Chris Z. - 1 year ago 291

R Question

I have proportion response data for 4 experimental groups, with 2 different statistics computed for each group. I want the following figure (which I can achieve):

I obtain this figure with the following code:

`Group<-c('a','b','c','d','a','b','c','d')`

Statistic<-c('Mean','Mean','Mean','Mean','d','d','d','d')

Val<-c(.75,.83,.79,.69,.5,.02,.1,.3)

dfm2<-data.frame(cbind(Group,Statistic,Val))

ggplot(dfm2,aes(x = Group,y = Val)) +

geom_bar(aes(fill = Statistic),position = dodge',stat='identity')

However, when I change the limits of the y-axis (to [0,1] since I have proportions) by adding the line of code:

`+ scale_y_continuous(limits=c(0, 1))`

I get

`Error: Discrete value supplied to continuous scale`

So I understand this means I have a non-continuous variable. I have tried converting my Statistic variable by using

`as.numeric()`

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Answer Source

The problem is the unnecessary use of `cbind`

inside `data.frame`

. `cbind`

creates a matrix. A matrix must have all values of the same mode (numeric, character, etc.). Since at least one of variables (two in this case) is character mode, `cbind`

coerces `Val`

to character as well. `data.frame`

converts the three character variables to factor. Either way, `Val`

is a discrete (categorical) value rather than numeric, resulting in an error when you use `scale_y_continuous`

.

Change to `dfm2 <- data.frame(Group,Statistic,Val)`

and the error will go away.

You can check the effect of `cbind`

and `data.frame`

on data types as follows:

```
cbind(Group, Statistic, Val)
Group Statistic Val
[1,] "a" "Mean" "0.75"
[2,] "b" "Mean" "0.83"
...
[7,] "c" "d" "0.1"
[8,] "d" "d" "0.3"
dfm2<-data.frame(cbind(Group,Statistic,Val))
str(dfm2)
'data.frame': 8 obs. of 3 variables:
$ Group : Factor w/ 4 levels "a","b","c","d": 1 2 3 4 1 2 3 4
$ Statistic: Factor w/ 2 levels "d","Mean": 2 2 2 2 1 1 1 1
$ Val : Factor w/ 8 levels "0.02","0.1","0.3",..: 6 8 7 5 4 1 2 3
dfm2 <- data.frame(Group,Statistic,Val)
str(dfm2)
'data.frame': 8 obs. of 3 variables:
$ Group : Factor w/ 4 levels "a","b","c","d": 1 2 3 4 1 2 3 4
$ Statistic: Factor w/ 2 levels "d","Mean": 2 2 2 2 1 1 1 1
$ Val : num 0.75 0.83 0.79 0.69 0.5 0.02 0.1 0.3
```

If you don't want `data.frame`

to convert strings to factors, add the argument `stringsAsFactors=FALSE`

.

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