hoang 2023-03-18
library(tidyverse)
library (readxl)
library(gridExtra)
library(ggrepel)
getwd()## [1] "C:/Users/hoang/Documents/Data Related/Data Projects/World Cup/World+Cup"
#GGPLOT Theme Setting
#FIRST STEP: READING AND CLEANING DATA
#1 WC Events, Date, Time, Location
WC_Events_uncleaned <- read_csv("data/world_cups.csv",col_names= TRUE)## Rows: 22 Columns: 9
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Host Country, Winner, Runners-Up, Third, Fourth
## dbl (4): Year, Goals Scored, Qualified Teams, Matches Played
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(WC_Events_uncleaned)## # A tibble: 6 × 9
## Year `Host Country` Winner `Runners-Up` Third Fourth `Goals Scored` `Qualified Teams` `Matches Played`
## <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1930 Uruguay Uruguay Argentina USA Yugoslavia 70 13 18
## 2 1934 Italy Italy Czechoslovakia Germany Austria 70 16 17
## 3 1938 France Italy Hungary Brazil Sweden 84 15 18
## 4 1950 Brazil Uruguay Brazil Sweden Spain 88 13 22
## 5 1954 Switzerland Germany FR Hungary Austria Uruguay 140 16 26
## 6 1958 Sweden Brazil Sweden France Germany FR 126 16 35
#remove white space between column name
names(WC_Events_uncleaned) <- gsub(" ", "",colnames(WC_Events_uncleaned))
#pivot data to longer
WC_Events <- WC_Events_uncleaned %>% pivot_longer(c(Winner, `Runners-Up`, Third, Fourth), names_to = "Standing", values_to = "Country")
#2 WC Matches
#The wC_Matches had not included results for WC 2022 joining the data with another one to retrieve the newest 2022 data
WC_Matches2022 <- read_csv("data/2022_world_cup_matches.csv", col_names = TRUE)## Rows: 64 Columns: 8
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Stage, Home Team, Away Team, Win Condition
## dbl (2): ID, Year
## lgl (1): Host Team
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
WC_MatchesB42022 <- read_csv("data/B42022_world_cup_matches.csv", col_names = TRUE)## Rows: 900 Columns: 10
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Stage, Home Team, Away Team, Win Condition
## dbl (4): ID, Year, Home Goals, Away Goals
## lgl (1): Host Team
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(WC_Matches2022)## # A tibble: 6 × 8
## ID Year Date Stage `Home Team` `Away Team` `Win Condition` `Host Team`
## <dbl> <dbl> <date> <chr> <chr> <chr> <chr> <lgl>
## 1 1 2022 2022-11-20 Group stage Qatar Ecuador <NA> TRUE
## 2 2 2022 2022-11-21 Group stage Senegal Netherlands <NA> FALSE
## 3 3 2022 2022-11-21 Group stage England Iran <NA> FALSE
## 4 4 2022 2022-11-21 Group stage United States Wales <NA> FALSE
## 5 5 2022 2022-11-22 Group stage France Australia <NA> FALSE
## 6 6 2022 2022-11-22 Group stage Denmark Tunisia <NA> FALSE
#Using the temp_data to join and retrieve the readable data
temp_data <- read_csv("data/Fifa_world_cup_matches.csv", col_names = TRUE)## Rows: 64 Columns: 88
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): team1, team2, possession team1, possession team2, possession in contest, date, category
## dbl (80): number of goals team1, number of goals team2, total attempts team1, total attempts team2, conceded team1, conceded team2, goal inside the penalty area team1, goal inside ...
