How to extract the density value from ggplot in r-CodePudding

How can I extract the values matrix of each density plot?

For example, I am interested in that when weight = 71, what will the density of orange, apple, pears, banana be ?

Below is the minimal example:

library(ggplot2)
set.seed(1234)
df = data.frame(
  fruits = factor(rep(c("Orange", "Apple", "Pears", "Banana"), each = 200)),
  weight = round(c(rnorm(200, mean = 55, sd=5),
                 rnorm(200, mean=65, sd=5),
                 rnorm(200, mean=70, sd=5),
                 rnorm(200, mean=75, sd=5)))
)
dim(df) [1] 800   2

ggplot(df, aes(x = weight))  
  geom_density()   
  facet_grid(fruits ~ ., scales = "free", space = "free")

CodePudding user response：

I think the underlying density in ggplot2 is similar to stats::density, and thus we can use that to build the same info



df %>% 
  group_by(fruits) %>%
  nest() %>% 
  ungroup() %>%  
  mutate(density = data %>% map(. %>% 
                               `[[`("weight") %>% 
                               density.default)) %>% 
  hoist(density, "x", "y") %>% 
  select(-density, -data) %>% 
  unnest(c(x,y)) %>% 
  group_by(fruits) %>%
  slice_min(abs(x - 71), n = 1) %>% 
  ungroup() %>%
  identity()
# A tibble: 5 x 3
  fruits     x       y
  <fct>  <dbl>   <dbl>
1 Apple   71.0 0.0409 
2 Banana  71.0 0.0574 
3 Orange  71.0 0.00131
4 Pears   71.0 0.0747 
5 Pears   71.0 0.0747

If you don't think that's enough, then this is an extraction from the plot, that you specified:


gg_density_plot %>% 
  ggplot_build() %>% 
  `[[`("data") %>% 
  `[[`(1) %>% 
  as_tibble() %>% 
  # glimpse() %>% 
  # count(PANEL) # panel is fruit
  group_by(PANEL) %>% 
  slice_min(abs(x - 71), n = 1) %>% 
  ungroup() %>% 
  select(PANEL, x, y, density)

# A tibble: 4 x 4
  PANEL     x       y density
  <fct> <dbl>   <dbl>   <dbl>
1 1      71.0 0.0410  0.0410 
2 2      71.0 0.0568  0.0568 
3 3      71.0 0.00135 0.00135
4 4      71.0 0.0747  0.0747

CodePudding user response：

Save the plot in a variable, build the data structure with ggplot_build and split the data by panel. Then interpolate with approx to get the new values.

g <- ggplot(df, aes(x = weight))  
  geom_density()   
  facet_grid(fruits ~ ., scales = "free", space = "free")

p <- ggplot_build(g)

# These are the columns of interest    
p$data[[1]]$x
p$data[[1]]$density
p$data[[1]]$PANEL

Split the list member p$data[[1]] by panel but keep only the x and density values. Then loop through the split data to interpolate by group of fruit.

sp <- split(p$data[[1]][c("x", "density")], p$data[[1]]$PANEL)

new_weight <- 71
sapply(sp, \(DF){
  with(DF, approx(x, density, xout = new_weight))
})
#  1          2          3           4         
#x 71         71         71          71        
#y 0.04066888 0.05716947 0.001319164 0.07467761

Or, without splitting the data previously, use by.

b <- by(p$data[[1]][c("x", "density")], p$data[[1]]$PANEL, \(DF){
  with(DF, approx(x, density, xout = new_weight))
})
do.call(rbind, lapply(b, as.data.frame))
#   x           y
#1 71 0.040668880
#2 71 0.057169474
#3 71 0.001319164
#4 71 0.074677607