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I have huge dataframe like this:
SN = c(1:100, 1:100, 1:100, 1:100) class = c(rep("A1", 100), rep("B2", 100), rep("C3", 100), rep("D4", 100)) # total 6000 levels myvar = rnorm(400) mydf = data.frame(SN, class, myvar) I want to "unmelt" to a table with each level as single column and myvar in filled:
SN A1 B2 C3 D4 .............and so on for all 6000 How can I achieve this, I know it is simple question, but I could not figure out.
838
We can also do the reverse of the melt operation which is also called as Pivoting. In Pivoting or Reverse Melting, we convert a column with multiple values into several columns of their own. The pivot() method on the dataframe takes two main arguments index and columns .
5. 4. In package reshape2 the opposite of melt is cast.
Pandas melt() function is used to change the DataFrame format from wide to long. It's used to create a specific format of the DataFrame object where one or more columns work as identifiers. All the remaining columns are treated as values and unpivoted to the row axis and only two columns - variable and value.
melt() function is useful to message a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are unpivoted to the row axis, leaving just two non-identifier columns, variable and value.
> dcast(mydf, SN ~ class) SN A1 B2 C3 D4 1 1 0.1461258 0.8325014 0.33562088 -0.07294576 2 2 0.5964182 0.4593710 -0.23652803 -1.52539568 3 3 2.0247742 -1.1235963 1.79875447 -1.87462227 4 4 0.8184004 1.3486721 0.76076486 -1.18311991 5 5 -0.6577212 0.3666741 -0.06057506 1.38825487 6 6 0.1590443 0.2043661 0.08161778 0.10421797 ... 
molten = melt( mydf , id.vars = c( "SN" , "class" ) , measure.vars = "myvar" ) casted = dcast( molten , SN~class ) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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