Selecting and Filtering Data
Selecting specific columns and filtering rows are core skills when working with data frames in R. The $ operator lets you access individual columns by name, making it easy to focus on the data you need. To filter rows, you use logical conditions to select only the rows that meet certain criteria. This approach helps you analyze relevant subsets of your data efficiently.
123456789101112131415# Create a sample data frame people <- data.frame( Name = c("Alice", "Bob", "Charlie", "Diana"), Age = c(23, 30, 28, 22), City = c("New York", "Los Angeles", "Chicago", "Houston") ) # Select the 'Age' column using the $ operator ages <- people$Age # Filter rows where Age is greater than 25 using subset() older_than_25 <- subset(people, Age > 25) print(ages) print(older_than_25)
First, the code creates a data frame named people with columns Name, Age, and City. To select a single column, the $ operator is used: people$Age retrieves all age values from the data frame and stores them in the variable ages. This gives you direct access to the Age column as a vector.
For filtering, the subset() function is used to extract only those rows where the Age column is greater than 25. The expression subset(people, Age > 25) checks each row in the people data frame and keeps only those where the Age value is more than 25. The result is stored in older_than_25, which contains only the rows for "Bob" and "Charlie" because their ages are 30 and 28, respectively. This process demonstrates how you can quickly focus on specific columns or rows in your data frame to streamline your analysis.
1. Which code correctly selects the Age column from the people data frame using the $ operator in R?
2. Which code filters the rows to include only people older than 30 from the data data frame?
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Selecting specific columns and filtering rows are core skills when working with data frames in R. The $ operator lets you access individual columns by name, making it easy to focus on the data you need. To filter rows, you use logical conditions to select only the rows that meet certain criteria. This approach helps you analyze relevant subsets of your data efficiently.
123456789101112131415# Create a sample data frame people <- data.frame( Name = c("Alice", "Bob", "Charlie", "Diana"), Age = c(23, 30, 28, 22), City = c("New York", "Los Angeles", "Chicago", "Houston") ) # Select the 'Age' column using the $ operator ages <- people$Age # Filter rows where Age is greater than 25 using subset() older_than_25 <- subset(people, Age > 25) print(ages) print(older_than_25)
First, the code creates a data frame named people with columns Name, Age, and City. To select a single column, the $ operator is used: people$Age retrieves all age values from the data frame and stores them in the variable ages. This gives you direct access to the Age column as a vector.
For filtering, the subset() function is used to extract only those rows where the Age column is greater than 25. The expression subset(people, Age > 25) checks each row in the people data frame and keeps only those where the Age value is more than 25. The result is stored in older_than_25, which contains only the rows for "Bob" and "Charlie" because their ages are 30 and 28, respectively. This process demonstrates how you can quickly focus on specific columns or rows in your data frame to streamline your analysis.
1. Which code correctly selects the Age column from the people data frame using the $ operator in R?
2. Which code filters the rows to include only people older than 30 from the data data frame?
Danke für Ihr Feedback!
