Question

我正在尝试使用dplyr按组数据进行处理，但它无法正常工作。任何帮助，将不胜感激。以下是数据样本。我想保留2014年的值，并使用滞后（midfs1）和值计算midfs1的其余值。以下是我对这个问题的尝试。

 t3 = t2 %>% 
 group_by(cz,btype) %>%
 mutate( midfs1 = ifelse(year == 2014,midfs1,
 lag(midfs1)*value+lag(midfs1)))

t2数据：

   cz    btype       year   midfs    value      midfs1
1    College     2014   5.4254  0.007582767 5.4254
1    College     2015   5.4779  0.007582767 NA
1    College     2016   5.5191  0.007582767 NA
1    College     2017   5.5616  0.007582767 NA
1    College     2018   5.6097  0.007582767 NA
1    Grocery     2012   4.8267  0.002697526 NA
1    Grocery     2013   4.8205  0.002697526 NA
1    Grocery     2014   4.8583  0.002697526 4.8583
1    Grocery     2015   4.8966  0.002697526 NA
1    Grocery     2016   4.9556  0.002697526 NA
1    Grocery     2017   5.0258  0.002697526 NA
1    Grocery     2018   5.0982  0.002697526 NA
1    Grocery     2019   5.1514  0.002697526 NA
1    Grocery     2020   5.1976  0.002697526 NA
1    Grocery     2021   5.2338  0.002697526 NA

Answer 1

解决复合增长问题：

t3 <-
  t2 %>% 
  group_by(cz, btype) %>%
  filter(year >= 2014) %>% 
  mutate(my_n = 1:n(),
         midfs2 = ifelse(year == 2014,
                         midfs1,
                         rep(midfs1[1]) * (1 + value) ^ lag(my_n, 1)))

# result

Source: local data frame [13 x 8]
Groups: cz, btype

   cz   btype year  midfs       value midfs1 my_n   midfs2
1   1 College 2014 5.4254 0.007582767 5.4254    1 5.425400
2   1 College 2015 5.4779 0.007582767     NA    2 5.466540
3   1 College 2016 5.5191 0.007582767     NA    3 5.507991
4   1 College 2017 5.5616 0.007582767     NA    4 5.549757
5   1 College 2018 5.6097 0.007582767     NA    5 5.591839
6   1 Grocery 2014 4.8583 0.002697526 4.8583    1 4.858300
7   1 Grocery 2015 4.8966 0.002697526     NA    2 4.871405
8   1 Grocery 2016 4.9556 0.002697526     NA    3 4.884546
9   1 Grocery 2017 5.0258 0.002697526     NA    4 4.897722
10  1 Grocery 2018 5.0982 0.002697526     NA    5 4.910934
11  1 Grocery 2019 5.1514 0.002697526     NA    6 4.924181
12  1 Grocery 2020 5.1976 0.002697526     NA    7 4.937465
13  1 Grocery 2021 5.2338 0.002697526     NA    8 4.950783

通过使用dplyr进行组处理 - 代码不起作用

1 个答案: