Create a scatter plot.

ggscatter(
  data,
  x,
  y,
  combine = FALSE,
  merge = FALSE,
  color = "black",
  fill = "lightgray",
  palette = NULL,
  shape = 19,
  size = 2,
  point = TRUE,
  rug = FALSE,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  facet.by = NULL,
  panel.labs = NULL,
  short.panel.labs = TRUE,
  add = c("none", "reg.line", "loess"),
  add.params = list(),
  conf.int = FALSE,
  conf.int.level = 0.95,
  fullrange = FALSE,
  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,
  parse = FALSE,
  cor.coef = FALSE,
  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_pubr(),
  ...
)

Arguments

data

a data frame

x, y

x and y variables for drawing.

combine

logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.

merge

logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.

color, fill

point colors.

palette

the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".

shape

point shape. See show_point_shapes.

size

Numeric value (e.g.: size = 1). change the size of points and outlines.

point

logical value. If TRUE, show points.

rug

logical value. If TRUE, add marginal rug.

title

plot main title.

xlab

character vector specifying x axis labels. Use xlab = FALSE to hide xlab.

ylab

character vector specifying y axis labels. Use ylab = FALSE to hide ylab.

facet.by

character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.

panel.labs

a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).

short.panel.labs

logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.

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 FactoMineR::coord.ellipse().

  • "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.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath.

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.

ggtheme

function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....

...

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

Examples

# Load data data("mtcars") df <- mtcars df$cyl <- as.factor(df$cyl) head(df[, c("wt", "mpg", "cyl")], 3)
#> wt mpg cyl #> Mazda RX4 2.620 21.0 6 #> Mazda RX4 Wag 2.875 21.0 6 #> Datsun 710 2.320 22.8 4
# 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") )
#> `geom_smooth()` using formula 'y ~ x'
# loess method: local regression fitting ggscatter(df, x = "wt", y = "mpg", add = "loess", conf.int = TRUE)
#> `geom_smooth()` using formula 'y ~ x'
# 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)