How to remove rows that have repeated elements? - r

Question

I have a dataframe that looks like this (but for every US county)
county
state
neighbor_county
neighbor_state
Baldwin County
AL
Clarke County
NA
Baldwin County
AL
Escambia County
FL
Baldwin County
AL
Mobile County
NA
Baldwin County
AL
Monroe County
NA
Barbour County
AL
Dale County
NA
Barbour County
AL
Henry County
NA
I am only interested in what states neighbor a county, so I want to remove repeated data to get this (step 1):
county
state
neighbor_state
Baldwin County
AL
NA
Baldwin County
AL
FL
Barbour County
AL
NA
And then change sort the dataframe like this (step 2):
county
state
neighbor_state_1
neighbor_state_2
neighbor_state_3
Baldwin County
AL
FL
NA
NA
Baldwin County
AL
NA
NA
NA
In step 1 I've deleted the "neighbor_county" column; however, I've not managed to remove the duplicates in the column "neighbor_state" for each distinct county. I have tried using the unique function but I can't seem to make it work such that it only removes duplicates of each distinct county.

Answer 1

For your first step you could drop the neighbour_county column and the use unique():
df$neighbor_county <- NULL
unique(df)
returns
county state neighbor_state
1 Baldwin_County AL NA
2 Baldwin_County AL FL
5 Barbour_County AL NA
An alternative using dplyr:
df %>%
select(-neighbor_county) %>%
distinct()
For your second step I make a suggestion:
library(tidyr)
library(dplyr)
df %>%
group_by(county) %>%
select(-neighbor_county) %>%
mutate(n = row_number()) %>%
pivot_wider(names_from=n, names_prefix="neighbor_state_", values_from=neighbor_state) %>%
ungroup()
returns
# A tibble: 2 x 6
county state neighbor_state_1 neighbor_state_2 neighbor_state_3 neighbor_state_4
<chr> <chr> <chr> <chr> <chr> <chr>
1 Baldwin_County AL 'NA' 'FL' 'NA' 'NA'
2 Barbour_County AL 'NA' 'NA' NA NA
but I'm not sure, if this is what you are looking for.
For removing doubled NA-values, you could use
df %>%
group_by(county) %>%
select(-neighbor_county) %>%
distinct() %>%
mutate(n = row_number()) %>%
pivot_wider(names_from=n, names_prefix="neighbor_state_", values_from=neighbor_state) %>%
ungroup()
Data
structure(list(county = c("Baldwin_County", "Baldwin_County",
"Baldwin_County", "Baldwin_County", "Barbour_County", "Barbour_County"
), state = c("AL", "AL", "AL", "AL", "AL", "AL"), neighbor_county = c("Clarke_County",
"Escambia_County", "Mobile_County", "Monroe_County", "Dale_County",
"Henry_County"), neighbor_state = c("'NA'", "'FL'", "'NA'", "'NA'",
"'NA'", "'NA'")), problems = structure(list(row = 6L, col = "neighbor_state",
expected = "", actual = "embedded null", file = "literal data"), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame")), class = "data.frame", row.names = c(NA,
-6L), spec = structure(list(cols = list(county = structure(list(), class = c("collector_character",
"collector")), state = structure(list(), class = c("collector_character",
"collector")), neighbor_county = structure(list(), class = c("collector_character",
"collector")), neighbor_state = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1L), class = "col_spec"))