Add a new row on basis of column values in R - r

Question

I am trying to get my head around this simple preprocessing task in R. I am trying to get the ideal value column as a row titled ideal in Product ID. I think the image below will shed more light on it.
> dput(df)
structure(list(Consumer = c(43L, 43L, 43L, 43L, 43L, 41L, 41L,
41L, 41L, 41L), Product = c(106L, 992L, 366L, 257L, 548L, 106L,
992L, 366L, 257L, 548L), Firm = c(1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L), Juicy = c(1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L
), Sweet = c(0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L), Ideal_Firm = c(1L,
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L), Ideal_Juicy = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Ideal_Sweet = c(1L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-10L))

Answer 1

Below is a solution:
df <- data.frame(
Consumer = c(rep(43, 5), rep(41, 5)),
Product = rep(sample(100:900,size = 5, replace = F), 2),
Firm = c(sample(rep(0:1, 5), replace = T)),
Juicy = c(sample(rep(0:1, 5), replace = T)),
Sweet = c(sample(rep(0:1, 5), replace = T)),
Ideal_Firm = 1,
Ideal_Juicy = c(rep(1, 5), rep(2, 5)),
Ideal_Sweet = c(rep(1, 5), rep(0, 5))
)
library(dplyr)
df <- merge(
# Bind the observation...
df %>% select(Consumer:Sweet) %>%
pivot_wider(id_cols = Consumer,names_from = Product,values_from = Firm:Sweet),
# ... to the ideal
df %>% group_by(Consumer) %>%
# Here I put mean, but it could be 1, median, min, max... If I understood correctly, it has to be 1?
summarise(across(Ideal_Firm:Ideal_Sweet, ~mean(.x))) %>%
# Rename so the column name has the form [characteristic]_ideal instead of Ideal_[characteristic]
# remove prefix Ideal_ ...
rename_at(.vars = vars(starts_with("Ideal_")),
.funs = funs(sub("Ideal_", "", .))) %>%
# ... add _Ideal as a suffix instead
rename_at(vars(-Consumer), function(x) paste0(x,"_Ideal"))
)
# Then manipulate to get into long form again
df <- df %>% pivot_longer(cols = !Consumer) %>%
separate(name, c("Characteristic", "Product")) %>%
pivot_wider(id_cols = Consumer:Product, names_from = Characteristic, values_from = value)
df