Lernen Creating and Modifying Variables

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When you work with data, you often need to create new variables or modify existing ones to prepare your dataset for analysis. In the Tidyverse, the mutate and transmute functions from the dplyr package allow you to efficiently add, change, or remove columns in your data frame. These functions are essential tools for transforming raw data into a format that is ready for modeling, visualization, or reporting.


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library(dplyr)
options(crayon.enabled = FALSE)

# Sample data frame
data <- tibble(
  name = c("Alice", "Bob", "Carol"),
  math_score = c(80, 90, 85),
  english_score = c(75, 88, 92)
)

# Using mutate to add and transform columns
data_mutated <- data %>%
  mutate(
    total_score = math_score + english_score,
    average_score = (math_score + english_score) / 2,
    math_score = math_score * 1.1 # increase math_score by 10%
  )

# Using transmute to create only new columns, dropping others
data_transmuted <- data %>%
  transmute(
    name,
    total_score = math_score + english_score,
    average_score = (math_score + english_score) / 2
  )

print(data_mutated)
print(data_transmuted)

The main difference between mutate and transmute is in what they return. When you use mutate, you add new columns or modify existing ones, but all other columns in the data frame remain. In contrast, transmute only keeps the columns you explicitly create or mention in your call; all other columns are dropped. Use mutate if you want to keep your original columns while adding or changing variables, and use transmute when you only want to keep the newly created variables or a specific subset of columns.

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Abschnitt 1. Kapitel 9

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Abschnitt 1. Kapitel 9