1. Snooker screen scrape LR (incremental).R

source("common-functions.R")
current_season <- 24
# Import the reference data containing the results URLs per season per division
# Only needs updating when a new season is added
ref_data <- read_csv("Snooker-Results-pages-per-season.csv") %>%
  mutate(sport = "Snooker") %>%
  filter(Season == current_season)

# Grab every match result and the link to the match details page
results_new <- pmap_dfr(unname(ref_data), get_season_division_results)
results_old <- read_csv("New-website-match-scores.csv",
                        col_types = cols(
                          fixture_date = col_date(),
                          season = col_integer(),
                          division = col_integer(),
                          home_team = col_character(),
                          away_team = col_character(),
                          home_score = col_integer(),
                          away_score = col_integer(),
                          URLs = col_character())) %>%
  filter(!is.na(home_score))
# Update results_new to include any results from result_old that are not from
# the current season
results_new <- results_new %>%
  bind_rows(results_old %>%
              filter(!season %in% results_new$season))

# Will definitely scrape any new results, plus any old results that we didn't
# have frame details for
# new_results_to_scrape <- setdiff(results_new, results_old)
new_results_to_scrape <- results_new %>%
  anti_join(results_old, by = c("fixture_date", "season", "division",
                                "home_team", "away_team", "home_score",
                                "away_score", "URLs")) %>%
  filter(!is.na(home_score)) %>%
  select(-c(home_score, away_score))
# Read in the formerly scraped frame scores
frame_scores_old <- read_csv("New-website-frame-scores.csv",
                             col_types = cols(
                               fixture_date = col_date(),
                               season = col_integer(),
                               division = col_integer(),
                               home_team = col_character(),
                               away_team = col_character(),
                               home_player_id = col_integer(),
                               home_player_name = col_character(),
                               home_score = col_integer(),
                               away_player_id = col_integer(),
                               away_player_name = col_character(),
                               away_score = col_integer()))
# Calculate which old results have no frame scores
old_results_to_scrape <- results_old %>%
  anti_join(frame_scores_old, by = c("fixture_date", "season", "division",
                                     "home_team", "away_team")) %>%
  filter(season == current_season) %>%
  select(-c(home_score, away_score))
# Create an empty dataframe
breaks_new <- data.frame(fixture_date = as.Date(character()),
                         season = numeric(),
                         division = numeric(),
                         player_id = character(),
                         player_name = character(),
                         high_break = integer(),
                         stringsAsFactors = FALSE)
# Scrape the likely fixtures that will now have match scores
frame_scores_new <-
  pmap_dfr(unname(new_results_to_scrape %>%
                    bind_rows(old_results_to_scrape)),
           scrape_match_page)

# Combine and filter out BYEs
frame_scores_total <- rbind(frame_scores_old, frame_scores_new)
frame_scores_total <- frame_scores_total %>%
  filter(home_player_id != "" & away_player_id != "")

# Close Selenium session opened in common functions
remDr$close()

# Write the results to a CSV file for use in the ELO ranking
write_csv(frame_scores_total, "New-website-frame-scores.csv")
write_csv(results_new, "New-website-match-scores.csv")
write_csv(breaks_new, "New-website-breaks.csv", append = TRUE)