Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/lkremer/ggpointdensity

:chart_with_upwards_trend: :bar_chart: Introduces geom_pointdensity(): A Cross Between a Scatter Plot and a 2D Density Plot.
https://github.com/lkremer/ggpointdensity

2d-density-plot density-visualization geom ggplot-extension ggplot2 ggplot2-enhancements ggplot2-geoms neighboring-points r r-package r-stats rstats scatter-plot visualization

Last synced: 1 day ago
JSON representation

:chart_with_upwards_trend: :bar_chart: Introduces geom_pointdensity(): A Cross Between a Scatter Plot and a 2D Density Plot.

Awesome Lists containing this project

README 

        
# ggpointdensity

[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/ggpointdensity)](https://cran.r-project.org/package=ggpointdensity) 

[![Downloads](https://cranlogs.r-pkg.org/badges/last-month/ggpointdensity?color=brightgreen)](https://cran.r-project.org/package=ggpointdensity)



Introduces `geom_pointdensity()`: A cross between a scatter plot and a 2D density plot.






## Installation

To install the package, type this command in R:

``` r

install.packages("ggpointdensity")



# Alternatively, you can install the latest

# development version from GitHub:

if (!requireNamespace("devtools", quietly = TRUE))

    install.packages("devtools")

devtools::install_github("LKremer/ggpointdensity")

```



## Motivation

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.






## Changelog

Added `method` argument and renamed the `n_neighbor` stat to `density`. The available options

are `method="auto"`,

`method="default"` and `method="kde2d"`. `default` is the regular n_neighbor calculation

as in the CRAN package. `kde2d` uses 2D kernel density estimation to estimate the point density

(credits to @slowkow).

This method is slower for few points, but faster for many (ca. >20k) points. By default,

`method="auto"` picks either `kde2d` or `default` depending on the number of points.



## Demo

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

``` r

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.

(Note: this here is the dev branch, where I decided to plot the density estimate

instead of n_neighbors now.)

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.

``` r

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...



``` r

# 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:

``` r

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.



``` r

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))

```






## Authors

Lukas PM Kremer ([@LPMKremer](https://twitter.com/LPMKremer/)) and Simon Anders ([@s_anders_m](https://twitter.com/s_anders_m/)), 2019