I have used scale to normalize the data. Example data is show below
structure(list(pp.pmhouravg = c(106.8181818182, 114.0833333333,
100.8333333333, 105, 102.4166666667, 117.8333333333), cc.cmhouravg = c(91.7272727273,
86.4166666667, 82.75, 84, 59.5833333333, 41.3333333333), ss.sdhouravg = c(49.2727272727,
46.8333333333, 47.5, 48.3333333333, 41, 45.5833333333), nn.ndhouravg = c(41.2727272727,
45.25, 34.0833333333, 27.75, 33.0833333333, 35.3333333333)), .Names = c("pp.pmhouravg",
"cc.cmhouravg", "ss.sdhouravg", "nn.ndhouravg"), row.names = c(NA,
6L), class = "data.frame")
and to normalize it I used
scale(df, center = T, scale = T)
I got the following normalized data:
pp.pmhouravg cc.cmhouravg ss.sdhouravg nn.ndhouravg
1 -0.1504657 0.8893812 0.9702219 0.8259116
2 0.9290599 0.6183329 0.1404438 1.4645030
3 -1.0397516 0.4311897 0.3672155 -0.3284185
4 -0.4206285 0.4949885 0.6506801 -1.3452993
5 -0.8044848 -0.7512149 -1.8438082 -0.4889786
6 1.4862706 -1.6826775 -0.2847531 -0.1277183
attr(,"scaled:center")
pp.pmhouravg cc.cmhouravg ss.sdhouravg nn.ndhouravg
107.83081 74.30177 46.42045 36.12879
attr(,"scaled:scale")
pp.pmhouravg cc.cmhouravg ss.sdhouravg nn.ndhouravg
6.729949 19.592842 2.939815 6.228196
How can I convert data back after normalization.
Let x be your original data (may be a data frame or a matrix) and sx be the scaled one (must be a matrix, as scale returns a matrix), you can do:
b <- attr(sx, "scaled:scale")
a <- attr(sx, "scaled:center")
rx <- sx * rep(b, each = nrow(sx)) + rep(a, each = nrow(sx))
The "de-scaled" data rx is of course also a matrix as sx is a matrix. You can make it a data frame by simply doing:
data.frame(rx)