I am trying to normalize a Y scale by converting all values to percentages.
Therefore, I need to divide every number in a column by the first number in that column. In Excel, this would be equivalent to locking a cell A1/$A1, B1/$A1, C1/$A1 then D1/$D1, E1/$D1...
The data needs to first meet four criteria (Time, Treatment, Concentration and Type) and the reference value changes at every new treatment. Each treatment has 4 concentrations (0, 0.1, 2 and 50). I would like for the values associated to each concentration to be divided by the reference value (when the concentration is equal to 0).
The tricky part is that this reference value changes every 4 rows.
I have tried doing this using ddply:
`MasterTable <- read.csv("~/Dropbox/Master-table.csv")`
MasterTable <- ddply(MasterTable, .(Time, Type, Treatment), transform, pc=(Value/Value$Concentration==0))
dplyr is very efficient here:
library(dplyr) result <- group_by(MasterTable, Time, Type, Treatment) %>% mutate(pc = Value / Value)