如何有条件地在行上创建计数器？ R

Question

我得到了这个公司数据集，我已经“完成了面板”，所以只要定量变量（销售额、工资）为 0，公司就会关闭。 NA 代表我已经完成了面板，这意味着所有公司的年份都相同，但 NA 表示该公司之前（或之后）不存在

我想为公司的第一次倒闭做一个柜台。

所以我的数据看起来像这样：

Year    Firm    sales   wages
2014    A        12      4   
2015    A        8       3
2016    A        0       0 
2017    A        NA      NA 
2018    A        NA      NA 

2014    B        NA      NA   
2015    B        8       3
2016    B        4       2 
2017    B        9       5 
2018    B        8       6 

2014    C        9       5   
2015    C        7       6
2016    C        0       0 
2017    C        0       0
2018    C        0       0

而想要的结果是这样的：

Year    Firm    sales   wages  Closure
2014    A        12      4        0
2015    A        8       3        0
2016    A        0       0        1
2017    A        NA      NA       2  # After the closure in 2016 it doesn't appear on the original dataset anymore
2018    A        NA      NA       3  # Same here

2014    B        NA      NA       0  # Here the firm has not been created yet
2015    B        NA      NA       0  # Here too
2016    B        4       2        0
2017    B        9       5        0
2018    B        8       6        0

2014    C        9       5        0
2015    C        7       6        0
2016    C        0       0        1 
2017    C        0       0        2 #After the closure it continues appearing because the firm has some debts or some pending
2018    C        0       0        3 #Here the same, still appears bc it still have obligations

我怎样才能做到这一点？

提前致谢。

Answer 1

也许这有帮助

library(dplyr)
library(tidyr)
df1 %>% 
   group_by(Firm) %>% 
   mutate(Closure = replace_na(cumsum(lead(is.na(sales) & 
       is.na(wages), default = TRUE)|(sales == 0 & wages == 0)), 0)) %>%
   ungroup

-输出

# A tibble: 15 x 5
#    Year Firm  sales wages Closure
#   <int> <chr> <int> <int>   <dbl>
# 1  2014 A        12     4       0
# 2  2015 A         8     3       0
# 3  2016 A         0     0       1
# 4  2017 A        NA    NA       2
# 5  2018 A        NA    NA       3
# 6  2014 B        NA    NA       0
# 7  2015 B         8     3       0
# 8  2016 B         4     2       0
# 9  2017 B         9     5       0
#10  2018 B         8     6       0
#11  2014 C         9     5       0
#12  2015 C         7     6       0
#13  2016 C         0     0       1
#14  2017 C         0     0       2
#15  2018 C         0     0       3

数据

df1 <- structure(list(Year = c(2014L, 2015L, 2016L, 2017L, 2018L, 2014L, 
2015L, 2016L, 2017L, 2018L, 2014L, 2015L, 2016L, 2017L, 2018L
), Firm = c("A", "A", "A", "A", "A", "B", "B", "B", "B", "B", 
"C", "C", "C", "C", "C"), sales = c(12L, 8L, 0L, NA, NA, NA, 
8L, 4L, 9L, 8L, 9L, 7L, 0L, 0L, 0L), wages = c(4L, 3L, 0L, NA, 
NA, NA, 3L, 2L, 5L, 6L, 5L, 6L, 0L, 0L, 0L)), class = "data.frame",
row.names = c(NA, 
-15L))