How to add split lines to the scatter plot to divide the scatter points into Top K

For df, it is assumed that the values of price_index and quantity_index have co-consistency.

   products price_index quantity_index   price_rank quantity_rank
1         a    95.00000       95.00000   high_price   high_volume
2         b    80.69012       94.53585   high_price   high_volume
3         c    74.41048       90.95081   high_price   high_volume
4         d    73.24853       89.82692 medium_price medium_volume
5         e    70.50442       79.71225 medium_price medium_volume
6         f    67.11609       75.43498    low_price    low_volume
7         g    64.14685       58.26419    low_price    low_volume
8         h    56.76375       56.16531    low_price    low_volume
9         i    55.76472       56.02838    low_price    low_volume
10        j    55.70475       50.24873    low_price    low_volume

I used the following code to draw a scatter plot, and added two dotted lines to x and y through geom_vline and geom_hline to distinguish top 3, top 5 and the rest of the data points. But now I manually set xintercept = c(68, 74) and yintercept = c(77, 90), how can I automatically set the value? For example: xintercept = c(avg(70.50442, 67.11609), avg(74.41048, 73.24853)) yintercept = c(avg(79.71225, 75.43498), avg(90.95081, 89.82692)). Thanks.

ggplot(df, aes(x = price_index, y = quantity_index, label = products)) +
   coord_fixed() +
   geom_point(colour = 'blue', size = 3, alpha=0.9) +
   scale_x_continuous(expand = c(0, 0), limits = c(50, 100),
                      breaks = seq(50, 100, 5)
   ) +
   scale_y_continuous(expand = c(0, 0), limits = c(50, 100),
                      breaks = seq(50, 100, 5)
   ) +
   geom_vline(xintercept = c(68, 74), color='black',
              linetype='dotted', size=1) +
   geom_hline(yintercept = c(77, 90), color='black',
              linetype='dotted', size=1)

Out:

Data samples:

df <- structure(list(products = c("a", "b", "c", "d", "e", "f", "g", 
"h", "i", "j"), price_index = c(95, 80.69011538, 74.41047705, 
73.24853055, 70.5044217, 67.1160916, 64.14685495, 56.76375355, 
55.76472446, 55.70475052), quantity_index = c(95, 94.53585227, 
90.95080999, 89.82692004, 79.71224701, 75.43498354, 58.26419203, 
56.16530529, 56.02838119, 50.24873055), price_rank = c("high_price", 
"high_price", "high_price", "medium_price", "medium_price", "low_price", 
"low_price", "low_price", "low_price", "low_price"), quantity_rank = c("high_volume", 
"high_volume", "high_volume", "medium_volume", "medium_volume", 
"low_volume", "low_volume", "low_volume", "low_volume", "low_volume"
)), class = "data.frame", row.names = c(NA, -10L))

>Solution :

Quite sure the data wrangling could be simplified but this is a quick approach to compute the intercepts for the dividing lines based on the mean:

library(dplyr)
library(ggplot2)

df <- df |>
  mutate(
    rank = rank(-price_index),
    top3 = rank %in% c(3, 4),
    top5 = rank %in% c(5, 6)
  )

top3 <- df |>
  filter(top3) |>
  summarise(across(c(price_index, quantity_index), mean))

top5 <- df |>
  filter(top5) |>
  summarise(across(c(price_index, quantity_index), mean))

top <- list(top3, top5) |> bind_rows()

ggplot(df, aes(x = price_index, y = quantity_index, label = products)) +
  coord_fixed() +
  geom_point(colour = "blue", size = 3, alpha = 0.9) +
  scale_x_continuous(
    expand = c(0, 0), limits = c(50, 100),
    breaks = seq(50, 100, 5)
  ) +
  scale_y_continuous(
    expand = c(0, 0), limits = c(50, 100),
    breaks = seq(50, 100, 5)
  ) +
  geom_vline(
    data = top, aes(xintercept = price_index), color = "black",
    linetype = "dotted", size = 1
  ) +
  geom_hline(
    data = top, aes(yintercept = quantity_index), color = "black",
    linetype = "dotted", size = 1
  )