Merge branch 'main' into 263-pre-requisites

Robinlovelace · web-flow · commit ca14dce244f3 · 2026-01-22T23:18:46.000Z
diff --git a/s1/images/avg_trip_length_weekday_weekend.png b/s1/images/avg_trip_length_weekday_weekend.png
diff --git a/s1/index.qmd b/s1/index.qmd
@@ -45,96 +45,7 @@ execute:
 
 See [openstreetmap.org](https://www.openstreetmap.org/#map=19/53.80689/-1.55637) or search for other open access datasets for more ideas
 
-<!-- 2. Work through the transport chapter of Geocomputation with R: https://r.geocompx.org/transport.html -->
 
-<!-- See https://github.com/ITSLeeds/TDS/blob/master/practicals/2-software.md -->
-
-<!-- - In terms of future work in an evolving job market? -->
-
-<!-- - In terms of the kinds of problems you want to solve? -->
-
-<!-- ## Sketching research methods (in groups of 2-4, 30 minutes) -->
-
-<!-- Starting with the 1000 'desire lines' dataset of Leeds, sketch-out some research ideas that cover -->
-
-<!-- 1) Hypotheses: generate two hypotheses that are falsifiable and 2 hypotheses that are not falsifiable -->
-
-<!-- 2) Input data: draw schematic representations of additional datasets that you could use alongside the desire lines dataset, with at least one at each of these levels: -->
-
-<!-- - Zones -->
-
-<!-- - Points -->
-
-<!-- - Routes -->
-
-<!-- - Route networks -->
-
-<!-- - Individual -->
-
-<!-- What temporal and spatial resolution could each one have? -->
-
-<!-- 3) Methods: using a flow diagram (e.g. as shown below) -->
-
-```{r schematic, echo=FALSE}
-# knitr::include_graphics("https://raw.githubusercontent.com/npct/pct-team/master/flow-model/flow-diag2.png")
-```
-
-<!-- ## Practical, group computer task (30 minutes) -->
-
-<!-- Create a github account (all). See: https://github.com -->
-
-<!-- Building on the follow code chunk (but with no copy-and-pasting), create a data frame that contains the names, coffee habits and like/dislike of bus travel for everyone in your group (just 1 computer per group): -->
-
-<!-- ```{r} -->
-
-<!-- person_name = c( -->
-<!--   "robin", -->
-
-<!--   "malcolm", -->
-
-<!--   "richard" -->
-
-<!-- ) -->
-
-<!-- n_coffee = c( -->
-
-<!--   5, -->
-
-<!--   1, -->
-
-<!--   0 -->
-
-<!-- ) -->
-
-<!-- like_bus_travel = c( -->
-
-<!--   TRUE, -->
-
-<!--   FALSE, -->
-
-<!--   TRUE -->
-
-<!-- ) -->
-
-<!-- personal_data = data.frame(person_name, n_coffee, like_bus_travel) -->
-
-<!-- personal_data -->
-
-<!-- ``` -->
-
-<!-- When you are complete, add your code to https://github.com/ITSLeeds/TDS/blob/master/code-r/01-person-data.R -->
-
-<!-- ## Learning outcomes -->
-
-```{r, echo=FALSE}
-# Identify available datasets and access and clean them
-# Combine datasets from multiple sources
-# Understand what machine learning is, which problems it is appropriate for compared with traditional statistical approaches, and how to implement machine learning techniques
-# Visualise and communicate the results of transport data science, and know about setting-up interactive web applications
-# Deciding when to use local computing power vs cloud services
-```
-
-<!-- - Articulate the relevance and limitations of data-centric analysis applied to transport problems, compared with other methods -->
 
 # Data Science foundations
 
@@ -241,7 +152,12 @@ crashes[[2]]
 
 ## Data science on real data
 
-To get some larger datasets, try the following (from Chapter 8 of RSRR)
+Work through the following example on road traffic data (recommended for most people) or the NTS data (for people more interested in travel survey data).
+You can do both if you have time.
+
+### UK Road Safety Data
+
+To get some larger datasets, try the following (from Chapter 8 of [RSRR](https://itsleeds.github.io/rrsrr/)):
 
 ::: {.panel-tabset group="language"}
 ## R
@@ -282,6 +198,166 @@ Let's go through these exercises together:
 
 -   We'll explore this together
 
+### UK National Travel Survey (NTS) data
+
+<details>
+
+Note: you will need to download the modified NTS 2022 data from your Minerva module page and place it in your working directory for this section to work.
+
+```{r import_dataset}
+#| eval: false
+# Read CSV file
+NTS_data <- read.csv("NTS2022_modifieddata.csv")
+
+# Look at the column names
+names(NTS_data)
+
+# Look at the data
+head(NTS_data)
+```
+
+You should see something like this:
+
+```
+> names(NTS_data)
+ [1] "IndividualID"            "avg_trip_length"
+ [3] "avg_trip_length_weekday" "avg_trip_length_weekend"
+ [5] "total_distance"          "total_distance_weekday"
+ [7] "total_distance_weekend"  "SD_triplength"
+ [9] "sd_Total_Distance_wknd"  "sd_Total_Distance_wk"
+ 
+ > head(NTS_data)
+  IndividualID avg_trip_length avg_trip_length_weekday avg_trip_length_weekend
+1   2023000001        4.080000                4.631579                2.333333
+2   2023000002        2.538462                2.400000                3.000000
+3   2023000003        5.916667                6.250000                5.250000
+```
+
+Visualising datasets is important when dealing with large volumes of data, as visualisations help convey complex information in an easily interpretable format. Consider the histogram plots of average trip lengths over a week in the UK.
+
+```{r avg-triplength-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+# Note: This requires ggplot2 library to be loaded first
+library(tidyverse) # Tidyverse contains ggplot2 and other useful packages
+ggplot(NTS_data, aes(x = avg_trip_length)) +
+  geom_histogram(binwidth = 1, fill = "darkgrey") +
+  labs(
+    title = "Avg. Trip Length in Whole Week",
+    x = "Trip Length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 50) 
+```
+
+Data exploration or "exploratory data analysis" (EDA) involves examining datasets in depth to uncover underlying patterns or differences. The direction of this investigation is largely guided by the research question.
+
+Consider different histogram plots for weekdays and weekends. Can you identify any differences between them? (Clue: Check the number of individuals between 0-1 Km)
+
+Think: What could be plausible reasons for such difference?
+
+```{r avg-triplength-weekday-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+ggplot(NTS_data, aes(x = avg_trip_length_weekday)) +
+  geom_histogram(binwidth = 1, fill = "darkblue") + 
+  labs(
+    title = "Avg. Trip Length on Weekdays",
+    x = "Trip Length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 50) 
+```
+
+```{r avg-triplength-weekend-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+ggplot(NTS_data, aes(x = avg_trip_length_weekend)) +
+  geom_histogram(binwidth = 1, fill = "darkred") + 
+  labs(
+    title = "Avg. Trip Length on Weekends",
+    x = "Trip Length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 50) 
+```
+
+You can more easily compare the two histograms when they are placed in the same plot, with transparency added to the bars:
+
+```{r avg-triplength-weekday-weekend-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+g_combined = ggplot() +
+  geom_histogram(data = NTS_data, aes(x = avg_trip_length_weekday), 
+                 binwidth = 1, fill = "darkblue", alpha = 0.5) + 
+  geom_histogram(data = NTS_data, aes(x = avg_trip_length_weekend), 
+                 binwidth = 1, fill = "darkred", alpha = 0.5) + 
+  labs(
+    title = "Avg. Trip Length on Weekdays (blue) and Weekends (red)",
+    x = "Trip Length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 50)
+# Then 'print' the plot to show it:
+g_combined
+```
+
+You can save the plot with `ggsave()`:
+
+```{r}
+#| eval: false
+ggsave("avg_trip_length_weekday_weekend.png", plot = g_combined, width = 8, height = 6)
+```
+
+And (this is how you can show figures in Quarto), in a quarto document (.qmd file) that you will use to write and submit your coursework, you can include the saved figure like this (we will come onto this later in the module):
+
+```
+![](avg_trip_length_weekday_weekend.png)
+```
+
+![Avg. Trip Length on Weekdays (blue) and Weekends (red)](images/avg_trip_length_weekday_weekend.png)
+
+```{r}
+#| eval: false
+#| echo: false
+# move the file:
+file.rename("avg_trip_length_weekday_weekend.png", "s1/images/avg_trip_length_weekday_weekend.png")
+```
+
+Don't they largely look the same? Can you stop here and infer that the trip length distributions for weekdays and weekends are largely similar? You might, depending on the resources at your disposal, but from an academic point of view we need to think about other potential dimensions where they could be different.
+
+Consider different histogram plots for 'Standard Deviation' of trip lengths over weekdays and weekends. Can you identify any differences between them? (Clue: Again, check the number of individuals with SD 0-2 Km)
+
+Think: What could be plausible reasons for such difference?
+
+```{r SD-triplength-weekday-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+ggplot(NTS_data, aes(x = sd_Total_Distance_wk)) +
+  geom_histogram(binwidth = 0.5, fill = "darkblue") + 
+  labs(
+    title = "SD of Trip Length on Weekdays",
+    x = "SD of trip length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 25) + ylim(0,1000) 
+```
+
+```{r SD-triplength-weekend-histogram, message=FALSE, warning=FALSE}
+#| eval: false
+ggplot(NTS_data, aes(x = sd_Total_Distance_wknd)) +
+  geom_histogram(binwidth = 0.5, fill = "darkred") + 
+  labs(
+    title = "SD of Trip Length on Weekends",
+    x = "SD of trip length (km)",
+    y = "Number of Individuals"
+  ) +
+  theme_minimal() +
+  xlim(0, 25) + ylim(0,1000) 
+```
+
+</details>
+
 # Self-study practical (1 hr)
 
