How do you perform data manipulation in R?

Performing data manipulation in R is essential for cleaning and transforming raw datasets into a format that is suitable for analysis. The two popular packages used for data manipulation in R are dplyr and tidyr. These packages provide functions that make data manipulation tasks more efficient and intuitive.

Filtering Data in R

Filtering data involves selecting specific rows based on certain conditions. In dplyr, the filter() function is used to keep rows that meet the specified criteria.

Sorting Data in R

Sorting data is arranging rows based on one or more variables. The arrange() function in dplyr is used for sorting data in ascending or descending order.

Grouping Data in R

Grouping data is used to split the dataset into groups based on one or more variables. The group_by() function in dplyr is used for grouping data.

Summarizing Data in R

Summarizing data involves calculating summary statistics like mean, median, sum, etc., for each group. The summarize() function in dplyr is used for this purpose.

Combining Data in R

Combining data involves merging or joining multiple datasets. Functions like left_join(), right_join(), full_join(), etc., in dplyr are used for combining data.

By using these functions in dplyr and tidyr, you can efficiently perform data manipulation tasks in R, making your data cleaning and transformation process much more streamlined and effective.