- tidyr. As the name suggests, we use tidyr to make the data 'tidy'.
- ggplot2. With ggplot2, you can create graphics declaratively.
- ggraph. ggraph is an extension of ggplot2.
- dplyr.
- tidyquant.
- dygraphs.
- leaflet.
- ggmap.
What are the best packages in R?
- R for Data Science.
- ggplot2 for Data Visualization.
- dplyr and dbplyr for Data Wrangling.
- mlr3 and caret for Machine Learning.
- knitr for Generating Reports.
- tidyverse for General Data Science Tasks.
- Keep Learning.
How do I list all packages in R?
To see what packages are installed, use the installed. packages() command. This will return a matrix with a row for each package that has been installed.
What packages do you need for R?
- ggplot2. ggplot2 is based on the 'Grammar of Graphics", which is a popular data visualization library.
- data. table.
- dplyr.
- tidyr.
- Shiny.
- plotly.
- knitr.
- mlr3.
What are R programming packages?
R packages are extensions to the R statistical programming language. R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).
What is the best R package?
- dpylr. This R package was developed to solve the data manipulation challenges from beginner to expert level.
- ggplot2. Ggplot2 Plot example.
- tidyr. Tidyr in action Source: Official Tidyr Github.
- lubridate.
- tibble.
- stringr.
- RMarkDown.
- Shiny.
Does R have packages?
R comes with standard (or base) packages, which contain the basic functions and data sets as well as standard statistical and graphical functions that allow R to work. There are also thousands other R packages available for download and installation from CRAN, Bioconductor and GitHub repositories.