## time (1): hour
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#Cleaning temp data to join converting data into lower case
temp_data$team1 <- str_to_title(tolower(temp_data$team1))
temp_data$team2 <- str_to_title(tolower(temp_data$team2))
#Joining and removing non-essential columns
WC_Matches2022 <- WC_Matches2022 %>% left_join(temp_data, by =c("Home Team"= "team1", "Away Team" = "team2")) %>% select(1:8, 12:13)
#Now Let's see what column this data have so we can rename column and do some cleaning
head(WC_Matches2022)## # A tibble: 6 × 10
## ID Year Date Stage `Home Team` `Away Team` `Win Condition` `Host Team` `number of goals team1` `number of goals team2`
## <dbl> <dbl> <date> <chr> <chr> <chr> <chr> <lgl> <dbl> <dbl>
## 1 1 2022 2022-11-20 Group stage Qatar Ecuador <NA> TRUE 0 2
## 2 2 2022 2022-11-21 Group stage Senegal Netherlands <NA> FALSE 0 2
## 3 3 2022 2022-11-21 Group stage England Iran <NA> FALSE 6 2
## 4 4 2022 2022-11-21 Group stage United States Wales <NA> FALSE 1 1
## 5 5 2022 2022-11-22 Group stage France Australia <NA> FALSE 4 1
## 6 6 2022 2022-11-22 Group stage Denmark Tunisia <NA> FALSE 0 0
#renaming column and relocate column and changing column type for union - here we can join Home goals with numberofgoalteam 1 and Away Goals with numberofgoalteam2
WC_Matches2022 <- WC_Matches2022 %>% rename('Home Goals' = 'number of goals team1', 'Away Goals' = 'number of goals team2') %>% relocate('Home Goals', .after = 'Home Team' )
WC_Matches2022 <- relocate(WC_Matches2022, 'Away Goals', .after = 'Away Team')
WC_Matches2022$`Home Goals` <- as.double(WC_Matches2022$`Home Goals`)
#Now that the data is sharing the same format we can union
WC_Matches <- union_all(WC_MatchesB42022, WC_Matches2022)
#removing space between column
names(WC_Matches) <- gsub(" ", "",colnames(WC_Matches))
#let's take a look at the clean version
head(WC_Matches)## # A tibble: 6 × 10
## ID Year Date Stage HomeTeam HomeGoals AwayGoals AwayTeam WinCondition HostTeam
## <dbl> <dbl> <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <lgl>
## 1 1 1930 1930-07-13 Group stage France 4 1 Mexico <NA> FALSE
## 2 2 1930 1930-07-13 Group stage United States 3 0 Belgium <NA> FALSE
## 3 3 1930 1930-07-14 Group stage Yugoslavia 2 1 Brazil <NA> FALSE
## 4 4 1930 1930-07-14 Group stage Romania 3 1 Peru <NA> FALSE
## 5 5 1930 1930-07-15 Group stage Argentina 1 0 France <NA> FALSE
## 6 6 1930 1930-07-16 Group stage Chile 3 0 Mexico <NA> FALSE
#3 International Matches - same logic
International_Matches <- read_csv("data/International_Matches.csv", col_names = TRUE)## Rows: 17769 Columns: 9
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Tournament, Home Team, Away Team, Win Conditions
## dbl (3): ID, Home Goals, Away Goals
## lgl (1): Home Stadium
## date (1): Date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
names(International_Matches) <- gsub(" ", "",colnames(International_Matches))
head(International_Matches)## # A tibble: 6 × 9
## ID Tournament Date HomeTeam HomeGoals AwayGoals AwayTeam WinConditions HomeStadium
## <dbl> <chr> <date> <chr> <dbl> <dbl> <chr> <chr> <lgl>
## 1 1 Friendly 1872-11-30 Scotland 0 0 England <NA> TRUE
## 2 2 Friendly 1873-03-08 England 4 2 Scotland <NA> TRUE
## 3 3 Friendly 1874-03-07 Scotland 2 1 England <NA> TRUE
## 4 4 Friendly 1875-03-06 England 2 2 Scotland <NA> TRUE
## 5 5 Friendly 1876-03-04 Scotland 3 0 England <NA> TRUE
## 6 6 Friendly 1876-03-25 Scotland 4 0 Wales <NA> TRUE
#4 World cup Squads and Group
