gistfile1.txt

rain_data <- read.csv("https://www2.sepa.org.uk/rainfall/api/Month/15201?csv=true",
                      header = T) |>
  dplyr::mutate(city = "Edinburgh") |>
  dplyr::bind_rows(read.csv("https://www2.sepa.org.uk/rainfall/api/Month/327234?csv=true",
                            header = T) |>
                     dplyr::mutate(city = "Glasgow")) |>
  # Oct 2024 data is incomplete at the time of this workshop!
  dplyr::filter(Timestamp != "Oct 2024") |>
  tidyr::separate(Timestamp, into = c("month", "year"), remove = F) |>
  # Make the years back into numbers 
  dplyr::mutate(year = as.numeric(as.character(year))) |>
  # Get month factor levels in right order
  dplyr::mutate(month = factor(month, levels = month.abb))


rain_data |>
  ggplot() +
  geom_point(aes(x = month,
                 y = Value,
                 colour = city),
             position = ggbeeswarm:::position_quasirandom(
               width = 0,
               dodge.width = 0.5)) +
  labs(title = "Rainfall in Glasgow and Edinburgh over the years",
       subtitle = "Each dot represents the total rainfall within a given month from 2014 to 2024.") +
  theme_minimal(base_size = 15) +
  scale_y_continuous(labels = function(x) paste(x, "mm")) +
  theme(axis.title = element_blank(),
        legend.title = element_blank())