Class
Shaliq Cochran
2022-10-05
R Markdown
This is how I went about doing assignment #2
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my_path <- "U:/" setwd(my_path) library(ggplot2) library(httr) library(lubridate) library(dplyr) library(data.table) df <- fread("gamesgithub.csv") cols_to_use <- c("season", "away_team", "away_score","home_team", "home_score") df <- subset(df, select = cols_to_use) df$winner <- ifelse(df$away_score > df$ home_score, df$away_team, df$home_team) df$loser <- ifelse(df$away_score > df$ home_score, df$home_team, df$away_team) df <- na.omit(df) View(df) df$score_diff <- abs(df$away_score - df$home_score) winners <- df %>% mutate(team = winner)%>% group_by(season, team) %>% summarise(win_count = n(), .groups = "keep") %>% data.frame() head(winners,10)## season team win_count ## 1 1999 ARI 6 ## 2 1999 ATL 5 ## 3 1999 BAL 8 ## 4 1999 BUF 11 ## 5 1999 CAR 8 ## 6 1999 CHI 6 ## 7 1999 CIN 4 ## 8 1999 CLE 2 ## 9 1999 DAL 8 ## 10 1999 DEN 6losers <- df %>% mutate(team = loser) %>% group_by(season, team) %>% summarise(lose_count = n(), .groups = "keep") %>% data.frame() df1 <- merge(x= winners, y = losers, by=c("season", "team"), all = TRUE) df1[is.na(df1)] <- 0 table(df1$season)## ## 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 ## 31 31 31 32 32 32 32 32 32 32 32 32 32 32 32 32 ## 2015 2016 2017 2018 2019 2020 2021 2022 ## 32 32 32 32 32 32 32 32table(df1$season, df1$team)## ## ARI ATL BAL BUF CAR CHI CIN CLE DAL DEN DET GB HOU IND JAX KC LA LAC LV ## 1999 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 ## 2000 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 ## 2001 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 ## 2002 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2003 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2004 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2005 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2006 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2007 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2008 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2009 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2010 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2011 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2012 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2013 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2014 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2015 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 ## 2016 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 ## 2017 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 ## 2018 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 ## 2019 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 ## 2020 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2021 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2022 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## ## MIA MIN NE NO NYG NYJ OAK PHI PIT SD SEA SF STL TB TEN WAS ## 1999 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2000 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2001 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2002 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2003 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2004 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2005 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2006 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2007 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2008 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2009 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2010 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2011 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2012 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2013 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2014 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2015 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ## 2016 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 ## 2017 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 ## 2018 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 ## 2019 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 ## 2020 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 ## 2021 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 ## 2022 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1df2 <- df1 %>% mutate(team=recode(team, "OAK" = "LV", "SD" = "LAC", "STL" = "LA"))%>% group_by(team)%>% summarise(wins = sum(win_count), losses = sum(lose_count), .groups = 'keep') %>% mutate(total_games = wins + losses, win_pc = 100*wins/total_games, loss_pc = 100*losses,total_games)%>% data.frame() df2## team wins losses total_games win_pc loss_pc ## 1 ARI 170 212 382 44.50262 21200 ## 2 ATL 187 199 386 48.44560 19900 ## 3 BAL 235 164 399 58.89724 16400 ## 4 BUF 177 203 380 46.57895 20300 ## 5 CAR 184 203 387 47.54522 20300 ## 6 CHI 182 199 381 47.76903 19900 ## 7 CIN 170 213 383 44.38642 21300 ## 8 CLE 124 251 375 33.06667 25100 ## 9 DAL 199 185 384 51.82292 18500 ## 10 DEN 209 178 387 54.00517 17800 ## 11 DET 131 245 376 34.84043 24500 ## 12 GB 249 152 401 62.09476 15200 ## 13 HOU 144 190 334 43.11377 19000 ## 14 IND 244 156 400 61.00000 15600 ## 15 JAX 151 229 380 39.73684 22900 ## 16 KC 212 179 391 54.21995 17900 ## 17 LA 194 198 392 49.48980 19800 ## 18 LAC 192 192 384 50.00000 19200 ## 19 LV 155 226 381 40.68241 22600 ## 20 MIA 182 197 379 48.02111 19700 ## 21 MIN 198 189 387 51.16279 18900 ## 22 NE 293 121 414 70.77295 12100 ## 23 NO 218 173 391 55.75448 17300 ## 24 NYG 187 201 388 48.19588 20100 ## 25 NYJ 169 215 384 44.01042 21500 ## 26 PHI 226 173 399 56.64160 17300 ## 27 PIT 250 149 399 62.65664 14900 ## 28 SEA 225 175 400 56.25000 17500 ## 29 SF 180 209 389 46.27249 20900 ## 30 TB 184 203 387 47.54522 20300 ## 31 TEN 205 185 390 52.56410 18500 ## 32 WAS 159 221 380 41.84211 22100ggplot(df2, aes(x=team, y = wins, fill = win_pc))+ geom_bar(stat="identity")
Diffferent Area to explain
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