How can I filter (dplyr) on the same dataset twice in a 'for' loop? R - r

Question

I have a dataset that looks like this:
Hospital.Name State heart attack
1 SOUTHEAST ALABAMA MEDICAL CENTER AL 14.3
2 MARSHALL MEDICAL CENTER SOUTH AL 18.5
3 ELIZA COFFEE MEMORIAL HOSPITAL AL 18.1
4 MIZELL MEMORIAL HOSPITAL AL Not Available
5 CRENSHAW COMMUNITY HOSPITAL AL Not Available
6 MARSHALL MEDICAL CENTER NORTH AL Not Available
7 ST VINCENT'S EAST AL 17.7
8 DEKALB REGIONAL MEDICAL CENTER AL 18.0
9 SHELBY BAPTIST MEDICAL CENTER AL 15.9
10 CALLAHAN EYE FOUNDATION HOSPITAL AL Not Available
11 HELEN KELLER MEMORIAL HOSPITAL AL 19.6
12 DALE MEDICAL CENTER AL 17.3
13 CHEROKEE MEDICAL CENTER AL Not Available
14 BAPTIST MEDICAL CENTER SOUTH AL 17.8
15 JACKSON HOSPITAL & CLINIC INC AL 17.5
16 GEORGE H. LANIER MEMORIAL HOSPITAL AL 15.4
17 ELBA GENERAL HOSPITAL AL Not Available
18 EAST ALABAMA MEDICAL CENTER AND SNF AL 16.3
19 WEDOWEE HOSPITAL AL Not Available
20 UNIVERSITY OF ALABAMA HOSPITAL AL 15.0
The goal is to retrieve the hospital name, for a given rank of hospital on 'heart attack' for every state. For example, here I am trying to retrieve the hospital name for the lowest score (rank=1) in the heart attack column, for every state in a data frame.
This is my attempt:
stateVec <- unique(df$State)
outcome <- 'heart attack'
name <- c()
st <- c()
stateVec <- c()
rank <- 1
for (i in 1:length(stateVec)) {
k <- stateVec[i]
df1 <- dplyr::filter(df, State==k)
rankVec <- unique(df[[outcome]])
rankVec <- sort(rankVec[rankVec != 'Not Available'])
key <- rankVec[rank]
df1 <- dplyr::filter(df1, get(outcome, envir = as.environment(df))==key)
df1 <- df1[order(df$Hospital.Name), , drop = F]
d <- df1[1,]
name <- d$Hospital.Name
st <- k
return(data.frame(st, name))
}
I receive the following error:
Error in filter_impl(.data, quo) : Result must have length 98, not 4706
I've tried recreating the problem with the mtcars dataset, and don't get the same error. Any help would be appreciated :)

Answer 1

I think this is what you are looking for.
desired_rank <- 1
df %>%
filter(!is.na(heart.attack)) %>%
group_by(State) %>%
arrange(heart.attack) %>%
slice(desired_rank) %>%
ungroup()
It remove's NA values for heart.attack;
Then groups by State;
Then sorts ascending on heart.attack;
Then returns the first hospital (so the hospital with lowest heart.attack value).
The output is a data.frame.