August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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112
｜
第
6
章
猜测并试验下面命令的执行结果：
print
（
df_base
）
df_base$colA
df_base$col
df_base$colB
创建一个
tibble
数据集并重复前面的命令。
使用
tidyr
与正则表达式整理数据
数据分析的关键技巧是理解数据集的结构并能够“重构”它们。
从工作流
程效率角度而言这很重要：超过一半的数据分析时间花在重组数据工作上
（（
Wickham 2014b
）
,
所以尽早将数据处理成合适的形式可为以后节约不少
时间。从计算效率而言，数据处理成“整洁”的格式也是有利的，因为在整
洁的数据上执行分析与绘图命令通常较快。
整理数据包括数据清理与数据重构。数据清理是重定格式与标记脏数据的过
程。
stringi
与
stringr
的包可以通过正则表达式帮助更新脏字符串；
assertive
与
assertr
包可在数据分析项目的一开始进行数据完整性的校验。
通常的数据
清理任务是将非标准文本字符串转换成
lubridate
简介所描述的数据格式
[
详
见 vignette("lubridate")
]
。整洁是个广泛的概念，当然也包括重构数据，
以便有利于数据分析与建模。表
6-1
和表
6-2
给出了重构的过程，由
Hadley
Wickham
提供，可通过如下代码加载：
library("efficient")
data(pew) # see ?pew - dataset from the efficient package
pew[1:3, 1:4] # take a look at the data
#> #
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