family = "")

theme_rgbg(base_size = 12, base_family = "")

theme_matrix(base_size = 12, base_family = "")

theme_tropical(base_size = 12, base_family = "")

theme_bluedark(base_size = 12, base_family = "")

theme_bluelight(base_size = 12, base_family = "")

theme_bvbw(base_size = 12, base_family = "")

theme_bvbg(base_size = 12, base_family = "")

Arguments

Details

theme_gray

The signature ggplot2 theme with a grey background and white gridlines, designed to put the data forward yet make comparisons easy.

theme_bw

The classic dark-on-light ggplot2 theme. May work better for presentations displayed with a projector.

theme_linedraw

A theme with only black lines of various widths on white backgrounds, reminiscent of a line drawings. Serves a purpose similar to theme_bw. Note that this theme has some very thin lines (<< 1 pt) which some journals may refuse.

theme_light

A theme similar to theme_linedraw but with light grey lines and axes, to direct more attention towards the data.

theme_dark

The dark cousin of theme_light, with similar line sizes but a dark background. Useful to make thin coloured lines pop out.

theme_darker

A darker cousing to theme_dark, with a dark panel background.

theme_minimal

A minimalistic theme with no background annotations.

theme_classic

A classic-looking theme, with x and y axis lines and no gridlines.

theme_rgbw

A theme with white background, red, green and blue axes and gridlines

theme_rgbg

A theme with grey background, red, green and blue axes and gridlines

theme_void

A completely empty theme.

theme_custom

Theme with custom basic colours

theme_matrix

Theme with very dark background and bright green features

theme_tropical

Theme with tropical colours

theme_bluelight

A blue theme with light background and dark features

theme_bluedark

A blue theme with dark background and light features

theme_bvbw

A black/vermillion/blue theme with white background, for colorblind sensitive readers, see references.

theme_bvbg

A black/vermillion/blue theme with grey background, for colorblind sensitive readers, see references.

Author(s)

Nicholas Hamilton

References

Okabe, Masataka, and Kei Ito. "How to make figures and presentations that are friendly to color blind people." University of Tokyo (2002). http://jfly.iam.u-tokyo.ac.jp/color/

Examples

#Create a list of the theme suffixes
themesOrg = c('gray','bw','linedraw','light',
              'dark','minimal','classic','void')
themesNew = c('custom','darker','rgbw','rgbg','tropical',
              'matrix','bluelight','bluedark','bvbw','bvbg')

#Iterate over all the suffixes, creating a list of plots
plotThemes = function(themes){
   grobs = lapply(themes,function(x){
     thmName = sprintf("theme_%s",x)
     thm = do.call(thmName,args=list(base_size=9))
     df  = data.frame(label=thmName)
     ggtern(df) + facet_wrap(~label) + thm
   })
   grobs
}

#Arrange the Original Themes
grid.arrange(grobs=plotThemes(themesOrg),top = "Collection of Themes (Original)")

#Arrange the New Themes
grid.arrange(grobs=plotThemes(themesNew),top = "Collection of Themes (New Themes)")

Description

label_formatter is a function that formats / parses labels for use in the grid.

Usage

label_formatter(label, ...)

Arguments

Description

Calculates the Labels for Major or Minor Gridlines based on the input limits.

Usage

labels_tern(
  limits = c(0, 1),
  breaks = breaks_tern(limits),
  format = "%g",
  factor = 100
)

Arguments

Author(s)

Nicholas Hamilton

Examples

labels_tern()
labels_tern(limits = c(0,.5))

Description

Modified version of the code provided in the drawMahal package

Usage

mahalanobis_distance(
  x,
  x.mean,
  x.cov,
  whichlines = c(0.975, 0.9, 0.75),
  m = 360
)

Arguments

Value

list containing mdX and mdY values.

Author(s)

Nicholas Hamilton

Description

Jitter ternary points to avoid overplotting.