WC_Groups <- read_csv("data/2022_world_cup_groups.csv", col_names = TRUE)## Rows: 32 Columns: 4
## ── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Group, Team, Final_Standing
## dbl (1): FIFA Ranking
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
names(WC_Groups) <- gsub(" ", "",colnames(WC_Groups))
head(WC_Groups)## # A tibble: 6 × 4
## Group Team FIFARanking Final_Standing
## <chr> <chr> <dbl> <chr>
## 1 A Qatar 50 Group Stage
## 2 A Ecuador 44 Group Stage
## 3 A Senegal 18 Top 16
## 4 A Netherlands 8 Top 8
## 5 B England 5 Top 8
## 6 B Iran 20 Group Stage
#Moving on to the analysis steps, some of the insights we looking to answer#1. Host Country Performance:
#Select the host country matches only
Host_Country_Matches <- filter(WC_Matches, HostTeam == TRUE)
head(Host_Country_Matches)## # A tibble: 6 × 10
## ID Year Date Stage HomeTeam HomeGoals AwayGoals AwayTeam WinCondition HostTeam
## <dbl> <dbl> <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <lgl>
## 1 9 1930 1930-07-18 Group stage Uruguay 1 0 Peru <NA> TRUE
## 2 14 1930 1930-07-21 Group stage Uruguay 4 0 Romania <NA> TRUE
## 3 17 1930 1930-07-27 Semi-finals Uruguay 6 1 Yugoslavia <NA> TRUE
## 4 18 1930 1930-07-30 Final Uruguay 4 2 Argentina <NA> TRUE
## 5 25 1934 1934-05-27 Round of 16 Italy 7 1 United States <NA> TRUE
## 6 29 1934 1934-05-31 Quarter-finals Italy 1 1 Spain <NA> TRUE
#We use this data to find the furthest round that the host country make to
Host_Country_Best_Stage <- Host_Country_Matches %>% group_by(Year) %>% top_n(1,Date)
head(Host_Country_Best_Stage)## # A tibble: 6 × 10
## # Groups: Year [6]
## ID Year Date Stage HomeTeam HomeGoals AwayGoals AwayTeam WinCondition HostTeam
## <dbl> <dbl> <date> <chr> <chr> <dbl> <dbl> <chr> <chr> <lgl>
## 1 18 1930 1930-07-30 Final Uruguay 4 2 Argentina <NA> TRUE
## 2 35 1934 1934-06-10 Final Italy 2 1 Czechoslovakia Extra time TRUE
## 3 48 1938 1938-06-12 Quarter-finals France 1 3 Italy <NA> TRUE
## 4 75 1950 1950-07-16 Final round Uruguay 2 1 Brazil <NA> TRUE
## 5 95 1954 1954-06-26 Quarter-finals Switzerland 5 7 Austria <NA> TRUE
## 6 136 1958 1958-06-29 Final Sweden 2 5 Brazil <NA> TRUE
Host_Country_Best_Stage$Stage <- fct_recode(Host_Country_Best_Stage$Stage, "Final Round" ="Final", "Group Stage" = "Final round", "Group Stage" = "Second group stage", "Group Stage"="Group stage", "Quarter-final Round" = "Quarter-finals", "Round of 16" = "Round of 16", "Third-Fourth Round"= "Third place")
#We create a new column in that indicate whether the host country could make to the top 4 that year
WC_Events_Host_Standing<- WC_Events %>%
group_by(Year) %>%
mutate(
HostFinalStanding = if_else(
HostCountry %in% head(Country, 4),
Standing,
"Out Top 4")) %>%
filter(HostCountry== Country | HostFinalStanding =="Out Top 4") %>% select(-Country, -Standing) %>% distinct()
head(WC_Events_Host_Standing)## # A tibble: 6 × 6
## # Groups: Year [6]
## Year HostCountry GoalsScored QualifiedTeams MatchesPlayed HostFinalStanding
## <dbl> <chr> <dbl> <dbl> <dbl> <chr>
## 1 1930 Uruguay 70 13 18 Winner
## 2 1934 Italy 70 16 17 Winner
## 3 1938 France 84 15 18 Out Top 4
## 4 1950 Brazil 88 13 22 Runners-Up
## 5 1954 Switzerland 140 16 26 Out Top 4
## 6 1958 Sweden 126 16 35 Runners-Up
#In order to see host performance over time, create a numeric column that numerises the Host Final Standing in each WC
