You're close, there are just a few changes you need to make:
Note that I removed colour = Treatment in the top ggplot() call because you don't want to colour by Treatment, you want to colour by Genotype. The aes() for geom_point() is where I've added colour = Genotype, since you want just the points coloured.
The other change was that I removed labels = c("...") from your scale_colour_manual(). If you are going to specify labels, you should specify them for all of the values you're using.
Note also that the scale_shape_manual() call specifies the same shape for both groups. I'm not sure if this is intentional or not, so I didn't touch it.
# CODE
library(tidyverse)
# REMOVE START FOR ANALYSIS
dta <- structure(list(
Location = structure(c(
2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L
), .Label = c("High", "Low", "Mid"), class = "factor"), Treatment = structure(c(
2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L
), .Label = c("Con", "Exp"), class = "factor"), Time = structure(c(
2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("Start", "Stop"), class = "factor"), Genotype = structure(c(
7L, 7L, 7L, 7L,
7L, 7L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 8L, 8L, 8L, 8L, 8L, 8L, 1L,
1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 6L,
6L, 6L, 6L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L,
7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 8L, 8L, 8L, 8L, 8L, 8L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 8L, 4L, 4L, 4L, 4L, 4L, 4L
), .Label = c(
"A", "B",
"C", "D", "E", "F", "G", "H"
), class = "factor"), Time.Location = structure(c(
5L,
6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 4L, 5L, 6L, 4L,
5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L,
6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L,
4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L,
5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L,
6L, 4L, 5L, 6L, 4L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L,
5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L,
6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L,
4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L,
5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L,
6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L,
4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L, 5L, 6L, 4L
), .Label = c(
"StartHigh",
"StartLow", "StartMid", "StopHigh", "StopLow", "StopMid"
), class = "factor"),
Location.Treatment = structure(c(
4L, 6L, 2L, 3L, 5L, 1L,
4L, 6L, 2L, 3L, 5L, 1L, 4L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L,
5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L,
6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L,
5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L,
6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L,
5L, 1L, 4L, 6L, 2L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L,
2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L,
1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L,
2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L,
1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L,
2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L,
1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L, 2L, 3L, 5L, 1L, 4L, 6L,
2L, 3L, 5L, 1L
), .Label = c(
"HighCon", "HighExp", "LowCon",
"LowExp", "MidCon", "MidExp"
), class = "factor"), CT = c(
4.61538,
3.96739, 7.34797, 3.58108, 2.89655, 2.7993, 10.56122, 10.68396,
15.57252, 6.79245, 9.23469, 9.18, 1.1087, 4.26136, 1.14504,
2.20238, 3.15789, 9.54082, 11.05263, 15.84783, 10.48986,
12.62195, 15.12931, 5.51471, 8.20313, 11.85811, 3.38115,
7.5, 9.69512, 8.64407, 11.30597, 14.42797, 8.8125, 11.82482,
11.53061, 6.97674, 9.62766, 10.88028, 5.50403, 9.73558, 8.56419,
11.84524, 16.34892, 18.15789, 10.58036, 14.80932, 12.06081,
12.96992, 9.86014, 12.45652, 6.625, 6.93396, 9.10714, 3.66142,
9.19811, 10.88346, 2.88851, 6.85096, 10.27778, 8.29787, 13.00885,
14.38017, 7.5, 11.77734, 13.84615, 2.22772, 5.28, 5.25641,
1.0514, 2.73256, 4.11111, 11.39098, 11.10236, 13.00781, 7.95259,
10.15748, 13.16327, 8.90625, 10.04587, 13.625, 6.27049, 9.27966,
10.94037, 5.80189, 7.76978, 7.34266, 3.80952, 3.75, 7.29545,
10.45872, 16.83206, 5.95238, 7.70833, 10.92391, 11.03659,
14.39338, 14.88281, 8.22917, 11.63603, 14.7561, 11.9469,
14.65649, 16.84615, 8.37209, 13.27982, 13.69128, 7.77778,
12.59124, 12.32955, 7.00472, 8.41121, 7.22222, 9.43878, 10.33613,
14.16667, 9.60526, 8.77232, 11.91589, 7.01786, 12.29592,
11.83673, 8.55634, 11.17347, 12.68836, 2.7551, 6, 7.21374,
2.52101, 4.03846, 4.80634, 5.49569, 4.78723, 6.02273, 3.04511,
3.59244, 2.48239, 1.54412, 5.74219, 7.68595, 1.33065, 2.625,
4.42164, 9.66942, 11.875, 17.91667, 10.81731, 13.05288, 16.23853,
11.93662, 14.31818, 14.09396, 7.82374, 15.5042, 10.86207,
6.87023, 11.69492, 12.65957, 3.48684, 5.29018, 7.89474, 10.53309,
17.05479, 16.63866, 7.43119, 12.06522, 12.05607, 6.14865,
10.44, 14.69512, 9.24757, 9.04018, 12.38255, 2.22222, 3.90756,
5.85616, 2.23958, 3.8125, 3.01056, 11.60256, 12.22222, 11.8007,
7.76316, 10.08197, 12.78777, 9.20455, 12.1875, 16.59449,
6.82331, 10.91518, 11.5748
)
), row.names = 192:381, class = "data.frame")
# Calculation of SE
data_summary <- function(x) {
m <- mean(x)
ymin <- m - sd(x) / sqrt(length(x))
ymax <- m + sd(x) / sqrt(length(x))
return(c(y = m, ymin = ymin, ymax = ymax))
}
pd1 <- position_dodge(0.5)
plot_CT <- ggplot(dta, aes(x = Location, y = CT, group = Treatment, shape = Treatment)) +
stat_summary(fun.data = data_summary, position = pd1, geom = "errorbar", width = 0.05) +
stat_summary(fun.data = data_summary, position = pd1, geom = "point", size = 2) +
geom_point(aes(colour = Genotype),
position = position_jitterdodge(dodge.width = 0.8, jitter.height = 0, jitter.width = 0.2),
alpha = 0.7
) +
labs(title = "", x = "", y = "CT (% dw)") +
# scale_color_manual(labels=c("Control", "Damaged"),values=c("red","black"),guide = guide_legend(reverse = TRUE) )+
scale_color_manual(values=c("red","black","#067c43","#89b651","#dc5b09","#e4a710","#92c5de","grey","#1d71b4","#7873a3")) +
scale_shape_manual(labels = c("Control", "Damaged"), name = "Treatment", values = c(16, 16), guide = guide_legend(reverse = TRUE)) +
# Style of background
theme_classic() +
# Change title
theme(plot.title = element_text(color = "black", size = 17, face = "bold")) +
# Font size axis
theme(
axis.text = element_text(size = 12),
axis.title = element_text(size = 17)
) +
scale_x_discrete("Location", labels = c("Low", "Mid", "High"), expand = c(0.1, 0.5)) +
coord_flip()
plot_CT

Created on 2019-04-08 by the reprex package (v0.2.1)