R手册(Common)--tidyverse+tibble

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

tibble

tibble 重塑了data.frame，可存储任意类型，包括list，tbl_df 等。

tibble()永远不会改变输入的类型（例如它永远不会将字符串转换为因子），永远不会改变变量的名称，并且它永远不会创建row.names()

创建tibble

`as_tibble(x)` #从现有对象创建
`tibble() `    #使用列向量创建
`tribble()`    #逐行布局生成

# 示例
as_tibble(iris)

tibble(x = runif(10), y = x * 2)
tibble(x =list(1,2), y = tibble("a","b"))

tribble(~colA,~colB, 
        "a",  1,
        "b",  2)

取子集	说明
[	返回data.frame
[[, $	返回子向量

函数

函数	说明
is_tibble(x)	判断
增加行/列
add_column(.data, …, .before = NULL, .after = NULL)	将列添加到数据框
add_row(.data, …, .before = NULL, .after = NULL)	将行添加到数据框
用于处理行名的工具
has_rownames(df)
remove_rownames(df)
rownames_to_column(df, var = “rowname”)
rowid_to_column(df, var = “rowid”)
column_to_rownames(df, var = “rowname”