Keep less recent duplicate row in R - r

Question

So, I have a dataset with bill numbers, day, month, year, and aggregate value. There are a bunch of bull number duplicates, and I want to keep the first ones. If there is a duplicate with the same day, month, and year, I want to keep the one with the highest amount in aggregate value.
For example, if the dataset now looks like this:
Bill Number Day Month Year Ag. Value
1 10 4 1998 10
1 11 4 1998 14
2 23 11 2001 12
2 23 11 2001 9
3 11 3 2005 8
3 12 3 2005 9
3 13 3 2005 4
I want the result to look like this:
Bill Number Day Month Year Ag. Value
1 10 4 1998 10
2 23 11 2001 12
3 11 3 2005 8
I'm not sure if there is a command I can use and just introduce all these arguments or if I should do it in stages, but either way I'm not sure how to begin. I used duplicate() and unique() and then got stuck.
Thanks!

Answer 1

library( data.table )
dt <- fread("Bill_Number Day Month Year Ag_Value
1 10 4 1998 10
1 11 4 1998 14
2 23 11 2001 12
2 23 11 2001 9
3 11 3 2005 8
3 12 3 2005 9
3 13 3 2005 4", header = TRUE)
dt[ !duplicated( Bill_Number), ]
# Bill_Number Day Month Year Ag_Value
# 1: 1 10 4 1998 10
# 2: 2 23 11 2001 12
# 3: 3 11 3 2005 8
or
dt[, .SD[1], by = .(Bill_Number) ] #other approach, a bit slower

Answer 2

duplicated() gives you the entries which are identical to earlier one (i.e. ones with smaller subscripts). Therefore, sorting your bill numbers by date (earliest to the top) and then removing duplicates should do the trick. Aggregating your columns day, month and year into one date-column might be helpful.

Answer 3

This answer uses dplyr package and satisfies your condition: "If there is a duplicate with the same day, month, and year, I want to keep the one with the highest amount in aggregate value."
library(data.table)
library(dplyr)
myData <- fread("Bill_Number Day Month Year Ag_Value
1 10 4 1998 10
1 11 4 1998 14
2 23 11 2001 12
2 23 11 2001 9
3 11 3 2005 8
3 12 3 2005 9
3 13 3 2005 4", header = TRUE)
myData <- as.tibble(myData) #tibble form
sData <- arrange(myData, Bill_Number, Year, Month, Day, desc(Ag_Value)) #sort the data with the required manner
fData <- distinct(sData, Bill_Number, .keep_all = 1) #final data
fData
# A tibble: 3 x 5
Bill_Number Day Month Year Ag_Value
<int> <int> <int> <int> <int>
1 1 10 4 1998 10
2 2 23 11 2001 12
3 3 11 3 2005 8

Answer 4

I used some loops and condition checks, and tried with a test set besides the "base" set you mentioned.
library(tidyverse)
#base dataset
billNumber <- c(1,1,2,2,3,3,3)
day <- c(10,11,23,23,11,12,13)
month <- c(4,4,11,11,3,3,3)
year <- c(1998,1998,2001,2001,2005,2005,2005)
agValue <- c(10,14,12,9,8,9,4)
#test dataset
billNumber <- c(1,1,2,2,3,3,3,4,4,4)
day <- c(10,11,23,23,11,12,13,15,15,15)
month <- c(4,4,11,11,3,3,3,6,6,6)
year <- c(1998,1998,2001,2001,2005,2005,2005,2020,2020,2020)
agValue <- c(10,14,9,12,8,9,4,13,15,8)
#build the dataset
df <- data.frame(billNumber,day,month,year,agValue)
#add a couple of working columns
df_full <- df %>%
mutate(
concat = paste(df$billNumber,df$day,df$month,df$year,sep="-"),
flag = ""
)
df_full
billNumber day month year agValue concat flag
1 1 10 4 1998 10 1-10-4-1998
2 1 11 4 1998 14 1-11-4-1998
3 2 23 11 2001 12 2-23-11-2001
4 2 23 11 2001 9 2-23-11-2001
5 3 11 3 2005 8 3-11-3-2005
6 3 12 3 2005 9 3-12-3-2005
7 3 13 3 2005 4 3-13-3-2005
#separate records with one/multi occurence as defined in the question
row_single <- df_full %>% count(concat) %>% filter(n == 1)
df_full_single <- df_full[df_full$concat %in% row_single$concat,]
row_multi <- df_full %>% count(concat) %>% filter(n > 1)
df_full_multi <- df_full[df_full$concat %in% row_multi$concat,]
#flag the rows with single occurence
df_full_single[1,]$flag = "Y"
for (row in 2:nrow(df_full_single)) {
if (df_full_single[row,]$billNumber == df_full_single[row-1,]$billNumber) {
df_full_single[row,]$flag = "N"
} else
{
df_full_single[row,]$flag = "Y"
}
}
df_full_single
#flag the rows with multi occurences
df_full_multi[1,]$flag = "Y"
for (row in 2:nrow(df_full_multi)) {
if (
(df_full_multi[row,]$billNumber == df_full_multi[row-1,]$billNumber) &
(df_full_multi[row,]$agValue > df_full_multi[row-1,]$agValue)
) {
df_full_multi[row,]$flag = "Y"
df_full_multi[row-1,]$flag = "N"
} else
{
df_full_multi[row,]$flag = "N"
}
}
df_full_multi
#rebuild full dataset and retrieve the desired output
df_full_final <- rbind(df_full_single,df_full_multi)
df_full_final <- df_full_final[df_full_final$flag == "Y",c(1,2,3,4,5)]
df_full_final <- df_full_final[order(df_full_final$billNumber),]
df_full_final
billNumber day month year agValue
1 1 10 4 1998 10
3 2 23 11 2001 12
5 3 11 3 2005 8