Question 1

Read file marks1.csv

dataset <- read.csv("marks1.csv")
dataset
##        X    X.1 test asgn Prsnt Final  q1 q2 q3   q4
## 1  60001  Ahmad   15   14    17    13 0.0  9  2  4.0
## 2  60003    Abu   26   13    18    22 3.0  5  8  6.0
## 3  60006   Samy   21   15    19    25 6.0  7  4  8.0
## 4  60008  Chong   25   10    17    14 2.0  3  4  5.0
## 5  60009   Paul   25   15    16    20 3.0  7  6  4.0
## 6  60011   John   18   15    19    22 4.0  7  4  7.0
## 7  60014   Devi   30   15    19    28 4.0  5  9 10.0
## 8  60015 Pillip   16   15    19    20 4.0  5  6  5.0
## 9  60023 Meilin   18   13    18    22 2.0  5  7  8.0
## 10 60025   Lily   30   14    18    24 5.5  6  5  7.5
## 11 60026  Jamil   12   10    12    12 1.0  5  1  6.0

Question 2

Check the data frame info using a few available functions

typeof(dataset)
## [1] "list"
class(dataset)
## [1] "data.frame"
summary(dataset)
##        X             X.1                 test            asgn      
##  Min.   :60001   Length:11          Min.   :12.00   Min.   :10.00  
##  1st Qu.:60007   Class :character   1st Qu.:17.00   1st Qu.:13.00  
##  Median :60011   Mode  :character   Median :21.00   Median :14.00  
##  Mean   :60013                      Mean   :21.45   Mean   :13.55  
##  3rd Qu.:60019                      3rd Qu.:25.50   3rd Qu.:15.00  
##  Max.   :60026                      Max.   :30.00   Max.   :15.00  
##      Prsnt           Final             q1              q2       
##  Min.   :12.00   Min.   :12.00   Min.   :0.000   Min.   :3.000  
##  1st Qu.:17.00   1st Qu.:17.00   1st Qu.:2.000   1st Qu.:5.000  
##  Median :18.00   Median :22.00   Median :3.000   Median :5.000  
##  Mean   :17.45   Mean   :20.18   Mean   :3.136   Mean   :5.818  
##  3rd Qu.:19.00   3rd Qu.:23.00   3rd Qu.:4.000   3rd Qu.:7.000  
##  Max.   :19.00   Max.   :28.00   Max.   :6.000   Max.   :9.000  
##        q3              q4        
##  Min.   :1.000   Min.   : 4.000  
##  1st Qu.:4.000   1st Qu.: 5.000  
##  Median :5.000   Median : 6.000  
##  Mean   :5.091   Mean   : 6.409  
##  3rd Qu.:6.500   3rd Qu.: 7.750  
##  Max.   :9.000   Max.   :10.000

Question 3

Check the names of the variables in the data frame

names(dataset)
##  [1] "X"     "X.1"   "test"  "asgn"  "Prsnt" "Final" "q1"    "q2"    "q3"   
## [10] "q4"

Question 4

Rename the first variable X to ID

names(dataset)[names(dataset) == "X"] <- "ID"
dataset
##       ID    X.1 test asgn Prsnt Final  q1 q2 q3   q4
## 1  60001  Ahmad   15   14    17    13 0.0  9  2  4.0
## 2  60003    Abu   26   13    18    22 3.0  5  8  6.0
## 3  60006   Samy   21   15    19    25 6.0  7  4  8.0
## 4  60008  Chong   25   10    17    14 2.0  3  4  5.0
## 5  60009   Paul   25   15    16    20 3.0  7  6  4.0
## 6  60011   John   18   15    19    22 4.0  7  4  7.0
## 7  60014   Devi   30   15    19    28 4.0  5  9 10.0
## 8  60015 Pillip   16   15    19    20 4.0  5  6  5.0
## 9  60023 Meilin   18   13    18    22 2.0  5  7  8.0
## 10 60025   Lily   30   14    18    24 5.5  6  5  7.5
## 11 60026  Jamil   12   10    12    12 1.0  5  1  6.0

Question 5

Rename the second variable X.1 to StuName

names(dataset)[names(dataset) == "X.1"] <- "StuName"
dataset
##       ID StuName test asgn Prsnt Final  q1 q2 q3   q4
## 1  60001   Ahmad   15   14    17    13 0.0  9  2  4.0
## 2  60003     Abu   26   13    18    22 3.0  5  8  6.0
## 3  60006    Samy   21   15    19    25 6.0  7  4  8.0
## 4  60008   Chong   25   10    17    14 2.0  3  4  5.0
## 5  60009    Paul   25   15    16    20 3.0  7  6  4.0
## 6  60011    John   18   15    19    22 4.0  7  4  7.0
## 7  60014    Devi   30   15    19    28 4.0  5  9 10.0
## 8  60015  Pillip   16   15    19    20 4.0  5  6  5.0
## 9  60023  Meilin   18   13    18    22 2.0  5  7  8.0
## 10 60025    Lily   30   14    18    24 5.5  6  5  7.5
## 11 60026   Jamil   12   10    12    12 1.0  5  1  6.0

Question 6

Remove the first two column from the data frame

dataset_remove <- dataset
dataset_remove[, c(1,2)] <- NULL
dataset_remove
##    test asgn Prsnt Final  q1 q2 q3   q4
## 1    15   14    17    13 0.0  9  2  4.0
## 2    26   13    18    22 3.0  5  8  6.0
## 3    21   15    19    25 6.0  7  4  8.0
## 4    25   10    17    14 2.0  3  4  5.0
## 5    25   15    16    20 3.0  7  6  4.0
## 6    18   15    19    22 4.0  7  4  7.0
## 7    30   15    19    28 4.0  5  9 10.0
## 8    16   15    19    20 4.0  5  6  5.0
## 9    18   13    18    22 2.0  5  7  8.0
## 10   30   14    18    24 5.5  6  5  7.5
## 11   12   10    12    12 1.0  5  1  6.0

Question 7

Use apply() function to sum all the marks in the data frame and put them in a new vector called Total and bind the vector to the data frame

dataset$total <- apply(dataset[, 3:10], 1, function(x) { sum(x) })
dataset
##       ID StuName test asgn Prsnt Final  q1 q2 q3   q4 total
## 1  60001   Ahmad   15   14    17    13 0.0  9  2  4.0    74
## 2  60003     Abu   26   13    18    22 3.0  5  8  6.0   101
## 3  60006    Samy   21   15    19    25 6.0  7  4  8.0   105
## 4  60008   Chong   25   10    17    14 2.0  3  4  5.0    80
## 5  60009    Paul   25   15    16    20 3.0  7  6  4.0    96
## 6  60011    John   18   15    19    22 4.0  7  4  7.0    96
## 7  60014    Devi   30   15    19    28 4.0  5  9 10.0   120
## 8  60015  Pillip   16   15    19    20 4.0  5  6  5.0    90
## 9  60023  Meilin   18   13    18    22 2.0  5  7  8.0    93
## 10 60025    Lily   30   14    18    24 5.5  6  5  7.5   110
## 11 60026   Jamil   12   10    12    12 1.0  5  1  6.0    59

Question 8

Using a user defined function called function(), use the apply() function to add variable 1 to variable 3,and write to a new variable in the data frame called CW.function(x) {x[1]+x[2]+x[3]}

dataset$CW <- apply(dataset[, 3:5], 1, function(x) { x[1]+x[2]+x[3] })
dataset
##       ID StuName test asgn Prsnt Final  q1 q2 q3   q4 total CW
## 1  60001   Ahmad   15   14    17    13 0.0  9  2  4.0    74 46
## 2  60003     Abu   26   13    18    22 3.0  5  8  6.0   101 57
## 3  60006    Samy   21   15    19    25 6.0  7  4  8.0   105 55
## 4  60008   Chong   25   10    17    14 2.0  3  4  5.0    80 52
## 5  60009    Paul   25   15    16    20 3.0  7  6  4.0    96 56
## 6  60011    John   18   15    19    22 4.0  7  4  7.0    96 52
## 7  60014    Devi   30   15    19    28 4.0  5  9 10.0   120 64
## 8  60015  Pillip   16   15    19    20 4.0  5  6  5.0    90 50
## 9  60023  Meilin   18   13    18    22 2.0  5  7  8.0    93 49
## 10 60025    Lily   30   14    18    24 5.5  6  5  7.5   110 62
## 11 60026   Jamil   12   10    12    12 1.0  5  1  6.0    59 34

The End, Thanks!