README.Rmd

---
title: "Project name"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# What is this project about?
summarize in three sentences what this project is about and what central features it has.

# Setup

## `renv`: Installing Packages
`renv` brings project-local R dependency management to our project. `renv` uses a lockfile (`renv.lock`) to capture the state of your library at some point in time.
Based on `renv.lock`, RStudio should automatically recognize that it's being needed, thereby downloading and installing the appropriate version of `renv` into the project library. After this has completed, you can then use `renv::restore()` to restore the project library locally on your machine.
When new packages are used, `install.packages()` does not install packages globally, it does in an environment only used for our project. You can find this library in `renv/library` (but it should not be necessary to look at it).
If `renv` fails, you will be presented something in the like of when you first start R after cloning the repo:

```
renv::restore()
This project has not yet been activated. Activating this project will ensure the project library is used during restore. Please see ?renv::activate for more details. Would you like to activate this project before restore? [Y/n]:
```

Follow along with `Y`  and `renv::restore()` will do its work downloading and installing all dependencies.
`renv` uses a local `.Rprofile` and `renv/activate.R` script to handle our project dependencies.

### Adding a new package
If you need to add a new package, you can install it as usual (`install.packages` etc.).
Then, to add your package to the `renv.lock`:

```
renv::snapshot()
```
and commit and push your `renv.lock`.

Other team members can then run `renv::restore()` to install the added package(s) on their laptop.

## Data

You need the following data files in order to run this project: 

```{r}
system2("tree", c("data/raw")) # works on mac and potentially linux. 
```

# Developer information
[the following can also be moved to the wiki if you decide to have one]

## Definition of Done
Default Definition of Done can be found [here](https://github.com/CorrelAid/definition-of-done). Adapt if needed.

## Code styling

# How to operate this project?
[the following can also be moved to the wiki if you decide to have one]

explain how the output(s) of this project can be handled/operated, for example:

- how to knit the report(s)
- where to create/find the data visualizations 
- how to update data 
- what would need to be updated if someone wanted to re-run your analysis with different data

# Limitations

be honest about the limitations of your project, e.g.: 

- methodological: maybe another model would be more suitable?
- reproducibility: what are limits of reproducibility? is there something hard-coded/specific to the data that you used?
- best practices: maybe some code is particularly messy and people working on it in the future should know about it in advance?