Label or Highlight Specific Rows in ggplot2

I have a great looking geom_tile plot, but I need a way to highlight specific rows or label specific rows based on a binary value.

Here is a small subset of data in wide format and resulting output:

df <- structure(list(bin_level = c(0,1), sequence = c("L19088.1", "chr1_43580199_43586187"), X236 = c("G", "."), X237 = c("G", "."), X238 = c("A", "a"), 
    X239 = c("T", "C"), X240 = c("A", "c"), X241 = c("G", "G"
    )), class = "data.frame", row.names = 1:2)


> df
  bin_level               sequence X236 X237 X238 X239 X240 X241
1         0               L19088.1    G    G    A    T    A    G
2         1 chr1_43580199_43586187    .    .    a    C    c    G

The actual dataset is much larger, with 1045 observations of 3096 variables.

My goal is to plot this massive dataset as a heatmap with colors for each different nucleotide and be able to differentiate between rows with bin_levels of 0 and 1.

The following code makes a great plot, but doesn't include the bin_level differences I need to see. I would like to highlight the entire row if the bin_level is 1, but I haven't been able to find anything on how to do such a thing. I am already using nucleotides for the aes fill variable, so I need something else. The best option I've come up with so far is to color the row labels. I used info from this post to try an ifelse statement to color based on the bin_level variable.

The biggest problems here are

1. Row axis titles are much too long and too many to look good
2. There are only 53 bin_level rows with a 1 (of 1045 total), so why does it look like a LOT more red than there should be?
3. I want the red labels (bin_level =1's) at the top of the plot, and the mix of black/red makes me think my arrange(bin_level) piece isn't working right.

Please let me know if you know of a better way to accomplish what I'm trying to accomplish, or can help make my code work better than it is currently. Thank you!

df %>%
  ## reshape to long table
  ## (one column each for sequence, position and nucleotide):
  pivot_longer(-c("Sequence", "bin_level"), ## stack all columns *except* sequence and bin_level
               names_to = 'position',
               values_to = 'nucleotide'
  ) %>%
  arrange(bin_level) %>%
  ## create the plot:
  ggplot() +
  geom_tile(aes(x = position, y = Sequence, fill = nucleotide),
            height = 1 ## adjust to visually separate sequences
  ) +
  scale_fill_manual(values = c('a'='#ea0064', 'c'='#008a3f', 'g'='#116eff',
                               't'='#cf00dc', '\U00B7'='#000000', 'X' ='#ffffff'
  )
  ) +
  labs(x = 'x-axis-title', y='Sequence') +
  ## remove x-axis (=position) elements: they'll probably be too dense:
  theme(axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.y = element_text(colour = ifelse(levels(df$bin_level)==1, "red", "black"))
  )

While passing a vector of colors to element_text() is a quick option in some cases IMHO in more general cases it is error prone and requires to keep an eye on the way you ordered your data. Instead I would suggest to have a look at the ggtext package which introduces the theme element element_markdown and allows for styling text using some HTML, CSS and markdown.

Moreover, besides the issue already pointed out by @I_O another issue is that you wrangle the data manipulation steps together with the plotting code in one pipeline. As a consequence while you arrange your data by bin_level you use the original unmanipulated, unarranged dataset df which by the way is still in wide format for the color assignment. That's why personally I would always recommend to split the data wrangling and the plotting except for very simple cases.

Finally, while your arranged your data by bin_level what really matters is the order of sequence, i.e. you have to set the order of sequence after arranging for which I use forecast::fct_inorder.

Note: To make your example more realistic I duplicated your dataset to add two more rows.

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

df_long <- df %>%
  pivot_longer(-c("sequence", "bin_level"),
    names_to = "position",
    values_to = "nucleotide"
  ) %>%
  arrange(bin_level) %>%
  mutate(
    sequence = if_else(bin_level == 1, paste0("<span style='color: red'>", sequence, "</span>"), sequence),
    sequence = forcats::fct_inorder(sequence))

ggplot(df_long) +
  geom_tile(aes(x = position, y = sequence, fill = nucleotide),
    height = 1
  ) +
  scale_fill_manual(values = c(
    "a" = "#ea0064", "c" = "#008a3f", "g" = "#116eff",
    "t" = "#cf00dc", "\U00B7" = "#000000", "X" = "#ffffff"
  )) +
  labs(x = "x-axis-title", y = "Sequence") +
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.y = ggtext::element_markdown()
  )

DATA

df <- structure(list(
  bin_level = c(0, 1), sequence = c("L19088.1", "chr1_43580199_43586187"), X236 = c("G", "."), X237 = c("G", "."), X238 = c("A", "a"),
  X239 = c("T", "C"), X240 = c("A", "c"), X241 = c("G", "G")
), class = "data.frame", row.names = 1:2)

df1 <- structure(list(
  bin_level = c(0, 1), sequence = c("L19088.2", "chr1_43580199_43586187.2"), X236 = c("G", "."), X237 = c("G", "."), X238 = c("A", "a"),
  X239 = c("T", "C"), X240 = c("A", "c"), X241 = c("G", "G")
), class = "data.frame", row.names = 1:2)

df <- dplyr::bind_rows(df, df1)