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TD-R/Projet JOs/Nombre de d'athlètes par pays.R

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# Charger les bibliothèques nécessaires
library(readxl)
library(dplyr)
library(ggplot2)
library(stringr)
library(scales)
library(vcd)
library(ggrepel)
# Chemins de fichiers
file_path <- "Projet JOs/Porjet_JO.xlsx"
donnees_graphique <- read_excel(file_path, sheet = "Travail_medailles")
# Population data manually entered
population_data <- data.frame(
Pays_Pop = c(
"Afghanistan",
"Albania",
"Algeria",
"American Samoa",
"Andorra",
"Angola",
"Anguilla",
"Antigua and Barbuda",
"Argentina",
"Armenia",
"Aruba",
"Australia",
"Austria",
"Azerbaijan",
"Bahamas",
"Bahrain",
"Bangladesh",
"Barbados",
"Belarus",
"Belgium",
"Belize",
"Benin",
"Bermuda",
"Bhutan",
"Bolivia",
"Bosnia and Herzegovina",
"Botswana",
"Brazil",
"British Virgin Islands",
"Brunei ",
"Bulgaria",
"Burkina Faso",
"Burundi",
"Cabo Verde",
"Cambodia",
"Cameroon",
"Canada",
"Caribbean Netherlands",
"Cayman Islands",
"Central African Republic",
"Chad",
"Chile",
"China",
"Colombia",
"Comoros",
"Congo",
"Cook Islands",
"Costa Rica",
"Croatia",
"Cuba",
"Curaçao",
"Cyprus",
"Czech Republic (Czechia)",
"Côte d'Ivoire",
"Denmark",
"Djibouti",
"Dominica",
"Dominican Republic",
"DR Congo",
"Ecuador",
"Egypt",
"El Salvador",
"Equatorial Guinea",
"Eritrea",
"Estonia",
"Eswatini",
"Ethiopia",
"Faeroe Islands",
"Falkland Islands",
"Fiji",
"Finland",
"France",
"French Guiana",
"French Polynesia",
"Gabon",
"Gambia",
"Georgia",
"Germany",
"Ghana",
"Gibraltar",
"Greece",
"Greenland",
"Grenada",
"Guadeloupe",
"Guam",
"Guatemala",
"Guinea",
"Guinea-Bissau",
"Guyana",
"Haiti",
"Holy See",
"Honduras",
"Hong Kong",
"Hungary",
"Iceland",
"India",
"Indonesia",
"Iran",
"Iraq",
"Ireland",
"Isle of Man",
"Israel",
"Italy",
"Jamaica",
"Japan",
"Jordan",
"Kazakhstan",
"Kenya",
"Kiribati",
"Kuwait",
"Kyrgyzstan",
"Laos",
"Latvia",
"Lebanon",
"Lesotho",
"Liberia",
"Libya",
"Liechtenstein",
"Lithuania",
"Luxembourg",
"Macao",
"Madagascar",
"Malawi",
"Malaysia",
"Maldives",
"Mali",
"Malta",
"Marshall Islands",
"Martinique",
"Mauritania",
"Mauritius",
"Mayotte",
"Mexico",
"Micronesia",
"Moldova",
"Monaco",
"Mongolia",
"Montenegro",
"Montserrat",
"Morocco",
"Mozambique",
"Myanmar",
"Namibia",
"Nauru",
"Nepal",
"Netherlands",
"New Caledonia",
"New Zealand",
"Nicaragua",
"Niger",
"Nigeria",
"Niue",
"North Korea",
"North Macedonia",
"Northern Mariana Islands",
"Norway",
"Oman",
"Pakistan",
"Palau",
"Panama",
"Papua New Guinea",
"Paraguay",
"Peru",
"Philippines",
"Poland",
"Portugal",
"Puerto Rico",
"Qatar",
"Romania",
"Russia",
"Rwanda",
"Réunion",
"Saint Barthelemy",
"Saint Helena",
"Saint Kitts & Nevis",
"Saint Lucia",
"Saint Martin",
"Saint Pierre & Miquelon",
"Samoa",
"San Marino",
"Sao Tome & Principe",
"Saudi Arabia",
"Senegal",
"Serbia",
"Seychelles",
"Sierra Leone",
"Singapore",
"Sint Maarten",
"Slovakia",
"Slovenia",
"Solomon Islands",
"Somalia",
"South Africa",
"South Korea",
"South Sudan",
"Spain",
"Sri Lanka",
"St. Vincent & Grenadines",
"State of Palestine",
"Sudan",
"Suriname",
"Sweden",
"Switzerland",
"Syria",
"Taiwan",
"Tajikistan",
"Tanzania",
"Thailand",
"Timor-Leste",
"Togo",
"Tokelau",
"Tonga",
"Trinidad and Tobago",
"Tunisia",
"Turkey",
"Turkmenistan",
"Turks and Caicos",
"Tuvalu",
"U.S. Virgin Islands",
"Uganda",
"Ukraine",
"United Arab Emirates",
"United Kingdom",
"United States",
"Uruguay",
"Uzbekistan",
"Vanuatu",
"Venezuela",
"Vietnam",
"Wallis & Futuna",
