ggplot-method: ggplot methods
In lawremi/ggbio: Visualization tools for genomic data
ggplot R Documentation
ggplot methods
Description
These methods extend ggplot to support several
types of Bioconductor objects, as well as some base types like
matrix. They return a ggbio object, which stores the original data
object. Please check the corresponding method for mold
to see how an object is coerced into a data.frame.
Usage
## S3 method for class 'Vector'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'Seqinfo'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'ExpressionSet'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'RsamtoolsFile'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'TxDbOREnsDb'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'BSgenome'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'matrix'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'character'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'SummarizedExperiment'
ggplot(data, mapping = aes(),
assay.id = 1L, ..., environment = parent.frame())
## S3 method for class 'GAlignments'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'VCF'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
Arguments
data
original data object.
mapping
the aesthetic mapping.
...
other arguments passed to specific methods.
environment
fall-back environment for evaluation of aesthetic symbols
assay.id
index of assay you are using when multiple assays exist.
Details
The biggest difference for objects returned by ggplot in
ggbio from ggplot2, is we always keep the original data copy, this
is useful because in ggbio, our starting point is not always
data.frame, many special statistical transformation is computed upon
original data objects instead of coerced data.frame. This is a hack
to follow ggplot2's API while allow our own defined components to
trace back to original data copy and do the transformation. For
objects supported by mold we transform them to
data.frame stored along the original data set, for objects which not
supported by mold method, we only store the original copy
for ggbio specific graphics.
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. You can always call qplot in
package ggplot2 or autoplot in ggbio for convenient usage.
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. 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.
The examples below illustrate how these methods of invoking
ggplot can be used in constructing a graphic.
Value
a return ggbio object, which is a subclass of ggplot
defined in ggplot2 package, but that's more, a '.data' list entry is
stored with the returned object.
Author(s)
Tengfei Yin
See Also
mold, ggbio
Examples
set.seed(1)
N <- 100
library(GenomicRanges)
## GRanges
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))
## automatically facetting and assign y
## this must mean geom_rect support GRanges object
ggplot(gr) + geom_rect()
ggplot(gr) + geom_alignment()
ggplot() + geom_alignment(gr)
## use pure ggplot2's geom_rect, no auto facet
ggplot(gr) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))
## GRangesList
grl <- split(gr, values(gr)$pair)
ggplot(grl) + geom_alignment()
ggplot(grl) + geom_rect()
ggplot(grl) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))
## IRanges
ir <- ranges(gr)
ggplot(ir) + geom_rect()
ggplot(ir) + layout_circle(geom = "rect")
## Seqinfo
seqlengths(gr) <- c(400, 500, 420)
ggplot(seqinfo(gr)) + geom_point(aes(x = midpoint, y = seqlengths))
## matrix
mx <- matrix(1:12, nrow = 3)
ggplot(mx, aes(x = x, y = y)) + geom_raster(aes(fill = value))
## row is the factor
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = value))
colnames(mx)
colnames(mx) <- letters[1:ncol(mx)]
mx
## has extra 'colnames'
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = colnames))
rownames(mx)
rownames(mx) <- LETTERS[1:nrow(mx)]
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = rownames))
## please check autoplot, matrix for more control
## Views
## ExpressionSet
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = value))
## please check autoplot,matrix method which gives you more control
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = sex))
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = type))
## Rle
library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
ggplot(xRle) + geom_tile(aes(x = x, y = y, fill = value))
## RleList
xRleList <- RleList(xRle, 2L * xRle)
xRleList
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
names(xRleList) <- c("a" ,"b")
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
## RangedSummarizedExperiment
library(SummarizedExperiment)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts,
counts2 = counts2),
rowRanges=rowRanges, colData=colData)
ggplot(sset) + geom_raster(aes(x = x, y = y , fill = value))
lawremi/ggbio documentation built on Nov. 1, 2023, 2:40 p.m.
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| ggplot | R Documentation |
ggplot methods
Description
These methods extend ggplot to support several
types of Bioconductor objects, as well as some base types like
matrix. They return a ggbio object, which stores the original data
object. Please check the corresponding method for mold
to see how an object is coerced into a data.frame.
Usage
## S3 method for class 'Vector'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'Seqinfo'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'ExpressionSet'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'RsamtoolsFile'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'TxDbOREnsDb'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'BSgenome'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'matrix'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'character'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'SummarizedExperiment'
ggplot(data, mapping = aes(),
assay.id = 1L, ..., environment = parent.frame())
## S3 method for class 'GAlignments'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
## S3 method for class 'VCF'
ggplot(data, mapping = aes(), ...,
environment = parent.frame())
Arguments
data |
original data object. |
mapping |
the aesthetic mapping. |
... |
other arguments passed to specific methods. |
environment |
fall-back environment for evaluation of aesthetic symbols |
assay.id |
index of assay you are using when multiple assays exist. |
Details
The biggest difference for objects returned by ggplot in
ggbio from ggplot2, is we always keep the original data copy, this
is useful because in ggbio, our starting point is not always
data.frame, many special statistical transformation is computed upon
original data objects instead of coerced data.frame. This is a hack
to follow ggplot2's API while allow our own defined components to
trace back to original data copy and do the transformation. For
objects supported by mold we transform them to
data.frame stored along the original data set, for objects which not
supported by mold method, we only store the original copy
for ggbio specific graphics.
