Most R users will agree that data frames provide a flexible and expressive structure for tabular data. While data frames are effective for small datasets, they are not ideal to use when processing data that is larger than a Gigabyte in size. Additionally, it is not easy to summarize data within the data frame itself; we need to load an additional package, such as plyr or reshape2, to perform advanced aggregation. Therefore, we would like to introduce how to use data.table and dplyr to perform descriptive statistics.
We first illustrate what these two packages do:
data.table: This is an extension ofdata.frame; it provides the ability to quickly aggregate and process large datasets. Additionally, it provides a much more readable and less confusing syntax compared to data frames.dplyr: This provides users with SQL-like functions so that we can quickly aggregate and summarize data from various sources.
These two packages can help users quickly and easily generate descriptive statistics...
