Controlling new column names from mutate_each?

gistfile1.txt

# Sample data: 2x2 manipulation, replicated 4x, multiple responses measured repeatedly over time.
# The response variables aren't related: y_also doesn't come "after" y_1 in any meaningful sense.
df = expand.grid(
	day=1:3, 
	replicate=1:4, 
	t1=c("ctrl", "more"), 
	t2=c("ambient", "manipulated"))
df$y_1 = rnorm(48)
df$y_other = runif(48)
df$y_also = rlnorm(48)
# ...other y-variables here...

# Want to compute differences from the baseline condition within each rep/day
# This works, but is tedious:
(df 
%>% group_by(day, replicate) 
%>% mutate(
	diff_1 = y_1 - y_1[t1 == "ctrl" & t2 == "ambient"], 
	diff_other = y_other - y_other[t1 == "ctrl" & t2 == "ambient"], 
	diff_also = y_also - y_also[t1 == "ctrl" & t2 == "ambient"])
	# ...
	)

# near-working mutate_each approach: 
# Numbers correct, but differences are named as "diff1", "diff2", "diff3", ...
# How to produce "diff_1", "diff_other", "diff_also", ...
# (preferably without a manual renaming step)?
(df 
%>% group_by(day, replicate) 
%>% mutate_each(
	funs=funs(. - .[t1 == "ctrl" & t2 == "ambient"]),
	diff=starts_with("y")))