Is there a way to show that the mean of a group is at zero?

I am trying to make a quick graph to show the means of a few groups. My y axis ranges from -2 to +2. Two of my groups have a mean of zero, therefore nothing shows up on the graph. Is there a good way to depict the two means at zero? Zero is a meaningful number. I was thinking like a small pink bar or blue bar.

Any suggestions? Sorry in advance for the messy code!

conseq_summary_table=structure(list(age_group = c("4", "4", "5", "5", "adult", "adult"), 
condition_motive = c("bad", "good", "bad", "good", "bad", "good"), 
group_conseq_mean = c(0, 0.192307692307, 0.133333333333, 
   -0.0333333333333, -0.710526315789, 0), 
conseq_sd = c(0.577350269189, 0.722797272709, 
   0.549891764241,0.611399643285, 0.450795268685, 0)), 
row.names = c(NA, -6L), 
class = c("grouped_df", "tbl_df", "tbl", "data.frame"), 
groups = structure(list(age_group = c("4", "5", "adult"), 
 .rows = list(1:2, 3:4, 5:6)), row.names = c(NA, -3L), 
 class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

Here is my current code:

    ggplot(conseq_summary_table, aes(age_group, conseq_mean )) +
      geom_bar(aes(fill = condition_motive), stat = "identity", position = "dodge",
               alpha = .7) +
      labs(title = "Summary of conseq", x = "Age Group", y = "Average conseq" ) +
        theme_minimal() +
      scale_y_continuous(expand = c(0,0),
                         limits = c(-2,2)) +
      geom_hline(yintercept=0)

Bar plot

Solution

I know it's not really my remit, but may I allow myself some comments on your plot?

For what you want to show, bar plots are not appropriate. I think one of the reasons you were struggling with this visualisation is just because of that. If you want to show mean and standard deviation, a much more appropriate visualisation are dots with error bars.
Whenever possible, calculate summary statistics within ggplot2! There is often no need of pre-processing your data. Now there are obviously several reasons why you may not have this data. Sometimes one can't get around pre-processing. But if you should have access to the raw data, your case would be a perfect example for the use of summary stats such as above mentioned or box plots.
Think about using facets! Separating your data visually helps the reader a lot to identify patterns and to "see the story". Your task would be to find the best facet for visualisation!
Don't use axis limits that do not make sense! In your case, stick to 1 rather than 2. As a matter of fact, ggplot often choses very reasonable limits - so you may not need to specify limits at all! And if you limit your axes, best to do this within coord_...() calls
The default colors are ggplot2's one big flaw However, there is an integrated set of color palettes based on https://colorbrewer2.org- you can chose color blind friendly palettes etc.

Below a suggestion how I would plot this based on the above comments:

library(ggplot2)

conseq_summary_table <- structure(list(age_group = c("4", "4", "5", "5", "adult", "adult"), condition_motive = c("bad", "good", "bad", "good", "bad", "good"), group_conseq_mean = c(0, 0.192307692307, 0.133333333333, -0.0333333333333, -0.710526315789, 0), conseq_sd = c(0.577350269189, 0.722797272709, 0.549891764241,0.611399643285, 0.450795268685, 0)), row.names = c(NA, -6L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"), groups = structure(list(age_group = c("4", "5", "adult"), .rows = list(1:2, 3:4, 5:6)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

ggplot(conseq_summary_table, aes(age_group, group_conseq_mean )) +
  geom_hline(yintercept=0) +
  geom_pointrange(aes(ymin= group_conseq_mean - conseq_sd, 
                      ymax = group_conseq_mean + conseq_sd, color = condition_motive),
                  position = position_dodge(width = 1)) +
  scale_color_brewer(palette = 'Set1') +
  scale_y_continuous() +
  facet_wrap(~ age_group, scales = 'free_x') # maybe try facetting by condition_motive instead

^{Created on 2020-03-24 by the reprex package (v0.3.0)}