Data Manipulation with dplyr The dplyr package provides a comprehensive suite of functions for manipulating and analyzing data. This allows you to perform va...
The dplyr package provides a comprehensive suite of functions for manipulating and analyzing data. This allows you to perform various data analysis tasks in a concise and efficient manner.
Filter:
filter() to select specific rows based on logical conditions.df %>% filter(age > 25) selects rows where age is greater than 25.Select:
select() to choose specific columns from a data frame.df %>% select(name, age) selects only the name and age columns.Mutate:
mutate() to add new columns to a data frame based on existing columns.df %>% mutate(gender = ifelse(sex == "female", "female", "male")) creates a new column called gender based on the sex column.Summarize:
summarize() to calculate various statistical summaries of data frame columns.df %>% summarize(mean(age), min(salary)) calculates the average age and minimum salary in the data frame.Arrange:
arrange() to sort data frame rows based on specific columns in ascending or descending order.df %>% arrange(age, name) sorts rows by age then by name.Group By and Aggregate:
group_by() and aggregate() to perform group-based calculations and summaries.df %>% group_by(gender) %>% aggregate(mean(salary)) calculates the average salary for each gender group