Host_Country_Data <- WC_Events_Host_Standing %>%
left_join(Host_Country_Best_Stage, by = "Year")
Host_Country_Data <- Host_Country_Data %>%
mutate(HostFinalStandingDetailed = case_when(HostFinalStanding== "Out Top 4" ~ (case_match(as.character(Stage),"Quarter-finals" ~ "Top 8", "Round of 16" ~ "Top 16", "Final round"~ "Top 4", .default = "Group Stage")), .default = HostFinalStanding))
Host_Country_Data <- Host_Country_Data %>%
mutate(HostFinalStandingNumber = case_match(HostFinalStandingDetailed, "Winner"~ 6,"Runners-Up" ~ 5, "Third" ~ 4, "Fourth" ~ 3,"Top 8" ~ 2, "Top 16"~1, .default=0))
#Let's graph the host country performance overtime
Host_Country_Performance_Chart <- ggplot(data = Host_Country_Data,mapping = aes( x = Year, y = HostFinalStandingNumber)) + geom_line(color = "red", size = 1, alpha = 0.5) + geom_text(aes(label = str_c(HostCountry,"\n", HostFinalStandingDetailed)), color = "black", vjust =0, size = 3) + theme(legend.position = "none") + ggtitle("Host Country Performance Over Time") + scale_x_continuous(limits = c(1930, 2022), breaks = seq(1930,2022,4)) + theme(axis.title.y = element_blank(),axis.text.y = element_blank(), legend.position = "none")
ggsave("charts/Host_Country_Performance_Overtime.png", plot = Host_Country_Performance_Chart, width = 8, height = 6, units = "in")
Host_Country_Performance_Chart
#Now Let's see how many times the host countries make it to each round in every WC
Host_Data_Summary <- fct_count(Host_Country_Data$HostFinalStandingDetailed) %>% rename(Standing = "f", Times = "n")
Host_Data_Summary$Standing <- fct_relevel (Host_Data_Summary$Standing,"Winner", "Runners-Up", "Third", "Fourth", "Top 8","Top 16", "Group Stage")## Warning: 1 unknown level in `f`: Top 8
Host_Country_Performance_Summarize <- ggplot(data = Host_Data_Summary, aes(x = Standing, y = Times)) +
geom_bar(stat = "identity", aes(fill = Times), color = "black") +
scale_fill_gradient(low = "grey", high = "red") +
ggtitle("Host Country Performance Summary") +
xlab("") +
geom_text(aes(label = Times), position = position_stack(vjust = 0.8), fontface = "bold") +
guides(fill = "none")
ggsave("charts/Host_Country_Performance_Summarize.png", plot = Host_Country_Performance_Summarize, width = 8, height = 6, units = "in")
Host_Country_Performance_Summarize
#Next let's look at the Average Goal Scored and Average Goal Concede by each Host Country
Summarised_HostPerformance <- WC_Matches %>%
filter(HostTeam==TRUE) %>%
group_by(Year) %>%
summarise(
Average_Goal_Scored = mean(HomeGoals),
Average_Goal_Conceded = mean(AwayGoals))
Summarised_QatarPerformance <- filter(Summarised_HostPerformance, Year == 2022 )
Qatar_Average_Goal_Scored <- Summarised_QatarPerformance$Average_Goal_Scored
Qatar_Average_Goal_Conceded <- Summarised_QatarPerformance$Average_Goal_Conceded
# Average Goal Scored Chart
Average_Goal_Scored <- ggplot(data = Summarised_HostPerformance, aes(x = Year, y = Average_Goal_Scored)) +
geom_line(color = "red", size = 1, alpha = 0.5) +
ggtitle("Host Country Average Goal Scored Per Match") +
theme(axis.title.y = element_text(size = 12), axis.text.y = element_text(size = 10)) +
