R: Vlookup for a 'for' loop - r

Question

i'm new to R and trying to use it in place of Excel (where i have more experience). I'm still working out the full 'for' logic, but not having the values to determine if it's working how i think it should is stopping me in my tracks. The goal is to generate what will be used as a factor with 3 levels; 0 = no duplicates, 1 is if duplicate, Oldest, 2 = if duplicate, newest.
I have a dataframe that looks like this
Person <- c("A", "B", "C", "C", "D", "E","E")
Date <- c(1/1/20, 1/1/20,12/25/19, 1/1/20, 1/1/20, 12/25/19, 1/1/20)
ID <- c(1,2,3,4,5,6,7)
DuplicateStatus <- c(0,0,0,0,0,0,0)
IdealResult <- c(0,0,1,2,0,1,2)
mydata <- cbind(Person, Date, ID, DuplicateStatus, IdealResult)
I am trying to use a for loop to evaluate if person duplicates. If a person does not duplicate, value= 0 and if they do duplicate, they should have a 1 for the oldest value and a 2 for the newest value (see ideal result). NOTE: I have already sorted the data to be by person and then date, so if duplicated, first appearance is oldest.
previous investigations of Vlookup in R answers here are aimed at merging datasets based on identical values in multiple datasets. Here, i am attempting to modify a column based on the relationship between columns, within a single dataset.
currentID = 0
nextID =0
for(i in mydata$ID){
currentID = i
nextID = currentID++1
CurrentPerson ##Vlookup function that does - find currentID in ID, return associated value in Person column in same position.
NextPerson ##Vlookup function that does - find nextID in ID, return associated value in Person column in same position.
if CurrentPerson = NextPerson, then DuplicateStatus at ID associated with current person should be 1, and DuplicateStatus at ID associated with NextPerson = 2.
**This should end when current person = total number of people
Thanks!

Answer 1

You really need to spend some time with a simple tutorial on R. Your cbind() function converts all of your data to a character matrix which is probably not what you want. Look at the results of str(mydata). Instead of looping, this creates an index number within each Person group and then zeros out the groups with a single observation:
mydata <- data.frame(Person, Date, ID, DuplicateStatus, IdealResult)
IR <- ave(mydata$ID, mydata$Person, FUN=seq_along)
IR
# [1] 1 1 1 2 1 1 2
tbl <- table(mydata$Person)
tozero <- mydata$Person %in% names(tbl[tbl == 1])
IR[tozero] <- 0
IR
# [1] 0 0 1 2 0 1 2

Answer 2

Is what you are looking for just to count the number of observations for a person, in one column (like a column ID)? If so, this will work using tidyverse:
Person <- c("A", "B", "C", "C", "D", "E","E")
Date <- c(1/1/20, 1/1/20,12/25/19, 1/1/20, 1/1/20, 12/25/19, 1/1/20)
ID <- c(1,2,3,4,5,6,7)
DuplicateStatus <- c(0,0,0,0,0,0,0)
IdealResult <- c(0,0,1,2,0,1,2)
mydata <- data.frame(Person, Date, ID, DuplicateStatus, IdealResult)
library(tidyverse)
mydata <- mydata %>%
group_by(Person) %>%
mutate(Duplicate = seq_along(Person))
mydata
# A tibble: 7 x 6
# Groups: Person [5]
Person Date ID DuplicateStatus IdealResult Duplicate
<fct> <dbl> <dbl> <dbl> <dbl> <int>
1 A 0.05 1 0 0 1
2 B 0.05 2 0 0 1
3 C 0.0253 3 0 1 1
4 C 0.05 4 0 2 2
5 D 0.05 5 0 0 1
6 E 0.0253 6 0 1 1
7 E 0.05 7 0 2 2

Answer 3

You could assign row number within each group provided if there are more than 1 row in each.
This can be implemented in base R, dplyr as well as data.table
In base R :
mydata$ans <- with(mydata, ave(ID, Person, FUN = function(x)
seq_along(x) * (length(x) > 1)))
# Person Date ID IdealResult ans
#1 A 0.0500000 1 0 0
#2 B 0.0500000 2 0 0
#3 C 0.0252632 3 1 1
#4 C 0.0500000 4 2 2
#5 D 0.0500000 5 0 0
#6 E 0.0252632 6 1 1
#7 E 0.0500000 7 2 2
Using dplyr:
library(dplyr)
mydata %>% group_by(Person) %>% mutate(ans = row_number() * (n() > 1))
and with data.table
library(data.table)
setDT(mydata)[, ans := seq_along(ID) * (.N > 1), Person]
data
mydata <- data.frame(Person, Date, ID, IdealResult)

Answer 4

I would argue that n() is the ideal function for you problem
library(tidyverse)
mydata <- mydata %>%
group_by(Person) %>%
mutate(Duplicate = n())