Plot Line Chart of Binary Variable Against Continuous Data

Question

I am looking for a way to help better visualize the relationship between a independent continuous variable and a binary response variable.

I am trying to understand how I can add a 2nd y axis to the existing plot I have below. I want to get a sense of the response rate over different numerical ranges visually.

How can I add in the response percent at any given histogram bin? For example if there were 10 observations in a bin and 2 were the positive class, then this would show a response of 20%.
Ideally it's possible that this would be dynamic in that I might change the # of bins. For instance, I have 10 here, I might want 20 the next time.
This would be a connected line-chart with the corresponding percentages from #1 on the right y axis.

Or in other words, I want a line chart of the positive class to be displayed as a line chart with % show in Y axis.

library(mlbench)
library(tidyverse)
data(Sonar)   ## from mlbench
library(ggplot2)

ggplot(Sonar, aes(x=V11, fill=Class)) +
  geom_histogram(col='black', bins = 10) +
  scale_fill_manual(values=c("purple", "green"))  +
  labs(title = "Count Left Y Axis; 'R' class percent of BIN in Right Y Axis" ,
       x = 'Variable Value in this case V33', y ='Count of Observations' )

Karolis Koncevičius · Accepted Answer · 2018-03-28 10:25:17Z

2

Not sure if this is what you are after but the description you gave sounded very similar to a conditional density plot.

ggplot probably has an alternative to this, but with base R:

cdplot(Class ~ V1, Sonar, col=c("cornflowerblue", "orange"), main="Conditional density plot")

And the result:

answered Mar 28, 2018 at 10:25

Karolis Koncevičius

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1 Comment

runningbirds Over a year ago

Yes, sorry ultimately I'm trying to plot continuous vs a categorical (binary) response. I'd like to see the percent positive of this response over various numerical ranges. This is in-line with that idea and helpful, but I'd like to be able to see the actual percentage of 'R' at any given numerical range of V1 in a cleaner way. It's also helpful to have some sense of the distribution of V1 or the frequency of V1 which this plots masks.

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