scale_x_continuous(limits = c(1930, 2022), breaks = seq(1930, 2022, 4)) +
scale_y_continuous(breaks = seq(0, max(Summarised_HostPerformance$Average_Goal_Scored), 0.5)) +
ylab("Average Goals") +
xlab("Year") +
geom_hline(aes(yintercept = Qatar_Average_Goal_Scored, linetype = "Average Goals Scored"),
color = "red", linetype = "dashed") +
scale_linetype_manual(values=c("dashed"), guide = "none") +
geom_label(aes(x = max(Year), y = Qatar_Average_Goal_Scored, label = "Qatar"),
color = "black", size = 4, vjust = 1, hjust = 0.2) +
geom_text(aes(label = round(Average_Goal_Scored, 1)), nudge_y = 0.1, hjust = -0.2, size = 3.5) +
theme(legend.position = "none")
ggsave("charts/Average_Goal_Scored_byHost.png", plot = Average_Goal_Scored, width = 8, height = 6, units = "in")
Average_Goal_Scored
# Chart with only Average Goal Conceded
Average_Goal_Conceded <- ggplot(data = Summarised_HostPerformance, aes(x = Year, y = Average_Goal_Conceded)) +
geom_line(color = "blue", size = 1, alpha = 0.5) +
ggtitle("Host Country Average Goal Conceded Per Match") +
theme(axis.title.y = element_text(size = 12), axis.text.y = element_text(size = 10)) +
scale_x_continuous(limits = c(1930, 2022), breaks = seq(1930, 2022, 4)) +
scale_y_continuous(breaks = seq(0, max(Summarised_HostPerformance$Average_Goal_Conceded), 0.5)) +
ylab("Average Goals Conceded") +
xlab("Year") +
geom_hline(aes(yintercept = Qatar_Average_Goal_Conceded, linetype = "Average Goals Conceded"),
color = "blue", linetype = "dashed") +
scale_linetype_manual(values=c("dashed"), guide = "none") +
geom_label(aes(x = max(Year), y = Qatar_Average_Goal_Conceded, label = "Qatar"),
color = "black", size = 4, vjust = 0.1, hjust = 0.2) +
geom_text(aes(label = round(Average_Goal_Conceded, 1)), nudge_y = -0.1, hjust = -0.2, size = 3.5) +
theme(legend.position = "none")
ggsave("charts/Average_Goal_Conceded_byHost.png", plot = Average_Goal_Conceded, width = 8, height = 6, units = "in")
Average_Goal_Conceded
#2 Based on recent form and historical dominance which countries under performed and over performed
#Manipulate data to see historical dominance
head(WC_Events)## # A tibble: 6 × 7
## Year HostCountry GoalsScored QualifiedTeams MatchesPlayed Standing Country
## <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1930 Uruguay 70 13 18 Winner Uruguay
## 2 1930 Uruguay 70 13 18 Runners-Up Argentina
## 3 1930 Uruguay 70 13 18 Third USA
## 4 1930 Uruguay 70 13 18 Fourth Yugoslavia
## 5 1934 Italy 70 16 17 Winner Italy
## 6 1934 Italy 70 16 17 Runners-Up Czechoslovakia
WC_dominance_summary <- WC_Events %>%
group_by(HostCountry, Standing) %>%
count() %>%
pivot_wider(names_from = Standing, values_from = n, values_fill = 0) %>% #transform this column values into multiple columns that count the number of times in top 4
rename(Winner = "Winner", `Runners-Up` = "Runners-Up", Third = "Third", Fourth = "Fourth")
WC_dominance_summary <- WC_dominance_summary %>% mutate(Times_in_Top4 = rowSums(across(Winner:Fourth)))
head(WC_dominance_summary)## # A tibble: 6 × 6
## # Groups: HostCountry [6]
## HostCountry Fourth `Runners-Up` Third Winner Times_in_Top4
## <chr> <int> <int> <int> <int> <dbl>
## 1 Argentina 1 1 1 1 4
## 2 Brazil 2 2 2 2 8
## 3 Chile 1 1 1 1 4
## 4 England 1 1 1 1 4
## 5 France 2 2 2 2 8
## 6 Germany 2 2 2 2 8
#Let's graph the country that dominate in WC and their achievement:
