example for alejandro as an example of avoiding dual y-axes

loss_summary_plot_example.R

library(tibble)
library(ggplot2)
library(dplyr)
library(tidyr)

sim_years <- 7
start_year <- 2014
report_pattern <- c(0.25, 0.45, 0.55, 0.75, 0.9, 0.95, 1)
paid_pattern <- c(0.1, 0.25, 0.45, 0.68, 0.85, 0.94, 0.98)
# assuming Poisson with lambda = 100, and Lognormal with mu = 7 and sigma = 2
set.seed(11235)

clms_per_year <- rpois(sim_years, lambda = 100)

rep_clms_per_year <- ceiling(rev(report_pattern) * clms_per_year)

calc_pct_paid <- function(yr, n) {
    mu <- rev(paid_pattern)[yr]
    sd <- 0.05
    pmax(0, pmin(1, rnorm(n, mean = mu, sd = sd)))
}

example_data <- tibble(
    yr = rep(seq_len(sim_years), times = rep_clms_per_year),
    inc = rlnorm(sum(rep_clms_per_year), meanlog = 7, sdlog = 2)
    ) %>%
    group_by(yr) %>%
    mutate(
        pct_paid = calc_pct_paid(yr, dplyr::n()),
        pd = pct_paid * inc,
        case = inc - pd,
        py = start_year + yr - 1
    ) %>%
    ungroup() %>%
    select(py, pd, case, inc)

plot_data <- example_data %>%
    group_by(py) %>%
    summarise(
        cnt = n(), 
        tot_pd = sum(pd), 
        tot_case = sum(case), 
        max_loss = max(pd + case),
        .groups = "drop"
    )

plot_data_agg_only <- plot_data %>%
    select(-max_loss) %>%
    pivot_longer(
        cols = c("tot_pd", "tot_case"), 
        names_to = "part", 
        values_to = "amt"
    )    

loss_plot <- ggplot(plot_data_agg_only) + 
    geom_col(aes(x = py, y = amt, fill = part)) +
    geom_point(
        aes(x = py, y = max_loss), 
        data = plot_data, 
        color = "black", 
        size = 3
    ) +
    geom_linerange(
        aes(x = py, ymin = 0, ymax = max_loss), 
        data = plot_data, 
        color = "black", 
        size = 1.5
    ) + 
    ylab("Total Loss Amount") + 
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.05)), 
        labels = function(x) paste0(x/1000, "k")
        )+
    theme(
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.y = element_text(size = 16),
        legend.position = "none"
    )

cnt_plot <- ggplot(plot_data, aes(x = py, y = cnt)) +
    geom_point(
        color = "blue", 
        size = 3
    ) +
    geom_line(
        aes(x = py, y = cnt), 
        color = "blue", 
        size = 1.5
    ) +
    ylab("Claim Count") +
    scale_x_continuous(
        n.breaks = 7, 
        expand = expansion(mult = c(0.13, 0.13))) +
    theme(
        axis.title.y = element_text(size = 16, colour = "blue")
    )
#using patchwork
#

library(patchwork)

loss_plot / cnt_plot + plot_layout(heights = c(4, 1), guides = "collect")

upper panel

• blue is paid
• orange is case
• black is max loss

lower panel

reported claim counts.