R: how to visualize the relationship between continuous and categorical data - r

Question

I have the following data.frame which contains 3 categorical variables (different types of vascular pathology) and 1 continuous variable (Output). I'm interested in seeing the relationship between Output and the different types of vascular pathologies, i.e. is higher/lower output associated with mild/severe pathology?
> dput(df)
structure(list(Vascular_Pathology_M = structure(c(1L, 2L, 3L,
1L, 1L, 2L, 4L, 3L, 1L, 2L), .Label = c("Absent", "Mild", "Mild/Moderate",
"Moderate/Severe", "Severe"), class = "factor"), Vascular_Pathology_F = structure(c(4L,
2L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L), .Label = c("Absent", "Mild",
"Mild/Moderate", "Moderate/Severe", "Severe"), class = "factor"),
Vascular_Pathology_O = structure(c(1L, 3L, 4L, 3L, 1L, 2L,
1L, 1L, 1L, 2L), .Label = c("Absent", "Mild", "Mild/Moderate",
"Moderate/Severe"), class = "factor"), Output = c(1.01789418758932,
1.05627630598801, 1.49233946102323, 1.38192374975672, 1.13097652937671,
0.861306979571144, 0.707820561413699, 1.16628243128399, 0.983163398006992,
1.23972603843843)), .Names = c("Vascular_Pathology_M", "Vascular_Pathology_F",
"Vascular_Pathology_O", "Output"), row.names = c(1L, 3L, 4L,
5L, 6L, 7L, 8L, 10L, 11L, 12L), class = "data.frame")
> df
Vascular_Pathology_M Vascular_Pathology_F Vascular_Pathology_O Output
1 Absent Moderate/Severe Absent 1.0178942
3 Mild Mild Mild/Moderate 1.0562763
4 Mild/Moderate Absent Moderate/Severe 1.4923395
5 Absent Absent Mild/Moderate 1.3819237
6 Absent Absent Absent 1.1309765
7 Mild Absent Mild 0.8613070
8 Moderate/Severe Mild Absent 0.7078206
10 Mild/Moderate Moderate/Severe Absent 1.1662824
11 Absent Absent Absent 0.9831634
12 Mild Absent Mild 1.2397260

Answer 1

You could look at the interaction of the various pathologies. For example, with a barplot
## Make the interaction variable
df$interact <- interaction(df[, 1:3], sep="_")
## Look at means of groups
library(dplyr)
df %>% group_by(interact) %>%
dplyr::summarise(Output = mean(Output)) -> means
ggplot(means, aes(interact, Output))+
geom_bar(stat="identity") +
theme(axis.text=element_text(angle=90)) +
xlab("Interaction")
or with points
ggplot(df, aes(interact, Output))+
geom_point() +
theme(axis.text=element_text(angle=45, hjust=1)) +
xlab("Interaction") +
geom_point(data=means, col="red") +
ylim(0, 1.6)

Answer 2

You can simply plot the output against the categorical variables
plot(df[, 1], df[, 4])
plot(df[, 2], df[, 4])
plot(df[, 3], df[, 4])

Answer 3

You have a 4 dimensional dataset. One option is to do a scatter plot (x/y = two dimensions), in a small multiple series (there's one more dimension), and map the Output variable to something visual like size (there's a fourth dimension).
Example, after putting your data in a data.frame called my_dat (since df is already assigned to a function in R). Points are jittered to show the multiple observations per point, and colored by Y position to help make clear which point goes with which category.
library(ggplot2)
my_dat$O_with_labels <-
factor(my_dat[, 3], labels=paste('Vasc Path O:', levels(my_dat[, 3])))
ggplot(my_dat,
aes(x=Vascular_Pathology_M, y=Vascular_Pathology_F)) +
geom_jitter(aes(size=Output, color=Vascular_Pathology_F)) +
facet_wrap(~O_with_labels) +
theme_bw() +
theme(axis.text.x = element_text(angle=45, hjust=1))