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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# gg1d <a href="https://selkamand.github.io/gg1d/"><img src="man/figures/logo.png" align="right" height="138" alt="gg1d website" /></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=gg1d)
[](https://github.com/selkamand/gg1d/actions/workflows/R-CMD-check.yaml)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://app.codecov.io/gh/selkamand/gg1d?branch=main)
[](https://github.com/selkamand/gg1d)
<!-- badges: end -->
Effortlessly visualize all columns in a data frame with vertically aligned plots and automatic plot selection based on variable type. Plots are fully interactive, and custom tooltips can be added.
A graphical user interface to all gg1d visualisations is available at https://selkamand.github.io/EDA/
**Why 1 dimensional plots?**
To understand trends in your data, especially correlative relationships between 2 or more features, it can be useful to densely stack visual representations of each feature vertically, regardless of data type. By unifying the $x$-axis across each plot, **gg1d** turns a series of 1D plots into an $n\text{-dimensional}$ visualization where $n = \text{number of columns in dataset}$.
Note the key idea of gg1d is to 'preserve the individual.' **gg1d** does **NOT** plot distributions of properties, but rather each value of a feature for each subject/observation in the dataset.
gg1d can be used for exploratory data analysis (EDA) or to produce publication quality graphics summarizing a dataset.
## Installation
``` r
install.packages("gg1d")
```
### Development Version
You can install the development version of gg1d from [GitHub](https://github.com/) with:
``` r
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("selkamand/gg1d")
```
## Quick Start
For examples of interactive gg1d plots see the [gg1d gallery](https://selkamand.github.io/gg1d/articles/gallery.html)
```{r, example, fig.width = 7, fig.height = 5}
# Load library
library(gg1d)
# Read data
path_gg1d <- system.file("example.csv", package = "gg1d")
df <- read.csv(path_gg1d, header = TRUE, na.strings = "")
# Plot data, sort by Glasses
gg1d(
df,
col_id = "ID",
col_sort = "Glasses",
interactive = FALSE,
verbose = FALSE,
options = gg1d_options(legend_nrow = 2)
)
```
## Customise Colours
Customise colours by supplying a named list to the `palettes` argument
```{r customise_colours, fig.width = 7, fig.height = 5}
gg1d(
df,
col_id = "ID",
col_sort = "Glasses",
palettes = list("EyeColour" = c(
Brown = "rosybrown4",
Blue = "steelblue",
Green = "seagreen"
)),
interactive = FALSE,
verbose = FALSE,
options = gg1d_options(legend_nrow = 2)
)
```
## Parallel Coordinate Plots
For datasets with many observations and mostly numeric features, parallel coordinate plots may be more appropriate.
```{r minibeans_class, fig.width = 7, fig.height = 5}
ggparallel(
data = minibeans,
col_colour = "Class",
order_columns_by = "auto",
interactive = FALSE
)
```
```{r minibeans_highlight, fig.width = 7, fig.height = 5}
ggparallel(
data = minibeans,
col_colour = "Class",
highlight = "DERMASON",
order_columns_by = "auto",
interactive = FALSE
)
```
```{r minibeans_noclass, fig.width = 7, fig.height = 5}
ggparallel(
data = minibeans,
order_columns_by = "auto",
interactive = FALSE
)
```
## Community Contributions
All types of contributions are encouraged and valued. See our [guide to
community contributions](https://selkamand.github.io/gg1d/CONTRIBUTING.html) for different ways to help.