Hi R- User
I have very big data set (5000 rows). I wanted to make classes based on a 
column of that table (that column has the data which is continuous .) After 
converting into different class, this class would be Unique ID. I want to run 
regression for each ID.
For example I have a data set 
> dput(dat)
structure(list(ID = c(0.1, 0.8, 0.1, 1.5, 1.1, 0.9, 1.8, 2.5, 
2, 2.5, 2.8, 3, 3.1, 3.2, 3.9, 1, 4, 4.7, 4.3, 4.9, 2.1, 2.4), 
    S = c(4L, 7L, 9L, 10L, 10L, 8L, 8L, 8L, 17L, 18L, 13L, 13L, 
    11L, 1L, 10L, 20L, 22L, 20L, 18L, 16L, 7L, 20L), en2 = c(-2.5767, 
    -2.5767, -2.5767, -2.5767, -2.5767, -2.5767, -2.5767, -2.5347, 
    -2.5347, -2.5347, -2.5347, -2.5347, -2.5347, -2.4939, -2.4939, 
    -2.4939, -2.4939, -2.4939, -2.4939, -2.4939, -2.4543, -2.4543
    ), en3 = c(-1.1785, -0.6596, -0.6145, -0.6437, -0.6593, -0.7811, 
    -1.1785, -1.1785, -1.1785, -0.6596, -0.6145, -0.6437, -0.6593, 
    -1.1785, -0.1342, -0.2085, -0.4428, -0.5125, -0.8075, -1.1785, 
    -1.1785, -0.1342), en4 = c(-1.4445, -1.3645, -1.1634, -0.7735, 
    -0.6931, -1.1105, -1.4127, -1.5278, -1.4445, -1.3645, -1.1634, 
    -0.7735, -0.6931, -1.0477, -0.8655, -0.1759, 0.1203, -0.2962, 
    -0.4473, -1.0436, -0.9705, -0.8953), en5 = c(-0.4783, -0.3296, 
    -0.2026, -0.3579, -0.5154, -0.5726, -0.6415, -0.3996, -0.4529, 
    -0.5762, -0.561, -0.6891, -0.7408, -0.6287, -0.4337, -0.4586, 
    -0.5249, -0.6086, -0.7076, -0.7114, -0.4952, 0.1091)), .Names = c("ID", 
"S", "en2", "en3", "en4", "en5"), class = "data.frame", row.names = c(NA, 
-22L))

Here ID has continuous value, I want to make groups with value 0-1, 1-2, 2-3, 
3-4 from the column ID. 
and then. I wanted to run regression with S (dependent variable) and en2 
(independent variable); again regression of S and en3 , and so on.
After that, I wanted to have a table with r2 and p value.

would you help me how I can do it? I was trying it manually - but it took so 
much time. therefore I thought to write you for your help. 

Thanks for your help.
Kristi



                                          
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