colors <- c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF") library(ggplot2) ggplot(df, aes(x = categorical_FOD_FODs, y = Frequency, fill = Condition_FODs)) + geom_bar(stat = "identity", position = position_dodge()) + scale_fill_manual(values = colors, labels = c("Verbal", "Vm", "Manual", "Mv")) + labs( x = "FODs (direction)", y = "Frequency of FODs", title = "FODs values (ascending/descending) in four condition", fill = "Conditions (renamed)" ) + theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold"), axis.title = element_text(size = 12, face = "bold"), axis.text = element_text(size = 10), legend.title = element_text(size = 10), legend.text = element_text(size = 10), panel.border = element_rect(color = "black", fill = NA, size = 1) ) I want you to improve this plot to show the result more clear than now. So please add error bars or confidence intervals to show the variability in the data.

library(tidyverse)
library(lme4)

FODs_direction <- read.csv("FODs_direction.csv", header = T)

ggplot(FODs_direction, aes(x = categorical_FOD_FODs, y = Frequency, fill = Condition_FODs)) +   
  geom_bar(stat = "identity", position = position_dodge()) +   
  scale_fill_manual(values = c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF"), 
                    labels = c("Verbal", "Vm", "Manual", "Mv")) +   
  labs(     
    x = "FODs (direction)",     
    y = "Frequency of FODs",  
    title = "FODs values (ascending/descending) in four condition",
    fill = "Conditions (renamed)"   
  ) +   
  theme_minimal() +
  theme(     
    plot.title = element_