WC_dominants <- WC_dominance_summary %>%
pivot_longer(cols = -c(HostCountry,Times_in_Top4), names_to = "Place", values_to = "Sum") %>%
mutate(Place = factor(Place, levels = c("Winner", "Runners-Up", "Third", "Fourth"))) %>%
mutate(Country = fct_reorder(HostCountry, Sum, sum)) %>%
ggplot(aes(x = Country, y = Sum, fill = Place)) +
geom_col(position = "stack", color = "black") +
geom_text(aes(label = ifelse(Sum > 0, Sum, "")), position = position_stack(vjust = 0.5), size = 3) +
coord_flip() +
scale_fill_manual(values = c("#5DA5DA", "#FAA43A", "#60BD68", "#F17CB0")) +
labs(x = "", y = "Performances", fill = "") +
ggtitle("Top Performers in World Cup") +
theme_minimal() +
scale_y_continuous(limits = c(0, 12), breaks = seq(0,12,2))
ggsave("charts/WC_dominants.png", plot = WC_dominants, width = 8, height = 6, units = "in")
WC_dominants
#Moving on to each country recent competition form, we use the International Matches data
head(International_Matches)## # A tibble: 6 × 9
## ID Tournament Date HomeTeam HomeGoals AwayGoals AwayTeam WinConditions HomeStadium
## <dbl> <chr> <date> <chr> <dbl> <dbl> <chr> <chr> <lgl>
## 1 1 Friendly 1872-11-30 Scotland 0 0 England <NA> TRUE
## 2 2 Friendly 1873-03-08 England 4 2 Scotland <NA> TRUE
## 3 3 Friendly 1874-03-07 Scotland 2 1 England <NA> TRUE
## 4 4 Friendly 1875-03-06 England 2 2 Scotland <NA> TRUE
## 5 5 Friendly 1876-03-04 Scotland 3 0 England <NA> TRUE
## 6 6 Friendly 1876-03-25 Scotland 4 0 Wales <NA> TRUE
#Data Transformation
International_Matches_Transformed <-
International_Matches %>% pivot_longer(cols = c(HomeTeam, AwayTeam), names_to = "Home_Away", values_to = "Country") %>% #pivoting Home Team and Away Team to one column only
mutate(Goal_Scored = ifelse (Home_Away == "HomeTeam", HomeGoals, AwayGoals)) %>% #Cleaning columns with conditions
filter( Country %in% (WC_Groups$Team)) #keep countries in the WC team only
#Let's evaluate the recent form by keeping the only top 5 most recent matches for each country:
International_Matches_Transformed_latest5 <- International_Matches_Transformed %>%
arrange(Country, Date) %>% # sort by Country and Date
group_by(Country) %>%
slice_tail(n = 5) # keep only the last 5 matches for each Country
#Based on the goal scored of home team and away team we can determine the result of the matches:
International_Matches_Transformed_latest5 <- International_Matches_Transformed_latest5 %>%
mutate(Result = case_when(
Home_Away == "HomeTeam" & HomeGoals > AwayGoals ~ "Win",
Home_Away == "HomeTeam" & HomeGoals < AwayGoals ~ "Loss",
Home_Away == "AwayTeam" & HomeGoals > AwayGoals ~ "Loss",
Home_Away == "AwayTeam" & HomeGoals < AwayGoals ~ "Win",
TRUE ~ "Draw"
))
#Using that result we assigned 3 points to a win, 1 point to a draw and 0 point to a loss for a country, the sum of point will be used to determine the recent form of a WC participants
International_Matches_Form <- International_Matches_Transformed_latest5 %>% select(Country, Result) %>%
mutate(Result_numeric = case_match(Result, "Win" ~ 3, "Draw" ~ 1, "Loss" ~ 0)) %>%
group_by(Country) %>%
summarise(Total_point = sum(Result_numeric)) %>%
mutate(Form_B4_WC = case_when(Total_point>10 ~ "A", Total_point <5 ~ "C", .default = "B"))%>%
arrange(desc(Total_point))
#Let's view the Match form data after all these steps so far
