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https://github.com/jpmonteagudo28/sequentially
An R package for non-linear sequence generation using easing functions
https://github.com/jpmonteagudo28/sequentially
Last synced: 16 days ago
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An R package for non-linear sequence generation using easing functions
- Host: GitHub
- URL: https://github.com/jpmonteagudo28/sequentially
- Owner: jpmonteagudo28
- License: gpl-3.0
- Created: 2024-12-05T15:12:32.000Z (18 days ago)
- Default Branch: main
- Last Pushed: 2024-12-05T15:58:16.000Z (18 days ago)
- Last Synced: 2024-12-05T16:35:54.094Z (18 days ago)
- Language: R
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
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%"
)
#library(sequentially)
```
# sequentially 
[](https://CRAN.R-project.org/package=sequentially)
[](https://github.com/mkenney/software-guides/blob/master/STABILITY-BADGES.md#work-in-progress)
[](https://app.codecov.io/gh/jpmonteagudo28/sequentially)
This package is born out of curiosity rather than necessity. `sequentially` creates non-linear and linear numeric sequences. By using non-linear interpolation the user can animate their data
in a way that is more visually pleasing than uniform, linear interpolation. The functions in this package represent a break from the **essential** but *boring* `seq()` family of functions–I wanted to plot numeric sequences that looked different and could be used in data visualization, motion animation, frame interpolation, UI/UX design, population dynamics, or other fields.
## Installation
You can install the development version of sequentially like so:
``` r
devtools::install_github("jpmonteagudo28/sequentially")
```
or download it from CRAN:
```r
install.packages("sequentially")
```
## What you get:
This is a basic example which shows you how to solve a common problem:
```{r example}
library(sequentially)
# Linear sequence from 0 to 10
t <- seq(0,1,length.out = 100)
lin_seq <- seq_smooth(0, 10, n = 100, type = "linear")
plot.new()
plot.window(range(t),range(lin_seq))
points(t,lin_seq,pch = 16, cex = .75,col = "red")
axis(1,tcl = 0.75,lwd = 0, family = "serif")
axis(2,lwd = 0, family = "serif", las = 1)
grid(2,3,col = "gray80",lty = "dotted", lwd = 0.50)
mtext("Linear Sequence",3,cex = 1.3, family = "serif")
# Elastic easing out sequence
elastic_seq <- seq_smooth(0, 10, n = 100, type = "elastic", ease = "out")
plot.new()
plot.window(range(t),range(elastic_seq))
points(t,elastic_seq,pch = 16, cex = .75,col = "red")
axis(1,tcl = 0.75,lwd = 0, family = "serif")
axis(2,lwd = 0, family = "serif", las = 1)
grid(2,3,col = "gray80",lty = "dotted", lwd = 0.50)
mtext("Ease-out Elastic Sequence",3,cex = 1.3, family = "serif")
```
What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
```{r cars}
```
You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.
You can also embed plots, for example:
```{r pressure, echo = FALSE}
```
## Related Software
* [tweenr](https://cran.r-project.org/web/packages/tweenr/tweenr.pdf) - an R package dealing with data animation.
* [displease](https://github.com/coolbutuseless/displease) - a related package used as inspiration for this one.
* [ofpennereasing](https://github.com/jesusgollonet/ofpennereasing) - Rcpp files containing Robert Penner's easing functions