问题与差异变量重叠的 ggplot2 直方图

Question

I have a dataframe (snapshot below - it actually has over 12,000 observations) where I've been able to use ggplot and construct a histogram to depict the distribution of each variable on it's own. I am now interested in super-imposing the 3 histograms I've constructed on top of one another to create a 'single' histogram.

I've tried to adapt the suggestion below from a similar question that was asked previously ,

d = data.frame(x = c(data1, data2), 
               type=rep(c("A", "B"), c(length(data1), length(data2))))
ggplot(d) + 
  geom_density(aes(x=x, colour=type))

but I keep getting the following error:

#combinedImputedDataVis is a data frame
distr <- combinedImputedDataVis[,37:39]
ggplot(distr) + geom_density(aes(x=c(rt,controlt,cleart),
                                 type=rep(c("rt","controlt","cleart"),c(length(rt),length(controlt),length(cleart)))))

Error: Aesthetics must be either length 1 or the same as the data (12687): x and type
Run `rlang::last_error()` to see where the error occurred.
In addition: Warning message:
Ignoring unknown aesthetics: type 

Not sure where I am going wrong, would appreciate a second set of eyes

Answer 1

Using your original data frame as shown in the image in your question:

d <- structure(list(rt = c(15, 173, 66, 167, 341), controlt = c(1294, 
181, 145, 835, 675), cleart = c(1603, 3274, 722, 1059, 2468)), 
class = "data.frame", row.names = c(NA, -5L))

d
#>    rt controlt cleart
#> 1  15     1294   1603
#> 2 173      181   3274
#> 3  66      145    722
#> 4 167      835   1059
#> 5 341      675   2468

Then if the data is in this format, it would be easiest to do a separate geom for each column:

ggplot(d) + 
  geom_density(aes(x = rt), fill = "red", alpha = 0.5) + 
  geom_density(aes(x = controlt), fill = "green", alpha = 0.5) + 
  geom_density(aes(x = cleart), fill = "blue", alpha = 0.5)

But it would be better to reshape your data to get all your variable names into one column and all their values in another. That way it is much easier to control various aspects of the plot:

library(dplyr)

d %>% 
  pivot_longer(everything()) %>%
  ggplot(aes(x = value, fill = name)) + 
  geom_density(alpha = 0.5)