Slight change to parsing Ken Pom Data

[meysubb]

Looks like readr::parse_number has changed a bit. It now expects a character before converting.

I would make a PR, but I forked into a repo of mine instead of a stand-alone fork. Thanks for providing these resources!

Made a slight change to your parsing function to handle that. The solution is as follows:

process_ken_pom_sheet <- function(dat){
  dat <- dat %>%
    clean_names() %>%
    mutate(seed = str_extract(x2, "[0-9]+")) %>% # extract seed where applicable
    mutate(x2 = gsub(" [0-9]+", "", x2)) # remove seed from school name
  names(dat) <- c("rank", "team", "conf", "wins_losses",
                  "adj_EM", "adj_offensive_efficiency", "adj_offensive_efficiency_seed",
                  "adj_defensive_efficiency", "adj_defensive_efficiency_seed",
                  "adj_tempo", "adj_tempo_seed", "luck", "luck_seed", "sos_adj_em",
                  "sos_adj_em_seed", "opposing_offenses", "opposing_offenses_seed",
                  "opposing_defenses", "opposing_defenses_seed", "ncsos_adj_em",
                  "ncsos_adj_em_seed", "year", "seed")
  
  dat <- dat[-1, ] %>%
    select(rank, everything()) %>%
    filter(!is.na(rank), !rank %in% c("Rank", "Rk")) %>%
    mutate(rank = as.numeric(rank)) %>%
    mutate_all(as.character) %>% 
    mutate_at(vars(adj_EM:year), parse_number) %>%
    mutate(seed = as.numeric(seed))
  dat
}

[sfirke]

thanks for pointing this out! I must have fixed this on my other machine and not pushed the changes? Or else... maybe it failed and I didn't notice and I made predictions using last year's KP data, hehe. I will check later and incorporate your addition if needed. Good luck on Kaggle!

[sfirke]

I don't have the bandwidth to compete this year but can merge pull requests if anyone wants to patch this or make other updates.