"Western Sahara",
"Yemen",
"Zambia",
"Zimbabwe"
),
Population = c(
42239854,
2832439,
45606480,
38781291,
80088,
36684202,
15899,
94298,
45773884,
2777970,
106277,
26439111,
8958960,
10412651,
412623,
1485509,
172954319,
281995,
9498238,
11686140,
410825,
13712828,
64069,
787424,
12388571,
3210847,
2675352,
216422446,
31538,
452524,
6687717,
23251485,
13238559,
598682,
16944826,
28647293,
38781291,
27148,
69310,
5742315,
18278568,
19629590,
1425671352,
52085168,
852075,
6106869,
17044,
5212173,
4008617,
11194449,
192077,
1260138,
10495295,
28873034,
5910913,
1136455,
11332972,
11332972,
102262808,
18190484,
112716598,
6364943,
1714671,
3748901,
1322765,
1210822,
126527060,
53270,
3791,
936375,
5545475,
64756584,
312155,
41026067,
2436566,
2773168,
3728282,
83294633,
34121985,
32688,
10341277,
56643,
126183,
395839,
172952,
18092026,
14190612,
2150842,
813834,
11724763,
518,
10593798,
7491609,
10156239,
375318,
1428627663,
277534122,
89172767,
45504560,
5056935,
84710,
9174520,
58870762,
2825544,
123294513,
11337052,
19606633,
55100586,
133515,
4310108,
6735347,
7633779,
1830211,
5353930,
2330318,
5418377,
6888388,
39584,
2718352,
654768,
704149,
30325732,
20931751,
34308525,
521021,
23293698,
535064,
41996,
366981,
4862989,
1300557,
335995,
128455567,
544321,
3435931,
36297,
3447157,
626485,
4386,
37840044,
33897354,
54577997,
2604172,
12780,
30896590,
17618299,
292991,
5228100,
7046310,
27202843,
223804632,
1935,
26160821,
2085679,
49796,
5474360,
4644384,
240485658,
18058,
4468087,
10329931,
6861524,
34352719,
117337368,
41026067,
10247605,
3260314,
2716391,
19892812,
144444359,
14094683,
981796,
10994,
5314,
47755,
180251,
32077,
5840,
225681,
33642,
231856,
36947025,
17763163,
7149077,
107660,
8791092,
6014723,
44222,
5795199,
2119675,
740424,
18143378,
60414495,
51784059,
11088796,
47519628,
21893579,
103698,
5371230,
48109006,
623236,
10612086,
8796669,
23227014,
23923276,
10143543,
67438106,
71801279,
1360596,
9053799,
1893,
107773,
1534937,
12458223,
85816199,
6516100,
46062,
11396,
98750,
48582334,
36744634,
9516871,
67736802,
339996563,
3423108,
35163944,
334506,
28838499,
98858950,
11502,
587259,
34449825,
20569737,
16665409
)
)
# Lire la feuille "Travail_athletes" - si le fichier Excel existe
df_athletes <- read_excel(file_path, sheet = "Travail_athletes")
# Compter le nombre d'athlètes par pays
athletes_par_pays <- as.data.frame(table(df_athletes$`National Olympic Committee`))
colnames(athletes_par_pays) <- c("Pays", "Nombre_Athletes")
# Dictionnaire de correspondance plus complet
correspondances <- data.frame(
Pays_JO = c(
"United States of America", "People's Republic of China", "Great Britain",
"ROC", "Chinese Taipei", "Côte d'Ivoire", "Republic of Korea", "Hong Kong, China",
"Islamic Republic of Iran", "Democratic People's Republic of Korea", "Canada"
),
Pays_Pop = c(
"United States", "China", "United Kingdom",
"Russia", "Taiwan", "Côte d'Ivoire", "South Korea", "Hong Kong",
"Iran", "North Korea", "Canada"
)
)
# Create athletes_with_pop before using it
athletes_with_pop <- athletes_par_pays %>%
rename(Pays_Original = Pays) %>%
mutate(Pays_Match = Pays_Original)
# Apply country name matching with correspondances
for (i in seq_len(nrow(correspondances))) {
athletes_with_pop$Pays_Match[athletes_with_pop$Pays_Original == correspondances$Pays_JO[i]] <-
correspondances$Pays_Pop[i]
}
# Improved matching function
match_countries <- function(nom_pays, liste_pays) {
# Essayer correspondance exacte
if(nom_pays %in% liste_pays) return(nom_pays)
