creating a function for processing my dataframe calculations - r

Question

I am doing systematic calculations for my created dataframe. I have the code for the calculations but I would like to:
1) Wite it as a function and calling it for the dataframe I created.
2) reset the calculations for next ID in the dataframe.
I would appreciate your help and advice on this.
The dataframe is created in R using the following code:
#Create a dataframe
dosetimes <- c(0,6,12,18)
df <- data.frame("ID"=1,"TIME"=sort(unique(c(seq(0,30,1),dosetimes))),"AMT"=0,"A1"=NA,"WT"=NA)
doserows <- subset(df, TIME%in%dosetimes)
doserows$AMT[doserows$TIME==dosetimes[1]] <- 100
doserows$AMT[doserows$TIME==dosetimes[2]] <- 100
doserows$AMT[doserows$TIME==dosetimes[3]] <- 100
doserows$AMT[doserows$TIME==dosetimes[4]] <- 100
#Add back dose information
df <- rbind(df,doserows)
df <- df[order(df$TIME,-df$AMT),]
df <- subset(df, (TIME==0 & AMT==0)==F)
df$A1[(df$TIME==0)] <- df$AMT[(df$TIME ==0)]
#Time-dependent covariate
df$WT <- 70
df$WT[df$TIME >= 12] <- 120
#The calculations are done in a for-loop. Here is the code for it:
#values needed for the calculation
C <- 2
V <- 10
k <- C/V
#I would like this part to be written as a function
for(i in 2:nrow(df))
{
t <- df$TIME[i]-df$TIME[i-1]
A1last <- df$A1[i-1]
df$A1[i] = df$AMT[i]+ A1last*exp(-t*k)
}
head(df)
plot(A1~TIME, data=df, type="b", col="blue", ylim=c(0,150))
The other thing is that the previous code assumes the subject ID=1 for all time points. If subject ID=2 when the WT (weight) changes to 120. How can I reset the calculations and make it automated for all subject IDs in the dataframe? In this case the original dataframe would be like this:
#code:
rm(list=ls(all=TRUE))
dosetimes <- c(0,6,12,18)
df <- data.frame("ID"=1,"TIME"=sort(unique(c(seq(0,30,1),dosetimes))),"AMT"=0,"A1"=NA,"WT"=NA)
doserows <- subset(df, TIME%in%dosetimes)
doserows$AMT[doserows$TIME==dosetimes[1]] <- 100
doserows$AMT[doserows$TIME==dosetimes[2]] <- 100
doserows$AMT[doserows$TIME==dosetimes[3]] <- 100
doserows$AMT[doserows$TIME==dosetimes[4]] <- 100
df <- rbind(df,doserows)
df <- df[order(df$TIME,-df$AMT),]
df <- subset(df, (TIME==0 & AMT==0)==F)
df$A1[(df$TIME==0)] <- df$AMT[(df$TIME ==0)]
df$WT <- 70
df$WT[df$TIME >= 12] <- 120
df$ID[(df$WT>=120)==T] <- 2
df$TIME[df$ID==2] <- c(seq(0,20,1))
Thank you in advance!

Answer 1

In general, when doing calculations on different subject's data, I like to split the dataframe by ID, pass the vector of individual subject data into a for loop, do all the calculations, build a vector containing all the newly calculated data and then collapse the resultant and return the dataframe with all the numbers you want. This allows for a lot of control over what you do for each subject
subjects = split(df, df$ID)
forResults = vector("list", length=length(subjects))
# initialize these constants
C <- 2
V <- 10
k <- C/V
myFunc = function(data, resultsArray){
for(k in seq_along(subjects)){
df = subjects[[k]]
df$A1 = 100 # I assume this should be 100 for t=0 for each subject?
# you could vectorize this nested for loop..
for(i in 2:nrow(df)) {
t <- df$TIME[i]-df$TIME[i-1]
A1last <- df$A1[i-1]
df$A1[i] = df$AMT[i]+ A1last*exp(-t*k)
}
head(df)
# you can add all sorts of other calculations you want to do on each subject's data
# when you're done doing calculations, put the resultant into
# the resultsArray and we'll rebuild the dataframe with all the new variables
resultsArray[[k]] = df
# if you're not using RStudio, then you want to use dev.new() to instantiate a new plot canvas
# dev.new() # dont need this if you're using RStudio (which doesnt allow multiple plots open)
plot(A1~TIME, data=df, type="b", col="blue", ylim=c(0,150))
}
# collapse the results vector into a dataframe
resultsDF = do.call(rbind, resultsArray)
return(resultsDF)
}
results = myFunc(subjects, forResults)

Answer 2

Do you want this:
ddf <- data.frame("ID"=1,"TIME"=sort(unique(c(seq(0,30,1),dosetimes))),"AMT"=0,"A1"=NA,"WT"=NA)
myfn = function(df){
dosetimes <- c(0,6,12,18)
doserows <- subset(df, TIME%in%dosetimes)
doserows$AMT[doserows$TIME==dosetimes[1]] <- 100
doserows$AMT[doserows$TIME==dosetimes[2]] <- 100
doserows$AMT[doserows$TIME==dosetimes[3]] <- 100
doserows$AMT[doserows$TIME==dosetimes[4]] <- 100
#Add back dose information
df <- rbind(df,doserows)
df <- df[order(df$TIME,-df$AMT),]
df <- subset(df, (TIME==0 & AMT==0)==F)
df$A1[(df$TIME==0)] <- df$AMT[(df$TIME ==0)]
#Time-dependent covariate
df$WT <- 70
df$WT[df$TIME >= 12] <- 120
#The calculations are done in a for-loop. Here is the code for it:
#values needed for the calculation
C <- 2
V <- 10
k <- C/V
#I would like this part to be written as a function
for(i in 2:nrow(df))
{
t <- df$TIME[i]-df$TIME[i-1]
A1last <- df$A1[i-1]
df$A1[i] = df$AMT[i]+ A1last*exp(-t*k)
}
head(df)
plot(A1~TIME, data=df, type="b", col="blue", ylim=c(0,150))
}
myfn(ddf)
For multiple calls:
for(i in 1:N) {
myfn(ddf[ddf$ID==i,])
readline(prompt="Press <Enter> to continue...")
}