mydata <- data.frame("ID"= c(1, 2, 3),
"AGE" = c(30, 40, 20), 
"GENDER" =c("F", "M", "M"))
print(mydata)
##   ID AGE GENDER
## 1  1  30      F
## 2  2  40      M
## 3  3  20      M
mydata[1,2] <- 28 #changed value in the first row and second column to 28
mydata2 <-mydata[,-3] #excluding the third variable

Create mydata3 which includes only the first and second row of mydata

mydata3 <- mydata[c(1,2),]
print(mydata3)
##   ID AGE GENDER
## 1  1  28      F
## 2  2  40      M
mydata$height <- c(178, 170, 190)
print(mydata)
##   ID AGE GENDER height
## 1  1  28      F    178
## 2  2  40      M    170
## 3  3  20      M    190
mydata$height <- mydata$height + 2
print(mydata)
##   ID AGE GENDER height
## 1  1  28      F    180
## 2  2  40      M    172
## 3  3  20      M    192
summary(mydata)
##        ID           AGE           GENDER              height     
##  Min.   :1.0   Min.   :20.00   Length:3           Min.   :172.0  
##  1st Qu.:1.5   1st Qu.:24.00   Class :character   1st Qu.:176.0  
##  Median :2.0   Median :28.00   Mode  :character   Median :180.0  
##  Mean   :2.0   Mean   :29.33                      Mean   :181.3  
##  3rd Qu.:2.5   3rd Qu.:34.00                      3rd Qu.:186.0  
##  Max.   :3.0   Max.   :40.00                      Max.   :192.0
summary(mydata[,-3])
##        ID           AGE            height     
##  Min.   :1.0   Min.   :20.00   Min.   :172.0  
##  1st Qu.:1.5   1st Qu.:24.00   1st Qu.:176.0  
##  Median :2.0   Median :28.00   Median :180.0  
##  Mean   :2.0   Mean   :29.33   Mean   :181.3  
##  3rd Qu.:2.5   3rd Qu.:34.00   3rd Qu.:186.0  
##  Max.   :3.0   Max.   :40.00   Max.   :192.0
mean(mydata$AGE)
## [1] 29.33333
sd(mydata$height)
## [1] 10.06645
#install.packages("psych")
library(psych)
 
describe(mydata)       
##         vars n   mean    sd median trimmed   mad min max range  skew kurtosis
## ID         1 3   2.00  1.00      2    2.00  1.48   1   3     2  0.00    -2.33
## AGE        2 3  29.33 10.07     28   29.33 11.86  20  40    20  0.13    -2.33
## GENDER*    3 3   1.67  0.58      2    1.67  0.00   1   2     1 -0.38    -2.33
## height     4 3 181.33 10.07    180  181.33 11.86 172 192    20  0.13    -2.33
##           se
## ID      0.58
## AGE     5.81
## GENDER* 0.33
## height  5.81
#install.packages("pastecs")
library(pastecs)
round(stat.desc(mydata[c(-1, -3)]), 2)
##                 AGE height
## nbr.val        3.00   3.00
## nbr.null       0.00   0.00
## nbr.na         0.00   0.00
## min           20.00 172.00
## max           40.00 192.00
## range         20.00  20.00
## sum           88.00 544.00
## median        28.00 180.00
## mean          29.33 181.33
## SE.mean        5.81   5.81
## CI.mean.0.95  25.01  25.01
## var          101.33 101.33
## std.dev       10.07  10.07
## coef.var       0.34   0.06