R - 基于条件的聚合分母,用于所有行的百分比计算

R - Aggregate denominator based on condition, for use in percentage calculation for all rows

我有这样的数据:

population <- c(101:110)
coverage  <- c(91:100)
area <- c("Cambridge", "Cambridge","Cambridge", "Cambridge","Cambridge", "Oxford", "Oxford","Oxford", "Oxford","Oxford")
all <- data.frame(population,coverage,area) 

然后我想要一段简洁的 R 代码来计算移动覆盖区域内的人口百分比。我知道是这样的(但不是这个):

coverage <- population x (coverage/100) / (aggregate(population, by=area, FUN=sum))

如何按地区计算人口总和,以用作所有行百分比计算中的分母?通常我会使用 aggregate 按地区获取人口,然后将其合并回数据框以用作分母,但这根本不是很优雅。我希望数据最终看起来像这样:

population <- c(101:110)
coverage  <- c(91:100)
area <- c("Cambridge", "Cambridge","Cambridge", "Cambridge","Cambridge", "Oxford", "Oxford","Oxford", "Oxford","Oxford")
percentage <- c(18, 18, 18, 18, 18, 19, 19, 19, 19, 19)
all <- data.frame(population,coverage,area, percentage) 

非常感谢您的帮助。

您可以使用 dplyrarea:

对计算进行分组
library(dplyr)
all %>% group_by(area) %>% mutate(percentage=population*(coverage/100)/sum(population))
##Source: local data frame [10 x 4]
##Groups: area [2]
##
##   population coverage      area percentage
##        <int>    <int>    <fctr>      <dbl>
##1         101       91 Cambridge  0.1784660
##2         102       92 Cambridge  0.1822136
##3         103       93 Cambridge  0.1860000
##4         104       94 Cambridge  0.1898252
##5         105       95 Cambridge  0.1936893
##6         106       96    Oxford  0.1884444
##7         107       97    Oxford  0.1922037
##8         108       98    Oxford  0.1960000
##9         109       99    Oxford  0.1998333
##10        110      100    Oxford  0.2037037

我想你想要 dplyr summarize 为此。

这是否达到你想要的效果?

图书馆(dplyr) all %>% group_by(area) %>% summarise(coveragePct=sum(覆盖率)/sum(人口))

你可以用 dplyr 做到这一点:

all.summary <- all %>%
    group_by(area) %>%
    mutate(percentage = population/sum(population)*(coverage/100))
all.summary


   population coverage      area percentage
        <int>    <int>    <fctr>      <dbl>
1         101       91 Cambridge  0.1784660
2         102       92 Cambridge  0.1822136
3         103       93 Cambridge  0.1860000
4         104       94 Cambridge  0.1898252
5         105       95 Cambridge  0.1936893
6         106       96    Oxford  0.1884444
7         107       97    Oxford  0.1922037
8         108       98    Oxford  0.1960000
9         109       99    Oxford  0.1998333
10        110      100    Oxford  0.2037037