dplyr：在突变本身中使用由 mutate 创建的列答案

【问题标题】：dplyr: using column created by mutate in the mutation itselfdplyr：在突变本身中使用由 mutate 创建的列
【发布时间】：2018-01-02 12:16:22
【问题描述】：

我有一个看起来像这样的数据框：

> df
# A tibble: 5,427 x 3
    cond desired   inc
   <chr>   <dbl> <dbl>
 1  <NA>       0     0
 2  <NA>       5     5
 3     X      10     5
 4     X       7     7
 5  <NA>      16    16
 6  <NA>      21     5
 7  <NA>      26     5
 8  <NA>      31     5
 9     X      37     6
10  <NA>       5     5

这已经包含了我想要的输出。我想要做的是将inc 的值相加，但如果在前一行的cond 列中有X，则重置总和。因此，例如在9 行中，我会从前一行（31）中获取desired-值，并从9（6）行中添加inc-值，得到37。在@987654329 行中@我只取inc-value，因为前一行的cond-column 是X。我使用循环解决了这个问题，但我想使用矢量化解决方案。到目前为止，我得到了这个：

df$test <- 0
df <- df %>% mutate(test = ifelse(is.na(lag(df$cond)), lag(test) + inc, inc))

如果我得到这个后运行第二行：

> df
# A tibble: 5,427 x 4
    cond desired   inc  test
   <chr>   <dbl> <dbl> <dbl>
 1  <NA>       0     0    NA
 2  <NA>       5     5     5
 3     X      10     5     5
 4     X       7     7     7
 5  <NA>      16    16    16
 6  <NA>      21     5     5
 7  <NA>      26     5     5
 8  <NA>      31     5     5
 9     X      37     6     6
10  <NA>       5     5     5

第二次运行后是这样的：

> df
# A tibble: 5,427 x 4
    cond desired   inc  test
   <chr>   <dbl> <dbl> <dbl>
 1  <NA>       0     0    NA
 2  <NA>       5     5    NA
 3     X      10     5    10
 4     X       7     7     7
 5  <NA>      16    16    16
 6  <NA>      21     5    21
 7  <NA>      26     5    10
 8  <NA>      31     5    10
 9     X      37     6    11
10  <NA>       5     5     5
# ... with 5,417 more rows

第三次：

> df
# A tibble: 5,427 x 4
    cond desired   inc  test
   <chr>   <dbl> <dbl> <dbl>
 1  <NA>       0     0    NA
 2  <NA>       5     5    NA
 3     X      10     5    NA
 4     X       7     7     7
 5  <NA>      16    16    16
 6  <NA>      21     5    21
 7  <NA>      26     5    26
 8  <NA>      31     5    15
 9     X      37     6    16
10  <NA>       5     5     5

那么，第五次之后：

> df
# A tibble: 5,427 x 4
    cond desired   inc  test
   <chr>   <dbl> <dbl> <dbl>
 1  <NA>       0     0    NA
 2  <NA>       5     5    NA
 3     X      10     5    NA
 4     X       7     7     7
 5  <NA>      16    16    16
 6  <NA>      21     5    21
 7  <NA>      26     5    26
 8  <NA>      31     5    31
 9     X      37     6    37
10  <NA>       5     5     5

