bio_corr: Correlation plot

In KatrionaGoldmann/BioOutputs: Visualisation of Biological Data

Correlation plot

Description

Create a correlation plot. Taken from kassambara/ggpubr just changed the default arguments

Usage

bio_corr(
  data,
  x,
  y,
  color = "black",
  fill = "lightgray",
  palette = NULL,
  shape = 19,
  size = 2,
  point = TRUE,
  rug = FALSE,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  add = "reg.line",
  add.params = list(color = "red", fill = "lightgray"),
  conf.int = TRUE,
  conf.int.level = 0.95,
  fullrange = TRUE,
  ellipse = FALSE,
  ellipse.level = 0.95,
  ellipse.type = "norm",
  ellipse.alpha = 0.1,
  ellipse.border.remove = FALSE,
  mean.point = FALSE,
  mean.point.size = ifelse(is.numeric(size), 2 * size, size),
  star.plot = FALSE,
  star.plot.lty = 1,
  star.plot.lwd = NULL,
  label = NULL,
  font.label = c(12, "plain"),
  font.family = "",
  label.select = NULL,
  repel = FALSE,
  label.rectangle = FALSE,
  cor.coef = TRUE,
  cor.coeff.args = list(),
  cor.method = "pearson",
  cor.coef.coord = c(NULL, NULL),
  cor.coef.size = 4,
  ggp = NULL,
  show.legend.text = NA,
  ggtheme = theme_classic(),
  ...
)

Arguments

x, y	x and y variables for drawing.
color, fill	point colors.
shape	point shape. See show_point_shapes.
point	logical value. If TRUE, show points.
rug	logical value. If TRUE, add marginal rug.
add	allowed values are one of "none", "reg.line" (for adding linear regression line) or "loess" (for adding local regression fitting).
add.params	parameters (color, size, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").
conf.int	logical value. If TRUE, adds confidence interval.
conf.int.level	Level controlling confidence region. Default is 95%. Used only when add != "none" and conf.int = TRUE.
fullrange	should the fit span the full range of the plot, or just the data. Used only when add != "none".
ellipse	logical value. If TRUE, draws ellipses around points.
ellipse.level	the size of the concentration ellipse in normal probability.
ellipse.type	Character specifying frame type. Possible values are "convex", "confidence" or types supported by stat_ellipse() including one of c("t", "norm", "euclid") for plotting concentration ellipses. "convex": plot convex hull of a set o points. "confidence": plot confidence ellipses arround group mean points as coord.ellipse()[in FactoMineR]. "t": assumes a multivariate t-distribution. "norm": assumes a multivariate normal distribution. "euclid": draws a circle with the radius equal to level, representing the euclidean distance from the center. This ellipse probably won't appear circular unless coord_fixed() is applied.
ellipse.alpha	Alpha for ellipse specifying the transparency level of fill color. Use alpha = 0 for no fill color.
ellipse.border.remove	logical value. If TRUE, remove ellipse border lines.
mean.point	logical value. If TRUE, group mean points are added to the plot.
mean.point.size	numeric value specifying the size of mean points.
star.plot	logical value. If TRUE, a star plot is generated.
star.plot.lty, star.plot.lwd	line type and line width (size) for star plot, respectively.
label	the name of the column containing point labels. Can be also a character vector with length = nrow(data).
font.label	a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of point labels. For example font.label = c(14, "bold", "red"). To specify only the size and the style, use font.label = c(14, "plain").
font.family	character vector specifying font family.
label.select	character vector specifying some labels to show.
repel	a logical value, whether to use ggrepel to avoid overplotting text labels or not.
label.rectangle	logical value. If TRUE, add rectangle underneath the text, making it easier to read.
cor.coef	logical value. If TRUE, correlation coefficient with the p-value will be added to the plot.
cor.coeff.args	a list of arguments to pass to the function stat_cor for customizing the displayed correlation coefficients. For example: cor.coeff.args = list(method = "pearson", label.x.npc = "right", label.y.npc = "top").
cor.method	method for computing correlation coefficient. Allowed values are one of "pearson", "kendall", or "spearman".
cor.coef.coord	numeric vector, of length 2, specifying the x and y coordinates of the correlation coefficient. Default values are NULL.
cor.coef.size	correlation coefficient text font size.
ggp	a ggplot. If not NULL, points are added to an existing plot.
show.legend.text	logical. Should text be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
...	other arguments to be passed to geom_point and ggpar.

Details

The plot can be easily customized using the function ggpar(). Read ?ggpar for changing:

• main title and axis labels: main, xlab, ylab

• axis limits: xlim, ylim (e.g.: ylim = c(0, 30))

• axis scales: xscale, yscale (e.g.: yscale = "log2")

• color palettes: palette = "Dark2" or palette = c("gray", "blue", "red")

• legend title, labels and position: legend = "right"

• plot orientation : orientation = c("vertical", "horizontal", "reverse")

See Also

stat_cor, stat_stars, stat_conf_ellipse and ggpar.

Examples

# Load data
data("mtcars")
df <- mtcars
df$cyl <- as.factor(df$cyl)
head(df[, c("wt", "mpg", "cyl")], 3)

# Basic plot
# +++++++++++++++++++++++++++
ggscatter(df, x = "wt", y = "mpg",
   color = "black", shape = 21, size = 3, # Points color, shape and size
   add = "reg.line",  # Add regressin line
   add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
   conf.int = TRUE, # Add confidence interval
   cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
   cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
   )

# loess method: local regression fitting
ggscatter(df, x = "wt", y = "mpg",
   add = "loess", conf.int = TRUE)


# Control point size by continuous variable values ("qsec")
ggscatter(df, x = "wt", y = "mpg",
   color = "#00AFBB", size = "qsec")


# Change colors
# +++++++++++++++++++++++++++
# Use custom color palette
# Add marginal rug
ggscatter(df, x = "wt", y = "mpg", color = "cyl",
   palette = c("#00AFBB", "#E7B800", "#FC4E07") )




# Add group ellipses and mean points
# Add stars
# +++++++++++++++++++
ggscatter(df, x = "wt", y = "mpg",
   color = "cyl", shape = "cyl",
   palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   ellipse = TRUE, mean.point = TRUE,
   star.plot = TRUE)


# Textual annotation
# +++++++++++++++++
df$name <- rownames(df)
ggscatter(df, x = "wt", y = "mpg",
   color = "cyl", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   label = "name", repel = TRUE)

KatrionaGoldmann/BioOutputs documentation built on May 21, 2022, 1:24 p.m.