R/viz_corelogic_shutdown_panel.R

In MattCowgill/macrodashboard: Shiny dashboard for Australian macroeconomic data

#' @import dplyr
#' @import tidyr
#' @import ggplot2
#' @importFrom readr read_csv
#' @import grattantheme
#' @importFrom patchwork plot_annotation

viz_corelogic_shutdown_panel <- function(data = load_data(),
                                         arg1 = NULL,
                                         arg2 = NULL) {
  df <- data[["corelogic_daily"]]
  df <- df[, -7]

  shutdown_date <- as.Date("2020-03-22")

  shutdown <- df %>%
    filter(date >= as.Date("2020-01-01")) %>%
    pivot_longer(
      cols = -date,
      names_to = "city"
    ) %>%
    # gather(key = city, value = value,
    #        -date) %>%
    mutate(city = tools::toTitleCase(city)) %>%
    group_by(city) %>%
    mutate(value = 100 * (value / value[date == shutdown_date]))


  labels <- shutdown %>%
    group_by(city) %>%
    filter(date == max(date)) %>%
    mutate(
      change = (value / 100) - 1,
      label = paste0(
        if_else(change > 0, "+", ""),
        round(change * 100, 1),
        "%"
      ),
      label_y = value - 1.5
    )

  top_of_range <- max(c(102, max(shutdown$value)))

  make_shutdown_graph <- function(cities) {
    if ("Melbourne" %in% cities) {
      shutdown_cols <- c(grattantheme::grattan_red, grattantheme::grattan_lightorange)
    } else {
      shutdown_cols <- c(
        grattantheme::grattan_darkred, grattantheme::grattan_darkorange,
        grattantheme::grattan_yellow
      )
    }

    shutdown_df <- shutdown %>%
      filter(city %in% cities)

    labels_df <- labels %>%
      filter(city %in% cities)

    shutdown_df %>%
      ggplot(aes(x = date, y = value, col = city)) +
      geom_hline(yintercept = 100) +
      geom_vline(xintercept = shutdown_date) +
      geom_line() +
      geom_point(
        data = labels_df,
        size = 2, stroke = 1.25, fill = "white", shape = 21
      ) +
      grattan_label(
        data = labels_df,
        aes(
          label = label,
          y = label_y
        ),
        nudge_x = -2,
        hjust = 0.75,
        size = 14
      ) +
      grattan_label(
        data = ~ filter(
          .,
          date == min(date)
        ),
        aes(label = city),
        size = 18,
        vjust = 1,
        hjust = 0,
        y = top_of_range - 0.1
      ) +
      facet_wrap(~city) +
      theme_grattan() +
      scale_y_continuous(
        limits = c(
          min(shutdown$value) - 2.5,
          max(c(102, shutdown$value))
        ),
        labels = function(x) paste0(x - 100, "%")
      ) +
      scale_x_date(
        date_labels = "%e\n%b",
        breaks = c(
          min(shutdown_df$date),
          shutdown_date,
          max(shutdown_df$date)
        ),
        expand = expansion(add = c(0, 10))
      ) +
      scale_colour_manual(values = shutdown_cols) +
      theme(
        strip.text = element_blank(),
        axis.title = element_blank(),
        axis.text.x = element_text(size = 14),
        panel.spacing = unit(1.5, "lines")
      )
  }

  melb_syd_graph <- make_shutdown_graph(c("Melbourne", "Sydney"))
  others_graph <- make_shutdown_graph(c("Perth", "Adelaide", "Brisbane"))

  shutdown_panel <- melb_syd_graph / others_graph

  shutdown_panel <- shutdown_panel +
    plot_annotation(
      title = "House prices by capital city",
      subtitle = "Cumulative change in Corelogic daily home value index since 22 March 2020",
      caption = "Source: Corelogic"
    ) &
    theme_grattan() +
      theme(
        strip.text = element_blank(),
        axis.title = element_blank(),
        axis.text = element_text(size = 14),
        panel.spacing = unit(1.25, "lines")
      )

  shutdown_panel
}