10 R Packages You Should Know in 2020

In this video I discuss 10 R packages that I would recommend learning for your average data scientist's need in 2020. While this is not an absolutely comprehensive list and there is some general preference towards more general packages
rather than more specific ones.

1) dplyr
https://rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf
"dplyr" is for your data wrangling and data manipulation needs.

2) ggplot2
https://rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
"ggplot2" is for your visualization needs.

3) tidyr
https://github.com/rstudio/cheatsheets/blob/master/data-import.pdf
"tidyr" is for getting your data into a "tidy" format where: a) every row is an observation, b) every column is a variable, c) every cell has an observation or NA

4) purrr
https://github.com/rstudio/cheatsheets/blob/master/data-import.pdfhttps://github.com/rstudio/cheatsheets/blob/master/purrr.pdf
"purrr" is for working with lists and for mapping functions to the elements of vectors or lists.

5) stringr
http://edrub.in/CheatSheets/cheatSheetStringr.pdf
"stringr" is for working with strings and regular expressions.

6) lubridate
https://evoldyn.gitlab.io/evomics-2018/ref-sheets/R_lubridate.pdf
"lubridate" is for working with dates and datetimes aka POSIXcts.

7) shiny
https://shiny.rstudio.com/images/shiny-cheatsheet.pdf
"shiny" is a tool for creating interactive web applications that can be accessed by end users.

8) rmarkdown
https://rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf
"rmarkdown" is a bundle of packages for creating documents and notebooks (.doc, .html, .pdf).

9) caret
https://rstudio.com/resources/cheatsheets/
"caret" is a front to back package for machine learning.

10) reticulate
https://rstudio.com/resources/cheatsheets/
"reticulate" is a package enabling calling Python from R.

Honorable mentions: data.table, DT, forcats, kableExtra, keras, plotly

Видео 10 R Packages You Should Know in 2020 канала RichardOnData