From 16fc6e6969d3ed3d06f1d6aecd8e91436ac2b398 Mon Sep 17 00:00:00 2001 From: robinlovelace Date: Thu, 22 Feb 2024 13:41:02 +0000 Subject: [PATCH] Add slides --- demos/demo-quarto-document.md | 39 +++ demos/demo-quarto-document.rmarkdown | 113 +++++++++ .../fig-crashes-per-day-1.png | Bin 0 -> 44334 bytes .../figure-gfm/unnamed-chunk-18-1.png | Bin 0 -> 49115 bytes slides/seminar-1-2024.Rmd | 227 ++++++++++++++++++ 5 files changed, 379 insertions(+) create mode 100644 demos/demo-quarto-document.md create mode 100644 demos/demo-quarto-document.rmarkdown create mode 100644 demos/demo-quarto-document_files/figure-commonmark/fig-crashes-per-day-1.png create mode 100644 practicals/1-intro_files/figure-gfm/unnamed-chunk-18-1.png create mode 100644 slides/seminar-1-2024.Rmd diff --git a/demos/demo-quarto-document.md b/demos/demo-quarto-document.md new file mode 100644 index 0000000..7084e85 --- /dev/null +++ b/demos/demo-quarto-document.md @@ -0,0 +1,39 @@ +# The impact of lockdowns on collisions + + +# Introduction + +This document explores the impact of lockdowns on the rate of +collisions. + +# Data and methods + +Datasets were taken from the STATS19 database and processed with the +`stats19` R package (Lovelace et al. 2019). + +# Results + +See the results in +Figure 1. + + + +# Conclusion + +# References + +
+ +
+ +Lovelace, Robin, Malcolm Morgan, Layik Hama, and Mark Padgham. 2019. +“Stats19 a Package for Working with Open Road Crash Data.” *Journal of +Open Source Software* 4 (33): 1181. +. + +
+ +
diff --git a/demos/demo-quarto-document.rmarkdown b/demos/demo-quarto-document.rmarkdown new file mode 100644 index 0000000..a761848 --- /dev/null +++ b/demos/demo-quarto-document.rmarkdown @@ -0,0 +1,113 @@ +--- +format: pdf +# Try replacing the above with this for PDF output +# format: pdf +title: "The impact of lockdowns on collisions" +number-sections: true +bibliography: references.bib +execute: + cache: refresh +--- + + +# Introduction + +This document explores the impact of lockdowns on the rate of collisions. + +# Data and methods + +Datasets were taken from the STATS19 database and processed with the `stats19` R package [@lovelace2019]. + +# Results + + +```{r} +#| include: false +library(tidyverse) +library(stats19) +# dl_stats19(year = 2020, type = "collision") +collisions_2020 = get_stats19(year = 2020, type = "collision") +``` + + +See the results of downloading national crash data from 2020 in @fig-crashes-per-day. + + +```{r} +#| label: fig-crashes-per-day +#| echo: false +#| fig-cap: "Crashes over time in 2020" +collisions_per_day = collisions_2020 |> + group_by(date) |> + summarise( + n = n() + ) +collisions_per_day |> + ggplot(aes(date, n)) + + geom_line(colour = "red") +``` + + +The equivalent trend for Leeds is shown in @fig-trend-leeds. + +The column names available to us are: + + +```{r} +names(collisions_2020) +``` + +```{r} +#| echo: false +#| label: fig-trend-leeds +# Check the local authority of crashes: +collisions_wy = collisions_2020 |> + filter(police_force == "West Yorkshire") +collisions_per_day_wy = collisions_wy |> + group_by(date) |> + summarise( + n = n() + ) +collisions_per_day_wy |> + ggplot(aes(date, n)) + + geom_line(colour = "red") + + +``` + + +Let's put them side-by-side. + + +```{r} +# collisions_per_day_combined = bind_rows( +# collisions_per_day |> mutate(Area = "GB"), +# collisions_per_day_wy |> mutate(Area = "West Yorksire") +# ) +# # Not sure why the scales here do not work: +# collisions_per_day_combined |> +# ggplot(aes(date, n)) + +# geom_line() + +# facet_grid(~Area, shrink = TRUE) +g1 = collisions_per_day |> + ggplot(aes(date, n)) + + geom_line(colour = "red") +g2 = collisions_per_day_wy |> + ggplot(aes(date, n)) + + geom_line(colour = "red") +library(patchwork) +``` + +```{r} +#| echo: false +#| layout-ncol: 2 +g1 +g2 +``` + + + +# Conclusion + +# References + diff --git a/demos/demo-quarto-document_files/figure-commonmark/fig-crashes-per-day-1.png b/demos/demo-quarto-document_files/figure-commonmark/fig-crashes-per-day-1.png new file mode 100644 index 0000000000000000000000000000000000000000..100b7722db3498b0e41024dd258f5101d3095eab GIT binary patch literal 44334 zcmZ^}V|bk1_Xav~(={raM00011Tuev-007Ma06@0CfC1k~E6_p$0AK()DJ5axBM1lxC@3fx z7#KJ>I0OU)BqSsh6x5e5U!b9(zkdA+0|WEz+c#KPSU5O1czF2l-@hXuARrx%F4>x+S&d$!