Creating a new column in RVest based on a cascading variable - r

Question

I'm writing a data scraper in using rvest which looks like this:
library(tidyverse)
library(rvest)
library(magrittr)
library(dplyr)
library(tidyr)
library(data.table)
library(zoo)
targets_url <- paste0("https://247sports.com/college/ohio-state/Season/2021-Football/Targets/")
targets <- map_df(targets_url, ~.x %>% read_html %>%
html_nodes(".ri-page__star-and-score .score , .position , .meta , .ri-page__name-link") %>%
html_text() %>%
str_trim %>%
str_split(" ") %>%
matrix(ncol = 4, byrow = T) %>%
as.data.frame)
df_structure <- apply(targets,2,as.character)
df_targets <- as.data.frame(df_structure)
You'll notice that it creates a dataframe with four variables and 53 rows.
But now go to the URL itself. You'll notice that the 53 rows correspond to certain subcategorizations: Top Target, High Choice, and Interested. Here's a picture showing an example:
What I'm trying to do is create a fifth column, which contains the subcategory. So for example, the three individuals who fall under "Top Target" will be assigned another column which lists them as "Top Target". Then the next 20 rows will have that fifth column reading as "High Choice" and so on. The reason I'm here is because I have no clue how to do that. What makes it even harder is that not every page will have the same numbers, here's an example of that. You'll see that while the picture from above only lists Top Target (3), this page now has Top Target (24). It varies for each page.
Would it be possible to alter my original script that would:
A) Creates that fifth column with the subcategory I mentioned above
B) Knows when it's suppose to switch to the next subcategory
C) Is agnostic to whatever the total number of people in each subcategory
EDITED script partially based on #Dave2e answer:
library(rvest)
library(dplyr)
library(stringr)
teams <- c("ohio-state","penn-state","michigan","michigan-state")
targets_url <- paste0("https://247sports.com/college/", teams, "/Season/2021-Football/Targets/")
# read the web page once! then extract the information requested
targets <- map_df(targets_url, ~.x %>% read_html %>%
html_nodes(".ri-page__star-and-score .score , .position , .meta , .ri-page__name-link") %>%
html_text() %>%
str_trim %>%
str_split(" ") %>%
matrix(ncol = 4, byrow = T) %>%
as.data.frame)
#find the headings and the players
list <- page %>% html_nodes("li.ri-page__list-item")
headers <- which(html_attr(list, "class") == "ri-page__list-item list-header")
#find the category
category <- list[headers] %>% html_node("b.name") %>% html_text()
#extract repeats from header
nrepeats<-as.integer(str_extract(category, "[0-9]+"))
categories <- rep(category, nrepeats)[1:nrow(targets)]
#create combined dataframe
answer <- cbind(categories, targets)

Answer 1

The headings are located at a "li" node with the class ="ri-page__list-item list-header". It is also convenient to note the heading contains the number of players underneath that heading.
This script, finds the heading nodes, extracts the number of players and then creates the vector of repeating headings to merge to the targets dataframe.
library(rvest)
library(dplyr)
library(stringr)
targets_url <- paste0("https://247sports.com/college/ohio-state/Season/2021-Football/Targets/")
# read the web page once! then extract the information requested
page <- read_html(targets_url)
targets <- page %>%
html_nodes(".ri-page__star-and-score .score , .position , .meta , .ri-page__name-link") %>%
html_text() %>%
str_trim %>%
str_split(" ") %>%
matrix(ncol = 4, byrow = T) %>%
as.data.frame
#find the headings and the players
list <- page %>% html_nodes("li.ri-page__list-item")
headers <- which(html_attr(list, "class") == "ri-page__list-item list-header")
#find the category
category <- list[headers] %>% html_node("b.name") %>% html_text()
#extract repeats from header
nrepeats<-as.integer(str_extract(category, "[0-9]+"))
categories <- rep(category, nrepeats)[1:nrow(targets)]
#create combined dataframe
answer <- cbind(categories, targets)
Update - finding the hidden data
The webpage dynamically hides some information if the list is too long. The cope can now handle that information. The code below finds the hidden JSON data (contained in a 'script' node and parses that data. It does return a list of players but not all of the same information.
#another option
#find the hidden JSON data
jsons <- page %>% html_nodes(xpath = '//*[#type ="application/ld+json"]')
allplayers <- jsonlite::fromJSON( html_text(jsons[2]))
#Similar list, provide URL to each players webpage
answer2 <- cbind(rep(category, nrepeats), allplayers$athlete)