How does R handle missing data in Data Frames?

a) Missing data is represented by NA, and R provides functions to handle NA values

b) Missing data is automatically removed from the data frame

c) Missing data is converted to zeros

d) Missing data is ignored and does not affect operations

Answer:

a) Missing data is represented by NA, and R provides functions to handle NA values

Explanation:

In R, missing data in data frames is represented by the special value NA. R provides several functions to handle NA values, such as removing them, replacing them, or excluding them from calculations. Handling missing data correctly is crucial for ensuring the accuracy of data analysis and statistical modeling.

# Creating a data frame with missing data
my_data_frame <- data.frame(
    Name = c("Alice", "Bob", NA),
    Age = c(25, NA, 35),
    Salary = c(50000, 60000, NA)
)

# Checking for NA values
na_check <- is.na(my_data_frame)
print(na_check)

# Removing rows with NA values
cleaned_data_frame <- na.omit(my_data_frame)
print(cleaned_data_frame)

In this example, the data frame my_data_frame contains NA values, representing missing data. The is.na() function checks for NA values, and the na.omit() function removes rows with any missing data. These tools allow you to manage missing data effectively, ensuring that your analysis is not compromised.