|Original author(s)||Hadley Wickham|
|Initial release||January 7, 2014|
1.0.0 / June 1, 2020
One of the core packages of the tidyverse in the R programming language, dplyr is primarily a set of functions designed to enable dataframe manipulation in an intuitive, user-friendly way. Data analysts typically use dplyr in order to transform existing datasets into a format better suited for some particular type of analysis, or data visualization.
For instance, someone seeking to analyze an enormous dataset may wish to only view a smaller subset of the data. Alternatively, a user may wish to rearrange the data in order to see the rows ranked by some numerical value, or even based on a combination of values from the original dataset.
Authored primarily by Hadley Wickham, dplyr was launched in 2014. On the dplyr web page, the package is described as "a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges."
While dplyr actually includes several dozen functions that enable various forms of data manipulation, the package features five primary verbs:
filter(), which is used to extract rows from a dataframe, based on conditions specified by a user;
select(), which is used to subset a dataframe by its columns;
arrange(), which is used to sort rows in a dataframe based on attributes held by particular columns;
mutate(), which is used to create new variables, by altering and/or combining values from existing columns; and
summarize(), also spelled summarise(), which is used to collapse values from a dataframe into a single summary.
In addition to its five main verbs, dplyr also includes several other functions that enable exploration and manipulation of dataframes. Included among these are:
count(), which is used to sum the number of unique observations that contain some particular value or categorical attribute;
rename(), which enables a user to alter the column names for variables, often to improve ease of use and intuitive understanding of a dataset;
slice_max(), which returns a data subset that contains the rows with the highest number of values for some particular variable;
slice_min(), which returns a data subset that contains the rows with the lowest number of values for some particular variable.
The dplyr package comes with five datasets. These are: band_instruments, band_instruments2, band_members, starwars, storms.