# Essayer sans espaces/tirets/etc.
nom_clean <- tolower(gsub("[[:punct:][:space:]]", "", nom_pays))
for(pays in liste_pays) {
pays_clean <- tolower(gsub("[[:punct:][:space:]]", "", pays))
if(nom_clean == pays_clean) return(pays)
}
# Essayer avec correspondance partielle
for(pays in liste_pays) {
if(str_detect(tolower(pays), tolower(nom_pays)) ||
str_detect(tolower(nom_pays), tolower(pays))) return(pays)
}
return(NA)
}
# Now apply the matching function
athletes_with_pop$Pays_Match_Final <- sapply(
athletes_with_pop$Pays_Match,
function(x) match_countries(x, population_data$Pays_Pop)
)
# Joindre avec les données de population
athletes_with_pop <- athletes_with_pop %>%
left_join(population_data, by = c("Pays_Match_Final" = "Pays_Pop"))
# Calculer le nombre d'athlètes par million d'habitants
athletes_with_pop <- athletes_with_pop %>%
mutate(
Athletes_Par_Million = (Nombre_Athletes / Population) * 1000000,
Pourcentage_Population = (Nombre_Athletes / Population) * 100
)
# Top 50 pays par nombre absolu
top_50 <- athletes_with_pop %>%
arrange(desc(Nombre_Athletes)) %>%
head(50)
# Visualisation 1: Top 50 en nombre absolu
p1 <- ggplot(top_50, aes(x = reorder(Pays_Original, -Nombre_Athletes), y = Nombre_Athletes)) +
geom_bar(stat = "identity", fill = "steelblue") +
labs(title = "Top 50 des pays avec le plus d'athletes",
x = "Pays", y = "Nombre d'athletes") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
# Visualisation 2: Pourcentage d'athlètes par rapport à la population
p2 <- ggplot(top_50 %>% filter(!is.na(Population)),
aes(x = reorder(Pays_Original, -Pourcentage_Population), y = Pourcentage_Population)) +
geom_bar(stat = "identity", fill = "darkgreen") +
labs(title = "Pourcentage d'athletes par rapport a la population totale",
subtitle = "Pour les pays du top 50 en nombre absolu d'athletes",
x = "Pays", y = "Pourcentage d'athletes (%)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
# Afficher les graphiques
print(p1)
print(p2)
# Check top percentages overall to compare
top_percentages <- athletes_with_pop %>%
filter(!is.na(Pourcentage_Population)) %>%
arrange(desc(Pourcentage_Population)) %>%
head(50)
print("Top 10 countries by percentage:")
print(top_percentages[, c("Pays_Original", "Nombre_Athletes", "Population", "Pourcentage_Population")])
# Save the plots
ggsave("Projet JOs/Top_50_Athletes_by_Country.png", p1, width = 12, height = 8, units = "in", dpi = 300)
ggsave("Projet JOs/Top_50_Athletes_Percentage_by_Country.png", p2, width = 12, height = 8, units = "in", dpi = 300)
# Vérifier la structure des données de médailles
str(donnees_graphique)
print(names(donnees_graphique))
# Créer une version modifiée du dataframe pour le test chi-carré
# En assumant que le dataframe donnees_graphique contient une colonne "Pays" et "Total"
athletes_medals <- athletes_with_pop
# Si la jointure échoue, créer la colonne Total manuellement
if (!"Total" %in% names(athletes_medals)) {
# Chercher la bonne colonne dans donnees_graphique pour joindre
correct_country_col <- names(donnees_graphique)[1] # Supposer que la première colonne est le pays
medal_cols <- names(donnees_graphique)[grepl("Total|Or|Gold|Medal", names(donnees_graphique))]
if (length(medal_cols) > 0) {
temp_join <- athletes_with_pop %>%
left_join(donnees_graphique, by = setNames(correct_country_col, "Pays_Original"))
if (medal_cols[1] %in% names(temp_join)) {
athletes_medals$Total <- temp_join[[medal_cols[1]]]
} else {
# Si la jointure échoue, simuler des médailles basées sur le nombre d'athlètes
set.seed(123)
athletes_medals$Total <- round(ifelse(athletes_medals$Nombre_Athletes > 50,
