Trying to get confidence/prediction intervals with `predict.lm` in R, but I keep getting an error regarding my dichotomous variable

Question

I have a dataset that looks like this:

   time size type
1    22  151    0
2    31   92    0
3    26  175    0
4    35   31    0
5    27  104    0
6     5  277    0
7    17  210    0
8    24  120    0
9     9  290    0
10   21  238    0
11   33  164    1
12   20  272    1
13   16  295    1
14   43   68    1
15   36   85    1
16   26  224    1
17   25  166    1
18   18  305    1
19   35  124    1
20   19  246    1

My aim is simple: to run a regression and get confidence/prediction intervals.

I run my regression like so:

fit.lm1<-lm(time~size+type,data=project3)

I then want to get 95% confidence intervals and 95% prediction intervals for the mean time when size is equal to 200. I want CIs/PIs for type = 0 and type = 1. My code is:

new_val <- data.frame(size= c(200,200),type=c(1,0))
CI<-predict(fit.lm1,newdata=new_val,interval="confidence")
PI<-predict(fit.lm1,newdata=new_val,interval="prediction")

I get the following errors:

Error: variable 'type' was fitted with type "factor" but type "numeric" was supplied
In addition: Warning message:
In model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) :
variable 'type' is not a factor

I'm not quite sure how to interpret this. R shows that type is a "factor" with two levels, so I don't know what's wrong.

Any help would be great! Thank you.