I have a question for you please :
My data :
Nb_obs <- as.vector(c( 2, 0, 6, 2, 7, 1, 8, 0, 2, 1, 1, 3, 11, 5, 9, 6, 4, 0,
This produces the plot with shaded areas using base R graphics.
The trick is to pair the x
values with the y
values.
plot(data$Nb_obst, data$Nb_obs, type = "n", xlab = "Number obst", ylab = "number obs", ylim = c(0, 25))
lines(data$Nb_obst, data$inf20, col = "dark red")
lines(data$Nb_obst, data$sup20, col = "dark red")
lines(data$Nb_obst, data$inf40, col = "red")
lines(data$Nb_obst, data$sup40, col = "red")
lines(data$Nb_obst, data$inf60, col = "dark orange")
lines(data$Nb_obst, data$sup60, col = "dark orange")
lines(data$Nb_obst, data$inf90, col = "yellow")
lines(data$Nb_obst, data$sup90, col = "yellow")
with(data, polygon(c(Nb_obst, rev(Nb_obst)), c(inf90, rev(sup90)), col = "yellow"))
with(data, polygon(c(Nb_obst, rev(Nb_obst)), c(inf60, rev(sup60)), col = "dark orange"))
with(data, polygon(c(Nb_obst, rev(Nb_obst)), c(inf40, rev(sup40)), col = "red"))
with(data, polygon(c(Nb_obst, rev(Nb_obst)), c(inf20, rev(sup20)), col = "dark red"))
The code for a ggplot
graph is a bit longer. There is a function geom_ribbon
perfect for this.
g <- ggplot(data)
g + geom_ribbon(aes(x = Nb_obst, ymin = sup60, ymax = sup90), fill = "yellow") +
geom_ribbon(aes(x = Nb_obst, ymin = sup40, ymax = sup60), fill = "dark orange") +
geom_ribbon(aes(x = Nb_obst, ymin = sup20, ymax = sup40), fill = "red") +
geom_ribbon(aes(x = Nb_obst, ymin = inf20, ymax = sup20), fill = "dark red") +
geom_ribbon(aes(x = Nb_obst, ymin = inf40, ymax = inf20), fill = "red") +
geom_ribbon(aes(x = Nb_obst, ymin = inf60, ymax = inf40), fill = "dark orange") +
geom_ribbon(aes(x = Nb_obst, ymin = inf90, ymax = inf60), fill = "yellow")
Data.
I will redo your dataset, simplifying its creation. You don't need as.vector
and if you are creating a data.frame
there is no need for the data.frame
method of cbind
, data.frame(.)
is enough.
Nb_obs <- c( 2, 0, 6, 2, 7, 1, 8, 0, 2, 1, 1, 3, 11, 5, 9, 6, 4, 0, 7, 9)
Nb_obst <- c(31, 35, 35, 35, 39, 39, 39, 39, 39, 41, 41, 42, 43, 43, 45, 45, 47, 48, 51, 51)
inf20 <- c(2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 4, 3, 5, 4)
sup20 <- c(3, 4, 4, 4, 5, 4, 4, 5, 4, 4, 5, 5, 5, 6, 5, 6, 6, 5, 7, 6)
inf40 <- c(1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 4, 3)
sup40 <- c(4, 5, 5, 5, 6, 5, 5, 6, 5, 5, 6, 6, 6, 7, 6, 7, 7, 7, 9, 7)
inf60 <- c(1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 2)
sup60 <- c(5, 6, 6, 6, 8, 7, 7, 7, 7, 7, 7, 7, 8, 9, 8, 9, 9, 9, 11, 9)
inf90 <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1)
sup90 <- c(10, 11, 11, 11, 15, 13, 13, 14, 12, 13, 13, 13, 14, 17, 15, 17, 17, 16, 21, 18)
data <- data.frame(Nb_obs, Nb_obst, inf20, sup20, inf40, sup40, inf60 , sup60, inf90 , sup90)
Cool question since I had to give myself a crash course in using LOESS for ribbons!
First thing I'm doing is getting the data into a long shape, since that's what ggplot
will expect, and since your data has some characteristics that are kind of hidden within values. For example, if you gather
into a long shape and have, say a column key
, with a value of "inf20" and another of "sup20", those hold more information than you currently have access to, i.e. the measure type is either "inf" or "sup", and the level is 20. You can extract that information out of that column to get columns of measure types ("inf" or "sup") and levels (20, 40, 60, or 90), then map aesthetics onto those variables.
So here I'm getting the data into a long shape, then using spread
to make columns of inf
and sup
, because those will become ymin
and ymax
for the ribbons. I made level
a factor and reversed its levels, because I wanted to change the order of the ribbons being drawn such that the narrow one would come up last and be drawn on top.
library(tidyverse)
data_long <- data %>%
as_tibble() %>%
gather(key = key, value = value, -Nb_obs, -Nb_obst) %>%
mutate(measure = str_extract(key, "\\D+")) %>%
mutate(level = str_extract(key, "\\d+")) %>%
select(-key) %>%
group_by(level, measure) %>%
mutate(row = row_number()) %>%
spread(key = measure, value = value) %>%
ungroup() %>%
mutate(level = as.factor(level) %>% fct_rev())
head(data_long)
#> # A tibble: 6 x 6
#> Nb_obs Nb_obst level row inf sup
#> <dbl> <dbl> <fct> <int> <dbl> <dbl>
#> 1 0 35 20 2 2 4
#> 2 0 35 40 2 2 5
#> 3 0 35 60 2 1 6
#> 4 0 35 90 2 0 11
#> 5 0 39 20 8 3 5
#> 6 0 39 40 8 2 6
ggplot(data_long, aes(x = Nb_obst, ymin = inf, ymax = sup, fill = level)) +
geom_ribbon(alpha = 0.6) +
scale_fill_manual(values = c("20" = "darkred", "40" = "red",
"60" = "darkorange", "90" = "yellow")) +
theme_light()
But it still has the issue of being jagged, so for each level I predicted smoothed values of both inf
and sup
versus Nb_obst
using loess
. group_by
and do
yield a nested data frame, and unnest
pulls it back out into a workable form. Feel free to adjust the span
parameter, as well as other loess.control
parameters that I know very little about.
data_smooth <- data_long %>%
group_by(level) %>%
do(Nb_obst = .$Nb_obst,
inf_smooth = predict(loess(.$inf ~ .$Nb_obst, span = 0.35), .$Nb_obst),
sup_smooth = predict(loess(.$sup ~ .$Nb_obst, span = 0.35), .$Nb_obst)) %>%
unnest()
head(data_smooth)
#> # A tibble: 6 x 4
#> level Nb_obst inf_smooth sup_smooth
#> <fct> <dbl> <dbl> <dbl>
#> 1 90 35 0 11.
#> 2 90 39 0 13.4
#> 3 90 48 0.526 16.7
#> 4 90 39 0 13.4
#> 5 90 41 0 13
#> 6 90 41 0 13
ggplot(data_smooth, aes(x = Nb_obst, ymin = inf_smooth, ymax = sup_smooth, fill = level)) +
geom_ribbon(alpha = 0.6) +
scale_fill_manual(values = c("20" = "darkred", "40" = "red",
"60" = "darkorange", "90" = "yellow")) +
theme_light()
Created on 2018-05-26 by the reprex package (v0.2.0).