https://github.com/robinlovelace/crash-data-vis-demo
Demo of crash data visualisation
https://github.com/robinlovelace/crash-data-vis-demo
Last synced: about 1 month ago
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Demo of crash data visualisation
- Host: GitHub
- URL: https://github.com/robinlovelace/crash-data-vis-demo
- Owner: Robinlovelace
- Created: 2024-03-25T14:39:02.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-25T15:03:04.000Z (about 1 year ago)
- Last Synced: 2025-04-03T16:12:19.466Z (about 2 months ago)
- Size: 4.88 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
# crash-data-vis-demo
The goal of crash-data-vis-demo is to demonstrate how to get and visualise data on road traffic casualties and road geometries.
```{r}
# Install the remotes package if not yet installed:
if (!"remotes" %in% installed.packages()) {
install.packages("remotes")
}
```
```{r}
# Check you have the packages installed:
remotes::install_cran(c("tidyverse", "stats19"))
```
```{r}
library(tidyverse) # for data manipulation and visualisation
library(stats19) # for getting road casualty data
library(tmap)
tmap_mode("view")
```
# Get road casualty data
```{r}
# Get road casualty data for 2022
cas = get_stats19(year = 2022, type = "cas")
col = get_stats19(year = 2022, type = "col")
```
If you want to get more data, you can change 2022 to 1979, which downloads all data from 1979 to the last year for which data is available.
We'll get the data for Edinburgh as follows:
```{r}
names(col)
table(col$police_force)
```
```{r}
col_scotland = col |>
filter(str_detect(police_force, "Scotland"))
```
Let's get a case study area:
```{r}
edinburgh_boundary = zonebuilder::zb_zone("Edinburgh", n_circles = 2)
```
We'll convert the collisions to `sf` objects so we can do a spatial join:
```{r}
col_sf = format_sf(col_scotland, lonlat = TRUE)
```
Now we can do a spatial filter:
```{r}
col_edinburgh = col_sf[edinburgh_boundary, ]
```
We can now plot these on a map:
```{r}
tm_shape(col_edinburgh) +
tm_dots(col = "accident_severity", size = 0.1) +
tm_layout(legend.position = c("left", "bottom"))
```