CODEWITHSUNDEEP
rggplot2
user1443547
user1443547

Reputation: 373

How to get geom_vline to honor facet_wrap?

I've poked around, but been unable to find an answer. I want to do a weighted geom_bar plot overlaid with a vertical line that shows the overall weighted average per facet. I'm unable to make this happen. The vertical line seems to a single value applied to all facets.

require('ggplot2')
require('plyr')

# data vectors
panel <- c("A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B")
instrument <-c("V1","V2","V1","V1","V1","V2","V1","V1","V2","V1","V1","V2","V1","V1","V2","V1")
cost <- c(1,4,1.5,1,4,4,1,2,1.5,1,2,1.5,2,1.5,1,2)
sensitivity <- c(3,5,2,5,5,1,1,2,3,4,3,2,1,3,1,2)

# put an initial data frame together
mydata <- data.frame(panel, instrument, cost, sensitivity)

# add a "contribution to" vector to the data frame: contribution of each instrument
# to the panel's weighted average sensitivity.
myfunc <- function(cost, sensitivity) {
  return(cost*sensitivity/sum(cost))
}
mydata <- ddply(mydata, .(panel), transform, contrib=myfunc(cost, sensitivity))

# two views of each panels weighted average; should be the same numbers either way
ddply(mydata, c("panel"), summarize, wavg=weighted.mean(sensitivity, cost))
ddply(mydata, c("panel"), summarize, wavg2=sum(contrib))

# plot where each panel is getting its overall cost-weighted sensitivity from. Also
# put each panel's weighted average on the plot as a simple vertical line.
#
# PROBLEM! I don't know how to get geom_vline to honor the facet breakdown. It
#          seems to be computing it overall the data and showing the resulting
#          value identically in each facet plot.
ggplot(mydata, aes(x=sensitivity, weight=contrib)) +
  geom_bar(binwidth=1) +
  geom_vline(xintercept=sum(contrib)) +
  facet_wrap(~ panel) +
  ylab("contrib")

Upvotes: 37

Views: 24314

Answers (3)

R.Sanchez
R.Sanchez

Reputation: 3646

Example using dplyr and facet_wrap incase anyone wants it.

library(dplyr)
library(ggplot2)

df1 <- mutate(iris, Big.Petal = Petal.Length > 4)
df2 <- df1 %>%
  group_by(Species, Big.Petal) %>%
  summarise(Mean.SL = mean(Sepal.Length))

ggplot() +
  geom_histogram(data = df1, aes(x = Sepal.Length, y = ..density..)) +
  geom_vline(data = df2, mapping = aes(xintercept = Mean.SL)) +
  facet_wrap(Species ~ Big.Petal)

enter image description here

Upvotes: 33

Chase
Chase

Reputation: 69201

If you pass in the presumarized data, it seems to work:

ggplot(mydata, aes(x=sensitivity, weight=contrib)) +
  geom_bar(binwidth=1) +
  geom_vline(data = ddply(mydata, "panel", summarize, wavg = sum(contrib)), aes(xintercept=wavg)) +
  facet_wrap(~ panel) +
  ylab("contrib") +
  theme_bw()

enter image description here

Upvotes: 37

Maiasaura
Maiasaura

Reputation: 32986

 vlines <- ddply(mydata, .(panel), summarize, sumc = sum(contrib))
 ggplot(merge(mydata, vlines), aes(sensitivity, weight = contrib)) + 
 geom_bar(binwidth = 1) + geom_vline(aes(xintercept = sumc)) + 
 facet_wrap(~panel) + ylab("contrib")

Upvotes: 4

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