https://github.com/const-ae/ggquadrilateral

A quadrilateral geom for ggplot2
https://github.com/const-ae/ggquadrilateral

ggplot-extension ggplot2 polygon quadrilateral rstats

Last synced: 9 days ago
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A quadrilateral geom for ggplot2

• Host: GitHub
• URL: https://github.com/const-ae/ggquadrilateral
• Owner: const-ae
• Created: 2019-08-28T09:01:38.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-28T11:14:17.000Z (over 5 years ago)
• Last Synced: 2024-11-06T14:00:51.597Z (about 2 months ago)
• Topics: ggplot-extension, ggplot2, polygon, quadrilateral, rstats
• Language: R
• Size: 86.9 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "70%"

)

set.seed(1)

```

# ggquadrilateral

`ggquadrilateral` provides a [ggplot2](https://ggplot2.tidyverse.org/index.html) geom that can draw

arbitrary quadrilaterals in a convenient way.

## Installation

You can install the latest version of ggquadrilateral from [GitHub](https://github.com/const-ae/ggquadrilateral) with:

```{r eval=FALSE}

devtools::install_github("const-ae/ggquadrilateral")

```

If you don't already have devtools installed, you can get it from CRAN with `install.packages("devtools")`.

## Example

First load `ggplot2` and the `ggquadrilateral` package

```{r example}

library(ggplot2)

library(ggquadrilateral)

```

The simplest example is to just define the positions of the four corners manually

```{r}

kite_df <- data.frame(

  left_tip_x = 2,

  left_tip_y = 7,

  top_tip_x = 3,

  top_tip_y = 8,

  right_tip_x = 4,

  right_tip_y = 7,

  bottom_tip_x = 3,

  bottom_tip_y = 3

)

kite_df

```

```{r}

ggplot(kite_df) +

  geom_quadrilateral(aes(x1=left_tip_x, y1 = left_tip_y,

                         x2 = top_tip_x, y2 = top_tip_y,

                         x3 = right_tip_x, y3 = right_tip_y,

                         x4 = bottom_tip_x, y4 = bottom_tip_y),

                     color = "black", fill = "purple", size=4) +

  xlim(-2, 8) + ylim(0, 10)

```

It can also be used to visualize more complex data.

We will now use it to draw the triangle mesh for

10 random points.

```{r}

df <- data.frame(x=rnorm(n=10, mean=0, sd=1),

                 y=rnorm(n=10, mean=0, sd=1))

ggplot(df, aes(x=x, y=y)) +

  geom_point()

```

We will use the [`tripack`](https://cran.r-project.org/web/packages/tripack/index.html) package

to calculate the Delauney triangulation.

```{r}

library(tripack)

triang <- as.data.frame(triangles(tri.mesh(df)))

triang_df <- data.frame(id = seq_len(nrow(triang)),

           p1x = df$x[triang$node1],

           p1y = df$y[triang$node1],

           p2x = df$x[triang$node2],

           p2y = df$y[triang$node2],

           p3x = df$x[triang$node3],

           p3y = df$y[triang$node3])

head(triang_df)

```

Using the `triang_df` that the coordinates for the 12 interpolating triangles we can make the plot:

```{r}

ggplot() +

  geom_quadrilateral(data=triang_df, 

                     mapping = aes(

                       x1 = p1x, y1 = p1y,

                       x2 = p2x, y2 = p2y,

                       x3 = p3x, y3 = p3y,

                       # We want triangles, so we make the

                       # fourth point identical to the third

                       x4 = p3x, y4 = p3y,

                       fill = as.factor(id)),

                     color = "black") +

  geom_point(data= df, aes(x=x, y=y), size = 3) 

```