Assign specific value based on two column in R - r

Question

I have a dataframe which looks like:
Student_ID Number Position
VB-123 10 2
VB-456 15 5
VB-789 25 25
VB-889 12 2
VB-965 15 7
VB-758 45 9
VB-245 25 25
I want to add new column and assign a value based on below conditions:
If only Number is duplicate in entire dataframe then Assign A
If only Position is duplicate in entire dataframe then assign B
If both Number and Position are duplicate then assign C
If none of the duplicate then assign D.
Output would looks like:
Student_ID Number Position Assign
VB-123 10 2 B
VB-456 15 5 A
VB-789 25 25 C
VB-889 12 2 B
VB-965 15 7 A
VB-758 45 9 D
VB-245 25 25 C

Answer 1

With dplyr,
library(dplyr)
students <- data.frame(Student_ID = c("VB-123", "VB-456", "VB-789", "VB-889", "VB-965", "VB-758", "VB-245"),
Number = c(10L, 15L, 25L, 12L, 15L, 45L, 25L),
Position = c(2L, 5L, 25L, 2L, 7L, 9L, 25L))
students2 <- students %>%
mutate_at(vars(Number, Position), funs(n = table(.)[as.character(.)])) %>%
mutate(Assign = case_when(Number_n > 1 & Position_n > 1 ~ 'C',
Number_n > 1 ~ 'A',
Position_n > 1 ~ 'B',
TRUE ~ 'D'))
students2
#> Student_ID Number Position Number_n Position_n Assign
#> 1 VB-123 10 2 1 2 B
#> 2 VB-456 15 5 2 1 A
#> 3 VB-789 25 25 2 2 C
#> 4 VB-889 12 2 1 2 B
#> 5 VB-965 15 7 2 1 A
#> 6 VB-758 45 9 1 1 D
#> 7 VB-245 25 25 2 2 C
As an alternative to the mutate_at line, you could use add_count twice, renaming as necessary. To remove the intermediary columns, tack on select(-matches('_n$')).
You can more or less replicate the logic in base by assigning to subsets:
students2 <- cbind(students, lapply(students[2:3], function(x) table(x)[as.character(x)]))
students2$Assign <- 'D'
students2$Assign[students2$Number.Freq > 1 & students2$Position.Freq > 1] <- 'C'
students2$Assign[students2$Number.Freq > 1 & students2$Position.Freq == 1] <- 'A'
students2$Assign[students2$Number.Freq == 1 & students2$Position.Freq > 1] <- 'B'
students2[4:7] <- NULL
students2
#> Student_ID Number Position Assign
#> 1 VB-123 10 2 B
#> 2 VB-456 15 5 A
#> 3 VB-789 25 25 C
#> 4 VB-889 12 2 B
#> 5 VB-965 15 7 A
#> 6 VB-758 45 9 D
#> 7 VB-245 25 25 C

Answer 2

Here is an option using base R. Create a list of column names as in the order of evaluatin ('l1'), pre assign 'D' to create the 'Assign' column in 'dat', loop through the sequence of 'l1', subset the columns of data based on the column names in 'l1', use duplicated to find the duplicate elements and reassign the 'Assign' column to the corresponding LETTER
l1 <- list("Number", "Position", c("Number", "Position"))
dat$Assign <- rep("D", nrow(dat))
for(i in seq_along(l1)){
df <- dat[l1[[i]]]
i1 <- duplicated(df)|duplicated(df, fromLast = TRUE)
dat$Assign <- replace(dat$Assign, i1, LETTERS[i])
}
-output
dat
# Student_ID Number Position Assign
#1 VB-123 10 2 B
#2 VB-456 15 5 A
#3 VB-789 25 25 C
#4 VB-889 12 2 B
#5 VB-965 15 7 A
#6 VB-758 45 9 D
#7 VB-245 25 25 C

Answer 3

A solution using dplyr.
library(dplyr)
dat2 <- dat %>% count(Number)
dat3 <- dat %>% count(Position)
dat4 <- dat %>% count(Number, Position)
dat5 <- dat %>%
left_join(dat2, by = "Number") %>%
left_join(dat3, by = "Position") %>%
left_join(dat4, by = c("Number", "Position")) %>%
mutate(Assign = case_when(
n > 1 ~ "C",
n.x > 1 & n.y == 1 ~ "A",
n.y > 1 & n.x == 1 ~ "B",
TRUE ~ "D"
)) %>%
select(-n.x, -n.y, -n)
dat5
# Student_ID Number Position Assign
# 1 VB-123 10 2 B
# 2 VB-456 15 5 A
# 3 VB-789 25 25 C
# 4 VB-889 12 2 B
# 5 VB-965 15 7 A
# 6 VB-758 45 9 D
# 7 VB-245 25 25 C
DATA
dat <- read.table(text = "Student_ID Number Position
'VB-123' 10 2
'VB-456' 15 5
'VB-789' 25 25
'VB-889' 12 2
'VB-965' 15 7
'VB-758' 45 9
'VB-245' 25 25",
header = TRUE, stringsAsFactors = FALSE)