I was looking to group coefficient from a single model in a dot and whisker plot:

Here is a sample:

    ## Simulate data
    set.seed(42)
    n <- 1e3
    
    d <- data.frame(
      # Covariates
      projects_0_300_minus2 = rnorm(n),
      projects_0_300_minus1 = rnorm(n),
      projects_0_300_0 = rnorm(n),
      projects_0_300_1 = rnorm(n),
      projects_0_300_2 = rnorm(n),
      projects_300_600_minus2 = rnorm(n),
      projects_300_600_minus1 = rnorm(n),
      projects_300_600_0 = rnorm(n),
      projects_300_600_1 = rnorm(n),
      projects_300_600_2 = rnorm(n),
      projects_600_800_minus2 = rnorm(n),
      projects_600_800_minus1 = rnorm(n),
      projects_600_800_0 = rnorm(n),
      projects_600_800_1 = rnorm(n),
      projects_600_800_2 = rnorm(n),
      # Individuals and firms
      id = factor(sample(20, n, replace=TRUE)),
      firm = factor(sample(13, n, replace=TRUE)),
      # Noise
      u = rnorm(n)
    )
    
    
    # Effects for individuals and firms
    id.eff <- rnorm(nlevels(d$id))
    firm.eff <- rnorm(nlevels(d$firm))
    
    # Left hand side
    d$y <- d$projects_0_300_minus2   0.5*d$projects_0_300_minus1   d$projects_0_300_0   0.5*d$projects_0_300_1  d$projects_0_300_2   d$projects_300_600_minus2  d$projects_300_600_minus1   0.5*d$projects_300_600_0  d$projects_300_600_1   0.5*d$projects_300_600_2    0.5*d$projects_600_800_0  d$projects_600_800_minus2   0.5*d$projects_600_800_minus1  d$projects_600_800_0   0.5*d$projects_600_800_1  d$projects_600_800_2  id.eff[d$id]   firm.eff[d$firm]   d$u
    
## extract predictor names
    d_pred <- subset(d, select = -c(id,firm,u,y))
    d_pred_names <- names(d_pred)
    
## model
    Formula <- formula(paste("y ~ ", paste(d_pred_names, collapse="   "),"| id   firm | 0 | 0"))
    results <- felm(Formula, data = d)
        
    #extract names for grouping of coefficient s
    projects_minus2 <- names(d[ , grepl( "00_minus2" , names( d ) ) ])
    projects_minus1 <- names(d[ , grepl( '00_minus1' , names( d ) ) ])
    projects_0 <- names(d[ , grepl( "00_0" , names( d ) ) ])
    projects_1 <- names(d[ , grepl( "00_1" , names( d ) ) ])
    projects_2 <- names(d[ , grepl( "00_2" , names( d ) ) ])

## plot individual BUT WANT TO GROUP THESE INTO ONE PLOT    
    Turnover_projects_minus2 <- jtools::plot_coefs(results, coefs = projects_minus2)   labs(title = 'Turnover_projects -2')
    Turnover_projects_minus1 <- jtools::plot_coefs(results, coefs = projects_minus1)   labs(title = 'Turnover_projects -1')
    Turnover_projects_0 <- jtools::plot_coefs(results, coefs = projects_0)   labs(title = 'Turnover_projects 0')
    Turnover_projects_1 <- jtools::plot_coefs(results, coefs = projects_1)   labs(title = 'Turnover_projects 1')
    Turnover_projects_2 <- jtools::plot_coefs(results, coefs = projects_2)   labs(title = 'Turnover_projects 2')
    
    
    ggarrange(Turnover_projects_minus2, Turnover_projects_minus1, Turnover_projects_0,Turnover_projects_1,Turnover_projects_2,
                    ncol = 2, nrow = 5)

Using this image as an example:

I would like the variables that include seperately: 0-300, 300-600, 600-800, which will be on the Y axis where the above image has 'wt' , 'cyl', 'disp'.

Then, for each one of these, they would grouped by 5 points that include 'minus2', 'minus1' , '0' , '1' , '2' like the above image where 'model1', 'model2', 'model3' are grouped together.

Something like this i think but couldnt get it work..:

Or

ggplot(plot_df, aes(x = var1, y = Estimate, color = var2))  
  geom_linerange(aes(ymin = lower, ymax = upper), size = 1,
                 position = position_dodge(width = 0.5))  
  geom_point(position = position_dodge(width = 0.5), size = 3)  
  coord_flip()  
  labs(x = NULL, color = NULL)  
  scale_color_brewer(palette = "Set1", labels = seq(-2, 2))  
  theme_minimal(base_size = 20)

^{Created on 2022-09-10 with reprex v2.0.2}