README.Rmd

---
title: "Demonstrating the PCT, for education and reproducibility"
output: github_document
---

Welcome to pct-demo, a small repo with a little code and data to how how the pct works.

The starting point is that regional data has already been generated.
See [pct-scripts](https://github.com/pctbike/pct-scripts) or an academic paper on the subject ([Lovelace et al. 2017](https://www.jtlu.org/index.php/jtlu/article/view/862)).

We also assume your have R and RStudio installed and running on your computer.
There is plenty of guidance online, notably on the RStudio website:  [rstudio.com](https://www.rstudio.com/products/rstudio/download/).

The first step is to download the the repo https://github.com/npct/pct-demo/archive/master.zip - that contains both code and data.
Note that it contains a .qgs file that can be opened with the open source program [QGIS](http://qgis.org/) - another powerful tool for analysing the geographical distribution of cycling potential.

The remainder of this tutorial is based on R code, which can be found in the [code](https://github.com/npct/pct-demo/tree/master/code) folder.
We encourage you to look over these scripts.
The script to load-in the data, for example, can be opened with the following command:

```{r, eval=FALSE}
file.edit("code/load-data.R")
```

Once that file is open we can run it line-by-line, e.g. by pressing `Ctl-Enter`.
Alternatively you can entirety of a script file with the `source()` function.

There are some dependencies: you need to have some packages installed.

```{r}
source("code/set-up.R")
```


To load the input data, we can run the following script:

```{r, results='hide'}
source("code/load-data.R")
```

## Zones, centroids, lines, routes

The input data we have just loaded can be seen by looking in the Environment tab in RStudio. Alternatively you can use the function `ls()`:

```{r}
ls()
```

This shows that we have loaded the following data objects:

- `z`, administrative zones about which we have cycling data
- `cents`, population-weighted centroids, one per zone
- `l`, straight 'desire lines' representing travel between one zone and another
- `rf` route-level data, like `l` but allocated to the road network

We can plot all these layers interactively as follows:

```{r}
library(tmap)
tmap_mode("view")
qtm(z, "bicycle") +
  qtm(cents) +
  qtm(l) +
  qtm(rf, col = "green")
```