tidyverse是一系列包的组合，构建了一套完整的数据分析生态链，提供了一套整洁的数据导入，分析和建模方法，刷新了R语言原有的数据科学体系。

tidyverse

Usage

  • small, in-memory data ( <2Gb): tidyverse
  • larger data (10-100 Gb): data.table
  • Parallel computing : need a system (like Hadoop or Spark)

core tidyverse packages

tidyverse_packages() #查看tidyverse内含的包
tidyverse_update() #更新tidyverse packages

library(tidyverse) will load the core tidyverse packages:

  • ggplot2, for data visualisation.
  • dplyr, for data manipulation.
  • tidyr, for data tidying.
  • readr, for data import.
  • purrr, for functional programming.(FP)
  • tibble, for tibbles, a modern re-imagining of data frames.

Import

As well as readr, for reading flat files, the tidyverse includes:

  • readxl for .xls and .xlsx sheets.
  • feather, for sharing with Python and other languages.
  • haven for SPSS, Stata, and SAS data.
  • jsonlite for JSON.
  • xml2 for XML.
  • httr for web APIs.
  • rvest for web scraping.
  • DBI for relational databases.

Wrangle

As well as tidyr, and dplyr, there are five packages designed to work with specific types of data:

  • stringr for strings.
  • lubridate for dates and date-times.
  • forcats for categorical variables (factors).
  • hms for time-of-day values.
  • blob for storing blob (binary) data.

Program

除了purrr处理函数式编程外，还有三个解决常规编程的包

  • rlang 提供了R的核心语言和tidyverse的工具
  • magrittr 管道函数
  • glue 提供了base::paste()的更加丰富的版本

Model

  • modelr, for modelling within a pipeline
  • broom, for turning models into tidy data