https://github.com/nacnudus/nzcrash
An R package to distribute New Zealand crash data in a convenient form
https://github.com/nacnudus/nzcrash
dataset government new-zealand r roads
Last synced: 14 days ago
JSON representation
An R package to distribute New Zealand crash data in a convenient form
- Host: GitHub
- URL: https://github.com/nacnudus/nzcrash
- Owner: nacnudus
- License: other
- Created: 2015-07-20T11:09:23.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2017-03-05T19:59:43.000Z (about 8 years ago)
- Last Synced: 2025-04-01T18:23:28.854Z (about 1 month ago)
- Topics: dataset, government, new-zealand, r, roads
- Language: R
- Homepage: https://github.com/nacnudus/nzcrash
- Size: 19.6 MB
- Stars: 4
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output:
md_document:
variant: markdown_github
---
```{r, message = FALSE}
library(nzcrash)
library(dplyr)
library(tidyr)
library(magrittr)
library(stringr)
library(ggplot2)
library(scales)
library(lubridate)
```
# nzcrash
This package redistributes [crash
statistics](http://www.nzta.govt.nz/resources/crash-analysis-system-data/)
already available from the New Zealand Transport Agency, but in a more
convenient form.
It's a large package (over 20 megabytes, compressed).
## Datasets
The `crashes` dataset describes most facts about a crash. The datasets `causes`,
`vehicles`, and `objects_struck` describe facts that are in a many-to-one
relationship with crashes. They can be joined to the `crashes` dataset by the
common `id` column. The `causes` dataset can additionally be joined to the
`vehicles` dataset by the combination of the `id` and `vehicle_id` columns.
This is most useful when the resulting table is also joined to the `crashes`
dataset.
## Up-to-date-ness
The data was last scraped from the NZTA website on `r Sys.Date()`. At
that time, the NZTA had published data up to `r max(crashes$date)`.
```{r}
dim(crashes)
dim(causes)
dim(vehicles)
dim(objects_struck)
```
## Accuracy
The [NZTA](http://www.transport.govt.nz/research/roadtoll/#5), doesn't agree with [itself](http://www.transport.govt.nz/research/roadtoll/annualroadtollhistoricalinformation/) about recent annual road tolls, and this dataset gives a third opinion.
```{r}
crashes %>%
filter(severity == "fatal") %>%
group_by(year = year(date)) %>%
summarize(fatalities = sum(fatalities))
```
## Severity
Crashes categorised as "fatal", "serious", "minor" or "non-injury", based on the
casualties. If there are any fatalities, then the crash is a "fatal" crash,
otherwise if there are any 'severe' injuries, the crash is a "serious" crash.
The definition of a 'severe' injury is not clear.
Minor and non-injury crashes are likely to be under-recorded since they often do
not involve the police, who write most of the crash reports upon which these
datasets are based.
A common mistake is to confuse the number of fatal crashes with the number of
fatalities.
```{r}
crashes %>% filter(severity == "fatal") %>% nrow
sum(crashes$fatalities)
```
## Dates and times
Three columns of the `crashes` dataset describe the date and time of the crash
in the NZST time zone (Pacific/Auckland).
* `date` gives the date without the time
* `time` gives the time where this is available, and NA otherwise. Times are
stored as date-times on the first of January, 1970.
* `datetime` gives the date and time in one value when both are available, and
NA otherwise. `date` is always available, however `time` is not.
When aggregating by some function of the date, e.g. by year, then always start
from the `date` column unless you also need the time. This ensures against
accidentally discounting crashes where a time is not recorded.
```{r, fig.show = "hold"}
crashes %>%
filter(is.na(time)) %>%
count(year = year(date)) %>%
ggplot(aes(year, n)) +
geom_line() +
ggtitle("Crashes missing\ntime-of-day information")
crashes %>%
filter(is.na(time)) %>%
count(year = year(date)) %>%
mutate(percent = n/sum(n)) %>%
ggplot(aes(year, percent)) +
geom_line() +
scale_y_continuous(labels = percent) +
ggtitle("Percent of crashes missing\ntime-of-day information")
```
## Location coordinates
`r percent(nrow(filter(crashes, !is.na(easting)))/nrow(crashes))` of
crashes have coordinates. These have been converted from the NZTM projection to
the WGS84 projection for convenience with packages like `ggmap`.
Because New Zealand is tall and skinny, you can easily spot the main population
centres with a simple histogram.
```{r}
crashes %>%
ggplot(aes(northing)) +
geom_histogram(binwidth = .1)
```
## Vehicles
There can be many vehicles in one crash, so vehicles are recorded in a separate
`vehicles` dataset that can be joined to `crashes` by the common `id` column.
```{r}
crashes %>%
inner_join(vehicles, by = "id") %>%
count(vehicle) %>%
arrange(desc(n))
```
## Objects struck
There can be many objects struck in one crash, so these are recorded in a separate
`objects_struck` dataset that can be joined to `crashes` by the common `id` column.
Q: What are more fatal, trees or lamp posts?
```{r}
crashes %>%
inner_join(objects_struck, by = "id") %>%
filter(object %in% c("Trees, shrubbery of a substantial nature"
, "Utility pole, includes lighting columns")
, severity != "non-injury") %>% # non-injury crashes are poorly recorded
count(object, severity) %>%
group_by(object) %>%
mutate(percent = n/sum(n)) %>%
select(-n) %>%
spread(severity, percent)
```
A: Trees (Don't worry, I know it's harder than that.)
## Causes
Causes can be joined either to the `crashes` dataset (by the common `id`
column), or to the `vehicles` dataset (by both of the commont `id` and
`vehicle_id`) columns.
The main cause groups are given in the `causes_category` column.
```{r}
crashes %>%
inner_join(causes, by = "id") %>%
group_by(cause_category, id) %>%
tally %>%
group_by(cause_category) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
mutate(cause_category = factor(cause_category, levels = cause_category)) %>%
ggplot(aes(cause_category, n)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
```
That's odd -- where are speed, alcohol, and restraints? They're given in `cause_subcategory`.
```{r}
causes %>%
filter(cause_subcategory == "Too fast for conditions") %>%
count(cause) %>%
arrange(desc(n))
```
There's nothing there about speed limit violations, because it's impossible to tell what speed a
vehicle was going at when it crashed.
More worryingly, how is "Alcohol test below limit" a cause for a crash?
Hopefully they filter those out when making policy decisions.
```{r}
levels(causes$cause) <- # Wrap facet labels
str_wrap(levels(causes$cause), 13)
crashes %>%
inner_join(causes, by = "id") %>%
filter(cause_subcategory %in% c("Alcohol or drugs")) %>%
group_by(cause, id) %>%
tally %>%
group_by(cause) %>%
summarize(n = n()) %>% # This extra step deals with many causes per crash
arrange(desc(n)) %>%
mutate(cause= factor(cause, levels = cause)) %>%
ggplot(aes(cause, n)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))
rm(causes) # Because we messed up the factor levels
```
This time, join `causes` to both `vehicles` and `crashes` to assess the
drunken cyclist menace.
```{r}
crashes %>%
filter(severity == "fatal") %>%
select(id) %>%
inner_join(vehicles, by = "id") %>%
filter(vehicle == "Bicycle") %>%
inner_join(causes, by = c("id", "vehicle_id")) %>%
count(cause) %>%
arrange(desc(n))
```
I think we all know what "Wandering or wobbling" means.