R: Scatter plot

ggscatter {ggpubr} | R Documentation

## Scatter plot

### Description

Create a scatter plot.

### Usage

    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,
      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 `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.
`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

`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)

[Package ggpubr version 0.1.6 Index]