Parsing a Hierarchy in a String Value - r

Question

I am trying to create an edge list from a single character vector. My list to be processed is over 93k elements long, but as an example I will provide a small excerpt.
The chracter strings are part of the ICD10 code hierarchy and the parent child relationships exist within the string. That means that a single string, "A0101", would have a parent of "A010"
It would look like this:
A00
A000
A001
A009
A01
A010
A0100
A0101
A02
A03
etc.
My vector does not contain any other data except the strings but i basically need to convert
dat <- c("A00", "A000", "A001", "A009", "A01", "A010", "A0100", "A0101", "A02")
into an edge list formatted as follows...
# (A00, A000)
# (A00, A001)
# (A00, A009)
# (A01, A010)
# (A010, A0100)
# (A010, A0101)

Answer 1

I am fairly certain there are more efficient ways to accomplish this but this excerpt of code should download the ICD10 CM data from the icd.data package. Use the children detection system from the icd package and then make extensive use of the tidyverse to return an edgelist. I had to get a bit creative to connect the "top" of the hierarchies since they do not include the chapters and sub chapters of ICD10 data as an individual 2 or 1 digit code.
Basically sub-chapters become 2 digit codes, chapters become 1 digit codes, and then there is a root node to connect everything at the top.
library(icd.data)
icd10 <- icd10cm2016
library(icd)
code_children <- lapply(icd10$code, children)
code_vec <- sapply(code_children, paste, collapse = ",")
code_df <- as.data.frame(code_vec, stringsAsFactors = F)
library(dplyr);library(stringr);library(tidyr)
code_df_new <- code_df %>%
mutate(parent = sapply(strsplit(code_vec,","), "[", 1)) %>%
separate(code_vec,
paste("code", 1:max(str_count(code_df$code_vec, ",")), sep ="."),
",",extra = "merge")
library(reshape2)
edgelist <- melt(code_df_new, id = "parent") %>%
filter(!is.na(value)) %>%
select(parent, child = value) %>%
arrange(parent)
edgelist <- subset(edgelist, edgelist$parent != edgelist$child)
edgelist <- subset(edgelist, nchar(edgelist$child) == nchar(edgelist$parent) + 1)
subchaps <- icd10 %>% select(three_digit, sub_chapter, chapter) %>%
mutate(two_digit = substr(three_digit, 1, 2)) %>%
select(parent = two_digit, child = three_digit) %>%
distinct()
chaps <- icd10 %>% select(three_digit, sub_chapter, chapter) %>%
mutate(
two_digit = substr(three_digit, 1, 2),
one_digit = substr(three_digit, 1, 1)) %>%
select(parent = one_digit, child = two_digit) %>%
distinct()
root <- icd10 %>% select(three_digit) %>%
mutate(parent = "root", child = substr(three_digit, 1, 1)) %>%
select(parent, child) %>%
distinct()
edgelist_final <- edgelist %>%
bind_rows(list(chaps, subchaps, root)) %>%
arrange(parent)
If anybody has any tips or methods to improve the efficiency of this code I am all ears. (eyes?)

Answer 2

On the assumption that the length of the node names in ICD10 fully define the order (with shorter ones being parents), here's an approach that connects each node with it's immediate parent, if available.
While I think the logic is legible here, I'd be curious to see what a more streamlined solution would look like.
# Some longer fake data to prove that it works acceptably
# with 93k rows (took a few seconds). These are just
# numbers of different lengths, converted to characters, but they
# should suffice if the assumption about length = order is correct.
set.seed(42)
fake <- runif(93000, 0, 500) %>%
magrittr::raise_to_power(3) %>%
as.integer() %>%
as.character()
# Step 1 - prep
library(dplyr); library(tidyr)
fake_2 <- fake %>%
as_data_frame() %>%
mutate(row = row_number()) %>%
# Step 2 - widen by level and fill in all parent nodes
mutate(level = str_length(value)) %>%
spread(level, value) %>%
fill(everything()) %>%
# Step 3 - Get two highest non-NA nodes
gather(level, code, -row) %>%
arrange(row, level) %>%
filter(!is.na(code)) %>%
group_by(row) %>%
top_n(2, wt = level) %>%
# Step 4 - Spread once more to get pairs
mutate(pos = row_number()) %>%
ungroup() %>%
select(-level) %>%
spread(pos, code)
Output on OP data
# A tibble: 9 x 3
row `1` `2`
<int> <chr> <chr>
1 1 A00 NA
2 2 A00 A000
3 3 A00 A001
4 4 A00 A009
5 5 A01 A009
6 6 A01 A010
7 7 A010 A0100
8 8 A010 A0101
9 9 A010 A0101
Output on 93k fake data
> head(fake, 10)
[1] "55174190" "50801321" "46771275" "6480673"
[5] "20447474" "879955" "4365410" "11434009"
[9] "5002257" "9200296"
> head(fake_2, 10)
# A tibble: 10 x 3
row `1` `2`
<int> <chr> <chr>
1 1 55174190 NA
2 2 50801321 NA
3 3 46771275 NA
4 4 6480673 46771275
5 5 6480673 20447474
6 6 6480673 20447474
7 7 4365410 20447474
8 8 4365410 11434009
9 9 5002257 11434009
10 10 9200296 11434009