dplyr: Create a new column with a complex operation using mutate() - r

Question

I'd like to create a new data frame (new.df) using an original (df) but with a new column (Age) using a complex operation with mutate function in dplyr package. My steps are:
# Artificial dataframe
IDtest<-c(1,1,1,1,1,1,2,2,2,3,3,3,3)
Class<-c(1,1,2,2,2,3,1,1,2,1,2,2,3)
Day<-c(0,47,76,100,150,173,0,47,76,0,47,76,100)
Area<-c(0.45,0.85,1.50,1.53,1.98,5.2,
0.36,0.58,1.2,
0.85,1.36,2.26,3.59)
df<-data.frame(cbind(IDtest, Class, Day, Area))
str(df)
#Split each IDtest
df[df[,1]==1,]
# IDtest Class Day Area
#1 1 1 0 0.45
#2 1 1 47 0.85
#3 1 2 76 1.50
#4 1 2 100 1.53
#5 1 2 150 1.98
#6 1 3 173 5.20
Inside each IDtest:
Last Area inside each Class minus a factor (If the Area < 1 = 0; < 2.9 = 1; < 8.9 = 3; < 24.9 = 9; > 25 = 25); and
Than 1) divided by make subtraction between last and first Area and divided by Day inside each Class
Than 1) and 2) sum Day inside each Class minus last Day. Finally sum of all divided by 365 and create a new column Age
#For Class 1
(0.85-0)/((0.85-0.45)/47) + (47 - 0)
#For Class 2
(1.98-1)/((1.98-0.85)/150) + (157 - 47)
#For Class 3
(5.20-3)/((5.2-1.98)/173) + (173 - 150)
#Final
Age<-((0.85-0)/((0.85-0.45)/47) + (47 - 0) +
(1.98-1)/((1.98-0.85)/150) + (157 - 47) +
(5.20-3)/((5.2-1.98)/173) + (173 - 150))/365
Age
#[1] 1.44702
# Desirable output
new.df
# IDtest Class Day Area Age
#1 1 1 0 0.45 1.44702
#2 1 1 47 0.85 1.44702
#3 1 2 76 1.50 1.44702
#4 1 2 100 1.53 1.44702
#5 1 2 150 1.98 1.44702
#6 1 3 173 5.20 1.44702
Please any ideas?

Answer 1

It's quite tricky, so I have made all steps separatedly, to make you easier detecting any possible missunderstanding.
Is it possible that a mistake exists in this line of yours?
(1.98-1)/((1.98-0.85)/150) + (157 - 47) # 157? wouldn't it be 150?
That said, my results for Class 1 are the same as yours, but please be careful with Class 2 and 3, because I'm not sure of properly understanding the second and third steps, I'm not absolutely sure about your use of "last" (i.e. "last" in the Class or the "previous" Class).
In the second step I use "last" in the Class and in third one I use a for loop to use "the previous". I think you can addapt the idea
df2 <- df %>%
group_by(IDtest, Class) %>%
mutate(
DayOrder = row_number()
)
df2 <- df2 %>%
mutate(step1a = Area[max(DayOrder)], # I divide step1 in several steps to make it clearer
minus = # what you want to substract
case_when(
step1a < 1 ~ 0,
step1a < 2.9 ~ 1,
step1a < 8.9 ~ 3,
step1a < 24.9 ~ 9,
step1a > 25 ~ 25
),
step1done = step1a - minus,
step2a = Area[max(DayOrder)] - Area[min(DayOrder)], # "Last" inside the same Class (as it is inside mutate, which is under group_by)
step2b = Day[max(DayOrder)],
step2done = step2a / step2b,
step1by2 = step1done / step2done
)
df2$step3 <- NA
for (i in 1:max(df2$Class)){
if(i == 1){
df2$step3[Class == i] <- max(df2$Day[df2$Class == i]) - 0 # quite silly
}else{
df2$step3[Class == i] <- max(df2$Day[df2$Class == i]) - max(df2$Day[df2$Class == i - 1]) # "Last" as the "previous" Class, not inside the same Class
}}
df2 %>%
mutate(
step3done = step1by2 + step3,
Age = step3done / 365 # Do you want "age" as a unique value?? not a value for each person? This case I would do this outside mutate and add as a new column
)
If I have misunderstood you, I hope you can at least take some ideas!