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Basic hexbin with R?

开发者 https://www.devze.com 2023-01-20 23:28 出处:网络
I have results from a survey. I am trying to create a graphic displaying the relationship of two variables: \"Q1\" and \"Q9.1\". \"Q1\" is the independent and \"Q9.1\" is the depende开发者_如何学编程n

I have results from a survey. I am trying to create a graphic displaying the relationship of two variables: "Q1" and "Q9.1". "Q1" is the independent and "Q9.1" is the depende开发者_如何学编程nt. Both variables have responses from like scale questions: -2,-1,0,1,2. A typical plot places the answers on top of each other - not very interesting or informative. I was thinking that hexbin would be the way to go. The data is in lpp. I have not been able to use "Q1" and "Q9.1" for x and y. However:

> is.numeric("Q1")
[1] FALSE
q1.num <- as.numeric("Q1")
Warning message:
NAs introduced by coercion 

The values for Q1 are (hundreds of instances of): -2,-1,0,1,2

How can I make a hexbin graph with this data? Is there another graph I should consider?

Error messages so far:

Warning messages:
1: In xy.coords(x, y, xl, yl) : NAs introduced by coercion
2: In xy.coords(x, y, xl, yl) : NAs introduced by coercion
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
5: In min(x) : no non-missing arguments to min; returning Inf
6: In max(x) : no non-missing arguments to max; returning -Inf


How about taking a slightly different approach? How about thinking of your responses as factors rather than numbers? You could use something like this, then, to get a potentially useful representation of your data:

# Simulate data for testing purposes
q1 = sample(c(-2,-1,0,1,2),100,replace=TRUE)
q9 = sample(c(-2,-1,0,1,2),100,replace=TRUE)
dat = data.frame(q1=factor(q1),q9=factor(q9))
library(ggplot2)
# generate stacked barchart
ggplot(dat,aes(q1,fill=q9)) + geom_bar()

You may want to switch q1 and q9 above, depending on the view of the data that you want.


Perhaps ggplot2's stat_binhex could sort that one for you?

Also, I find scale_alpha useful for dealing with overplotting.

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