Vertical equivalent of position_dodge for geom_point on categorical scale

I would transform y_factor to numeric and use continuous y-axis. Trick is to add to "noise" y numeric values by n group.

df_with_overlap <- df_with_overlap %>%
    # Transform y factors to numbers
    mutate(y_num = as.numeric(y_factor)) %>%
    # Add scaling factor by n group 
    mutate(y_num = y_num + case_when(n == 1 ~  0,
                                     n == 2 ~ -0.1,
                                     n == 3 ~  0.1))

# Plot y numeric values
ggplot(df_with_overlap, aes(x, y_num, color = n)) + 
    geom_point() +
    # On y-axis put original labels and no one will notice that it's actually a continuous scale
    scale_y_continuous(breaks = 1:5, 
                       labels = levels(df_with_overlap$y_factor)) +
    labs(y = "y_factor")

Thanks to @aosmith for suggesting ggstance::position_dodgev(). It's exactly what I was looking for. I increased the oversampling so the effect is more obvious.

df <- expand.grid(
  y_factor = paste0('factor_',1:5),
  x =1:100
)%>%as.tbl

seed<-1
df_with_overlap<-df%>%
  sample_frac(1.5,replace = TRUE)%>%
  group_by(y_factor,x)%>%
  mutate(n=factor(1:n()))

ggplot(data=df_with_overlap, aes(x=x, y=y_factor, col=n))+
  geom_point(position=ggstance::position_dodgev(height=0.3))