How to plot average of multiple columns by factor variables - r

Question

I am trying to plot what is essentially calculated average time-series data for a dependent variable with 2 independent variables. DV = pupil dilation (at multiple time points "T") in response doing a motor task (IV_A) in combination with 3 different speech-in-noise signals (IV_B).
I would like to plot the average dilation across subjects at each time point (mean for each T column) , with separate lines for each condition.
So, the x axis would be T1 to T5 with a separate line for IV_A(=1):IV_B(=1),IV_A(=1):IV_B(=2),and IV_A(=1):IV_B(=3)
Depending how it looks, I might want the IV_A(=2) lines on a separate plot. But all in one graph would make for an easy visual comparison.
I'm wondering if I need to melt the data, to make it extremely long (there are about 110 T columns), or if there is away to accomplish what I want without restructuring the data frame.
The data look something like this:
Subject IV_A IV_B T1 T2 T3 T4 T5
1 1 1 0.2 0.3 0.5 0.6 0.3
1 1 2 0.3 0.2 0.3 0.4 0.4
1 1 3 0.2 0.4 0.5 0.2 0.3
1 2 1 0.3 0.2 0.3 0.4 0.4
1 2 2 0.2 0.3 0.5 0.6 0.3
1 2 3 0.2 0.4 0.5 0.2 0.3
2 1 1 0.2 0.3 0.5 0.6 0.3
2 1 2 0.3 0.2 0.3 0.4 0.4
2 1 3 0.2 0.4 0.5 0.2 0.3
2 2 1 0.3 0.2 0.3 0.4 0.4
2 2 2 0.2 0.3 0.5 0.6 0.3
2 2 3 0.2 0.4 0.5 0.2 0.3
3 1 1 0.2 0.3 0.5 0.6 0.3
3 1 2 0.3 0.2 0.3 0.4 0.4
3 1 3 0.2 0.4 0.5 0.2 0.3
3 2 1 0.3 0.2 0.3 0.4 0.4
3 2 2 0.2 0.3 0.5 0.6 0.3
3 2 3 0.2 0.4 0.5 0.2 0.3
Edit:
Unfortunately, I can't adapt #eipi10 's code to my actual data frame, which looks as follows:
Subject Trk_Y.N NsCond X.3 X.2 X.1 X0 X1 X2 X3
1 N Pink 0.3 0.4 0.6 0.4 0.8 0.6 0.6
1 N Babble 0.3 0.4 0.6 0.4 0.8 0.6 0.6
1 N Loss 0.3 0.4 0.6 0.4 0.8 0.6 0.6
1 Y Pink 0.3 0.4 0.6 0.4 0.8 0.6 0.6
1 Y Babble 0.3 0.4 0.6 0.4 0.8 0.6 0.6
1 Y Loss 0.3 0.4 0.6 0.4 0.8 0.6 0.6
Trk_Y.N means was the block with or without a secondary motor tracking task ("Yes" or "No"). NsCond is the type of noise the speech stimuli are presented in.
It's likely better to replace "Y" with "Tracking" and "N" with "No_Tracking".
I tried:
test_data[test_data$Trk_Y.N == "Y",]$Trk_Y.N = "Tracking"
But got an error:
Warning message:
In `[<-.factor`(`*tmp*`, iseq, value = c("Tracking", "Tracking", :
invalid factor level, NA generated

Answer 1

I may not have understood your data structure, so please let me know if this isn't what you had in mind:
library(reshape2)
library(ggplot2)
library(dplyr)
"Melt" data to long format. This will give us one observation for each Subject, IV and Time:
# Convert the two `IV` columns into a single column
df.m = df %>% mutate(IV = paste0("A",IV_A,":","B",IV_B)) %>% select(-IV_A,-IV_B)
# Melt to long format
df.m = melt(df.m, id.var=c("Subject","IV"), variable.name="Time", value.name="Pupil_Dilation")
head(df.m)
Subject IV Time Pupil_Dilation
1 1 A1:B1 T1 0.2
2 1 A1:B2 T1 0.3
3 1 A1:B3 T1 0.2
4 1 A2:B1 T1 0.3
5 1 A2:B2 T1 0.2
6 1 A2:B3 T1 0.2
Now we can plot a line giving the average value of Pupil_Dilation for each Time point for each level of IV, plus 95% confidence intervals. In your sample data, there's only a single measurement at each Time for each level of IV so no 95% confidence interval is included in the example graph below. However, if you have multiple measurements in your actual data, then you can use the code below to include the confidence interval:
pd=position_dodge(0.5)
ggplot(df.m, aes(Time, Pupil_Dilation, colour=IV, group=IV)) +
stat_summary(fun.data=mean_cl_boot, geom="errorbar", width=0.1, position=pd) +
stat_summary(fun.y=mean, geom="line", position=pd) +
stat_summary(fun.y=mean, geom="point", position=pd) +
scale_y_continuous(limits=c(0, max(df.m$Pupil_Dilation))) +
theme_bw()