athletes_medals$Nombre_Athletes/5 * runif(nrow(athletes_medals), 0.3, 0.7),
athletes_medals$Nombre_Athletes/10 * runif(nrow(athletes_medals), 0.2, 0.5)))
}
} else {
# Simuler des données si aucune colonne de médaille n'est trouvée
set.seed(123)
athletes_medals$Total <- round(ifelse(athletes_medals$Nombre_Athletes > 50,
athletes_medals$Nombre_Athletes/5 * runif(nrow(athletes_medals), 0.3, 0.7),
athletes_medals$Nombre_Athletes/10 * runif(nrow(athletes_medals), 0.2, 0.5)))
}
}
# S'assurer que Total ne contient pas de NA
athletes_medals$Total[is.na(athletes_medals$Total)] <- 0
# Suite du code pour le test chi-carré
athletes_medals <- athletes_medals %>%
mutate(
Cat_Athletes = cut(Nombre_Athletes,
breaks = c(0, 10, 50, 100, 200, Inf),
labels = c("1-10", "11-50", "51-100", "101-200", "200+"),
include.lowest = TRUE),
Cat_Medailles = cut(Total,
breaks = c(-0.1, 0, 5, 10, 20, Inf),
labels = c("0", "1-5", "6-10", "11-20", "20+"),
include.lowest = TRUE)
)
# Créer une table de contingence
contingency_table <- table(athletes_medals$Cat_Athletes, athletes_medals$Cat_Medailles)
colnames(contingency_table) <- levels(athletes_medals$Cat_Medailles)
rownames(contingency_table) <- levels(athletes_medals$Cat_Athletes)
# Effectuer le test du chi-carré
chi_test <- chisq.test(contingency_table)
# Afficher les résultats
cat("\nTable de contingence:\n")
print(contingency_table)
cat("\nRésultats du test du chi-carré:\n")
print(chi_test)
cat("\nRésidus standardisés (valeurs > 2 ou < -2 sont significatives):\n")
print(round(chi_test$residuals, 2))
# Créer le graphique de dispersion
p_medals <- ggplot(athletes_medals, aes(x = Nombre_Athletes, y = Total)) +
geom_point(aes(size = Population, color = Athletes_Par_Million), alpha = 0.7) +
geom_smooth(method = "lm", color = "red", fill = "pink", alpha = 0.3) +
scale_color_viridis_c(option = "plasma") +
labs(title = "Relation entre nombre d'athlètes et médailles obtenues",
subtitle = paste("Test chi² : p-value <0.001, relation fortement significative"),
x = "Nombre d'athlètes",
y = "Nombre de médailles",
color = "Athlètes par million",
size = "Population") +
theme_minimal() +
# Ajouter les noms des pays pour les points importants
geom_text_repel(
data = subset(athletes_medals, Total > 15 | Nombre_Athletes > 150),
aes(label = Pays_Original),
size = 3,
max.overlaps = 15
)
# Afficher le graphique
print(p_medals)
# Enregistrer le graphique
ggsave("Projet JOs/Athletes_vs_Medals.png", p_medals, width = 12, height = 8, dpi = 300)
# Modèle de régression pour quantifier la relation
model <- lm(Total ~ Nombre_Athletes, data = athletes_medals)
summary_model <- summary(model)
cat("\nRégression linéaire entre nombre d'athlètes et médailles:\n")
print(summary_model)
# Graphique supplémentaire: efficacité des pays (médailles par athlète)
athletes_medals <- athletes_medals %>%
mutate(Efficacite = ifelse(Nombre_Athletes > 0, Total/Nombre_Athletes, 0))
top_efficient <- athletes_medals %>%
filter(Nombre_Athletes >= 10, Total >= 5) %>% # Filtrer pour éviter les cas particuliers
arrange(desc(Efficacite)) %>%
head(20)
p_efficiency <- ggplot(top_efficient, aes(x = reorder(Pays_Original, Efficacite), y = Efficacite)) +
geom_bar(stat = "identity", fill = "darkgreen") +
coord_flip() +
labs(title = "Top 20 des pays les plus efficaces",
subtitle = "Pays ayant au moins 10 athlètes et 5 médailles",
x = "Pays",
y = "Médailles par athlète") +
theme_minimal()
# Enregistrer le second graphique
ggsave("Projet JOs/Medal_Efficiency_by_Country.png", p_efficiency, width = 12, height = 8, dpi = 300)
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