counter function of reshape2 :: melt (tear)

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counter function of reshape2 :: melt (tear)

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#1 by (3 votes)
#2 by (3 votes)

2

I'm working with data.frame and it's organized in long format. But I'd like to put it in wide format based on a variable ( FAT2 ) such that the column layouts would be: AVA , FAT1 , Banana , Ingá , Gliricídia , Pupunha .

However, I would not like to convert to array, and then again to data.frame . The melt function of the reshape2 package does the opposite of what I need because it stacks the variables.

data:

dados<-structure(list(AVA = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("Março de 2016", 
"Agosto de 2016", "Dezembro de 2016", "Março de 2017", "Agosto de 2017", 
"Fevereiro de 2018", "Abril de 2018", "Agosto de 2018"), class = c("ordered", 
"factor")), FAT1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Antes", "Após"), class = "factor"), 
    FAT2 = structure(c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
    5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L
    ), .Label = c("Banana", "Gliricídia", "Ingá", "Pupunha", 
    "Solteiro"), class = "factor"), LUX = c(39018.33, 38870, 
    40375, 39686.67, 53363.33, 55666.67, 56888.33, 57768.33, 
    5029.33, 4758, 4810.67, 5044.67, 17840.67, 2917.33, 8508.67, 
    9960.67, 3014, 4704.67, 5712, 3815.33, 4683.33, 49213.33, 
    54353.33, 57226.67, 13939.33, 13869.33, 5100.67, 15864, 1809.47, 
    1814.53, 6084.67, 2357.33, 28333.33, 37586.67, 35640, 36833.33, 
    55886.67, 59886.67, 63193.33, 63346.67, 25313.33, 36593.33, 
    24466.67, 38420, 26700, 29106.67, 36746.67, 52300, 25586.67, 
    13106.67, 2782.67, 15677.33, 18310.67, 2864.67, 1893.13, 
    2330, 5256.67, 5313.33, 3916, 5219.33, 5176.67, 6183.33, 
    2959.33, 3823.33, 803.93, 1815.87, 1629.93, 1886.47, 7705.33, 
    2350, 2322.6, 1715.8, 25313.33, 23700, 38420, 28253.33, 26700, 
    11562.67, 49326.67, 52300, 9258, 7078, 6374, 7147, 6435, 
    8366, 9639, 129220, 3481, 1973.8, 4097, 3584, 4189, 1573, 
    3312, 2488, 2886, 2908, 4489, 4047, 4641, 3429, 3434, 4903, 
    4341, 4352, 5019, 8046, 4060, 4552, 5445, 7159, 7870, 4004, 
    5660, 9790, 8772, 6728, 129010, 128520, 27106.67, 19360, 
    28766.67, 25513.33, 29606.67, 30206.67, 21666.67, 34660, 
    15920, 18108.67, 6322, 2402.67, 19686.67, 28853.33, 2898, 
    3403.33, 2437.33, 4086, 25520, 22993.33, 2664, 4850, 3688, 
    3084.67, 22528.67, 4182, 24286.67, 4442.67, 29561.54, 29606.67, 
    28740, 34120, 16304.67, 12944.67, 25466.67, 25513.33, 26006.67, 
    30836.36, 35130, 34645.45, 15926.67, 16664, 24580, 15746, 
    15780.67, 37533.33, 63600, 21560, 12336, 15016.67, 8820.67, 9112, 29880, 35580, 31173.33, 21893.33, 5828, 8477.33, 8122.67, 
    13715.33, 7430, 14023.33, 13144.67, 6759.33, 5126.67, 7038.67, 
    13430.67, 13701.33, 8657.33, 14273.33, 21368, 18332, 16500, 
    16000, 14870.67, 14990.67, 13547.33, 14310, 14806.67, 13180, 
    39786.67, 58420, 56646.67, 59280, 60213.33, 60633.33, 61533.33, 
    64240, 46886.67, 55386.67, 9316, 43553.33, 5883.33, 5913.33, 
    39906.67, 13561.33, 29660, 11585.33, 25340, 8721.33, 57513.33, 
    58613.33, 5214, 60060, 9409.33, 36626.67, 22033.33, 7980.67, 
    7192, 5508, 57680, 9765.33, 39020, 35806.67, 56393.33, 50346.67, 
    23554, 54246.67, 63540, 62333.33, 14585, 54200, 55500, 18350, 
    18340, 54100, 58200, 12260, 17285, 15520, 6565, 5650, 34116.67, 
    4145, 27601.33, 3215, 2110, 2425, 4955, 5000, 2955, 3230, 
    9165, 4550, 3310, 6505, 4375, 4635, 4260, 3635, 4205, 3480, 
    10820, 12205, 16245, 13600, 17000, 13425.33, 11295, 11950, 
    4350, 5040, 6060, 16640, 10440, 24000, 29100, 30700, 3280, 
    3150, 2810, 2735, 17050, 20650, 9645, 10050, 4775, 4370, 
    5575, 5340, 3490, 6555, 5060, 5015, 2375, 2320, 4015, 3265, 
    7570, 6380, 28550, 26750, 4100, 3110, 14715, 15260, 4915, 
    4740, 27850, 17625, 3015, 3760, 4460, 4720, 5115, 5655, 6030, 
    9560, 2015, 2070, 2015, 2135, 2320, 2050, 2895, 2855, 2085, 
    2355, 2160, 2000, 2600, 2955, 3050, 3020, 2010, 2030, 2990, 
    2890, 3785, 3880, 4610, 3890, 2165, 2835, 4625, 4650, 4045, 
    4190, 6715, 6340, 4640, 5425, 8295, 16705, 28450, 17340, 
    11920, 16360, 4455, 4690, 4580, 4485, 11455, 10970, 13070, 
    11050, 3080, 3650, 3425, 3225, 9400, 9245, 9250, 7560, 4015, 
    3930, 14690, 15655, 24650, 25050, 10755, 9665, 4560, 2800, 
    6540, 15120, 23650, 24500, 11780, 11835, 14585, 54200, 55500, 
    18350, 18340, 54100, 58200, 12260, 17285, 15520, 6565, 5650, 
    38184, 35550, 19769.33, 10050, 14730, 43700, 7765, 53400, 
    3490, 6555, 9165, 4550, 3310, 6505, 4375, 4635, 4260, 3635, 
    4205, 3480, 10820, 12205, 16245, 13600, 16700, 16920, 11295, 
    11940, 6378.67, 8702, 12030.67, 12763.33, 11382.67, 10196.67, 
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r data.frame reshape

