August 2018
Intermediate to advanced
227 pages
4h 16m
Chinese
Content preview from 高效R语言编程
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高效数据木匠
｜
129
合并数据集
有时通过与其他数据的合并可大大增强数据集的作用。比如说
，如果将全局
ghg_ems 数据集与地理数据合并
，我们能够可视化气候污染的空间分布。基
于本章的目的
，我们将 ghg_ems 与
ggmap
提供的
world
数据连接，演示了数
据连接的概念以及方法（也称为合并
）。
library("ggmap")
world = map_data("world")
names(world)
#> [1] "long" "lat" "group" "order" "region" "subregion"
对
world
的新数据集
和
ghg_ems 进行可视化比较
（例如
View(world);
View(ghg_ems))。显然
world
的新数据集中的 region 列与 ghg_ems 中的
Country 列包含的信息相同。这将是连接变量，在 world 中对其改名将使连
接更高效。
world = rename(world, Country = region)
ghg_ems$All = rowSums(ghg_ems[3:7])
确保两个连接变量有相同的类（组合字符与因子列将导致灾难）。
ghg_ems$Country 与 world$Country 存在多大的重合 ? 我们可使用 %in% 操
作符查看下，该操作符可查看一个向量有多少元素与另外一个向量匹配。本
例中就是查清 ghg_ems 中有多少国家（不重复）的名字出现在 world ...
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