Chapter 6 Practice

6.1 Exercise

60:00

Use the transport_expenditure data to create a time series showing household expenditure on transport over time. The data shows household spending per week in pounds on different types of transport and subcategories of transport, such as petrol, between 2002 and 2021.

In your analysis, try to use as many components of creating a complex chart in ggplot2 covered in this course.

1. Inspect the transport_expenditure data and decide on an area you would like to analyse or compare.
2. Tidy and manipulate the data in preparation to create a time series chart.
3. Create a multi-line time series chart data in ggplot2.
4. Adapt or change the scale of your chart to one you find appropriate for your analysis.
5. Add a title, and label your x and y axis.
6. Create a theme by changing text elements, line elements, legend and background elements.
7. Add an annotation to enhance the readability or narrative of your chart.

This is a preview of the data you will be using:

transport_group_category	spend_category	spend_subcategory	year	spend_per_week_GBP
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2002	10.7
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2003	11.3
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2004	11.4
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2005	10.1
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2006	8.0
Motoring and bicycle costs	Purchase of vehicles	New cars and vans	2007	7.8

Solution

Code

# Q1 & Q2: Data tidying 
practice_data <- transport_expenditure %>%
  dplyr::filter(transport_group_category == "Motoring and bicycle costs") %>%
  dplyr::select(year, spend_category, spend_per_week_GBP) %>%
  dplyr::group_by(year, spend_category) %>%
  dplyr::summarise(spend_per_week_GBP = sum(spend_per_week_GBP)) %>%
  dplyr::mutate(year = as.numeric(year))

# Q7 Creating labels that will be used for annotation
practice_labels <- practice_data %>%
  dplyr::filter(year == "2021")

ggplot(practice_data, aes(x = year, y = spend_per_week_GBP, color = spend_category, group = spend_category)) +
  # Q3 Using geom_line() to create a multi-line time series chart
  geom_line(linewidth = 1) +
  # Q4 Adapting the scale of the chart using scale continuous function for x and y
  scale_y_continuous(
    limits = c(0, 30),
    breaks = seq(from = 0, to = 30, by = 5),
    expand = c(0, 0)
  ) +
  scale_x_continuous(
    limits = c(min(practice_data$year), max(practice_data$year) + 6),
    breaks = seq(from = 2006, to = 2021, by = 5)
  ) +
  dftplotr::scale_colour_dft(palette = "joyful.journey") +
  # Q5 Adding a title and labelling the x and y axis
  labs(
    title = "Household spend (£) on motor and cycling vehicles per week, 2002 to 2021",
    x = "Year",
    y = "GBP (£)"
  ) +
  # Q6 Creating a theme 
  theme(
    axis.text.x = element_text(size = 10),
    axis.text.y = element_text(size = 10),
    axis.title.y = element_text(size = 12),
    axis.title.x = element_text(size = 12),
    plot.title = element_text(lineheight = 0.8, face = "bold", hjust = 0.5),
    panel.background = element_rect(fill = "white"),
    panel.grid.major.y = element_line(colour = "grey", linetype = "dashed", linewidth = 0.2),
    legend.position = "none"
  ) +
  # Q7 Adding a annotation to enhance readability of the chart
  geom_text_repel(
    data = practice_labels, aes(x = year, 
                                y = spend_per_week_GBP, 
                                group = spend_category, 
                                label = spend_category),
    colour = "grey8", 
    segment.color = 'NA', 
    nudge_x = 1.2, 
    nudge_y = 0, 
    size = 3.5,
    hjust = 0
  )