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https://github.com/Hy4m/linkET
Everything is Linkable
https://github.com/Hy4m/linkET
correlation feature-selection ggplot2 heatmap r visualization
Last synced: about 1 month ago
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Everything is Linkable
- Host: GitHub
- URL: https://github.com/Hy4m/linkET
- Owner: Hy4m
- Created: 2021-06-06T14:14:27.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-06-14T16:16:52.000Z (over 1 year ago)
- Last Synced: 2024-08-03T23:24:10.135Z (5 months ago)
- Topics: correlation, feature-selection, ggplot2, heatmap, r, visualization
- Language: R
- Homepage:
- Size: 2.63 MB
- Stars: 129
- Watchers: 2
- Forks: 38
- Open Issues: 4
-
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 = "100%"
)
```
# linkET
The goal of `linkET` is to visualize simply and directly a matrix heatmap based
on 'ggplot2'. **Note: all functions of network have been merged in netET package.**
## Installation
You can install the released version of `linkET` from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("Hy4m/linkET", force = TRUE)
packageVersion("linkET")
```
## Example
This is a basic example which shows you how to solve a common problem:
### Data processing
```{r data_processing}
library(linkET)
## matrix_data
matrix_data(list(mtcars = mtcars))
## md_tbl
matrix_data(list(mtcars = mtcars)) %>%
as_md_tbl()
## as method
as_matrix_data(mtcars)
as_md_tbl(mtcars)
## special function for correlation matrix
correlate(mtcars) %>%
as_matrix_data()
correlate(mtcars) %>%
as_md_tbl()
```
### Heatmap
```{r heatmap}
library(ggplot2)
matrix_data(list(mtcars = mtcars)) %>%
hyplot(aes(fill = mtcars)) +
geom_tile()
as_md_tbl(mtcars) %>%
hyplot(aes(size = mtcars)) +
geom_point(shape = 21, fill = NA)
correlate(mtcars) %>%
as_md_tbl() %>%
qcorrplot() +
geom_square()
library(vegan)
data("varespec")
data("varechem")
correlate(varespec[1:30], varechem) %>%
qcorrplot() +
geom_square() +
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))
qcorrplot(varespec[1:30], type = "lower") +
geom_square() +
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu"))
## you can set your style
set_corrplot_style()
qcorrplot(mtcars) + geom_square()
## reset to default style
set_default_style()
```
## mantel test
```{r mantel}
library(dplyr)
data("varechem", package = "vegan")
data("varespec", package = "vegan")
mantel <- mantel_test(varespec, varechem,
spec_select = list(Spec01 = 1:7,
Spec02 = 8:18,
Spec03 = 19:37,
Spec04 = 38:44)) %>%
mutate(rd = cut(r, breaks = c(-Inf, 0.2, 0.4, Inf),
labels = c("< 0.2", "0.2 - 0.4", ">= 0.4")),
pd = cut(p, breaks = c(-Inf, 0.01, 0.05, Inf),
labels = c("< 0.01", "0.01 - 0.05", ">= 0.05")))
qcorrplot(correlate(varechem), type = "lower", diag = FALSE) +
geom_square() +
geom_couple(aes(colour = pd, size = rd),
data = mantel,
curvature = nice_curvature()) +
scale_fill_gradientn(colours = RColorBrewer::brewer.pal(11, "RdBu")) +
scale_size_manual(values = c(0.5, 1, 2)) +
scale_colour_manual(values = color_pal(3)) +
guides(size = guide_legend(title = "Mantel's r",
override.aes = list(colour = "grey35"),
order = 2),
colour = guide_legend(title = "Mantel's p",
override.aes = list(size = 3),
order = 1),
fill = guide_colorbar(title = "Pearson's r", order = 3))
```
## pairs plot
```{r}
qpairs(iris) + geom_pairs()
```
## Citation
To cite linkET in publications use:
Houyun Huang(2021). linkET: Everything is Linkable. R package version 0.0.3.