>-rm8%!O_vt$;rvt+1bU##nsi-&CSi--QB~(!_(8# z%gf8#+uO&-$Jf`_&(H7Y&!4}3{qpzs4+sbd3=9kk3JMMm4habf4Gj$o3kweqkBEqf zjEszmii(boj){qhjg5_qi;ItsPe@2eOiWBlN=i;nPDx2gO-)TpOG{5r&&bHg%*@Qn 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zx3@Q7N~ZD^oE#ku4Gn(|wml^;t*!am+n49$bOA-eb0L9&%|O(_lnC=TI;R89P^-pr z1`v7^6B9c-JK)>a_815T=F0SJ=4UERh6H^cZ`X85cMc8+Nl8g@an%7+tFE4ylw{6~ z_xSW=^8p0S9sdi%5)>S~y|a_S>q5uOtXMQ7=zcC%qV_6HOB7U{r}Q~|Kruf0uVvM!O!sFj^zLP9k&_*{sEA*xV%{DXMO+w4~T+EDF6Tf literal 0 HcmV?d00001 diff --git a/slides/seminar-1-2024.Rmd b/slides/seminar-1-2024.Rmd new file mode 100644 index 0000000..d04eef1 --- /dev/null +++ b/slides/seminar-1-2024.Rmd @@ -0,0 +1,227 @@ +--- +title: "Data science in transport planning" +subtitle: '
Guest Seminar
Module: Transport Data Science' +author: "Dr Robin Lovelace
Guest speaker: Tom Van Vuren MBE" +date: 'Institute for Transport Studies `r # Sys.Date()`
' +output: + xaringan::moon_reader: + # css: ["default", "its.css"] + # chakra: libs/remark-latest.min.js + lib_dir: libs + nature: + highlightStyle: github + highlightLines: true +bibliography: + # - references.bib + - ../tds.bib +--- + +background-image: url(https://c1.staticflickr.com/2/1216/1096671706_571a263b63_b.jpg) +background-position: 50% 50% +class: center, bottom, inverse + +# Credit: Mandeep Lota via [flickr](https://www.flickr.com/photos/deepster2k/1096671706) + +```{r setup, include=FALSE} +knitr::opts_chunk$set(eval = TRUE, echo = FALSE) +options(htmltools.dir.version = FALSE) +library(RefManageR) +BibOptions(check.entries = FALSE, + bib.style = "authoryear", + cite.style = 'alphabetic', + style = "markdown", + first.inits = FALSE, + hyperlink = FALSE, + dashed = FALSE) +my_bib = c( + ReadBib("../tds.bib") +) +library(tmap) +library(tidyverse) +``` + +--- + +# Housekeeping + +Session timings: + +- Welcome and refreshments: 13:45-14:00 +- Talk by Tom Van Vuren: 14:00-14:45 +- Questions: 14:45-15:00 +- Break: 5 minutes +- Practical session part 2 (for Transport Data Science Students) + +--- + +# The history of TDS + +- 2017: Transport Data Science created, led by Dr Charles Fox, Computer Scientist, author of Transport Data Science book `r Citep(my_bib, "fox_data_2018", .opts = list(cite.style = "authoryear"))` +- The focus was on databases and Bayesian methods +- 2019: I inherited the module, which was attended by ITS students +- Summer 2019: Python code published in the module 'repo': + - [github.com/ITSLeeds](https://github.com/ITSLeeds/TDS/tree/master/code-python) +- January 2020: Transport Data Science module available to Data Science and Data Analytics MSc students +- March 2020: Switch to online teaching +- 2021-2023: Updated module, focus on methods +- 2024: Switch to combined practical sessions and lectures +- 2025+: Expand, online course? book? stay in touch! + +```{r, echo=FALSE, eval=TRUE} +# knitr::include_graphics("https://images.springer.com/sgw/books/medium/9783319729527.jpg") +# blogdown::shortcode("1239930988416897033") +``` + + + +??? + +Skip this bit + +--- + +# Essential reading + + + +- Chapter 12, [Transportation](http://geocompr.robinlovelace.net/transport.html) of Geocomputation with R, a open book on geographic data in R (available free [online](http://geocompr.robinlovelace.net/)) `r Citep(my_bib, "lovelace_geocomputation_2018", .opts = list(cite.style = "authoryear"))` +- Reproducible Road Safety Research with R (RRSRR): https://itsleeds.github.io/rrsrr/ + + +```{r geocompr-cover, echo=FALSE, out.height="300px"} +knitr::include_graphics(c( + "https://geocompr.robinlovelace.net/images/cover.png", + "https://r.geocompx.org/13-transport_files/figure-html/desire-1.png" + )) +``` + +--- + +## Timetable: January and February + +```{r, message=FALSE} +library(tidyverse) +timetable = read_csv("../timetable.csv") +timetable = timetable %>% + mutate(`Duration (Hours)` = duration) %>% + select(-description, -duration) %>% + rename_with(str_to_title) +``` + +```{r, echo=FALSE} +timetable %>% + slice(1:8) %>% + knitr::kable() +``` + +--- + +## Timetable: March to May + +```{r, echo=FALSE} +timetable %>% + slice(9:nrow(timetable)) %>% + knitr::kable() +``` + +--- + +## Objectives + + + +- Understand the structure of transport datasets +- Understand how to obtain, clean and store transport related data +- Gain proficiency in command-line tools for handling large transport datasets +- Produce data visualizations, static and interactive + +```{r, echo=FALSE} +# Understand the structure of transport datasets: spatial, temporal and demographic +# Understand how to obtain, clean and store transport related data +# Gain proficiency in command-line tools for handling large transport datasets +# Learn machine learning and data modelling techniques +# Produce data visualizations, static and interactive +# Learn where to find large transport datasets and assess data quality +``` + +- Learn how to join together the components of transport data science into a cohesive project portfolio + +--- + +## Assessment (for those doing this as credit-bearing) + +- You will build-up a portfolio of work +- 100% coursework assessed, you will submit by **Friday 19th May**: + - **a pdf document up to 10 pages long with figures, tables, references explaining how you used data science to research a transport problem** + - **reproducible code contained in an RMarkdown (.Rmd) document that produced the report** +- Written in RMarkdown - will be graded for reproducibility +- Code chunks and figures are encouraged +- You will submit a non-assessed 2 page pdf + Rmd report by **18th March** + +--- + +## What is transport data science? + +- The application of data science to transport datasets and problems + +-- + +- Raising the question... + +-- + +- What is data science? + + + +-- + +- A discipline "that allows you to turn raw data into understanding, insight, and knowledge" `r Citep(my_bib, "grolemund_r_2016", .opts = list(cite.style = "authoryear"))` + +-- + +In other words... + +- Statistics that is actually useful! + +### Deeper question: why transport data science? + +- You must answer that question + +--- + +## What is science? + +.pull-left[ +- Scientific knowledge is hypotheses that can be falsified +- Science is the process of *generating falsifiable hypotheses* and *testing them* +- In a reproducible way +- Systematically + +![](https://media3.giphy.com/media/3ohhworAhxSEHT3zDa/200w.webp?cid=3640f6095c57e8d15767723367d0c596) +] + +-- + +.pull-right[ + +- Falsifiability is central to the scientific process `r Citep(my_bib, "popper_logic_1959", .opts = list(cite.style = "authoryear"))` +- All of which requires software conducive to reproducibility + +![](https://duckduckgo.com/i/f2692e7b.jpg) +] + +--- + +# Introducing Tom and the guest seminar + +![](https://private-user-images.githubusercontent.com/1825120/307013437-c78ff59e-5cf6-4766-9e39-07d7fa250be0.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.cQYNNlvNgtHVDyDIxhmi3sASvCfFGE_46edbc3rCopA) + +--- + +# References + +```{r, 'refs', results="asis", echo=FALSE} +PrintBibliography(my_bib) +# RefManageR::WriteBib(my_bib, "refs-geostat.bib") +```