Vincent La Foote Carrier Vincent La Foote Carrier - 2 months ago 35
R Question

ggplot2: geom_smooth select observations connections (equivalence to geom_path())

I am using

ggplot2
to create vertical profiles of the ocean. My raw data set creates "spikes" so to make smooth curves. I am hoping to use
geom_smooth()
. I also want the line to progress according to the order of the observations (and not according to the x axis). When I use
geom_path()
, it works for the original plot, but not for the resulting
geom_smooth()
(see picture below).

melteddf = Storfjorden %>% melt(id.vars = "Depth")
ggplot(melteddf, aes(y = Depth, x = value)) +
facet_wrap(~ variable, nrow = 1, scales = "free_x") +
scale_y_reverse() +
geom_smooth(span = 0.5,se = FALSE) +
geom_path()


enter image description here
Therefore is there a way to make sure the smooth curve progress according to the order of observations, instead of the a axis?

Subset of my data:

head(Storfjorden)
Depth Salinity Temperature Fluorescence
1 0.72 34.14 3.738 0.01
2 0.92 34.14 3.738 0.02
3 1.10 34.13 3.739 0.03
4 1.80 34.14 3.740 0.06
5 2.80 34.13 3.739 0.02
6 3.43 34.14 3.739 0.05

Answer

The data that you provided is quite minimal, but we can make it work.

Using some of the tidyverse packages we can fit separate loess functions to each of the variables.

What we do, essentially, is

  1. Group our data by variable (group_by).
  2. Use do to fit a loess function to each group.
  3. Use augment to create predictions from that loess model, in this case for a 1000 values within the range of the data (for that variable).

.

# Load the packages
library(dplyr)
library(broom)

lo <- melteddf %>% 
  group_by(variable) %>% 
  do(augment(loess(value ~ Depth, data = .), 
             newdata = data.frame(Depth = seq(min(.$Depth), max(.$Depth), l = 1000))))

Now we can use that predicted data in a new geom_path call:

ggplot(melteddf, aes(y = Depth, x = value)) + 
  facet_wrap(~ variable, nrow = 1, scales = "free_x") + 
  scale_y_reverse() +
  geom_path(aes(col = 'raw')) +
  geom_path(data = lo, aes(x = .fitted, col = 'loess'))

(I map simple character vectors to the color of both lines to create a legend.)

Result:

enter image description here