Introduces `geom_pointdensity()`

: A cross between a scatter plot and a 2D density plot.

To install the package from R, use:

Alternatively, you can install the development version from GitHub:

```
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("LKremer/ggpointdensity")
```

There are several ways to visualize data points on a 2D coordinate system: If you have lots of data points on top of each other, `geom_point()`

fails to give you an estimate of how many points are overlapping. `geom_density2d()`

and `geom_bin2d()`

solve this issue, but they make it impossible to investigate individual outlier points, which may be of interest.

`geom_pointdensity()`

aims to solve this problem by combining the best of both worlds: individual points are colored by the number of neighboring points. This allows you to see the overall distribution, as well as individual points.

Generate some toy data and visualize it with `geom_pointdensity()`

:

```
library(ggplot2)
library(dplyr)
library(viridis)
library(ggpointdensity)
dat <- bind_rows(
tibble(x = rnorm(7000, sd = 1),
y = rnorm(7000, sd = 10),
group = "foo"),
tibble(x = rnorm(3000, mean = 1, sd = .5),
y = rnorm(3000, mean = 7, sd = 5),
group = "bar"))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis()
```

Each point is colored according to the number of neighboring points. The distance threshold to consider two points as neighbors (smoothing bandwidth) can be adjusted with the `adjust`

argument, where `adjust = 0.5`

means use half of the default bandwidth.

```
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = .1) +
scale_color_viridis()
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity(adjust = 4) +
scale_color_viridis()
```

Of course you can combine the geom with standard `ggplot2`

features such as facets…

```
# Facetting by group
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
facet_wrap( ~ group)
```

… or point shape and size:

```
dat_subset <- sample_frac(dat, .1) # smaller data set
ggplot(data = dat_subset, mapping = aes(x = x, y = y)) +
geom_pointdensity(size = 3, shape = 17) +
scale_color_viridis()
```

Zooming into the axis works as well, keep in mind that `xlim()`

and `ylim()`

change the density since they remove data points. It may be better to use `coord_cartesian()`

instead.

```
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
xlim(c(-1, 3)) + ylim(c(-5, 15))
ggplot(data = dat, mapping = aes(x = x, y = y)) +
geom_pointdensity() +
scale_color_viridis() +
coord_cartesian(xlim = c(-1, 3), ylim = c(-5, 15))
```

Lukas Kremer and Simon Anders (2019)