head(International_Matches_Form)## # A tibble: 6 × 3
## Country Total_point Form_B4_WC
## <chr> <dbl> <chr>
## 1 Brazil 15 A
## 2 Argentina 13 A
## 3 Croatia 13 A
## 4 Netherlands 13 A
## 5 Senegal 13 A
## 6 Serbia 13 A
WC_participants_recentform <-ggplot(data = International_Matches_Form, aes(x = Total_point, y = reorder(Country,Total_point), color = Form_B4_WC)) +
geom_point(size = 4) +
scale_color_manual(values = c("green", "blue", "red"), name = "Form_B4_WC") +
labs(x = "Total Points", y = "Country", title = "Team Performance Summary Based on last 5 matches") +
theme_bw() +
scale_x_continuous(breaks = seq(0, 15, by = 2))
ggsave("charts/WC_participants_recentform.png", plot = WC_participants_recentform, width = 8, height = 6, units = "in")
WC_participants_recentform
#Create a new tibble that join recent forms(numerical) to WC 2022 Countries
WC2022_Performance <-WC_Groups %>% left_join(International_Matches_Form, by = c("Team" = "Country")) %>%
left_join(WC_dominance_summary, by = c("Team" = "HostCountry")) %>%
replace_na(list(Times_in_Top4 = 0)) %>%
arrange(Team) %>%
mutate(Final_Standing_numeric = case_match(Final_Standing,"Winner" ~ 1, "Runners-Up"~2,"Third Place" ~ 3, "Fourth Place" ~ 4, "Top 8" ~ 5, "Top 16" ~ 6, .default = 7)) # numerise the final standing of WC2022 participants to display on graph
WC2022_Performance$Final_Standing <- factor(WC2022_Performance$Final_Standing, levels = c("Group Stage", "Top 16", "Top 8", "Fourth Place", "Third Place", "Runners-Up", "Winner"))
WC_participants_Standing_vsForm <-ggplot(data = WC2022_Performance, aes(x = Final_Standing, y = Times_in_Top4, label = Team)) +
geom_point(size =2 , color = "black") +
ggrepel::geom_text_repel(aes(color = Form_B4_WC), size = 4, show.legend = TRUE, max.overlaps = Inf, nudge_x = 0.2, nudge_y = 0.2) +
scale_color_manual(values = c("green", "blue", "red"), name = "Form_B4_WC") +
labs(x = "Final Standing", y = "Times in Top 4", title = "Team Performance Summary Based on last 5 matches") +
theme_bw() +
scale_x_discrete(limits = c("Group Stage", "Top 16", "Top 8", "Fourth Place", "Third Place", "Runners-Up", "Winner"))
ggsave("charts/WC_participants_Standing_vsForm.png", plot = WC_participants_Standing_vsForm, width = 8, height = 6, units = "in")
WC_participants_Standing_vsForm
#3 Argentina 2022 road to glory compare to France 2018
#Let's filter for Argentina and Frances matches only
WC_Matches_Transformed <-
WC_Matches %>% pivot_longer(cols = c(HomeTeam, AwayTeam), names_to = "Home_Away", values_to = "Country") %>%
mutate(Goal_Scored = ifelse (Home_Away == "HomeTeam", HomeGoals, AwayGoals)) %>%
filter((Country == "France" & Year == 2018) | (Country =="Argentina" & Year == 2022))
#See above for transformation logic
WC_Matches_Transformed <- WC_Matches_Transformed %>%
mutate(Result = case_when(
Home_Away == "HomeTeam" & HomeGoals > AwayGoals ~ "Win",
Home_Away == "HomeTeam" & HomeGoals < AwayGoals ~ "Loss",
Home_Away == "AwayTeam" & HomeGoals > AwayGoals ~ "Loss",
Home_Away == "AwayTeam" & HomeGoals < AwayGoals ~ "Win",
TRUE ~ "Draw")) %>%
mutate(Result_numeric_streak = case_match(Result, "Win" ~ 1, "Draw" ~ 0, "Loss" ~ -1)) %>%
arrange(Country,Date) %>%
group_by(Country) %>%
mutate(match_index = row_number())
# plot Year vs. Result_Numeric with Country as the grouping variable
Road_to_Glory <- ggplot(WC_Matches_Transformed, aes(x = match_index, y = Result_numeric_streak, color = Country)) +
