Example

mydata <- data.frame("ID" = c(1, 2, 3, 4),
                     "Age" = c(20, 22, 18, 24),
                     "Gender" = c("M", "F", "M", "M"))

print(mydata) 

##   ID Age Gender
## 1  1  20      M
## 2  2  22      F
## 3  3  18      M
## 4  4  24      M

mean(mydata$Age)

## [1] 21

average age is 21

mydata$Height <- c(180, 170, 176, 177)

mydata$Weight <- c(76, 60, 72, 73)

mydata$BMI <- mydata$Weight / (mydata$Height/100)^2

mydata2 <- mydata[   ,  c(2, 4)]

mydata3 <- mydata2[-3, ]

summary(mydata[ , c(-1, -3)])

##       Age           Height          Weight           BMI       
##  Min.   :18.0   Min.   :170.0   Min.   :60.00   Min.   :20.76  
##  1st Qu.:19.5   1st Qu.:174.5   1st Qu.:69.00   1st Qu.:22.62  
##  Median :21.0   Median :176.5   Median :72.50   Median :23.27  
##  Mean   :21.0   Mean   :175.8   Mean   :70.25   Mean   :22.69  
##  3rd Qu.:22.5   3rd Qu.:177.8   3rd Qu.:73.75   3rd Qu.:23.34  
##  Max.   :24.0   Max.   :180.0   Max.   :76.00   Max.   :23.46

#install.packages("pastecs")

library(pastecs) 
stat.desc(mydata [, c(-1, -3)])

##                     Age       Height      Weight        BMI
## nbr.val       4.0000000   4.00000000   4.0000000  4.0000000
## nbr.null      0.0000000   0.00000000   0.0000000  0.0000000
## nbr.na        0.0000000   0.00000000   0.0000000  0.0000000
## min          18.0000000 170.00000000  60.0000000 20.7612457
## max          24.0000000 180.00000000  76.0000000 23.4567901
## range         6.0000000  10.00000000  16.0000000  2.6955444
## sum          84.0000000 703.00000000 281.0000000 90.7629323
## median       21.0000000 176.50000000  72.5000000 23.2724482
## mean         21.0000000 175.75000000  70.2500000 22.6907331
## SE.mean       1.2909944   2.09662427   3.5207717  0.6447345
## CI.mean.0.95  4.1085205   6.67239416  11.2046669  2.0518330
## var           6.6666667  17.58333333  49.5833333  1.6627305
## std.dev       2.5819889   4.19324854   7.0415434  1.2894691
## coef.var      0.1229519   0.02385917   0.1002355  0.0568280

round(stat.desc(mydata[ , c(-1, -3) ]), 2)

##                Age Height Weight   BMI
## nbr.val       4.00   4.00   4.00  4.00
## nbr.null      0.00   0.00   0.00  0.00
## nbr.na        0.00   0.00   0.00  0.00
## min          18.00 170.00  60.00 20.76
## max          24.00 180.00  76.00 23.46
## range         6.00  10.00  16.00  2.70
## sum          84.00 703.00 281.00 90.76
## median       21.00 176.50  72.50 23.27
## mean         21.00 175.75  70.25 22.69
## SE.mean       1.29   2.10   3.52  0.64
## CI.mean.0.95  4.11   6.67  11.20  2.05
## var           6.67  17.58  49.58  1.66
## std.dev       2.58   4.19   7.04  1.29
## coef.var      0.12   0.02   0.10  0.06

mydata_M <- mydata[ mydata$Gender == "M" ,  ]

mydata_M1 <- mydata[ mydata$Gender == "M" & mydata$Age >= 20 ,  ]