How to visualise data where one variable is continuous and the other is categorical? This question is very simple but I have been struggling in getting the right script for this.
My data set goes as follows:
HZ  Condition
  5     A
  6     A
  3     A
  4     A
 10     B
  8     B
 11     B
 13     B      

I want to build a scatter plot to visualize the correlation between HZ in A and Hz in B.
I thought it should be represented as x being "HZ" interacting with "Condition_A" and y being "HZ" interacting with "Condition_B", and grouping being "Condition".
Perhaps I am not tidying up my data properly, but I wonder if there is a way to make this directly, without adding more columns (as this is part of a data set with many other variables).

Thank you all for your replies.
The reason why I was not using boxplot is that, as Nick Cox pointed out, I want to show the distribution of HZ data points. I should have explained it better in my original post, but the aim is to check if HZ is increasing or decreasing in each condition, and that is why at first I thought having x-axis with "HZxA" and y-axis with "HZ$B". I will be using this variable to control for effect size another variable. So I will try to use Nick Cox visual representation example, thank you!
I also did not mention that I am using R, so the scripts are very appreciated indeed, thank you Robert Long and Cdalitz.
 A: I like to show the raw data but use a method that scales to large N better than dot plots, so I use spike histograms stratified by the categorical variable.  I add quantile intervals at the bottom of the histograms.  If using R plotly graphics you can turn the quantile intervals off and on and likewise for the mean and median.  An example may be found here along with other displays such as extended box plots and simple spike histograms.
A: A couple of options spring to mind. The first is a simple dot plot:
HZ <- c(5,6,3,4,10,8,11,13)
Cond <- c(rep("A",4), rep("B",4))
dt <- data.frame(HZ, Cond)
library(ggplot2)
ggplot(dt, aes(y = HZ, x = Cond, color = Cond)) + geom_point()


The second is a boxplot:
ggplot(dt, aes(y = HZ, x = Cond, color = Cond)) + geom_boxplot()


A: The question didn't mention R and how to code this in R or any other software is off-topic here in any case.
As an in-principle answer, consider also

My point is not this example -- many other displays would work well too -- but some small principles that apply to posts like this.

*

*Box plots don't use the space available with 2 groups very well, and that is true for any other small number of groups. The box plot here is a simplified summary with whiskers to the extremes, defensible because detail is shown separately.


*There is scope to show the individual data points too. With integer values the scope for ties in a full dataset is very high, so some way to show ties directly is helpful. Here the quantile plot display would work to show ties clearly: stacking or jittering data points could work almost or exactly as well.


*There is no reason to suppose that median and quartiles are the only summaries of interest. The horizontal lines here show means, which happen to be identical in the data example. In other datasets, other summaries might make more sense, say geometric means or trimmed means.
A: These are univariate data. Thus you are presumably not looking for a scatter plot, but for a boxplot:
boxplot(HZ ~ Condition, x)

where x denotes the data frame containing your data. This is only a display af summary statistics (median, quartiles). For showing the original points, the only builtin function seems to be dotchart:
dotchart(x$HZ, labels=x$Condition, col=as.numeric(x$Condition)

But this gives each point its own row. Apparently, with builtin tools, a workaround is required: First draw an empty plot, then add the points with points:
# we must use border="white",
# because type="n" is ignored for categorial x-values
plot(x$Condition, x$HZ, border="white")
points(x$Condition, x$HZ, col=x$Condition)

