geom_point {ggplot2} | R Documentation

## Points, as for a scatterplot

### Description

The point geom is used to create scatterplots.

### Usage

    geom_point(mapping = NULL, data = NULL, stat = "identity",
      position = "identity", ..., na.rm = FALSE, show.legend = NA,
      inherit.aes = TRUE)

### Arguments

`mapping` | Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data` | The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame.`, and will be used as the layer data.
`stat` | The statistical transformation to use on the data for this layer, as a string.
`position` | Position adjustment, either as a string, or the result of a call to a position adjustment function.
`...` | other arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`na.rm` | If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`show.legend` | logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes.
`inherit.aes` | If `FALSE`, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. `borders`.

### Details

The scatterplot is useful for displaying the relationship between two continuous variables, although it can also be used with one continuous and one categorical variable, or two categorical variables. See `geom_jitter` for possibilities.

The bubblechart is a scatterplot with a third variable mapped to the size of points. There are no special names for scatterplots where another variable is mapped to point shape or colour, however.

The biggest potential problem with a scatterplot is overplotting: whenever you have more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot. There is no one solution to this problem, but there are some techniques that can help. You can add additional information with `geom_smooth`, `geom_quantile` or `geom_density_2d`. If you have few unique x values, `geom_boxplot` may also be useful. Alternatively, you can summarise the number of points at each location and display that in some way, using `stat_sum`. Another technique is to use transparent points, e.g. `geom_point(alpha = 0.05)`.

### Aesthetics

`geom_point` understands the following aesthetics (required aesthetics are in bold):

- `x`

- `y`

- `alpha`

- `colour`

- `fill`

- `shape`

- `size`

- `stroke`

### See Also

`scale_size` to see scale area of points, instead of radius, `geom_jitter` to jitter points to reduce (mild) overplotting

### Examples

    p <- ggplot(mtcars, aes(wt, mpg))
    p + geom_point()

    # Add aesthetic mappings
    p + geom_point(aes(colour = factor(cyl)))
    p + geom_point(aes(shape = factor(cyl)))
    p + geom_point(aes(size = qsec))

    # Change scales
    p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = "blue")
    p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)

    # Set aesthetics to fixed value
    ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)

    # Varying alpha is useful for large datasets
    d <- ggplot(diamonds, aes(carat, price))
    d + geom_point(alpha = 1/10)
    d + geom_point(alpha = 1/20)
    d + geom_point(alpha = 1/100)

    # For shapes that have a border (like 21), you can colour the inside and
    # outside separately. Use the stroke aesthetic to modify the width of the
    # border
    ggplot(mtcars, aes(wt, mpg)) +
      geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)

    # You can create interesting shapes by layering multiple points of
    # different sizes
    p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
    p + geom_point(aes(colour = factor(cyl)), size = 4) +
      geom_point(colour = "grey90", size = 1.5)
    p + geom_point(colour = "black", size = 4.5) +
      geom_point(colour = "pink", size = 4) +
      geom_point(aes(shape = factor(cyl)))

    # These extra layers don't usually appear in the legend, but we can
    # force their inclusion
    p + geom_point(colour = "black", size = 4.5, show.legend = TRUE) +
      geom_point(colour = "pink", size = 4, show.legend = TRUE) +
      geom_point(aes(shape = factor(cyl)))

    # geom_point warns when missing values have been dropped from the data set
    # and not plotted, you can turn this off by setting na.rm = TRUE
    mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
    ggplot(mtcars2, aes(wt, mpg)) + geom_point()
    ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE)

[Package ggplot2 version 2.1.0 Index]