# Sample data frame
df <- data.frame(
A = c(1, NA, 3, 4, 5),
B = c("a", "b", NA, "d", "e"),
C = c("x", "y", "z", NA, "w")
)
# Remove rows with missing values
df_without_na <- na.omit(df)
# Replace missing values with another value
df_replaced_na <- replace(df, is.na(df), "replacement_value")
# Remove duplicate rows
df_without_duplicates <- unique(df)
# Print the results
print("Data frame without missing values:")
print(df_without_na)
print("\nData frame with missing values replaced:")
print(df_replaced_na)
print("\nData frame without duplicate rows:")
print(df_without_duplicates)
licate rows:")
print(df_without_duplicates)
```
We create a sample data frame called `df` with three columns (`A`, `B`, and `C`). Each column contains some missing values represented by `NA`.
Here's what each operation does:
1. Remove rows with missing values: We use the `na.omit()` function to remove any rows containing `NA` values and assign the result to a new data frame called `df_without_na`.
2. Replace missing values with another value: We use the `replace()` function to replace all `NA` values in the original data frame `df` with a specified replacement value (in this case, "replacement_value"). The result is stored in `df_replaced_na`.
3. Remove duplicate rows: We use the `unique()` function to remove duplicate rows from the original data frame `df` and store the result in `df_without_duplicates`.
Finally, we print the resulting data frames to see the output.
Note that the functions `na.omit()`, `replace()`, and `unique()` create new data frames and do not modify the original data frame unless you assign the result back to the same variable.
