Q1. Using functions

BOD_list <- as.list(BOD)
head(BOD_list, 6)
## $Time
## [1] 1 2 3 4 5 7
## 
## $demand
## [1]  8.3 10.3 19.0 16.0 15.6 19.8

Q2. Converting matrix into DataFrame

df1 <- data.frame(
  Name = c("a", "b", "c", "d"),
  Age = c(12, 34, 23, 67),
  BMI = c(22, 12, 10, 11),
  Country = c("Malaysia", "India", "Spain", "England")
)
head(df1, 6)
##   Name Age BMI  Country
## 1    a  12  22 Malaysia
## 2    b  34  12    India
## 3    c  23  10    Spain
## 4    d  67  11  England

Q3. Accessing 1st & 2nd rows

df <- data.frame(
  Name = c(
    "Raju", "Kumar", "Chandru", "Deepak", "Raina",
    "Dhoni", "Hema", "Vandana", "Bela", "Anand"
  ),
  Age = c(12, 34, 23, 67, 23, 43, 23, 38, 34, 12),
  Gender = c("M", "M", "M", "M", "M", "M", "F", "F", "F", "M"),
  Country = c(
    "Malaysia", "India", "Spain", "England", "Malaysia",
    "Malaysia", "India", "Egypt", "Italy", "Peru"
  )
)

first_two_rows <- df[1:2, ]
head(first_two_rows, 6)
##    Name Age Gender  Country
## 1  Raju  12      M Malaysia
## 2 Kumar  34      M    India

Q4. Accessing 1st & 2nd columns

first_two_columns <- df[, 1:2]
head(first_two_columns, 6)
##      Name Age
## 1    Raju  12
## 2   Kumar  34
## 3 Chandru  23
## 4  Deepak  67
## 5   Raina  23
## 6   Dhoni  43

Q5. Sorting the dataframe

sorted_df <- df[order(df$Age), ]
head(sorted_df, 6)
##       Name Age Gender  Country
## 1     Raju  12      M Malaysia
## 10   Anand  12      M     Peru
## 3  Chandru  23      M    Spain
## 5    Raina  23      M Malaysia
## 7     Hema  23      F    India
## 2    Kumar  34      M    India

Q6. Adding a new column

df_with_score <- df
df_with_score$Score <- c(85, 90, 78, 88, 90, 92, 80, 90, 87, 100)
head(df_with_score, 6)
##      Name Age Gender  Country Score
## 1    Raju  12      M Malaysia    85
## 2   Kumar  34      M    India    90
## 3 Chandru  23      M    Spain    78
## 4  Deepak  67      M  England    88
## 5   Raina  23      M Malaysia    90
## 6   Dhoni  43      M Malaysia    92

Q7. Filtering rows with a condition

age_over_30 <- df[df$Age > 30, ]
head(age_over_30, 6)
##      Name Age Gender  Country
## 2   Kumar  34      M    India
## 4  Deepak  67      M  England
## 6   Dhoni  43      M Malaysia
## 8 Vandana  38      F    Egypt
## 9    Bela  34      F    Italy

Q8. Renaming columns

renamed_df <- df
names(renamed_df) <- c("Person", "Years", "Gender", "Nation")
head(renamed_df, 6)
##    Person Years Gender   Nation
## 1    Raju    12      M Malaysia
## 2   Kumar    34      M    India
## 3 Chandru    23      M    Spain
## 4  Deepak    67      M  England
## 5   Raina    23      M Malaysia
## 6   Dhoni    43      M Malaysia

Q9. Selecting multiple conditions

selected_rows <- df1[df1$Age > 20 & df1$BMI < 15, ]
head(selected_rows, 6)
##   Name Age BMI Country
## 2    b  34  12   India
## 3    c  23  10   Spain
## 4    d  67  11 England

Q10. Summarizing a column

average_age <- mean(df1$Age)
head(average_age, 6)
## [1] 34