Usage

position_jitter_tern(x = NULL, y = NULL, z = NULL)

Arguments

Author(s)

Nicholas Hamilton

See Also

Other position adjustments: position_nudge_tern()

Description

This is useful if you want to nudge labels a little ways from their points, input data will normalised to sum to unity before applying the particular nudge, so the nudge variables should be as a fraction ie (0,1)

Usage

position_nudge_tern(x = 0, y = 0, z = 0)

Arguments

Author(s)

Nicholas Hamilton

See Also

Other position adjustments: position_jitter_tern()

Description

Get predictions with standard errors into data frame

Usage

predictdf2d(model, xseq, yseq)

Arguments

Description

Define the ternary continuous position scales (T, L & R).

Usage

scale_T_continuous(
  name = waiver(),
  limits = NULL,
  breaks = waiver(),
  minor_breaks = waiver(),
  labels = waiver(),
  ...
)

scale_L_continuous(
  name = waiver(),
  limits = NULL,
  breaks = waiver(),
  minor_breaks = waiver(),
  labels = waiver(),
  ...
)

scale_R_continuous(
  name = waiver(),
  limits = NULL,
  breaks = waiver(),
  minor_breaks = waiver(),
  labels = waiver(),
  ...
)

Arguments

Author(s)

Nicholas Hamilton

Description

strip_unapproved is an internal function which essentially 'deletes' layers from the current ternary plot in the event that such layers are not one of the approved layers. For a layer to be approved, it must use an approved geometry, and also an approved stat. Refer to approved_layers for the current list of approved geometries and stats

Usage

strip_unapproved(layers)

Arguments

Value

strip_unapproved returns a list of approved layers (may be empty if none are approved).

Description

tern_limits (or its aliasses) appends new T, L and R ternary continuous scales, where the maximum scale value is specified, and, where the minimums for each are solved.

Usage

tern_limit(T = 1, L = 1, R = 1, ...)

limit_tern(...)

Arguments

Details

The contra value (ie minimum value) for the T, L and R species is solved using linear equations, therefore, if the solution is degenerate, or, the solution results in a zero range in either of the proposed scales, then a warning message will be reported and an empty list returned. Note that limits_tern(...), limit_tern(...) and tern_limit(...) are all aliasses for the main function, tern_limits(...) and can be used interchangeably.

Value

Either an empty list (when no solution can be found), or a list containing one of each of scale_X_continuous (X = T, L, R)

Author(s)

Nicholas Hamilton

See Also

scale_T_continuous, scale_L_continuous and scale_R_continuous

Examples

#Display a non-zoomed and zoomed plot side by side
data(Feldspar)
df.lims = data.frame(Ab = c(1,.25,.25), 
                     An = c(0,.75,.00), 
                     Or = c(0,.00,.75))
#Build the non-zoomed plot
A = ggtern(Feldspar,aes(Ab,An,Or)) +
 stat_density_tern(geom='polygon',aes(fill=..level..,alpha=..level..)) + 
 geom_point() + 
 geom_mask() + 
 geom_polygon(data=df.lims,color='red',alpha=0,size=0.5) +
 guides(color='none',fill='none',alpha='none') + 
 labs(title = "Non-Zoomed")

#Build the zoomed plot
B = A + 
  tern_limits(T=max(df.lims$An), L=max(df.lims$Ab), R=max(df.lims$Or)) +
  labs(title = "Zoomed")

#Arrange the above plots side by side for illustration
grid.arrange(A,B,ncol=2,top="Demonstration of Limiting Region")

Description

Functions to transform data from the ternary to cartesian spaces and vice-versa.

Usage

tlr2xy(data, coord, ..., inverse = FALSE, scale = TRUE, drop = FALSE)

xy2tlr(data, coord, ..., inverse = FALSE, scale = TRUE)

Arguments

Details

tlr2xy transforms from the ternary to cartesian spaces, an inverse transformation transforms between cartesian to ternary spaces

xy2tlr transforms from the cartesian to ternary spaces, an inverse transformation transforms between ternary to cartesian spaces, it is the reciprocal to tlr2xy, therefore an inverse transformation in xy2tlr function is the same as the forward transformation in tlr2xy

Author(s)

Nicholas Hamilton

Examples

data(Feldspar)
dfm = plyr::rename(Feldspar,c("Ab"="x","An"="y","Or"="z"))
crd = coord_tern()
fwd = tlr2xy(dfm,crd)
rev = tlr2xy(fwd,crd,inverse = TRUE)

Description

Custom theme elements for ggtern

Arguments