asked by anonymous 10.12.2018 / 15:51

2 answers

3

First, you need to create an identifier variable:

dados$id <- 1:nrow(dados)

Next, the variables you want to stay on the line need to be on the left side of the formula, and in the right column. Finally, you need to tell which function you want to aggregate the data. In this case, I put sum , which will not make any difference, just keep the original data (because you have only 1 value) for each combination:

reshape2::dcast(dados, id + AVA + FAT1 ~ FAT2, fun.aggregate = sum, value.var = "LUX")
     id               AVA  FAT1   Banana Gliricídia     Ingá   Pupunha  Solteiro
1     1     Março de 2016 Antes     0.00       0.00     0.00      0.00  39018.33
2     2     Março de 2016 Antes     0.00       0.00     0.00      0.00  38870.00
3     3     Março de 2016 Antes     0.00       0.00     0.00      0.00  40375.00
4     4     Março de 2016 Antes     0.00       0.00     0.00      0.00  39686.67
5     5     Março de 2016 Antes     0.00       0.00     0.00      0.00  53363.33
6     6     Março de 2016 Antes     0.00       0.00     0.00      0.00  55666.67
7     7     Março de 2016 Antes     0.00       0.00     0.00      0.00  56888.33
8     8     Março de 2016 Antes     0.00       0.00     0.00      0.00  57768.33
9     9     Março de 2016 Antes     0.00       0.00  5029.33      0.00      0.00
10   10     Março de 2016 Antes     0.00       0.00  4758.00      0.00      0.00
11   11     Março de 2016 Antes     0.00       0.00  4810.67      0.00      0.00
12   12     Março de 2016 Antes     0.00       0.00  5044.67      0.00      0.00
13   13     Março de 2016 Antes     0.00       0.00 17840.67      0.00      0.00
14   14     Março de 2016 Antes     0.00       0.00  2917.33      0.00      0.00
15   15     Março de 2016 Antes     0.00       0.00  8508.67      0.00      0.00
16   16     Março de 2016 Antes     0.00       0.00  9960.67      0.00      0.00

