在ggplot中平滑

Question

I have this ggplot

ggplot(dt.1, aes(x=pctOAC,y=NoP, fill=Age)) +
    geom_bar(stat="identity",position=position_dodge()) +
    geom_smooth(aes(x=pctOAC,y=NoP, colour=Age), se=F, method="loess",show_guide = FALSE,lwd=0.7) +
    theme(legend.position=c(.2,0.8))

dt1 <- structure(list(Age = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("o80", "u80"), class = "factor"), NoP = c(47L, 5L, 33L, 98L, 287L, 543L, 516L, 222L, 67L, 14L, 13L, 30L, 1L, 6L, 17L, 30L, 116L, 390L, 612L, 451L, 146L, 52L), pctOAC = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)), .Names = c("Age", "NoP", "pctOAC"), row.names = c(NA, -22L), class = "data.frame")

I would like to have the smooth lines constrained to lie above zero, perhaps something similar to a kernel density. In fact if I had the underlying data, I expect a kernel density is exactly what I would want, but I only have the aggregated data. Is there any way to do this ? I tried using different method= in the geom_smooth , but the small dataset seems to prevent it. I wondered about using stat_function but I don't have much clue about how to proceed with finding a suitable function to plot.

Answer 1

Another possibility is to use method="glm" with a spline curve and a log link (ie also tried method="gam" , but its automatic complexity adjustment wanted to reduce the wiggliness too much:

library(splines)
ggplot(dt.1, aes(x=pctOAC,y=NoP, fill=Age)) +
    geom_bar(stat="identity",position=position_dodge()) +
    geom_smooth(aes(colour=Age), se=F,
                method="glm",
                formula=y~ns(x,8),
                family=gaussian(link="log"),
                show_guide = FALSE,lwd=0.7) +
    theme(legend.position=c(.2,0.8))

Answer 2

How about geom_density() ?

ggplot(dt1, aes(x=pctOAC,y=NoP, colour=Age, fill=Age)) +
  geom_bar(stat="identity",position=position_dodge()) +
  geom_density(stat="identity", fill=NA) +
  theme(legend.position=c(.2,0.8))