Error when using apply and t-test - r

Question

I can't find the bug in my code, and/or the flaw in my logic. I have a matrix, X, of 0's and 1's and a vector y of continuous values and I want to do a 2 sample t-test in R where the rows of X indicate the different groups of y.
For example:
x = matrix(rbinom(60,1,.5),ncol=10)
y = abs(rnorm(ncol(x)))
apply(x,1,function(x,y=y)t.test(y[x==1],y[x==0]))
So using this code I would have expected to get 6 t-tests where each row of X corresponds to the two groups of y. However, I get this error when I run my code:
Error in t.test(y[x == 1], y[x == 0]) :
promise already under evaluation: recursive default argument reference or earlier problems?
Can someone explain the error and modify my code to get what I want.

Answer 1

The problem comes from your re-use of variable names in your function arguments. This should work:
apply(x,1,function(x.f,y.f=y)t.test(y.f[x.f==1],y.f[x.f==0]))

Answer 2

What about
apply(x,1,function(x,z)t.test(y[x==1],y[x==0]),y)
If you want to use the second argument within the function, you should also pass it to apply

Answer 3

Following works:
> apply(x,1,function(a)t.test(y[a==1],y[a==0]))
[[1]]
You should give better names to data in data.frames and vectors so that x and y etc can be used as general variables. Also there is no need to send y to the function since it will be same for all tests.
Output:
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = 0.43835, df = 5.377, p-value = 0.6782
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.6356057 0.9036413
sample estimates:
mean of x mean of y
0.5807408 0.4467230
[[2]]
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = -0.80208, df = 5.5382, p-value = 0.4555
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.0985419 0.5644195
sample estimates:
mean of x mean of y
0.4337110 0.7007722
[[3]]
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = 0.58194, df = 7.3884, p-value = 0.5779
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5584942 0.9283034
sample estimates:
mean of x mean of y
0.6329878 0.4480832
[[4]]
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = 1.1148, df = 4.8236, p-value = 0.3174
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4919082 1.2308641
sample estimates:
mean of x mean of y
0.7622223 0.3927443
[[5]]
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = 0.23436, df = 5.5539, p-value = 0.8231
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.7818960 0.9439901
sample estimates:
mean of x mean of y
0.5729543 0.4919073
[[6]]
Welch Two Sample t-test
data: y[a == 1] and y[a == 0]
t = -1.015, df = 7.9168, p-value = 0.3401
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1.0152988 0.3954558
sample estimates:
mean of x mean of y
0.3855747 0.6954962
For only p values:
> apply(x,1,function(a)t.test(y[a==1],y[a==0])$p.value)
[1] 0.6781895 0.4555338 0.5779255 0.3173567 0.8231019 0.3400979