Density plot, scale it to 0-1

Question

Doubt regarding density plot what is the scale being plotted in the Y axis.Is it possible to scale it to 1?.

This is my code

w <- read.csv("Normal_Myeloid_Dev_stages/Myeloid_non_coding_non_CDS_CORRVALUE.txt",header = TRUE)
head(w)
#names(w)[1] = "Sample"
head(w)

#w.plot <- melt(w) 

df <- w

df_melt=melt(df,id.vars="Sample")
#head(df_melt)
tail(df_melt)
df_melt$Group <- gsub('[0-9]', '',df_melt$variable)
head(df_melt)

p1 <- ggplot(aes(x=value, colour=Group), data=df_melt)
p1 + geom_density()

ggplot(df_melt, aes(x = value,y=..density.., fill = Group)) + geom_density(alpha = 0.5)+
  theme_bw(base_size=30)+
  theme(axis.text.x=element_text(angle = 45, size=45, face="bold", hjust = 1), 
        axis.text.y=element_text(angle=0, size=50, face="bold", vjust=0.5),
        plot.title = element_text(size=40, face="bold"), 
        legend.title=element_blank(), 
        legend.key.size=unit(1, "cm"),      #Sets overall area/size of the legend
        legend.text=element_text(size=30)) 

dev.off()

Second question regarding finding the overlapping area between various condition let say I have four condition and i want to find out what is the overlapping or intersecting area between them .

I tried on of the stack solution firstly is it the right way to do it for finding overlap? if yes can i try finding all the condition overlap such as I have HSC,CMP,GMP and Mono.

FDensity = approxfun(density(df_melt$value[df_melt$Group=="CMP"], from=.80, to=1))
MDensity = approxfun(density(df_melt$value[df_melt$Group=="Mono"], from=.80, to=1))
plot(FDensity, xlim=c(.80,1), ylab="Density")
curve(MDensity, add=TRUE)

FminusM = function(x) { FDensity(x) - MDensity(x) }
Intersect = uniroot(FminusM, c(.8, 1))$root
points(Intersect, FDensity(Intersect), pch=20, col="red")

integrate(MDensity, .80,Intersect)$value + 
  integrate(FDensity, Intersect, 1)$value

Any suggestion or help would be really appreciated

My dataframe small subset

dput(head(df))
structure(list(Sample = structure(c(9L, 10L, 11L, 12L, 1L, 2L
), .Label = c("CMP1", "CMP2", "CMP3", "CMP4", "GMP1", "GMP2", 
"GMP3", "GMP4", "HSC1", "HSC2", "HSC3", "HSC4", "Mono1", "Mono2", 
"Mono3", "Mono4"), class = "factor"), HSC1 = c(1, 0.901758000194052, 
0.880971458703505, 0.900098712568466, 0.873507054094022, 0.906315987662777
), HSC2 = c(0.901758000194052, 1, 0.945122894369186, 0.955453795442752, 
0.844777154811663, 0.960260413763721), HSC3 = c(0.880971458703505, 
0.945122894369186, 1, 0.931891528389177, 0.829521543809914, 0.93636611655036
), HSC4 = c(0.900098712568466, 0.955453795442752, 0.931891528389177, 
1, 0.852155096935692, 0.947192034704188), CMP1 = c(0.873507054094022, 
0.844777154811663, 0.829521543809914, 0.852155096935692, 1, 0.88406635204624
), CMP2 = c(0.906315987662777, 0.960260413763721, 0.93636611655036, 
0.947192034704188, 0.88406635204624, 1), CMP3 = c(0.883085447599108, 
0.928929790213059, 0.937278860333014, 0.923139729112196, 0.870595169861622, 
0.958330253497026), CMP4 = c(0.903500691840647, 0.931015449611016, 
0.915510499719426, 0.949501343959892, 0.89151869084162, 0.960206335031192
), GMP1 = c(0.859320159223793, 0.833454007185579, 0.821479398953591, 
0.837605565046685, 0.856892209387879, 0.8612685942226), GMP2 = c(0.894680165774456, 
0.944129985346879, 0.92461599550388, 0.937108867595306, 0.875930650641323, 
0.966512850930959), GMP3 = c(0.881416749993221, 0.925001567707159, 
0.923015420025178, 0.920758302664238, 0.863998116268766, 0.95069438729762
), GMP4 = c(0.887230702792805, 0.911614382740949, 0.898704725226052, 
0.938310378760322, 0.875956580458754, 0.93865124308053), Mono1 = c(0.80834274546097, 
0.803706049167148, 0.785535924609134, 0.794438848049474, 0.794402126200663, 
0.810382054101301), Mono2 = c(0.829928035079747, 0.868021129791583, 
0.848929312833536, 0.852953184941131, 0.818074585324121, 0.880490870699251
), Mono3 = c(0.834028389107401, 0.859818911060746, 0.852694253707916, 
0.85039434449057, 0.82251614206182, 0.872740655210769), Mono4 = c(0.832524159512094, 
0.858444209302146, 0.84397886802844, 0.862850406374668, 0.818436260136434, 
0.870293804485003)), row.names = c(NA, 6L), class = "data.frame")

Answer 1

I'm not fully to have understood your second questions, but first, if you want to scale the density to 1, you can use y= ..scaled.. in your aes:

library(tidyverse)
df %>% pivot_longer(., everything(), names_to = "Variable", values_to = "Value") %>%
  ggplot(., aes(x = Value, y = ..scaled.., fill = Variable)) + geom_density(alpha = 0.4)

Now regarding the second question, if you are looking for the overlap between each of your density, you can use the package overlapping as specified in the post you are referring: https://stackoverflow.com/questions/41914257/calculate-area-of-overlapping-density-plot-by-ggplot-using-r?noredirect=1&lq=1

library(overlapping)
library(lattice)
x <- list(D1 = D1, D2 = D2, D3 = D3, D4 = D4)
overlap(x, plot = TRUE)

And if you want to extract the value of overlap, you can get it:

OV <- overlap(x)$OV

> OV
    D1-D2     D1-D3     D1-D4     D2-D3     D2-D4     D3-D4 
0.5971760 0.3171644 0.7566528 0.4926057 0.6683083 0.3756224 

Is it what you are looking for ? Or did I misunderstood your question ?

Dummy Dataset

# Dummy example:
D1 = rnorm(100, mean =1, sd = 2)
D2 = rnorm(100, mean = 2, sd = 2)
D3 = rnorm(100, mean = 3, sd = 1)
D4 = rnorm(100,mean = 1.5, sd = 1.5)

df = data.frame(D1,D2,D3,D4)

---

EDIT: Using reproducible example provided in the question For plotting geom_density of your data:

library(tidyverse)
df %>% pivot_longer(., -Sample, names_to = "Variable", values_to = "Value") %>% 
  mutate(New_Var = gsub("\\d","",Variable)) %>%
  ggplot(., aes(x = Value, y = ..scaled.., fill = New_Var))+
  geom_density(alpha = 0.4)

And to find the overlap:

d<- df %>% pivot_longer(., -Sample, names_to = "Variable", values_to = "Value") %>% mutate(New_Var = gsub("\\d","",Variable))
a <- split(d$Value, unique(d$New_Var))

g <- overlap(a, plot = TRUE)$OV

I don't think there is an option in the overlap function to scaled each overlap plot to 1 but these plot are using ggplot and so, I imagine you can extract their points and scaled them by plotting them separately in ggplot2.

Hope it answers your question