R Code Wizard-R Programming Assistant

Example

Using dplyr's filter() to extract rows based on specific conditions. Example: filter(df, age > 50)

Scenario

A healthcare data analyst might use this to select patient records where the age is greater than 50 to focus on a senior patient cohort for a study on arthritis prevalence.

Example

Using dplyr's select() to focus on certain columns of interest. Example: select(df, starts_with('income'))

Scenario

A financial analyst can use this function to retrieve all columns related to income from a dataset during an audit of income distribution among different demographics.

Example

Using dplyr's summarise() along with group_by() to compute summaries like means or counts. Example: df %>% group_by(gender) %>% summarise(average_age = mean(age, na.rm = TRUE))

Scenario

In market research, summarizing data by customer demographics can help identify which gender groups are purchasing certain products more frequently, assisting in targeted marketing strategies.

Example

Using dplyr's mutate() to create new columns based on existing data. Example: mutate(df, new_salary = salary * 1.1)

Scenario

A HR manager might use this to calculate adjusted salaries after a planned salary increase of 10% for budget projection reports.

Example

Using dplyr's inner_join(), left_join(), etc., to merge datasets based on a common key. Example: left_join(employees, departments, by = 'dept_id')

Scenario

An operations analyst may merge employee and department data to evaluate staffing levels and departmental costs, facilitating resource planning.