Accessing Columns in Data Frames
Data frames always have named columns, and you can use these names to extract specific columns. There are two main approaches.
Using Square Brackets
You can specify the column name inside square brackets.
Example
1234567name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") test <- data.frame(name, age, gender) # Extracting the name with square brackets test[, "name"]
Using the Dollar Sign
Data frames also support a shorthand operator, $, which allows you to access a column by name without quotes.
Example
1234567name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") test <- data.frame(name, age, gender) # Extracting the name with dollar sign test$name
Swipe to start coding
You have the mtcars dataset.
Your tasks are:
- Extract the
cylcolumn values using square brackets. - Extract the
dispcolumn values using the dollar$sign.
Lösung
Danke für Ihr Feedback!
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Accessing Columns in Data Frames
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Data frames always have named columns, and you can use these names to extract specific columns. There are two main approaches.
Using Square Brackets
You can specify the column name inside square brackets.
Example
1234567name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") test <- data.frame(name, age, gender) # Extracting the name with square brackets test[, "name"]
Using the Dollar Sign
Data frames also support a shorthand operator, $, which allows you to access a column by name without quotes.
Example
1234567name <- c("Alex", "Julia", "Finn") age <- c(24, 43, 32) gender <- c("M", "F", "M") test <- data.frame(name, age, gender) # Extracting the name with dollar sign test$name
Swipe to start coding
You have the mtcars dataset.
Your tasks are:
- Extract the
cylcolumn values using square brackets. - Extract the
dispcolumn values using the dollar$sign.
Lösung
Danke für Ihr Feedback!
single