ggplot Assigns Colors by Alphabetical Order Despite Factor Levels

September 2, 2022

ggplot is refusing to cooperate with color assignments. It continues to assign colors based on alphabetical order, even after I’ve assigned Factor levels. Please note: I have simplified my code here. This is actually a part of a much more complicated function that creates many plots from distinct datasets and is the reasoning behind the use of [] to call appropriate coloring.

Thanks for any help!

Example Data

LUT <- data.frame(
  Mutation = c("Wildtype", "Deletion"),
  ConditionID = c("Control_Rep1","Treatment_Rep1"),
  Treatment = c("Control", "Treatment")
  )

data <- data.frame(
  bp = c(1:10),
  cAvgCPM = c(10.5, 10.4, 10.4, 10.4, 10.3, 12.3, 12.4, 11.9, 12.4, 13.4),
  cSEM = c(1.8, 1.8, 1.8, 1.8, 1.7, 1.7, 1.9, 1.7, 1.8, 2.1),
  tAvgCPM = c(6.6, 6.6, 6.6, 6.6, 6.6, 7.4, 7.4, 7.1, 7.2, 8.0),
  tSEM = c(0.7, 0.8, 0.8, 0.8, 0.8, 1.0, 1.0, 0.9, 0.5, 0.8)
  )

I always want my Control group to be plotted in black and my Treatment group to be plotted in red. ConditionID can be used to identify the appropriate group for a dataset but it is not the value that I want displayed in the Legend.

Here is an example that generates the plot exactly how I want it, but this is only true because the values in LUT$Treatment are in the correct alphabetical order.

Plot Correct

plot <- ggplot()+
  geom_ribbon(data = data, aes(x=bp, ymax=cAvgCPM+cSEM, ymin=cAvgCPM-cSEM, fill = paste0(LUT$Treatment[LUT$ConditionID == "Control_Rep1"])), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=cAvgCPM, col = paste0(LUT$Treatment[LUT$ConditionID == "Control_Rep1"])), size = .75, alpha = 0.7)+
  geom_ribbon(data = data, aes(x=bp, ymax=tAvgCPM+tSEM, ymin=tAvgCPM-tSEM, fill = paste0(LUT$Treatment[LUT$ConditionID == "Treatment_Rep1"])), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=tAvgCPM, col = paste0(LUT$Treatment[LUT$ConditionID == "Treatment_Rep1"])), size = .75, alpha = 0.7)+
  scale_fill_manual(values = c("#333333", "red"))+
  scale_color_manual(values = c("#333333", "red"))+
  ylab("CPM")+
  theme(text = element_text(family = "Arial", color = "#545454", size = 25),
        panel.background = element_rect("white", color = "#333333", size = 1.2),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.y = element_text(colour = "#333333"),
        legend.title = element_text(colour = "#333333", size=12),
        legend.text = element_text(colour = "#333333", size=12)) +
  guides(fill = "none") +
  guides(color=guide_legend(title="Strain"))

However, if I instead want to include LUT$Mutation in my Legend the plot now displays the incorrect coloring: Wildtype (the Control condition) is now red.

Plot Incorrect

plot <- ggplot()+
  geom_ribbon(data = data, aes(x=bp, ymax=cAvgCPM+cSEM, ymin=cAvgCPM-cSEM, fill = paste0(LUT$Mutation[LUT$ConditionID == "Control_Rep1"])), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=cAvgCPM, col = paste0(LUT$Mutation[LUT$ConditionID == "Control_Rep1"])), size = .75, alpha = 0.7)+
  geom_ribbon(data = data, aes(x=bp, ymax=tAvgCPM+tSEM, ymin=tAvgCPM-tSEM, fill = paste0(LUT$Mutation[LUT$ConditionID == "Treatment_Rep1"])), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=tAvgCPM, col = paste0(LUT$Mutation[LUT$ConditionID == "Treatment_Rep1"])), size = .75, alpha = 0.7)+
  scale_fill_manual(values = c("#333333", "red"))+
  scale_color_manual(values = c("#333333", "red"))+
  ylab("CPM")+
  theme(text = element_text(family = "Arial", color = "#545454", size = 25),
        panel.background = element_rect("white", color = "#333333", size = 1.2),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.y = element_text(colour = "#333333"),
        legend.title = element_text(colour = "#333333", size=12),
        legend.text = element_text(colour = "#333333", size=12)) +
  guides(fill = "none") +
  guides(color=guide_legend(title="Strain"))

I’ve tried:

factoring to add levels
Adding variabled such as scale_colour_discrete, scale_colour_identity
Assigning colors for each plot outside of aes()

For instance, adding levels to LUT$Mutation still produces a plot with the Wildtype plotted in red.

LUT <- LUT %>%
  dplyr::mutate(Mutation = factor(Mutation, levels=c("Wildtype", "Deletion")))

>Solution :

You had some unneeded paste0’s that I removed, I also refactored the fill/colour labelling variables so they can be set once and reused, and also used them to construct the colorscale.Finally I think its better to control the guides such that color is none and its fill that is renamed to strain


(from_lut <- LUT$Mutation[LUT$ConditionID == "Control_Rep1"])
(from_lut2 <- LUT$Mutation[LUT$ConditionID == "Treatment_Rep1"])
(colorscale <- c("#333333", "red"))
names(colorscale) <- c(from_lut,from_lut2)
colorscale

plot <- ggplot()+
  geom_ribbon(data = data, aes(x=bp, ymax=cAvgCPM+cSEM, ymin=cAvgCPM-cSEM, fill = from_lut), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=cAvgCPM, col = from_lut), size = .75, alpha = 0.7)+
  geom_ribbon(data = data, aes(x=bp, ymax=tAvgCPM+tSEM, ymin=tAvgCPM-tSEM, fill = from_lut2), size = 1.2, alpha = 0.25)+
  geom_line(data = data, aes(x=bp, y=tAvgCPM, col = from_lut2), size = .75, alpha = 0.7)+
  scale_fill_manual(values = colorscale)+
  scale_color_manual(values = colorscale)+
  ylab("CPM")+
  theme(text = element_text(family = "Arial", color = "#545454", size = 25),
        panel.background = element_rect("white", color = "#333333", size = 1.2),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.title.y = element_text(colour = "#333333"),
        legend.title = element_text(colour = "#333333", size=12),
        legend.text = element_text(colour = "#333333", size=12)) +
  guides(fill=guide_legend(title="Strain")) + guides(color = "none")