QUESTION that I have to solve: The winner of each match is the team who has the highest score. Each team scores 3 points for a win and 1 point for a draw and the team with the most points at the end of the season is the season winner. Write the code to compute each team’s points after the first 5 rounds of matches and the full season. Provide the results for a season in a table (two tables, one table for each set of results), ordering teams by their points (i.e. a results ladder).
So I need to compute each team Point by season. But there are 16 teams and I wanna find a smarter way rather than subset each team separately.
After calculation I will need to put each team score in an order
Here are the dataset that I already take out the season I wanna work on
This is my current solution but it is still not giving the correct answer
season2015 <- df[df$Season == 2015,]
season2015 %>%
distinct(homeTeam) #to see how many teams there are
#Subset each team
UnitedStatesChimeras <- season2015[season2015$homeTeam == "United States Chimeras",]
Durhamstrang <- season2015[season2015$homeTeam == "Durhamstrang",]
EnglandNightmares <- season2015[season2015$homeTeam == "England Nightmares",]
ScotlandSquirrels <- season2015[season2015$homeTeam == "Scotland Squirrels",]
WalesLongshots <- season2015[season2015$homeTeam == "Wales Longshots",]
IrelandGriffins <- season2015[season2015$homeTeam == "Ireland Griffins",]
FranceThestrals <- season2015[season2015$homeTeam == "France Thestrals",]
GermanyWerewolves <- season2015[season2015$homeTeam == "Germany Werewolves",]
TurkeyKnights <- season2015[season2015$homeTeam == "Turkey Knights",]
PakistanSwans <- season2015[season2015$homeTeam == "Pakistan Swans",]
MalaysiaTornadoes <- season2015[season2015$homeTeam == "Malaysia Tornadoes",]
DenmarkHorntails <- season2015[season2015$homeTeam == "Denmark Horntails",]
FranceHippogriffsFirsts <- season2015[season2015$homeTeam == "France Hippogriffs Firsts",]
NewZealandDaemons <- season2015[season2015$homeTeam == "New Zealand Daemons",]
AustraliaCenturions <- season2015[season2015$homeTeam == "Australia Centurions",]
JapanFalcons <- season2015[season2015$homeTeam == "Japan Falcons",]
#Compute the point
UnitedStatesChimeras <- sum(UnitedStatesChimeras$homePoint, UnitedStatesChimeras$awayPoint)
Durhamstrang <- sum(Durhamstrang$homePoint, Durhamstrang$awayPoint)
EnglandNightmares <- sum(EnglandNightmares$homePoint, EnglandNightmares$awayPoint)
ScotlandSquirrels <- sum(ScotlandSquirrels$homePoint, ScotlandSquirrels$awayPoint)
WalesLongshots <- sum(WalesLongshots$homePoint, WalesLongshots$awayPoint)
IrelandGriffins <- sum(IrelandGriffins$homePoint, IrelandGriffins$awayPoint)
FranceThestrals <- sum(FranceThestrals$homePoint, FranceThestrals$awayPoint)
GermanyWerewolves <- sum(GermanyWerewolves$homePoint, GermanyWerewolves$awayPoint)
TurkeyKnights <- sum(TurkeyKnights$homePoint, TurkeyKnights$awayPoint)
PakistanSwans <- sum(PakistanSwans$homePoint, PakistanSwans$awayPoint)
MalaysiaTornadoes <- sum(MalaysiaTornadoes$homePoint, MalaysiaTornadoes$awayPoint)
DenmarkHorntails <- sum(DenmarkHorntails$homePoint, DenmarkHorntails$awayPoint)
FranceHippogriffsFirsts <- sum(FranceHippogriffsFirsts$homePoint, FranceHippogriffsFirsts$awayPoint)
NewZealandDaemons <- sum(NewZealandDaemons$homePoint, NewZealandDaemons$awayPoint)
AustraliaCenturions <- sum(AustraliaCenturions$homePoint, AustraliaCenturions$awayPoint)
JapanFalcons <- sum(JapanFalcons$homePoint, JapanFalcons$awayPoint)
season2015_totalpoint <- c(UnitedStatesChimeras,
Durhamstrang,
EnglandNightmares,
ScotlandSquirrels,
WalesLongshots,
IrelandGriffins,
FranceThestrals,
GermanyWerewolves,
TurkeyKnights,
PakistanSwans,
MalaysiaTornadoes,
DenmarkHorntails,
FranceHippogriffsFirsts,
NewZealandDaemons,
AustraliaCenturions,
JapanFalcons
)
lapply(season2015_totalpoint, sort,decreasing=TRUE) #put the point of each team in order
I need a smarter, better way to do this. My method is a mess
Please help!!!
CodePudding user response：
You say there are 16 teams, but there are 22 in the dataset? Anyways, here is a way to calculate the total points per season for each team.
library(tidyverse)
df <- read_csv("competitionResults.csv") %>%
janitor::clean_names()
# Repliace OPs dataset
df <- df %>%
mutate(snitch_catcher = case_when(snitch == home_team ~ "home",
TRUE ~ "away"),
home_score = home_goals * 10,
away_score = away_goals * 10,
home_point = case_when(home_score > away_score ~ 3,
home_score == away_score ~ 1,
TRUE ~ 0),
away_point = case_when(away_score > home_score ~ 3,
away_score == home_score ~ 1,
TRUE ~ 0),
match_number = 1:nrow(.))
# Points as home_team per season
home <- df %>%
group_by(team = home_team, season) %>%
summarise(home_points = sum(home_point)) %>%
arrange(team, season)
# Points as away team per season
away <- df %>%
group_by(team = away_team, season) %>%
summarise(away_points = sum(away_point)) %>%
arrange(team, season)
left_join(home, away) %>%
mutate(total_points = home_points away_points)
# A tibble: 174 x 5
# Groups: team [22]
team season home_points away_points total_points
<chr> <dbl> <dbl> <dbl> <dbl>
1 Australia Centurions 2012 21 22 43
2 Australia Centurions 2013 26 13 39
3 Australia Centurions 2014 18 10 28
4 Australia Centurions 2015 24 22 46
5 Australia Centurions 2016 28 28 56
6 Australia Centurions 2017 24 23 47
7 Australia Centurions 2018 34 24 58
8 Australia Centurions 2019 37 29 66
9 Australia Centurions 2020 32 37 69
10 Australia Centurions 2021 26 32 58
11 China Nifflers 2021 28 46 74
12 Denmark Horntails 2012 17 15 32
13 Denmark Horntails 2013 19 12 31
14 Denmark Horntails 2014 30 14 44
15 Denmark Horntails 2015 24 21 45
16 Denmark Horntails 2016 15 18 33
17 Denmark Horntails 2017 19 15 34
18 Denmark Horntails 2018 26 35 61
19 Denmark Horntails 2019 28 19 47
20 Denmark Horntails 2020 34 27 61
21 Denmark Horntails 2021 30 33 63
22 Durhamstrang 2012 21 24 45
23 Durhamstrang 2013 18 25 43
24 Durhamstrang 2014 31 26 57
25 Durhamstrang 2015 22 15 37
26 Durhamstrang 2016 28 27 55
27 Durhamstrang 2017 20 13 33
28 Durhamstrang 2018 36 27 63
29 Durhamstrang 2019 19 29 48
30 Durhamstrang 2020 27 26 53
# ... with 144 more rows