---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
# Please put your title here to include it in the file below.
Title <- "Title of your paper goes here"
```

# simplerc

## Overview
simplerc is a tool for visualizing random walks. Using simplerc, you can simulate a random walk or fit using your own data. Fitting gives two parameters: mean and standard deviation, which describes the random walk. simplerc can also be used to plot the cumulative distribution of the random walk as a line plot.

## Example
First, we simulate random walk data with the function `simulate_walk`, which takes number of steps, mean, and standard deviation as inputs.

```{}
t <- 100
mu <- 0.5
sigma <- 2

walk <- simulate_walk(t, mu, sigma)
plot_walk(walk)
```

Next, we will try to fit our simulated random walk data.
```{}
params <- fit_walk(walk)
params
```

Finally, we can see that we have recovered the correct random walk parameters from our fit.




[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh///master?urlpath=rstudio)

This repository contains the data and code for our paper:

> Authors, (YYYY). _`r Title`_. Name of journal/book <https://doi.org/xxx/xxx>

Our pre-print is online here:

> Authors, (YYYY). _`r Title`_. Name of journal/book, Accessed `r format(Sys.Date(), "%d %b %Y")`. Online at <https://doi.org/xxx/xxx>


### How to cite

Please cite this compendium as:

> Authors, (`r format(Sys.Date(), "%Y")`). _Compendium of R code and data for `r Title`_. Accessed `r format(Sys.Date(), "%d %b %Y")`. Online at <https://doi.org/xxx/xxx>

## Contents

The **analysis** directory contains:

  - [:file\_folder: paper](/analysis/paper): R Markdown source document
    for manuscript. Includes code to reproduce the figures and tables
    generated by the analysis. It also has a rendered version,
    `paper.docx`, suitable for reading (the code is replaced by figures
    and tables in this file)
  - [:file\_folder: data](/analysis/data): Data used in the analysis.
  - [:file\_folder: figures](/analysis/figures): Plots and other
    illustrations
  - [:file\_folder:
    supplementary-materials](/analysis/supplementary-materials):
    Supplementary materials including notes and other documents
    prepared and collected during the analysis.

## How to run in your broswer or download and run locally

This research compendium has been developed using the statistical programming
language R. To work with the compendium, you will need
installed on your computer the [R software](https://cloud.r-project.org/)
itself and optionally [RStudio Desktop](https://rstudio.com/products/rstudio/download/).

You can download the compendium as a zip from from this URL:
[master.zip](/archive/master.zip). After unzipping:
- open the `.Rproj` file in RStudio
- run `devtools::install()` to ensure you have the packages this analysis depends on (also listed in the
[DESCRIPTION](/DESCRIPTION) file).
- finally, open `analysis/paper/paper.Rmd` and knit to produce the `paper.docx`, or run `rmarkdown::render("analysis/paper/paper.Rmd")` in the R console

### Licenses

**Text and figures :**  [CC-BY-4.0](http://creativecommons.org/licenses/by/4.0/)

**Code :** See the [DESCRIPTION](DESCRIPTION) file

**Data :** [CC-0](http://creativecommons.org/publicdomain/zero/1.0/) attribution requested in reuse

### Contributions

We welcome contributions from everyone. Before you get started, please see our [contributor guidelines](CONTRIBUTING.md). Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.