Split barplot using position = "fill" into separate facets - r

Question

I created a bar graph in ggplot using stat = "count" and position = "fill" to show the proportional occurrence of each feature per year (below). I find the readability of this graph rather poor and therefore I'd like to split the graph into facets. However, if I add facet_wrap(~Features), it just fills the bars in every separate facet. How can I prevent this from happening?
The code for my original graph is:
data %>% ggplot(aes(x = Year, fill = Features)) + geom_bar(stat = "count", position = "fill") + theme_classic() + theme(axis.text.x = element_text(angle = 90)) + scale_y_continuous(labels = scales::percent)
I've tried:
data %>% ggplot(aes(x = Year)) + stat_count(geom = "bar", aes(y = ..prop..)) + facet_wrap(~Features) + theme_classic() + theme(axis.text.x = element_text(angle = 90))
but this calculates the proportion within the facet rather than within each year.
Any ideas how I can solve this (using ggplot, rather than by restructuring my data)?
A little about my data:
I have a data frame of features (factor) with for each feature the year (factor) in which this feature was observed. The same feature can occur several times per year, so there are several rows with the same entry for year and feature.

Answer 1

This should work. First, I'll make some data that has similar properties:
labs <- c("Digital labels", "Produce ID (barcode)",
"Smart labels", "Product Recommendation",
"Shopping list", "Product Browsing",
"Product ID (computer vision)",
"Navigation (in-store)", "Product ID (RFID)",
"Other")
years <- vector(mode="list", length=13)
years[[1]] <- c(1,2)
years[[2]] <- c(1,2,8)
years[[3]] <- c(1,2,4,10)
years[[4]] <- c(1,2,3,4,5,6,8,9,10)
years[[5]] <- c(2,3,4,5,8,10)
years[[6]] <- c(1:6, 10)
years[[7]] <- c(1:6, 10)
years[[8]] <- 1:10
years[[9]] <- c(1,3,6,9,10)
years[[10]] <- c(1:5, 7,9,10)
years[[11]] <- 1:10
years[[12]] <- c(1:6, 8:10)
years[[13]] <- c(1,2,3,6,8,9,10)
y <- 2008:2020
dat <- NULL
for(i in 1:13){
tmp <- tibble(
Features = sample(years[[i]], runif(1,600,1000), replace=TRUE),
Year = y[i]
) %>%
mutate(Features = factor(Features, levels=1:10, labels=labs))
dat <- rbind(dat, tmp)
}
Next, here's the original plot like the one you made initially.
dat %>%
ggplot(aes(x = Year, fill = Features)) +
geom_bar(stat = "count", position = "fill") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
scale_y_continuous(labels = scales::percent)
And here's how that would translate into different facets. The key is to make the percentages by hand first and then plot them directly.
agdat %>% filter(Features != "Other") %>%
ggplot(aes(x=Year, y=pct)) +
geom_bar(stat="identity") +
facet_wrap(~Features, ncol=3) +
labs(x="Year", y="Percent") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90)) +
scale_y_continuous(labels = scales::percent)