| Type: | Package |
| Title: | An Enhanced Chart for Simple and Truthful Representation of Single Observations over Multiple Classes |
| Version: | 1.1.0 |
| Date: | 2017-04-10 |
| Maintainer: | Nikos Sidiropoulos <nikos.sidiro@gmail.com> |
| Description: | The sinaplot is a data visualization chart suitable for plotting any single variable in a multiclass data set. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class. |
| Depends: | R(≥ 3.1.0), plyr(≥ 1.8.4) |
| Suggests: | rmarkdown, knitr, RColorBrewer |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| LazyData: | TRUE |
| VignetteBuilder: | knitr |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-04-21 14:28:00 UTC; bfh139 |
| Author: | Nikos Sidiropoulos [aut, cre], Sina Hadi Sohi [aut], Nicolas Rapin [aut], Frederik Otzen Bagger [aut] |
| Repository: | CRAN |
| Date/Publication: | 2017-04-21 17:58:31 UTC |
Expression data from 2095 AML/ALL and healthy bone marrow cells.
Description
Expression data from 2095 AML/ALL and healthy bone marrow cells.
Usage
data(blood)
Format
A data frame with 2095 rows and 2 columns (Class (AML/ALL subtype), Gene expression values).
Source
http://servers.binf.ku.dk/bloodspot/
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13159
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15434
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61804
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14468
sinaplot
Description
The SinaPlot is a data visualization chart suitable for plotting any single variable in a multiclass dataset. It is an enhanced jitter strip chart, where the width of the jitter is controlled by the density distribution of the data within each class.
Usage
sinaplot(x, ...)
## Default S3 method:
sinaplot(x, groups = NULL, method = c("density",
"counts"), scale = TRUE, adjust = 0.75, bins = 50, bin_limit = 1,
maxwidth = 1, seed = NULL, plot = TRUE, add = FALSE, log = FALSE,
labels = NULL, xlab = "", ylab = "", col = NULL, pch = NULL, ...)
## S3 method for class 'formula'
sinaplot(formula, data = NULL, ..., subset,
na.action = NULL, xlab, ylab)
Arguments
x |
numeric vector or a data frame or a list of numeric vectors to be plotted. |
... |
arguments to be passed to |
groups |
optional vector of |
method |
choose the method to spread the samples within the same
bin along the x-axis. Available methods: "density" and "counts".
See |
scale |
a logical that indicates whether the width of each group should
be scaled relative to the group with the highest density.
Default: |
adjust |
adjusts the bandwidth of the density kernel when
|
bins |
number of bins to divide the y-axis into when
|
bin_limit |
if the samples within the same y-axis bin are more
than |
maxwidth |
control the maximum width the points can spread into. Values between 0 and 1. |
seed |
a single value that controls the random sample jittering. Set to an integer to enable plot reproducibility. Default NULL. |
plot |
logical. When |
add |
logical. If true add boxplot to current plot. |
log |
logical. If true it uses a logarihmic scale on the y-axis. |
labels |
labels for each group. Recycled if necessary. By default, these are inferred from the data. |
xlab, ylab |
axis labels. |
pch, col |
plotting characters and colors, specified by group. Recycled if necessary. |
formula |
a formula, such as y ~ grp, where y is a numeric vector of data values to be split into groups according to the grouping variable grp (usually a factor). |
data |
a data.frame (or list) from which the variables in formula should be taken. |
subset |
an optional vector specifying a subset of observations to be used for plotting. |
na.action |
a function which indicates what should happen when the data contain NAs. The default is to ignore missing values in either the response or the group. |
Details
There are two available ways to define the x-axis borders for the samples to spread within:
method = "density"A density kernel is estimated along the y-axis for every sample group. The borders are then defined by the density curve. Tuning parameter
adjustcan be used to control the density bandwidth in the same way it is used indensity.method = "counts":The borders are defined by the number of samples that occupy the same bin and the parameter
maxwidthin the following fashion:xBorder = nsamples * maxwidth
Value
x |
discrete x-coordinates, split by group |
y |
input values |
group |
input groups |
scaled |
final x-coordinates, adjusted by sinaplot |
NULL
NULL
Examples
## sinaplot on a formula:
data("blood", package = "sinaplot")
boxplot(Gene ~ Class, data = blood)
sinaplot(Gene ~ Class, data = blood, pch = 20, add = TRUE)
## sinaplot on a data.frame:
df <- data.frame(Uni05 = (1:100)/21, Norm = rnorm(100),
`5T` = rt(100, df = 5), Gam2 = rgamma(100, shape = 2))
boxplot(df)
sinaplot(df, add = TRUE, pch = 20)
## sinaplot on a list:
bimodal <- c(rnorm(300, -2, 0.6), rnorm(300, 2, 0.6))
uniform <- runif(500, -4, 4)
normal <- rnorm(800,0,3)
distributions <- list(uniform = uniform, bimodal = bimodal, normal = normal)
boxplot(distributions, col = 2:4)
sinaplot(distributions, add = TRUE, pch = 20)
## sinaplot on a vector:
x <- c(rnorm(200, 4, 1), rnorm(200, 5, 2), rnorm(400, 6, 1.5))
groups <- c(rep("Cond1", 200), rep("Cond2", 200), rep("Cond3", 400))
sinaplot(x, groups)
par(mfrow = c(2, 2))
sinaplot(x, groups, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, adjust = 1/6, pch = 20, col = 2:4)
sinaplot(x, groups, scale = FALSE, adjust = 3, pch = 20, col = 2:4)
#blood
par(mfrow = c(1,1))
sinaplot(blood$Gene, blood$Class)
old.mar <- par()$mar
par(mar = c(9,4,4,2) + 0.1)
groups <- levels(blood$Class)
sinaplot(blood$Gene, blood$Class, pch = 20, xaxt = "n", col = rainbow(18))
axis(1, at = 1:length(groups), labels = FALSE)
text(1:length(groups), y = par()$usr[3] - 0.1 * (par()$usr[4] - par()$usr[3]),
xpd = TRUE, srt = 45, adj = 1, labels = groups)
par(mar = old.mar)