Creación data.frame

id<-c(1:30)

edadP<-rnorm(n=30, mean = 45, sd=10)
edad<-c(round(edadP,0))

#función rep -> replica valores en x, con each le digo cuántas repeticiones de cada valor
#sample me "toma una muestra"/ aleatoriza

genero <- rep(c(0,1), each=15)
gener<-sample(genero)

trat<-sample(c("A", "B", "C"), size=30, replace = TRUE)

mediapeso<-70
desvpeso<-7
pesoP<-rnorm(n=30, mean = mediapeso, sd=desvpeso)
peso<-c(round(pesoP, 1))
peso
##  [1] 69.4 74.2 78.9 74.7 74.9 73.3 67.0 76.0 60.1 70.0 71.9 86.4 62.3 64.9 63.5
## [16] 76.6 68.2 71.0 75.1 73.6 71.4 78.3 63.0 64.7 78.3 58.4 58.9 77.2 68.8 84.2
altu<-rnorm(n=30, mean = 166, sd=10)
alt<-c(round(altu, 0))
datos<-data.frame(id,edad,gener,trat,peso,alt)
datos
##    id edad gener trat peso alt
## 1   1   50     0    A 69.4 165
## 2   2   47     0    B 74.2 154
## 3   3   53     0    C 78.9 161
## 4   4   46     1    C 74.7 183
## 5   5   50     1    A 74.9 154
## 6   6   43     0    A 73.3 148
## 7   7   47     0    B 67.0 172
## 8   8   49     0    A 76.0 177
## 9   9   59     1    A 60.1 164
## 10 10   29     0    A 70.0 161
## 11 11   50     1    B 71.9 149
## 12 12   41     0    B 86.4 153
## 13 13   49     1    B 62.3 169
## 14 14   39     0    B 64.9 167
## 15 15   37     1    A 63.5 169
## 16 16   52     1    C 76.6 170
## 17 17   44     1    B 68.2 168
## 18 18   51     0    A 71.0 169
## 19 19   48     0    C 75.1 167
## 20 20   41     0    A 73.6 184
## 21 21   47     1    A 71.4 157
## 22 22   57     1    B 78.3 156
## 23 23   52     0    C 63.0 157
## 24 24   47     1    C 64.7 164
## 25 25   57     1    B 78.3 172
## 26 26   46     1    A 58.4 168
## 27 27   49     1    B 58.9 160
## 28 28   43     0    C 77.2 146
## 29 29   47     0    C 68.8 190
## 30 30   47     1    B 84.2 186

Búsqueda de información

summary(datos)
##        id             edad           gener         trat          
##  Min.   : 1.00   Min.   :29.00   Min.   :0.0   Length:30         
##  1st Qu.: 8.25   1st Qu.:44.50   1st Qu.:0.0   Class :character  
##  Median :15.50   Median :47.00   Median :0.5   Mode  :character  
##  Mean   :15.50   Mean   :47.23   Mean   :0.5                     
##  3rd Qu.:22.75   3rd Qu.:50.00   3rd Qu.:1.0                     
##  Max.   :30.00   Max.   :59.00   Max.   :1.0                     
##       peso            alt       
##  Min.   :58.40   Min.   :146.0  
##  1st Qu.:65.42   1st Qu.:157.0  
##  Median :71.65   Median :166.0  
##  Mean   :71.17   Mean   :165.3  
##  3rd Qu.:75.78   3rd Qu.:169.8  
##  Max.   :86.40   Max.   :190.0

Creación variable y nueva base de datos

IMCp<-c((peso/alt)^2*100)
IMC<-round(IMCp, 0)
IMC
##  [1] 18 23 24 17 24 25 15 18 13 19 23 32 14 15 14 20 16 18 20 16 21 25 16 16 21
## [26] 12 14 28 13 20
datos2<-data.frame(datos, IMC)

subdividir los datos por género y volver a unir

Df_hombres<-subset(datos, datos$gener<1)
Df_mmujeres<-subset(datos, datos$gener>0)

rbind(Df_hombres, Df_mmujeres)
##    id edad gener trat peso alt
## 1   1   50     0    A 69.4 165
## 2   2   47     0    B 74.2 154
## 3   3   53     0    C 78.9 161
## 6   6   43     0    A 73.3 148
## 7   7   47     0    B 67.0 172
## 8   8   49     0    A 76.0 177
## 10 10   29     0    A 70.0 161
## 12 12   41     0    B 86.4 153
## 14 14   39     0    B 64.9 167
## 18 18   51     0    A 71.0 169
## 19 19   48     0    C 75.1 167
## 20 20   41     0    A 73.6 184
## 23 23   52     0    C 63.0 157
## 28 28   43     0    C 77.2 146
## 29 29   47     0    C 68.8 190
## 4   4   46     1    C 74.7 183
## 5   5   50     1    A 74.9 154
## 9   9   59     1    A 60.1 164
## 11 11   50     1    B 71.9 149
## 13 13   49     1    B 62.3 169
## 15 15   37     1    A 63.5 169
## 16 16   52     1    C 76.6 170
## 17 17   44     1    B 68.2 168
## 21 21   47     1    A 71.4 157
## 22 22   57     1    B 78.3 156
## 24 24   47     1    C 64.7 164
## 25 25   57     1    B 78.3 172
## 26 26   46     1    A 58.4 168
## 27 27   49     1    B 58.9 160
## 30 30   47     1    B 84.2 186