I am trying to visualize my data separately as a bar graph and as a dot plot connected by a line.
The experimental design includes 2 treatments, 3 levels for each treatment, and 2 diets as independent variables and weight measurement as a dependent variable. Each sample (e.g. treatment "a" level "1" diet "l" is duplicated. Below is a sample data frame (the response variable values are simplified):
df <- data.frame(treatment=c('a','a','a','b','b','b','a','a','a','b','b','b',
'a','a','a','b','b','b','a','a','a','b','b','b',
'a','a','a','b','b','b','a','a','a','b','b','b',
'a','a','a','b','b','b','a','a','a','b','b','b'),
level=c(1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3,
1,2,3,1,2,3,1,2,3,1,2,3),
diet=c('l','l','l','l','l','l','h','h','h','h','h','h',
'l','l','l','l','l','l','h','h','h','h','h','h',
'l','l','l','l','l','l','h','h','h','h','h','h',
'l','l','l','l','l','l','h','h','h','h','h','h'),
rep=c(1,1,1,1,1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,2,2,2,2,
1,1,1,1,1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,2,2,2,2),
weight=c(100,75,50,50,25,12.5,100,75,50,50,25,12.5,
100,75,50,50,25,12.5,100,75,50,50,25,12.5,
200,150,100,100,50,25,200,150,100,100,50,25,
200,150,100,100,50,25,200,150,100,100,50,25))
Using a linear mixed model, I see that treatment and level effects are individually significant.
fit_df <- lmer(weight ~ treatment*level*diet + (1|rep), data=df)
I have also run emmeans to see pairwise contrasts between each combination of treatment and level.
(emm_wt <- emmeans(fit_df, specs=pairwise~treatment*level))
Then, I want to visualize the result shown below in a bar graph and a dot plot connected by a line. For the bar graph, the y-axis is emmean, x-axis is treatment*level, and error bars show emmean±SE.
$emmeans
treatment level emmean SE df lower.CL upper.CL
a 1 150.0 7.98 27.7 133.64 166.4
b 1 75.0 7.98 27.7 58.64 91.4
a 2 112.5 7.98 27.7 96.14 128.9
b 2 37.5 7.98 27.7 21.14 53.9
a 3 75.0 7.98 27.7 58.64 91.4
b 3 18.8 7.98 27.7 2.39 35.1
Results are averaged over the levels of: diet
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
The code below produces something similar to what I am looking for, but I am not sure how to add a line connecting the dots by the treatment (a1 to a3 and b1 to b3)... It would also be nice to assign colors by the treatment (e.g. red for a and blue for b).
plot(emm_wt[[1]],
CIs=TRUE,
PIs=TRUE,
comparisons=TRUE,
colors=c("black","dark grey","grey","red"),
alpha=0.05,
adjust="tukey") +
theme_bw() +
coord_flip()
If anybody has any insights as to how I could visualize this, please let me know. Thank you in advance!
