Software:Dplyr
 | |
| Original author(s) | Hadley Wickham, Romain François, Lionel Henry, Kirill Müller, Davis Vaughan |
|---|---|
| Initial release | January 7, 2014 |
| Written in | R |
| License | MIT License |
| Website | dplyr |
dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language.[1] 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.[2][3]
For instance, someone seeking to analyze a large 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. Functions within the dplyr package will allow a user to perform such tasks.
dplyr was launched in 2014.[4] 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."[5]
The five core verbs
While dplyr actually includes several dozen functions that enable various forms of data manipulation, the package features five primary verbs or actions:[6]
- 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.
Additional functions
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.
Built-in datasets
The dplyr package comes with five datasets. These are: band_instruments, band_instruments2, band_members, starwars, storms.
Copyright & license
The copyright to dplyr is held by Posit PBC, formerly RStudio PBC. dplyr was originally released under a GPL license[citation needed], but in 2022, Posit changed the license terms for the package to the "more permissive" MIT License.[7] The main difference between the two types of license is that the MIT license allows subsequent re-use of code within proprietary software, whereas a GPL license does not.
References
- ↑ Wickham, Hadley; Averick, Mara; Bryan, Jennifer; Chang, Winston; McGowan, Lucy D'Agostino; François, Romain; Grolemund, Garrett; Hayes, Alex et al. (2019-11-21). "Welcome to the Tidyverse" (in en). Journal of Open Source Software 4 (43): 1686. doi:10.21105/joss.01686. ISSN 2475-9066.
- ↑ Yadav, Rohit (2019-10-29). "Python's Pandas vs R's Tidyverse: Who Comes Out On Top?" (in en-US). https://analyticsindiamag.com/pythons-pandas-vs-rs-tidyverse-who-wins/.
- ↑ Krill, Paul (2015-06-30). "Why R? The pros and cons of the R language" (in en). https://www.infoworld.com/article/2940864/r-programming-language-statistical-data-analysis.html.
- ↑ "Introducing dplyr" (in en-us). 17 January 2014. https://blog.rstudio.com/2014/01/17/introducing-dplyr/.
- ↑ "Function reference" (in en). https://dplyr.tidyverse.org/reference/index.html.
- ↑ Grolemund, Garrett; Wickham, Hadley. 5 Data transformation | R for Data Science. https://r4ds.had.co.nz/.
- ↑ "A Grammar of Data Manipulation" (in en). https://dplyr.tidyverse.org.
 |  |
