Winter-Olympic Medals es un dataset de investigación para ver toda la información sobre las medallas ganadas en los Juegos Olímpicos de Invierno hasta el 2006.Los datos que manejan este sitio web fueron proporcionados por el periódico The Guardian en el Reino Unido.
http://winterolympicsmedals.com/
df = read.table("http://winterolympicsmedals.com/medals.csv",header=TRUE,sep =",",stringsAsFactors = TRUE )
head(df)#Mostrar cabezera
## Year City Sport Discipline NOC Event Event.gender
## 1 1924 Chamonix Skating Figure skating AUT individual M
## 2 1924 Chamonix Skating Figure skating AUT individual W
## 3 1924 Chamonix Skating Figure skating AUT pairs X
## 4 1924 Chamonix Bobsleigh Bobsleigh BEL four-man M
## 5 1924 Chamonix Ice Hockey Ice Hockey CAN ice hockey M
## 6 1924 Chamonix Biathlon Biathlon FIN military patrol M
## Medal
## 1 Silver
## 2 Gold
## 3 Gold
## 4 Bronze
## 5 Gold
## 6 Silver
summary(df)#Estadísticos básicos
## Year City Sport
## Min. :1924 Turin : 252 Biathlon : 162
## 1st Qu.:1968 Salt Lake City: 234 Bobsleigh : 130
## Median :1988 Innsbruck : 214 Curling : 21
## Mean :1981 Nagano : 205 Ice Hockey: 68
## 3rd Qu.:1998 Lillehammer : 183 Luge : 108
## Max. :2006 Albertville : 171 Skating : 747
## (Other) :1016 Skiing :1039
## Discipline NOC Event Event.gender
## Speed skating :448 NOR : 278 individual: 190 M:1362
## Cross Country S:389 USA : 213 500m : 132 W: 791
## Alpine Skiing :359 URS : 186 1500m : 109 X: 122
## Figure skating :203 AUT : 179 downhill : 95
## Biathlon :162 GER : 158 1000m : 94
## Ski Jumping :113 FIN : 147 slalom : 94
## (Other) :601 (Other):1114 (Other) :1561
## Medal
## Bronze:753
## Gold :764
## Silver:758
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str(df)#Estructura 2275 obs y 8 variables
## 'data.frame': 2275 obs. of 8 variables:
## $ Year : int 1924 1924 1924 1924 1924 1924 1924 1924 1924 1924 ...
## $ City : Factor w/ 52 levels "Albertville",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Sport : Factor w/ 7 levels "Biathlon","Bobsleigh",..: 6 6 6 2 4 1 6 6 6 6 ...
## $ Discipline : Factor w/ 15 levels "Alpine Skiing",..: 6 6 6 3 8 2 6 15 15 15 ...
## $ NOC : Factor w/ 45 levels "AUS","AUT","BEL",..: 2 2 2 3 6 15 15 15 15 15 ...
## $ Event : Factor w/ 67 levels "10000m","1000m",..: 48 48 56 41 47 54 56 1 1 7 ...
## $ Event.gender: Factor w/ 3 levels "M","W","X": 1 2 3 1 1 1 3 1 1 1 ...
## $ Medal : Factor w/ 3 levels "Bronze","Gold",..: 3 2 2 1 2 3 3 2 3 2 ...
Participaron 2275 deportistas
table(df$Medal)->medallas#Creo una tabla de la columna medal
medallas # Tabla tridimensional
##
## Bronze Gold Silver
## 753 764 758
table(df$City)->ciudad
summary(ciudad)
## Number of cases in table: 2275
## Number of factors: 1
genero = subset(df, (Event.gender !="X"),select=c(7))#Filtro por hombre y mujer
table(droplevels(genero))#Elimino lo restante y lo convierto en una tabla
##
## M W
## 1362 791
max(df$Year)
## [1] 2006
oro = subset(df, (Year >="1960"& Year<="1996"))
subset(oro, (Medal =="Gold"),select=c(1,5,8))->r
row.names(r)= 1:nrow(r)
summary(droplevels(r))
## Year NOC Medal
## Min. :1960 URS : 71 Gold:411
## 1st Qu.:1972 NOR : 40
## Median :1980 GDR : 39
## Mean :1980 USA : 34
## 3rd Qu.:1992 AUT : 25
## Max. :1994 SWE : 24
## (Other):178