Library(tidyverse) library(knitr) library(brms) # DATA LOADING & PROCESSING #=# civ_id % left_join(civ_id, by = c( "civilization" = "id")) %>% left_join(map_id, by = c( "map_id" = "map_id")) %>% group_by(id) %>% mutate( player = seq_along(civ)) %>% filter( sum(player) = 3, victory >= 0) %>% ungroup() %>% mutate( elo = case_when( average_elo 1650 ~ ">1650"), elo = fct_relevel(elo, c( "% count(map) %>% arrange( desc(n)) %>% mutate( prop = n / sum(n), cumprop = cumsum(prop)) %>% slice( 1 : 15) %>% pull(map) # convert to a wide format # df1 = civ*civ*elo # elo analysis df1 % ungroup() %>% select(id, elo, civ, victory, player) %>% pivot_wider( names_from = player, values_from = c(victory, civ)) %>% group_by(civ_1, civ_2, elo) %>% summarise( victory_1 = sum(victory_1), victory_2 = sum(victory_2), count = n() ) # df2 = civ*civ*top15map # maptype analysis df2 % ungroup() %>% filter(map %in% topmap) %>% select(id, map, civ, victory, player) %>% pivot_wider( names_from = player, values_from = c(victory, civ)) %>% group_by(civ_1, civ_2, map) %>% summarise( victory_1 = sum(victory_1), victory_2 = sum(victory_2), count = n() ) # HELPER FUNCTIONS #=# Function to extract a civ v civ matchup # and combine probabilities (e.g.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |