"Crossing off" tiles on a heatmap

Question

For a heatmap made using ggplot and geom_tile, how would you "cross off" a tile based on a conditional value?

The heatmap shows counts of the number of times an animal performed a behavior between 1990-2020. Rows are animal IDs, columns are years.

Years go from 1990-2020 but not all animals are alive throughout that time frame (ie, some born later than 1990 or die earlier than 2020)

So I want to cross off any tiles where an animal isn't alive, or before it was born.

Data look like this (shortened to 5 rows for brevity):

data <- data.frame(date = structure(c(8243, 8243, 8243, 8248, 8947), class = "Date"), 
                       year = c("1992", "1992", "1992", "1992", "1994"), 
                       event.id = c(8L, 8L, 8L, 10L, 11L), 
                       id = c("L5", "L58", "L73", "L21", "L5"),
                       birth = c(1964L, 1980L, 1986L, 1950L, 1964L), 
                       death = c(2012L, 2003L, NA, NA, 2012L))

NA means the animal is still alive and it wouldn't be crossed off since before it was born.

Any help to create this is greatly appreciated!

Code looks like this:

heatmap <- data %>%
mutate(x = case_when(year %in% 1990:1999 ~ "1990-1999",
                   TRUE ~ year)) %>%
mutate(y = paste(id)) %>%
group_by(x, y, .drop  = FALSE) %>%
summarize(count = n()) %>%
arrange(y)

ggplot(data = heatmap, aes(x, y, fill = count)) +
geom_tile()

EDIT Current heat map.

Answer 1

Here's how you could use color to indicate NA, like suggested by @Gregor Thomas.

Transforming your data to "complete":

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

hm <- dat %>%
  mutate(x = case_when(year < 1960 ~ "Pre-1960", 
                       year %in% 1960:1969 ~ "1960-1969",
                       year %in% 1970:1979 ~ "1970-1979",
                       year %in% 1980:1989 ~ "1980-1989",
                       year %in% 1990:1999 ~ "1990-1999",
                       TRUE ~ year)) %>%
  mutate(y = paste(matriline, id)) %>%
  group_by(x, y, .drop  = FALSE) %>%
  summarize(count = n()) %>%
  ungroup() %>%
  tidyr::complete(x, y) %>%
  arrange(y) %>%
  tidyr::separate(y, into = c("ym", "yid"), sep = " ", remove = FALSE)

Then define a color for NA:

ggplot(data = hm, aes(x, yid, fill = count)) +
  geom_tile() +
  scale_fill_gradient(low = "white", high = "red", na.value = "grey50") +
  scale_x_discrete(position = "top", drop = FALSE) +
  scale_y_discrete(limits=rev) +
  labs(x = NULL, y = NULL) +
  facet_wrap( ~ ym, strip.position = "left", dir = "v", ncol = 1) +
  theme(panel.spacing = unit(0.2, "lines"),
        strip.background = element_blank(),
        strip.placement = "outside",
        axis.text.x = element_text(angle = 45, hjust = -0.02))

Data:

ids <- c("J11", "J16", "J17", "J02", "J22", "J26", "J27", "J30")
matrilines <- c("J02","J04", "K11", "L20", "P90", "K100", "R22")

dat <- data.frame(year = as.character(sample(1960:2018, 1000, replace = TRUE)),
                  id = sample(ids, 1000, replace = TRUE),
                  matriline = sample(matrilines, 1000, replace = TRUE))