Data

ID<-c(16762,16439,16211,16790,16443,16998,
      16543,16779,16945,16111,16224,16980,
      16779,16000,16111,16224,16400,16327)
Name<-c(NA,NA,"Ibraheem","Fahd",
        "Majeda",NA,"Mohammed","Remas",
        "Rteel","Abdalrhman",NA,"Tala",
        "Remas","Nadiah",NA,"Mhdi",
        "Lila",NA)
Age<-c(30,NA,29,NA,27,9,32,9,NA,29,28,9,
       9,30,NA,28,42,NA )
Sex<-c("M","M","M","M","F","F","M","F",
       "F","M","M","F","F","F","M","M",
       "F","F")

data<-data.frame(ID,Name,Age,Sex)

Test the missing values

is.na(data)
##          ID  Name   Age   Sex
##  [1,] FALSE  TRUE FALSE FALSE
##  [2,] FALSE  TRUE  TRUE FALSE
##  [3,] FALSE FALSE FALSE FALSE
##  [4,] FALSE FALSE  TRUE FALSE
##  [5,] FALSE FALSE FALSE FALSE
##  [6,] FALSE  TRUE FALSE FALSE
##  [7,] FALSE FALSE FALSE FALSE
##  [8,] FALSE FALSE FALSE FALSE
##  [9,] FALSE FALSE  TRUE FALSE
## [10,] FALSE FALSE FALSE FALSE
## [11,] FALSE  TRUE FALSE FALSE
## [12,] FALSE FALSE FALSE FALSE
## [13,] FALSE FALSE FALSE FALSE
## [14,] FALSE FALSE FALSE FALSE
## [15,] FALSE  TRUE  TRUE FALSE
## [16,] FALSE FALSE FALSE FALSE
## [17,] FALSE FALSE FALSE FALSE
## [18,] FALSE  TRUE  TRUE FALSE
#Identify count of missing values 
sum(is.na(data))
## [1] 11
#Identify mean of missing values 
mean(is.na(data))
## [1] 0.1527778
#list rows of data that have missing values
data[!complete.cases(data),]
##       ID  Name Age Sex
## 1  16762  <NA>  30   M
## 2  16439  <NA>  NA   M
## 4  16790  Fahd  NA   M
## 6  16998  <NA>   9   F
## 9  16945 Rteel  NA   F
## 11 16224  <NA>  28   M
## 15 16111  <NA>  NA   M
## 18 16327  <NA>  NA   F
#list rows of data that no have missing values
data[complete.cases(data),]
##       ID       Name Age Sex
## 3  16211   Ibraheem  29   M
## 5  16443     Majeda  27   F
## 7  16543   Mohammed  32   M
## 8  16779      Remas   9   F
## 10 16111 Abdalrhman  29   M
## 12 16980       Tala   9   F
## 13 16779      Remas   9   F
## 14 16000     Nadiah  30   F
## 16 16224       Mhdi  28   M
## 17 16400       Lila  42   F

Data

ID<-c(16762,16439,16211,16790,16443,16998,
      16543,16779,16945,16111,16224,16980,
      16779,16000,16111,16224,16400,16327)
Name<-c(NA,NA,"Ibraheem","Fahd",
        "Majeda",NA,"Mohammed","Remas",
        "Rteel","Abdalrhman",NA,"Tala",
        "Remas","Nadiah",NA,"Mhdi",
        "Lila",NA)
Age<-c(30,NA,29,NA,27,9,32,9,NA,29,28,9,
       9,30,NA,28,42,NA )
Sex<-c("M","M","M","M","F","F","M","F",
       "F","M","M","F","F","F","M","M",
       "F","F")

data<-data.frame(ID,Name,Age,Sex)

1)Replace the missing values with zero

data[is.na(data)] = 0
data
##       ID       Name Age Sex
## 1  16762          0  30   M
## 2  16439          0   0   M
## 3  16211   Ibraheem  29   M
## 4  16790       Fahd   0   M
## 5  16443     Majeda  27   F
## 6  16998          0   9   F
## 7  16543   Mohammed  32   M
## 8  16779      Remas   9   F
## 9  16945      Rteel   0   F
## 10 16111 Abdalrhman  29   M
## 11 16224          0  28   M
## 12 16980       Tala   9   F
## 13 16779      Remas   9   F
## 14 16000     Nadiah  30   F
## 15 16111          0   0   M
## 16 16224       Mhdi  28   M
## 17 16400       Lila  42   F
## 18 16327          0   0   F

Data

ID<-c(16762,16439,16211,16790,16443,16998,
      16543,16779,16945,16111,16224,16980,
      16779,16000,16111,16224,16400,16327)
Name<-c(NA,NA,"Ibraheem","Fahd",
        "Majeda",NA,"Mohammed","Remas",
        "Rteel","Abdalrhman",NA,"Tala",
        "Remas","Nadiah",NA,"Mhdi",
        "Lila",NA)
Age<-c(30,NA,29,NA,27,9,32,9,NA,29,28,9,
       9,30,NA,28,42,NA )
Sex<-c("M","M","M","M","F","F","M","F",
       "F","M","M","F","F","F","M","M",
       "F","F")

data<-data.frame(ID,Name,Age,Sex)

2)Replace the missing values with column mean

data$Age[is.na(data$Age)]<-mean(data$Age,na.rm=TRUE)
data$Age
##  [1] 30.00000 23.92308 29.00000 23.92308 27.00000  9.00000 32.00000  9.00000
##  [9] 23.92308 29.00000 28.00000  9.00000  9.00000 30.00000 23.92308 28.00000
## [17] 42.00000 23.92308

Data

ID<-c(16762,16439,16211,16790,16443,16998,
      16543,16779,16945,16111,16224,16980,
      16779,16000,16111,16224,16400,16327)
Name<-c(NA,NA,"Ibraheem","Fahd",
        "Majeda",NA,"Mohammed","Remas",
        "Rteel","Abdalrhman",NA,"Tala",
        "Remas","Nadiah",NA,"Mhdi",
        "Lila",NA)
Age<-c(30,NA,29,NA,27,9,32,9,NA,29,28,9,
       9,30,NA,28,42,NA )
Sex<-c("M","M","M","M","F","F","M","F",
       "F","M","M","F","F","F","M","M",
       "F","F")

data<-data.frame(ID,Name,Age,Sex)

3)Remove all missing values

na.omit(data)
##       ID       Name Age Sex
## 3  16211   Ibraheem  29   M
## 5  16443     Majeda  27   F
## 7  16543   Mohammed  32   M
## 8  16779      Remas   9   F
## 10 16111 Abdalrhman  29   M
## 12 16980       Tala   9   F
## 13 16779      Remas   9   F
## 14 16000     Nadiah  30   F
## 16 16224       Mhdi  28   M
## 17 16400       Lila  42   F