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. You can always call qplot in
package ggplot2 or autoplot in ggbio for convenient usage.
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. 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.
The examples below illustrate how these methods of invoking
ggplot can be used in constructing a graphic.
Value
a return ggbio object, which is a subclass of ggplot
defined in ggplot2 package, but that's more, a '.data' list entry is
stored with the returned object.
Author(s)
Tengfei Yin
See Also
mold, ggbio
Examples
set.seed(1)
N <- 100
library(GenomicRanges)
## GRanges
gr <- GRanges(seqnames =
sample(c("chr1", "chr2", "chr3"),
size = N, replace = TRUE),
IRanges(
start = sample(1:300, size = N, replace = TRUE),
width = sample(70:75, size = N,replace = TRUE)),
strand = sample(c("+", "-", "*"), size = N,
replace = TRUE),
value = rnorm(N, 10, 3), score = rnorm(N, 100, 30),
sample = sample(c("Normal", "Tumor"),
size = N, replace = TRUE),
pair = sample(letters, size = N,
replace = TRUE))
## automatically facetting and assign y
## this must mean geom_rect support GRanges object
ggplot(gr) + geom_rect()
ggplot(gr) + geom_alignment()
ggplot() + geom_alignment(gr)
## use pure ggplot2's geom_rect, no auto facet
ggplot(gr) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))
## GRangesList
grl <- split(gr, values(gr)$pair)
ggplot(grl) + geom_alignment()
ggplot(grl) + geom_rect()
ggplot(grl) + ggplot2::geom_rect(aes(xmin = start, ymin = score,
xmax = end, ymax = score + 1))
## IRanges
ir <- ranges(gr)
ggplot(ir) + geom_rect()
ggplot(ir) + layout_circle(geom = "rect")
## Seqinfo
seqlengths(gr) <- c(400, 500, 420)
ggplot(seqinfo(gr)) + geom_point(aes(x = midpoint, y = seqlengths))
## matrix
mx <- matrix(1:12, nrow = 3)
ggplot(mx, aes(x = x, y = y)) + geom_raster(aes(fill = value))
## row is the factor
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = value))
colnames(mx)
colnames(mx) <- letters[1:ncol(mx)]
mx
## has extra 'colnames'
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = colnames))
rownames(mx)
rownames(mx) <- LETTERS[1:nrow(mx)]
ggplot(mx, aes(x = x, y = row)) + geom_raster(aes(fill = rownames))
## please check autoplot, matrix for more control
## Views
## ExpressionSet
library(Biobase)
data(sample.ExpressionSet)
sample.ExpressionSet
set.seed(1)
## select 50 features
idx <- sample(seq_len(dim(sample.ExpressionSet)[1]), size = 50)
eset <- sample.ExpressionSet[idx,]
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = value))
## please check autoplot,matrix method which gives you more control
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = sex))
ggplot(eset) + geom_tile(aes(x = x, y = y, fill = type))
## Rle
library(IRanges)
lambda <- c(rep(0.001, 4500), seq(0.001, 10, length = 500),
seq(10, 0.001, length = 500))
xVector <- rpois(1e4, lambda)
xRle <- Rle(xVector)
ggplot(xRle) + geom_tile(aes(x = x, y = y, fill = value))
## RleList
xRleList <- RleList(xRle, 2L * xRle)
xRleList
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
names(xRleList) <- c("a" ,"b")
ggplot(xRleList) + geom_tile(aes(x = x, y = y, fill = value)) +
facet_grid(group~.)
## RangedSummarizedExperiment
library(SummarizedExperiment)
nrows <- 200; ncols <- 6
counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
counts2 <- matrix(runif(nrows * ncols, 1, 1e4), nrows)
rowRanges <- GRanges(rep(c("chr1", "chr2"), c(50, 150)),
IRanges(floor(runif(200, 1e5, 1e6)), width=100),
strand=sample(c("+", "-"), 200, TRUE))
colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 3),
row.names=LETTERS[1:6])
sset <- SummarizedExperiment(assays=SimpleList(counts=counts,
counts2 = counts2),
rowRanges=rowRanges, colData=colData)
ggplot(sset) + geom_raster(aes(x = x, y = y , fill = value))
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