---
title: "Introduction to R for Road Safety"
author: "Robin Lovelace"
institute: "Institute for Transport Studies, University of Leeds"
date: "`r Sys.Date()`"
output:
  xaringan::moon_reader:
    lib_dir: libs
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

background-image: url(https://user-images.githubusercontent.com/1825120/61066243-12dc6d80-a3fd-11e9-8805-826a47c553f6.png)

# About the course team and location

- Robin Lovelace
- Haruko
- Pangiotis
- Martyna 

---

# About the course

- What it is...
  - An opportunity to learn R with support
  - Based on recent, cutting edge software
  - By people experienced teachers/researchers/developers
  
--
  
- What it is not
  - A course on statistical modelling


---

# About the package

See https://docs.ropensci.org/stats19/

![](https://docs.ropensci.org/stats19/reference/figures/README-unnamed-chunk-2-1.png)

---

# R and RStudio demo

Actions speak louder than words...

![](https://raw.githubusercontent.com/ITSLeeds/TDS/master/courses/2day/images/rstudio-ui.png)

---

# Over to you

Work through Section 2 of the exercises

- Projects and scripts
- Writing and running code
- Vewing Objects
- Autocompletion
- Getting help

---

# Time data

Section 3.4 shows how to change classes in R

Time is represented by special classes:

```{r}
library(lubridate)
x = today()
class(x)
day(x)
month(x)
year(x)
weekdays(x)
```

---

# Time representations

```{r}
as.Date("2019-10-17") # works
# as.Date("2019 10 17") # fails
ymd("2019 10 17") # works
dmy("17/10/2019") # works
```

---

# Subsetting time objects

```{r}
c_sample = stats19::accidents_sample
c_sample$date
c_sample$date_formatted = dmy(c_sample$date)
c_sample$date_formatted > ymd("2017-08-01")
```

---
# Next up

- Try running the code and answer the questions in Section 5
- Continue working through Sections 2 and 3

## After lunch

Spatial data (maps!)
---

# Spatial data and maps

```{r, message=FALSE}
library(sf)
crashes = readr::read_csv("https://github.com/ropensci/stats19/releases/download/1.0.0/crashes.csv")
crashes_sf = crashes # create copy of crashes dataset
crashes_sf$longitude = c(-1.3, -1.2, -1.1)
crashes_sf$latitude = c(50.7, 50.7, 50.68)
crashes_sf = st_as_sf(crashes_sf, coords = c("longitude", "latitude"), crs = 4326)
plot(crashes_sf)
```

---

# Doing spatial data with R

- Context: spatial ecosystem (see section [1.4 of Geocomputation with R - package ecosystem](https://geocompr.robinlovelace.net/intro.html#rs-spatial-ecosystem))
- [Exercises](https://geocompr.robinlovelace.net/attr.html#exercises-1): Section 6 of the handout

- Further reading: [Section 3.2 to 3.2.2](https://geocompr.robinlovelace.net/attr.html#vector-attribute-manipulation) of handouts

---

# Practical

- Work through Section 6.1 and try exercises 1 to 3
- Take a short read of [Section 1.4](https://geocompr.robinlovelace.net/intro.html#rs-spatial-ecosystem) of Geocomputation with R (first challenge, find the resource online!)
- Return to the practical

---

# Afternoon session

- Talk on Road Safety 1

- Applying the methods to stats19 data - live demo

    How to access data with stats19
    Key stats19 functions
    Excercises: analysing road crash data on the Isle of Wight

- Continue working through the handouts
- Talk on Road Safety 2
- Homework - ensure that you have at least read-over the handout Sections 1:5

---

# Day 2

- Prioirty: consolidate knowledge from day 1
- Continuing to work through the handouts starting on Section 6
- Demonstrations of counting number of crashes on roads
- Talk on network analysis 
- Talk on R in a professional setting
- Working on your own data

--

- Missed homework! Ensure that you have at least read-over the handout Sections 1:5


---

# The R learning curve

See video: https://www.youtube.com/watch?v=7oyiPBjLAWY&feature=youtu.be&t=357

![](https://i.imgur.com/CBdpkug.jpg)

---

# Agenda

09:30-11:00 Point pattern analysis

- Visualising data with tmap
- Spatial and temporal subsetting
- Aggregation

11:15-12:30 Road network data

- Desire lines: using origin-destination data
- Downloading road network data from OSM
- Buffers on road networks

**Lunch**

13:30-15:00 Analysing crash data on road network

**Break**
  
15:15-15:30: Talk on Road Safety 3

15:30-16:30 Applying the methods to your own data

---

# Bonus extras

![](final-figure.png)

--

Merging, forecasting, network analysis

---

# Visualising spatial data practical


- Foundations of sf: try Sections 6.2 and 6.3 (building on new subsetting knowledge)
- Making maps: try out the exercises in Section 7

- From break until lunch, either:
  - Continue working through the practicals (Sections 2 to 5), or
  - Dive into processing large stats19 dataset (Section 8)

- Sections 1:5 R vital
- Section 3 on subsetting (vital)
- Section 4 on packages (important)
- Section 5 on times (can do later)

--

- Live demo + questions

---

# Further information
