如何平滑ggplot中密度为plot的曲线？

Question

I'm trying to overlay density plots for an outcome variable that is expressed as an integer scale (1-7). Right now I'm using:

ggplot(dface, aes(Current.Mood, fill = NewCode))+ geom_density(alpha = 0.1)

That gets me:

For some reason I don't understand, ggplot is putting valleys in between the integer values (pictured below) Does anyone know how I can get the plot to smooth these over?

Does anyone know how I can smooth these out? They are making the plot very hard to interpret and don't really reflect what's happening in my data.

Answer 1

geom_density(bw=..) is useful here.

      bw: The smoothing bandwidth to be used. If numeric, the standard
          deviation of the smoothing kernel. If character, a rule to
          choose the bandwidth, as listed in 'stats::bw.nrd()'.

ggplot(mtcars, aes(cyl)) + geom_density(bw = 0.1) + labs(title = "bw = 0.1")
ggplot(mtcars, aes(cyl)) + geom_density() + labs(title = "bw default")
ggplot(mtcars, aes(cyl)) + geom_density(bw = 2) + labs(title = "bw = 2")

Or, as MrFlick suggested, you can use adjust= :

  adjust: A multiplicate bandwidth adjustment. This makes it possible
          to adjust the bandwidth while still using the a bandwidth
          estimator. For example, 'adjust = 1/2' means use half of the
          default bandwidth.

ggplot(mtcars, aes(cyl)) + geom_density(adjust = 0.5) + labs(title = "adjust = 0.5")
ggplot(mtcars, aes(cyl)) + geom_density(adjust = 0.9) + labs(title = "adjust = 0.9")

Answer 2

Your choice of data visualization is not ideal. You want to compare the outcome variables across the 1-7 scale of different questions/groups. You probably want to map the frequency of the outcome variable to a geom_line or geom_area or both.

Using survey data from Kaggle .

library(tidyverse)

my_data <- read_csv("~/Downloads/archive/test.csv")

plot_data <- my_data %>%
  select(id, `Inflight wifi service`:`Food and drink`) %>%
  pivot_longer(`Inflight wifi service`:`Food and drink`, names_to = "question", values_to = "response") %>%
  count(question, response) %>%
  group_by(question) %>%
  mutate(freq = n / sum(n))

ggplot(plot_data) +
  geom_area(aes(x = response, fill = question, y = freq), alpha = 0.5)