In-class Exercise 6: Visualising and Analysing Time-Oriented Data

View the slides to learn more about:

• Characteristics of time-series data

• A short visual history of time-series graphs

• Time-series patterns

• Time-series data visualization Methods

  • Line graph
  • Control chart
  • Slopegraph
  • Cycle plot
  • Horizon graph
  • Sunburst diagram
  • Calendar Heatmap
  • Stream Graph

• Interactive techniques for time-series data visualisation

• Animation techniques for time-series visualisation

1 Tableau

Visitor arrival by country

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2 Hirizon Plot

2.1 Loading R Package

pacman::p_load(ggHoriPlot, ggthemes, tidyverse)

2.2 Dataset

averp <- read_csv("../../data/AVERP.csv") %>%
  mutate(`Date` = dmy(`Date`))

averp %>% 
  filter(Date >= "2018-01-01") %>%
  ggplot() +
  geom_horizon(aes(x = Date, y=Values), 
               origin = "midpoint", 
               horizonscale = 6)+
  facet_grid(`Consumer Items`~.) +
    theme_few() +
  scale_fill_hcl(palette = 'RdBu') +
  theme(panel.spacing.y=unit(0, "lines"), strip.text.y = element_text(
    size = 5, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=7),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank()
    ) +
    scale_x_date(expand=c(0,0), date_breaks = "3 month", date_labels = "%b%y") +
  ggtitle('Average Retail Prices of Selected Consumer Items (Jan 2018 to Dec 2022)')