R ifelse loop on unique values always resolves FALSE - r

Question

I am newish to R and having trouble with a for loop over unique values.
with the df:
id = c(1,2,2,3,3,4)
rank = c(1,2,1,3,3,4)
df = data.frame(id, rank)
I run:
df$dg <- logical(6)
for(i in unique(df$id)){
ifelse(!unique(df$rank), df$dg ==T, df$dg == F)
}
I am trying to mark the $dg variable as T providing that rank is different for each unique id and F if rank is the same within each id.
I am not getting any errors, but I am only getting F for all values of $dg even though I should be getting a mix.
I have also used the following loop with the same results:
for(i in unique(df$id)){
ifelse(length(unique(df$rank)), df$dg ==T, df$dg == F)
}
I have read other similar posts but the advice has not worked for my case.
From Comments:
I want to mark dg TRUE for all instances of an id if rank changed at all for a given id. Im looking to say for a given ID which has anywhere between 1-13 instances, mark dg TRUE if rank differs across instances.

Answer 1

Update: How to identify groups (ids) that only have one rank?
After clarification that OP provided this would be a solution for this particular case:
library(dplyr)
df %>%
group_by(id) %>%
mutate(dg = ifelse( length(unique(rank))>1 | n() == 1, T, F))
For another data-set that has also an id, which has duplicates but also non-duplicate rank (presented below) this would be the output:
df2 %>%
group_by(id) %>%
mutate(dg = ifelse( length(unique(rank))>1 | n() == 1, T, F))
#:OUTPUT:
# Source: local data frame [9 x 3]
# Groups: id [5]
#
# # A tibble: 9 x 3
# id rank dg
# <dbl> <dbl> <lgl>
# 1 1 1 TRUE
# 2 2 2 TRUE
# 3 2 1 TRUE
# 4 3 3 FALSE
# 5 3 3 FALSE
# 6 4 4 TRUE
# 7 5 1 TRUE
# 8 5 1 TRUE
# 9 5 3 TRUE
Data-no-2:
df2 <- structure(list(id = c(1, 2, 2, 3, 3, 4, 5, 5, 5), rank = c(1, 2, 1, 3, 3, 4, 1, 1, 3
)), .Names = c("id", "rank"), row.names = c(NA, -9L), class = "data.frame")
How to identify duplicated rows within each group (id)?
You can use dplyr package:
library(dplyr)
df %>%
group_by(id, rank) %>%
mutate(dg = ifelse(n() > 1, F,T))
This will give you:
# Source: local data frame [6 x 3]
# Groups: id, rank [5]
#
# # A tibble: 6 x 3
# id rank dg
# <dbl> <dbl> <lgl>
# 1 1 1 TRUE
# 2 2 2 TRUE
# 3 2 1 TRUE
# 4 3 3 FALSE
# 5 3 3 FALSE
# 6 4 4 TRUE
Note: You can simply convert it back to a data.frame().
A data.table solution would be:
dt <- data.table(df)
dt$dg <- ifelse(dt[ , dg := .N, by = list(id, rank)]$dg>1,F,T)
Data:
df <- structure(list(id = c(1, 2, 2, 3, 3, 4), rank = c(1, 2, 1, 3,
3, 4)), .Names = c("id", "rank"), row.names = c(NA, -6L), class = "data.frame")
# > df
# id rank
# 1 1 1
# 2 2 2
# 3 2 1
# 4 3 3
# 5 3 3
# 6 4 4
N. B. Unless you want a different identifier rather than TRUE/FALSE, using ifelse() is redundant and costs computationally. #DavidArenburg