However, if you want to add the data, just:

reshape2::dcast(dados[, -5], AVA + FAT1 ~ FAT2, fun.aggregate = sum, value.var = "LUX")

                 AVA  FAT1    Banana Gliricídia      Ingá   Pupunha  Solteiro
1      Março de 2016 Antes 182722.66   60839.33  58870.01 380706.67 381636.66
2      Março de 2016  Após  37847.99   20229.93  82551.81 255576.00 269646.67
3     Agosto de 2016 Antes  30737.00   42974.00  24697.80 300354.00 183517.00
4     Agosto de 2016  Após  69323.33  177468.22  97594.67 206847.82 216886.68
5   Dezembro de 2016 Antes  77500.66  101928.00 163812.00 118205.34 211390.67
6   Dezembro de 2016  Após 256707.32  156195.33 220407.33 385240.67 460753.33
7      Março de 2017 Antes  34390.00   34405.00 114098.00 106540.33 285535.00
8      Março de 2017  Após  40180.00   81225.00  69370.00  92315.00 126330.00
9     Agosto de 2017 Antes  20225.00   26085.00  18355.00  35565.00  42315.00
10    Agosto de 2017  Após  48835.00  108410.00  64755.00 100785.00 109135.00
11 Fevereiro de 2018 Antes 143355.00   34405.00 148573.33 109725.00 285535.00
12 Fevereiro de 2018  Após  51843.99   83628.00  59205.34  96237.33 102267.35
13     Abril de 2018 Antes  51885.33   72475.33  63535.34  96642.01 105252.68
14     Abril de 2018  Após  37445.99   51784.69  43426.00  65078.01  67080.67
15    Agosto de 2018 Antes  38343.33   96391.66  63484.67 131195.32 330319.32
16    Agosto de 2018  Após  71098.00  122475.34  84400.68 116604.00 130174.01

Then it returns the data.frame with the sum of each species of FAT2 to the value of LUX .

10.12.2018 / 16:45

How to calculate difference of dates that are a previous row with MYSQL? Difference between the 2 expressions?

score 3 · Accepted Answer

Using the tidyverse , you first need to create a LUX and"% "to" data.frame ". then spread it (% with%):

library(tidyverse)

dados %>% 
  group_by(AVA, FAT1, FAT2) %>% 
  summarise(LUX = sum(LUX)) %>% 
  spread(FAT2, LUX)

# Groups:   AVA, FAT1 [16]
   AVA               FAT1   Banana Gliricídia    Ingá Pupunha Solteiro
   <ord>             <fct>   <dbl>      <dbl>   <dbl>   <dbl>    <dbl>
 1 Março de 2016     Antes 182723.     60839.  58870. 380707.  381637.
 2 Março de 2016     Após   37848.     20230.  82552. 255576   269647.
 3 Agosto de 2016    Antes  30737      42974   24698. 300354   183517 
 4 Agosto de 2016    Após   69323.    177468.  97595. 206848.  216887.
 5 Dezembro de 2016  Antes  77501.    101928  163812  118205.  211391.
 6 Dezembro de 2016  Após  256707.    156195. 220407. 385241.  460753.
 7 Março de 2017     Antes  34390      34405  114098  106540.  285535 
 8 Março de 2017     Após   40180      81225   69370   92315   126330 
 9 Agosto de 2017    Antes  20225      26085   18355   35565    42315 
10 Agosto de 2017    Após   48835     108410   64755  100785   109135 
11 Fevereiro de 2018 Antes 143355      34405  148573. 109725   285535 
12 Fevereiro de 2018 Após   51844.     83628   59205.  96237.  102267.
13 Abril de 2018     Antes  51885.     72475.  63535.  96642.  105253.
14 Abril de 2018     Após   37446.     51785.  43426   65078.   67081.
15 Agosto de 2018    Antes  38343.     96392.  63485. 131195.  330319.
16 Agosto de 2018    Após   71098     122475.  84401. 116604   130174.

The intermediate table only needed to be created because there was more than one row for each set of spread() + AVA . If the information were already unique for each group, it would be enough to use FAT1 .