Question

例如，我有需要根据以前的历史记录创建变量的数据

 created<- c(2009,2010,2010,2011, 2012, 2011)
 person <- c(A, A, A, A, B, B)
 location<- c('London','Geneva', 'London', 'New York', 'London', 'London')
 df <- data.frame (created, person, location)

我想创建一个名为“ existing”的变量，该变量考虑到前几年的情况，并查看他/她是否住过该地方，如果该地方老了（他们住在那里，则给出的值为0）。？

 library(dplyr) 
 df %>% group_by(person) %>% mutate (existing=0)

  existing<- c(1, 1, 0, 1, 0,1)

Answer 1

另一个dplyr选项可能是：

df %>%
 group_by(person, location) %>%
 mutate(existing = +(1:n() == 1))

  created person location existing
    <dbl> <fct>  <fct>       <int>
1    2009 A      London          1
2    2010 A      Geneva          1
3    2010 A      London          0
4    2011 A      New York        1
5    2012 B      London          1
6    2011 B      London          0

如果需要排序：

df %>%
 group_by(person, location) %>%
 arrange(created, .by_group = TRUE) %>%
 mutate(existing = +(1:n() == 1))

Answer 2

基于OP的更新信息，我们需要先按arrange和年份（person）created的数据，然后使用duplicated。

library(dplyr)

df %>% 
   arrange(person, created) %>% 
   group_by(person) %>% 
   mutate(existing = +(!duplicated(location)))

#  created person location existing
#    <dbl> <fct>  <fct>       <int>
#1    2009 A      London          1
#2    2010 A      Geneva          1
#3    2010 A      London          0
#4    2011 A      New York        1
#5    2011 B      London          1
#6    2012 B      London          0

Answer 3

您可以尝试

with(df, ave(location, person, FUN = function(i)as.integer(!duplicated(i))))
#[1] "1" "1" "0" "1" "1" "0"

Answer 4

使用data.table的另一个选项：

setDT(df)[order(person, created), existing := c(1L, rep(0L, .N-1L)), .(person, location)]

输出：

   created person location existing
1:    2009      A   London        1
2:    2010      A   Geneva        1
3:    2010      A   London        0
4:    2011      A New York        1
5:    2012      B   London        0
6:    2011      B   London        1

根据先前的历史记录创建新变量

4 个答案: