r - 分组并计数以获得接近的价格

Question

我想按country计算status是open的次数，以及status是closed的次数。然后计算每个closerate的country。

数据:

customer <- c(1,2,3,4,5,6,7,8,9)
country <- c('BE', 'NL', 'NL','NL','BE','NL','BE','BE','NL')
closeday <- c('2017-08-23', '2017-08-05', '2017-08-22', '2017-08-26', 
'2017-08-25', '2017-08-13', '2017-08-30', '2017-08-05', '2017-08-23')
closeday <- as.Date(closeday)

df <- data.frame(customer,country,closeday)

添加 status:

df$status <- ifelse(df$closeday < '2017-08-20', 'open', 'closed') 

  customer country   closeday status
1        1      BE 2017-08-23 closed
2        2      NL 2017-08-05   open
3        3      NL 2017-08-22 closed
4        4      NL 2017-08-26 closed
5        5      BE 2017-08-25 closed
6        6      NL 2017-08-13   open
7        7      BE 2017-08-30 closed
8        8      BE 2017-08-05   open
9        9      NL 2017-08-23 closed

计算 closerate

closerate <- length(which(df$status == 'closed')) / 
(length(which(df$status == 'closed')) + length(which(df$status == 'open')))

[1] 0.6666667

显然，这是总数的 closerate。面临的挑战是获取每个 closerate的 country。我尝试通过以下方式将 closerate计算添加到 df中:

df$closerate <- length(which(df$status == 'closed')) / 
(length(which(df$status == 'closed')) + length(which(df$status == 'open')))

但这给所有行的 closerate为0.66，因为我没有分组。我相信我不应该使用长度函数，因为可以通过分组来完成计数。我阅读了一些有关使用 dplyr对每个组的逻辑输出进行计数的信息，但是这没有解决。

这是所需的输出:

Answer 1

aggregate(list(output = df$status == "closed"),
          list(country = df$country),
          function(x)
              c(close = sum(x),
                open = length(x) - sum(x),
                rate = mean(x)))
#  country output.close output.open output.rate
#1      BE         3.00        1.00        0.75
#2      NL         3.00        2.00        0.60

在注释中有一个使用 table的解决方案，它似乎已被删除。无论如何，您也可以使用 table

output = as.data.frame.matrix(table(df$country, df$status))
output$closerate = output$closed/(output$closed + output$open)
output
#   closed open closerate
#BE      3    1      0.75
#NL      3    2      0.60