How to test amount of differences in T-test in R for large samples? We have good discussions on hypothesis testing in large data: With large amount of data, even tinny difference between two samples can be detected, and we are almost certain to reject NULL hypothesis.
Effect size is one "fix", but I am interested in other fix, where, in addition to say they are different, but say how much the difference are.
I not know how to do it in R. Let us assume we are doing two sample T test, how can I say I want to test if the means are different by say certain amount say $0.1$ ?
The following code is test if two means are equal. How to modify it to test if two means are difference by $0.1$?
sample1=rnorm(1e5)
sample2=rnorm(1e5)
t.test(sample1,sample2)


    Welch Two Sample t-test

data:  sample1 and sample2
t = -0.5542, df = 2e+05, p-value = 0.5794
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.01124444  0.00628720
sample estimates:
    mean of x     mean of y 
-0.0002658978  0.0022127222 

Edit: 
I was trying to ask if the amount difference on mean of two samples within range of $-0.1$ to $0.1$, not exactly to equal $0.1$.
Reading Peter Flom 's comment, modifying T test will not meet my needs?
 A: R code questions are actually off-topic here but it is:
t.test(sample1, sample2, alternative = "two.sided", mu = 0.1)

I think the question should stay here, however, because it raises a statistical question. My view is that looking for an effect larger than a certain amount makes a lot of sense, but it can be hard to choose an amount. 
A: There is not a usual way to use a t-test to test if the absolute difference is larger than 0.1, or even to test if the difference lies within a given range.
However, your goal of assessing how large the difference is can be achieved by using the confidence interval. It's already in your results, since R and most statistical packages produce a confidence interval when told to perform a t-test:
95 percent confidence interval:
 -0.01124444  0.00628720

A: I'm not going to attempt to answer the R portion of this question, but I wanted to comment on this: "Effect size is one "fix", but I am interested in other fix, where, in addition to say they are different, but say how much the difference are."
The purpose of effect size is to attach a measure of magnitude of effect, or difference, between the populations (or treatments).
If you are not familiar with effect sizes, I recommend you read:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/
and
http://www.leeds.ac.uk/educol/documents/00002182.htm

'Effect size' is simply a way of quantifying the size of the difference between two groups.

As a statistical consultant, I always push for reporting of effect sizes to any statistical product.
An Edit: 
I want to comment on this: "I was trying to ask if the amount difference on mean of two samples within range of −0.1−0.1 to 0.10.1, not exactly to equal 0.10.1."
I think there needs to be some clarity on this. What exactly is -0.1 to 0.1? Is this a confidence interval? Acceptance region?
A: You can just subtract or add 0.1 to one of the datasets.
