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Data Cleaning and Wrangling in R

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Definition

Variable transformation means changing the scale or distribution of a variable, often to meet analysis assumptions or improve interpretability. Common transformations include taking the logarithm, square root, or standardizing values. Recoding involves changing the values of a variable, such as converting numeric codes to meaningful labels or grouping continuous values into categories. For example, transforming income using the log function or recoding age into age groups.

Transforming and recoding variables are essential skills in data cleaning and wrangling. You may need to transform variables to normalize distributions, reduce skewness, or prepare them for certain statistical models. Common transformations include the logarithmic transformation (using log()), square root transformation (using sqrt()), or scaling values. Recoding is used when you want to change the meaning or grouping of values, such as categorizing continuous variables into bins or converting codes to descriptive categories. This is especially useful for simplifying analysis, making data more interpretable, or meeting the requirements of specific methods.


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# Simulate a dataset
library(dplyr)
set.seed(123)
data <- data.frame(
  id = 1:10,
  income = c(25000, 48000, 32000, 54000, 29000, 41000, 37000, 62000, 31000, 45000)
)

# Create a new variable: log-transformed income
data <- data %>%
  mutate(log_income = log(income))

print(data)

You can also recode variables using functions like case_when() or ifelse() in dplyr. These tools allow you to create new variables or modify existing ones based on conditions. For instance, you might want to flag incomes above a certain threshold or assign descriptive labels based on value ranges.


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# Simulate age data and create age groups
set.seed(456)
data2 <- data.frame(
  id = 1:10,
  age = sample(18:70, 10, replace = TRUE)
)

data2 <- data2 %>%
  mutate(
    age_group = case_when(
      age < 30 ~ "Young",
      age >= 30 & age < 50 ~ "Middle-aged",
      age >= 50 ~ "Senior"
    )
  )

print(data2)

You should consider transforming or recoding variables when preparing data for modeling, visualization, or reporting. Transformations are often used to address skewed distributions or meet statistical assumptions, while recoding is useful for grouping values, creating flags, or improving interpretability. Choosing the right approach depends on your analysis goals and the characteristics of your data.

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Avsnitt 1. Kapitel 7

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Avsnitt 1. Kapitel 7

Transforming and Recoding Variables

1. What dplyr function is used to create new variables?

2. How can you recode values in a variable using dplyr?

3. Give an example of when you might want to transform a variable.