Having geom_vline() get along well with facet_grid(): a failed mapping example

Question

Using the data generated from here , I would like to have ggplot2 create a plot similar to the one below.

Desirable plot panels with undesirable labeling ( facet_wrap ):

The objective in this plot is to allow visual comparison between the mean of the distribution of first column (Approach1) with the mean of density from each column. The script to create the density plots is as follows:

ggplot(surg_df, aes(x=op_tm, col=color_hex)) +
  geom_density(aes(fill=color_hex), alpha=0.3) +
  geom_density(data = base_comp_df, col='#a269ff', alpha=1, size=0.5) +
  geom_vline(data=avg_surg_df, aes(xintercept= avg_op_tm), 
             size=1, col=avg_surg_df$color_hex, linetype="dashed") +
  geom_vline(data=avg_comp_df, aes(xintercept= avg_op_tm+2), 
             size=1, colour='#a269ff', linetype="dashed") +
  annotate(geom= "text",
           label=paste("mean diff: ", 
                       as.character(floor(avg_comp_df$avg_op_tm-avg_surg_df$avg_op_tm)), 
                       sep=""), 
           col='black', size=4, x=100, y=0.006) +
  geom_segment(aes(x = avg_op_tm, y=0.006, xend = avg_surg_df$avg_op_tm, 
                   yend = 0.006, colour = "red") , 
               size=1, data = avg_comp_df) +    
  facet_wrap(~surg_grp) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_colour_identity(guide="none", breaks=base_surg_df$color_hex) +
  scale_fill_identity(guide="legend", breaks=base_surg_df$color_hex,
                      name="Surgical Approaches", 
                      labels=base_surg_df$surg_apprch) 

The above plot was desirably created using facet_wrap() with grouping by variable surg_grp . But when I decided to give it a less messy look by the advantage of labeling from facet_grid() instead, things went out of control. What I managed to create so far by facet_grid() was the following lousy collection of density plots that each comes with 3 ridiculous vertical line for each panel :)) The lines turn out to be the average values across each column picked from variable avg_op_tm from avg_surg_df calculated table of summaries.

Undesirable plot panels, desirable labeling ( facet_grid ):

As you notice in the script below, unlike the previous plot, there is only one geom_vline and the three lines on each panel comes from that single line:

ggplot(surg_df)+
  geom_density(aes(x=op_tm, col=color_hex, fill=color_hex), alpha=0.3) +
  scale_fill_identity("Approaches", guide="legend", breaks=base_surg_df$color_hex, 
                      labels=base_surg_df$surg_apprch,
                      aesthetics = "fill")+    
  scale_colour_identity(guide="none",breaks=base_surg_df$color_hex)+
  geom_density(data = base_comp_df, aes(x=op_tm), alpha=1, col='#a269ff', size=0.5) +
  geom_vline(data=avg_surg_df, aes(xintercept= avg_op_tm), size=1,
             linetype="dashed")+
  annotate(geom= "text",
           label=paste("mean diff: ", 
                       as.character(floor(avg_surg_df$avg_op_tm)), sep=""), 
           col='black', size=4, x=100, y=0.006)+
  facet_grid(rows = vars(condition_grp), cols=vars(surg_apprch), scales = 'free')

The community is rich with similar Q&As around facet_wrap() + geom_vline() but not so much helpful questions and answers about facet_grid() + geom_vline() . How can I have geom_vline() use two grouping parameters that have been fed to facet_wrap ( condition_grp and surg_apprch ) and get it to map data correctly? What educational point have I failed to learn that made my approach with facet_grid() fail?

Any help is highly appreciated.

Update

> head(avg_comp_df)
  surg_grp avg_op_tm Cnt surg_apprch color_hex
1       A1  309.5494  74   Approach1   #a269ff
2       A2  309.5494  74   Approach2   #00CC00
3       A3  309.5494  74   Approach3   #FFAA93
4       A4  309.5494  74   Approach4   #5DD1FF
5       B1  263.0835  71   Approach1   #a269ff
6       B2  263.0835  71   Approach2   #00CC00

> head(surg_df) #used to create 12 different curves
  surg_grp surg_apprch condition_grp    op_tm color_hex
1       A1   Approach1      Benign-1 287.2103   #a269ff
2       A1   Approach1      Benign-1 261.2655   #a269ff
3       A1   Approach1      Benign-1 308.9267   #a269ff
4       A1   Approach1      Benign-1 257.9060   #a269ff
5       A1   Approach1      Benign-1 408.0310   #a269ff
6       A1   Approach1      Benign-1 405.4334   #a269ff

> head(avg_surg_df)
  surg_grp avg_op_tm Cnt surg_apprch color_hex
1       A1  309.5494  74   Approach1   #a269ff
2       A2  378.4466 113   Approach2   #00CC00
3       A3  242.9890 101   Approach3   #FFAA93
4       A4  273.0774  71   Approach4   #5DD1FF
5       B1  263.0835  71   Approach1   #a269ff
6       B2  243.1910  85   Approach2   #00CC00

> head(base_comp_df)  #to create similar orchid control distributions in each row 
  surg_grp surg_apprch condition_grp    op_tm color_hex
1       A1   Approach1      Benign-1 287.2103   #a269ff
2       A1   Approach1      Benign-1 261.2655   #a269ff
3       A1   Approach1      Benign-1 308.9267   #a269ff
4       A1   Approach1      Benign-1 257.9060   #a269ff
5       A1   Approach1      Benign-1 408.0310   #a269ff
6       A1   Approach1      Benign-1 405.4334   #a269ff

> head(base_surg_df) #to make the legend
  surg_apprch condition_grp surg_grp color_hex
1   Approach1      Benign-1       A1   #a269ff
2   Approach2      Benign-1       A2   #00CC00
3   Approach3      Benign-1       A3   #FFAA93
4   Approach4      Benign-1       A4   #5DD1FF
5   Approach1      Benign-2       B1   #a269ff
6   Approach2      Benign-2       B2   #00CC00

Answer 1

You need to precalculate the means in a separate dataframe that replicates the structure of your facet_grid in its groups. A rough and dirty example with mtcars :

library(dplyr)
library(ggplot2)

mean_df <- mtcars %>% 
  group_by(am, vs) %>% 
  summarise(mean_val = mean(mpg))

ggplot(mtcars) +
  geom_freqpoly(aes(x = mpg)) +
  geom_vline(data = mean_df, aes(xintercept = mean_val), colour = "red") +
  facet_grid(am ~ vs)