geom_line(size = 1) + geom_point(size =2) +
geom_text(aes(label = paste(Result, ifelse(is.na(WinCondition),"",WinCondition), sep = "\n")),
hjust = -0.1, size = 3, show.legend = FALSE, color = "black") +
labs(title = "Road to glory Argentina 2022 vs 2018 France",
x = "Match Index",
y = "Result",
color = "Country") +
scale_color_manual(values = c("#75AADB", "darkblue")) +
facet_wrap(~Country, ncol = 2) +
theme(axis.text.y = element_blank())
ggsave("charts/Road_to_Glory.png", plot = Road_to_Glory, width = 8, height = 6, units = "in")
Road_to_Glory
#Let's compare the number of Goal Scored and Conceded between the two Countries
head(WC_Matches_Transformed)## # A tibble: 6 × 14
## # Groups: Country [1]
## ID Year Date Stage HomeGoals AwayGoals WinCondition HostTeam Home_Away Country Goal_Scored Result Result_numeric_streak match_index
## <dbl> <dbl> <date> <chr> <dbl> <dbl> <chr> <lgl> <chr> <chr> <dbl> <chr> <dbl> <int>
## 1 8 2022 2022-11-22 Group stage 1 2 <NA> FALSE HomeTeam Argentina 1 Loss -1 1
## 2 24 2022 2022-11-26 Group stage 2 0 <NA> FALSE HomeTeam Argentina 2 Win 1 2
## 3 39 2022 2022-11-30 Group stage 0 2 <NA> FALSE AwayTeam Argentina 2 Win 1 3
## 4 50 2022 2022-12-03 Round of 16 2 1 <NA> FALSE HomeTeam Argentina 2 Win 1 4
## 5 58 2022 2022-12-09 Quarter-finals 2 2 Argentina win on penalties (3 -4) FALSE AwayTeam Argentina 2 Draw 0 5
## 6 61 2022 2022-12-13 Semi-finals 3 0 <NA> FALSE HomeTeam Argentina 3 Win 1 6
Total_Goal_Scored <- WC_Matches_Transformed %>%
group_by(Country) %>%
summarize(Total_Goals_Scored = sum(ifelse(Home_Away == "HomeTeam", HomeGoals, AwayGoals)),
Total_Goals_Conceded = sum(ifelse(Home_Away == "HomeTeam", AwayGoals, HomeGoals))) %>%
ggplot(aes(x = Country, y = Total_Goals_Scored, fill = Country)) +
geom_col(position = "dodge") +
geom_text(aes(label=Total_Goals_Scored), position=position_dodge(width=0.9), vjust=-0.5) +
facet_wrap(~ .) +
labs(title = "Total Goals Scored",
x = NULL, y = NULL) +
guides(fill=FALSE) +
scale_fill_manual(values = c("#75AADB", "darkblue")) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))
ggsave("charts/Total_Goal_Scored_FranceArgentina.png", plot = Total_Goal_Scored, width = 8, height = 6, units = "in")
Total_Goal_Conceded <- WC_Matches_Transformed %>%
group_by(Country) %>%
summarize(Total_Goals_Scored = sum(ifelse(Home_Away == "HomeTeam", HomeGoals, AwayGoals)),
Total_Goals_Conceded = sum(ifelse(Home_Away == "HomeTeam", AwayGoals, HomeGoals))) %>%
ggplot(aes(x = Country, y = Total_Goals_Conceded, fill = Country)) +
geom_col(position = "dodge") +
geom_text(aes(label=Total_Goals_Conceded), position=position_dodge(width=0.9), vjust=-0.5) +
facet_wrap(~ .) +
labs(title = "Total Goals Conceded",
x = NULL, y = NULL) +
guides(fill=FALSE) +
scale_fill_manual(values = c("#75AADB", "darkblue")) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))
ggsave("charts/Total_Goal_Conceded_FranceArgentina.png", plot = Total_Goal_Conceded , width = 8, height = 6, units = "in")
grid.arrange(Total_Goal_Scored + scale_y_continuous(limits = c(0, 20), expand = c(0, 0)),
Total_Goal_Conceded + scale_y_continuous(limits = c(0, 20), expand = c(0, 0)),
ncol = 2, widths = c(4, 4), top = "Argentina vs France World Cup Stats")
#rmarkdown::render("R_Script.R", "github_document")