如何按组获得最小值 [英] How to get a minimum value by group

问题描述

我有一个像这样的数据框

I have a data frame looks like this

library(dplyr)
test.df <- data.frame(id=c(1,1,1,3,3,3,3),
                      date=c("2016-02-13","2016-06-01",
                             "2016-09-01","2015-08-02",
                             "2015-09-21","2016-12-01",
                             "2017-02-11"))

test.df$date <- as.Date(test.df$date,format='%Y-%m-%d')

id    date
1   2016-02-13          
1   2016-06-01          
1   2016-09-01          
3   2015-08-02          
3   2015-09-21          
3   2016-12-01          
3   2017-02-11  

我想创建一个新变量 first.login 来获取每个 id 的第一个日期.输出将如下所示

And I want to create a new variable first.login to get first date of each id. The output will look like this

id    date      first.login
1   2016-02-13  2016-02-13
1   2016-06-01  2016-02-13      
1   2016-09-01  2016-02-13      
3   2015-08-02  2015-08-02      
3   2015-09-21  2015-08-02      
3   2016-12-01  2015-08-02      
3   2017-02-11  2015-08-02

我尝试使用这样的代码

new.df <- test.df %>% 
  group_by(id) %>% 
  mutate(first.log = min(date))

但这给出的结果是提取整个数据框的最早日期，而不是在每个 ID 组中.

But this gives the result that extracts earliest date for the whole data frame, not within each ID group.

id    date      first.login
1   2016-02-13  2015-08-02
1   2016-06-01  2015-08-02      
1   2016-09-01  2015-08-02      
3   2015-08-02  2015-08-02      
3   2015-09-21  2015-08-02      
3   2016-12-01  2015-08-02      
3   2017-02-11  2015-08-02

这不应该是一项棘手的任务，但我想知道我犯了什么错误?如何在每个 id 组中获得最早的?

This shouldn't be a tricky task, but I was wondering what mistake did I make? How can I get the earliest within each id group?

更新:我之前尝试过使用 summarize，

Update: I've tried to use summarize before,

new.df <- test.df %>% 
  group_by(id) %>% 
  summarize(first.login = min(date))

但它返回单行和单列.

first.log
2015-08-02

事实证明这些代码没有问题；我只需要在其中指定 dplyr::mutate.

It turns that there's nothing wrong with these codes; I just need to specify dplyr::mutate in it.

推荐答案

这是一个循序渐进的 R 基础解决方案:

Here's a step-by-step R base solution:

# renaming for easy handle
x <- test.df$date
g <- test.df$id
# getting min
split(x, g) <- lapply(split(x, g), min)
# merging
test.df$first.login <- do.call("c", split(x, g))
#printting result
test.df
  id       date first.login
1  1 2016-02-13  2016-02-13
2  1 2016-06-01  2016-02-13
3  1 2016-09-01  2016-02-13
4  3 2015-08-02  2015-08-02
5  3 2015-09-21  2015-08-02
6  3 2016-12-01  2015-08-02
7  3 2017-02-11  2015-08-02

实际上这就是 ave 在内部的工作方式

Actually this is how ave Works inside

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