Transforming and Mutating Columns
In data science, you often need to create new columns or modify existing ones to prepare your dataset for modeling. This process is called feature engineering, and it can involve calculating ratios, categorizing continuous variables, or transforming data to better fit the requirements of statistical models. Deriving new features or recoding variables can help you capture important patterns, improve model performance, and make your data more interpretable.
1234567891011121314library(dplyr) # Sample data frame data <- tibble( name = c("Alice", "Bob", "Carol", "Dan"), age = c(25, 32, 28, 40), income = c(50000, 60000, 52000, 70000) ) # Create a new column: income per year of age data <- data %>% mutate(income_per_age = income / age) print(as.data.frame(data))
The mutate() function from the dplyr package lets you add new columns or change existing ones in a data frame. You provide the name of the new column on the left and a formula or function on the right. In the example, income_per_age = income / age creates a new column by dividing each person's income by their age. You can use arithmetic operations, built-in R functions, or even custom functions inside mutate(). The original columns remain unless you overwrite them by using the same column name.
Be careful when creating or modifying columns: if the new value is a single number, R will recycle it across all rows, which may not be what you want. Also, if you use the same column name in mutate(), you will overwrite the original values. Double-check your code to avoid unintentional data loss or errors.
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In data science, you often need to create new columns or modify existing ones to prepare your dataset for modeling. This process is called feature engineering, and it can involve calculating ratios, categorizing continuous variables, or transforming data to better fit the requirements of statistical models. Deriving new features or recoding variables can help you capture important patterns, improve model performance, and make your data more interpretable.
1234567891011121314library(dplyr) # Sample data frame data <- tibble( name = c("Alice", "Bob", "Carol", "Dan"), age = c(25, 32, 28, 40), income = c(50000, 60000, 52000, 70000) ) # Create a new column: income per year of age data <- data %>% mutate(income_per_age = income / age) print(as.data.frame(data))
The mutate() function from the dplyr package lets you add new columns or change existing ones in a data frame. You provide the name of the new column on the left and a formula or function on the right. In the example, income_per_age = income / age creates a new column by dividing each person's income by their age. You can use arithmetic operations, built-in R functions, or even custom functions inside mutate(). The original columns remain unless you overwrite them by using the same column name.
Be careful when creating or modifying columns: if the new value is a single number, R will recycle it across all rows, which may not be what you want. Also, if you use the same column name in mutate(), you will overwrite the original values. Double-check your code to avoid unintentional data loss or errors.
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