ggplot facet grid within a factor - r

Question

Consider data that looks like this
fitem<-rep(rep(1:16,each=3),2)
fsubs<-factor(rep(rep(paste('sub',1:3,sep=''),16),2))
ftime<-factor(as.character(rep(c('a','b'),each=48)))
fcounts<-as.numeric(round(runif(96,1,10)))
fdf<-data.frame(fsubs,fitem,fcounts,ftime)
head(df)
fsubs fitem fcounts ftime
1 sub1 1 8 a
2 sub2 1 10 a
3 sub3 1 4 a
4 sub1 2 4 a
5 sub2 2 1 a
6 sub3 2 6 a
I would like to plot a facet grid that shows the counts for the two time points ('a','b'), subject-wise. I can't seem to figure out how to plot this in ggplot
here is my ugly attempt to do it
fdf_counts<-data.frame()
for (i in unique(fdf$fsubs)){
fdf_counts<-append(fdf_counts,cbind(fdf%>%filter(fsubs==i,ftime=='a')%>%dplyr::select(fcounts),
fdf%>%filter(fsubs==i,ftime=='b')%>%dplyr::select(fcounts)))
fdf_counts<-data.frame(fdf_counts)
}
s1<-ggplot(fdf_counts,aes(x=fcounts,y=fcounts.1))+geom_point()+geom_smooth(method='lm')+labs(x='a',y='b',title='sub1')
s2<-ggplot(fdf_counts,aes(x=fcounts.2,y=fcounts.3))+geom_point()+geom_smooth(method='lm')+labs(x='a',y='b',title='sub2')
s3<-ggplot(fdf_counts,aes(x=fcounts.4,y=fcounts.5))+geom_point()+geom_smooth(method='lm')+labs(x='a',y='b',title='sub3')
plot_grid(s1,s2,s3)#from 'cowplot' package
How can I do this with using the original fdf data.frame? Especially as the # of subs increase
Or for example if I wanted to plot one scatter plot across all of the subs with fcounts against eachother with ftime(a) as x axis and ftime(b) as y axis?

Answer 1

Consider a merge solution with data frame by itself on fsubs and fitem (being sequential number of items per fsubs and ftime grouping). This approach allows you to keep your long, tidy data format which is ideal format for ggplot since you can then facet_grid using fsubs without iteration.
mdf <- merge(subset(fdf, ftime=="a"),
subset(fdf, ftime=="b"),
by=c("fsubs", "fitem"),
suffixes=c("", "_"))
ggplot(mdf, aes(x=fcounts, y=fcounts_)) +
geom_point() +
geom_smooth(method='lm') +
labs(x='a', y='b') +
facet_grid(~fsubs)

Answer 2

This should get you close:
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
fitem<-rep(rep(1:16,each=3),2)
fsubs<-factor(rep(rep(paste('sub',1:3,sep=''),16),2))
ftime<-factor(as.character(rep(c('a','b'),each=48)))
fcounts<-as.numeric(round(runif(96,1,10)))
fdf<-tibble(fsubs,fitem,fcounts,ftime)
fdf <- fdf %>%
group_by(ftime) %>%
mutate(row_id = row_number()) %>%
pivot_wider(values_from = fcounts,
names_from = ftime)
ggplot(data = fdf, aes(x = a, y = b)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(fsubs ~ ., ncol = 1)
The tidyr function pivot_wider allows us to create the shape of the data we need without explicit loops: create new columns a and b with values from fcounts. We do need to create a unique row id to make this work.
By the way, when I run your code the plots look different from what you posted in the question.
With this output:

Answer 3

Just tried to create a visualization that would analyze all 4 variables. Got a geom_histogram
```{r}
fitem<-rep(rep(1:16,each=3),2)
fsubs<-factor(rep(rep(paste('sub',1:3,sep=''),16),2))
ftime<-factor(as.character(rep(c('a','b'),each=48)))
fcounts<-as.numeric(round(runif(96,1,10)))
fdf<-data.frame(fsubs,fitem,fcounts,ftime)
fdf_counts<-data.frame()
for (i in unique(fdf$fsubs)){
fdf_counts<-append(fdf_counts,cbind(fdf%>%filter(fsubs==i,ftime=='a')%>%dplyr::select(fcounts),
fdf%>%filter(fsubs==i,ftime=='b')%>%dplyr::select(fcounts)))
fdf_counts<-data.frame(fdf_counts)
}
ggplot(data = fdf, mapping = aes(x = fdf$fsubs, y = fdf$fcounts, fill = fdf$fitem)) + geom_bar(stat = "identity", position = "dodge") + facet_grid(cols = vars(ftime))
```