 **Read and try to complete the exercises in Chapters 1 to 5 of the book [Reproducible Road Safety Research with R](https://itsleeds.github.io/rrsrr/).**
@@ -311,4 +387,4 @@ For details on installing packages see [here](https://docs.ropensci.org/stats19/
 
 -   Think of a research question that you could answer with data science, and write it down in a .qmd file. Include a sketch of the data you would need to answer the question.
 
--   Sign-up to the Cadence platform as outlined at [itsleeds.github.io/tds/s2/#the-cadence-platform](https://itsleeds.github.io/tds/s2/#the-cadence-platform)
+-   Sign-up to the Cadence platform as outlined at [itsleeds.github.io/tds/s2/#the-cadence-platform](https://itsleeds.github.io/tds/s2/#the-cadence-platform)
diff --git a/s4/index.qmd b/s4/index.qmd
@@ -99,6 +99,7 @@ We will start with a simple map of the world. Load the `world` object from the `
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 #| output: false
 world = spData::world
@@ -120,6 +121,7 @@ Use some basic R functions to explore the `world` object. e.g. `class(world)`, `
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| warning: false
 plot(world)
 ```
@@ -143,6 +145,7 @@ Note that this makes a map of each column in the data frame. Try some other plot
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 plot(world[3:6])
 plot(world["pop"])
 ```
@@ -167,6 +170,7 @@ Load the `nz` and `nz_height` datasets from the `spData` package.
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 #| output: false
 nz = spData::nz
@@ -185,6 +189,7 @@ We can use `tidyverse` functions like `filter` and `select` on `sf` objects in t
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 #| output: false
 canterbury = nz |> filter(Name == "Canterbury")
@@ -241,6 +246,7 @@ In this section we will look at basic transport data in the R package **stplanr*
 Load the  `stplanr` package as follows:
 
 ```{r}
+#| eval: false
 #| echo: true
 #| output: false
 library(stplanr)
@@ -255,6 +261,7 @@ First we will load some sample data:
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 od_data = stplanr::od_data_sample
 zone = stplanr::cents_sf
@@ -274,6 +281,7 @@ Now we will rename one of the columns from `foot` to `walk`
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 od_data = od_data |>
   rename(walk = foot)
@@ -292,6 +300,7 @@ Next we will made a new dataset `od_data_walk` by taking `od_data` and piping it
 
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 od_data_walk = od_data |>
   filter(walk > 0) |>
@@ -313,6 +322,7 @@ We can use the generic `plot` function to view the relationships between variabl
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 plot(od_data_walk)
 ```
 
@@ -328,6 +338,7 @@ R has built in modelling functions such as `lm` lets make a simple model to pred
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 #| echo: true
 model1 = lm(proportion_walk ~ proportion_drive, data = od_data_walk)
 od_data_walk$proportion_walk_predicted = model1$fitted.values
@@ -349,6 +360,7 @@ We can use the `ggplot2` package to graph our model predictions.
 ::: {.panel-tabset}
 ## R
 ```{r}
+#| eval: false
 ggplot(od_data_walk) +
   geom_point(aes(proportion_drive, proportion_walk)) +
   geom_line(aes(proportion_drive, proportion_walk_predicted))