我在 mutate-command 本身中使用了我通过 mutate 创建的列，我猜这是导致这种行为/问题的原因。有什么方法可以达到我想要的结果吗？提前致谢！

数据框：

structure(list(cond = c(NA, NA, "X", "X", NA, NA, NA, NA, "X", 
NA, NA, NA, NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, "X", 
NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, "X", NA, NA, "X", 
NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, "X", NA, 
NA, NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, 
NA, "X", NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, 
NA, NA, NA, "X", NA, NA, NA, "X", NA, NA, NA, NA, "X", NA, NA, 
NA, NA, NA, NA, NA, NA, "X", NA, NA, "X", NA, NA, NA, NA, "X", 
NA, NA, NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, 
NA, "X", NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, "X", NA, NA, 
NA, NA, NA, NA, NA, "X", NA, NA, NA, "X", "X", NA, NA, NA, NA, 
NA, NA, NA, NA, "X", "X", NA, "X", NA, NA, NA, NA, NA, NA, NA, 
NA, "X", NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, "X", 
NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, "X", NA, NA, NA, NA, 
"X", NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, 
"X", NA, NA, NA, NA, NA, NA, "X", NA, NA, NA, NA, "X", NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "X", NA, "X", 
NA, "X", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, "X", NA, NA, NA), desired = c(0, 5, 10, 7, 16, 21, 26, 
31, 37, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 5, 10, 15, 20, 
30, 7, 15, 21, 25, 40, 45, 55, 12, 20, 25, 30, 35, 40, 45, 50, 
55, 60, 65, 70, 75, 5, 10, 15, 20, 22, 30, 35, 45, 50, 55, 60, 
65, 70, 75, 9, 14, 19, 24, 29, 34, 39, 44, 5, 7, 10, 2, 7, 12, 
17, 22, 27, 5, 10, 15, 20, 25, 30, 35, 38, 4, 7, 12, 17, 22, 
27, 32, 37, 39, 13, 18, 23, 28, 33, 38, 43, 48, 53, 5, 10, 15, 
20, 25, 30, 35, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 5, 10, 
15, 20, 2, 10, 15, 20, 25, 5, 10, 15, 20, 25, 30, 35, 40, 45, 
5, 8, 12, 5, 10, 14, 19, 24, 5, 10, 15, 20, 25, 30, 35, 40, 45, 
5, 10, 15, 20, 25, 28, 33, 38, 5, 11, 5, 10, 15, 20, 25, 30, 
35, 40, 45, 12, 17, 22, 27, 32, 37, 42, 47, 5, 10, 15, 20, 5, 
5, 10, 15, 20, 25, 30, 35, 40, 45, 5, 5, 10, 5, 10, 15, 20, 25, 
30, 35, 40, 45, 5, 10, 15, 20, 5, 10, 15, 20, 25, 30, 34, 39, 
44, 5, 10, 15, 20, 25, 30, 5, 10, 15, 20, 25, 5, 10, 15, 20, 
25, 5, 10, 15, 20, 25, 29, 5, 10, 15, 20, 23, 25, 30, 35, 40, 
5, 15, 20, 25, 30, 35, 40, 5, 10, 15, 20, 25, 5, 10, 15, 20, 
25, 28, 33, 38, 43, 48, 53, 58, 71, 76, 81, 5, 10, 5, 10, 5, 
10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 5, 
10, 15), inc = c(0, 5, 5, 7, 16, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 10, 7, 8, 6, 4, 15, 5, 10, 12, 8, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 2, 8, 5, 10, 5, 5, 
5, 5, 5, 5, 9, 5, 5, 5, 5, 5, 5, 5, 5, 2, 3, 2, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 3, 4, 3, 5, 5, 5, 5, 5, 5, 2, 13, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 2, 8, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
3, 4, 5, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
3, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 12, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 
5, 5, 5, 5, 3, 2, 5, 5, 5, 5, 10, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 13, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5)), .Names = c("cond", 
"desired", "inc"), row.names = c(NA, -300L), class = c("tbl_df", 
"tbl", "data.frame"))

【问题讨论】：

cond 列是 x，即使在行 9 中也是如此。因此，根据您规定的规则，总和也应设置在那里。为什么行9 与行3 或4 不同？
X 影响下一行，因此第 9 行中的 X 重置总和，第 10 行 inc 成为总和。第 3 行和第 4 行也是如此：在第 4 行和第 5 行中，所需的值与该行的 inc 相同。

标签： r dplyr

【解决方案1】：

这是一个使用ave() 函数和上面的df 结构的示例。为了清楚起见，我将显示所有步骤，但如果需要，可以减少这些步骤。

library(dplyr)
df %>% 
  mutate(prevcond = lag(cond)) %>%
  mutate(flag = ifelse(is.na(prevcond) | prevcond !='X', 0, 1)) %>% 
  mutate(counter = cumsum(flag)) %>% 
  mutate(desired2 = ave(inc, counter, FUN = cumsum))

【讨论】：

【解决方案2】：

为了达到您想要的输出，我们必须首先创建一个分组列，每次上一行等于X 时都会重置。为此，我们将row_number() 与zoo::na.locf() 结合使用。那么我们就可以简单的使用cumsum()：

library(dplyr)
library(zoo)
df %>% group_by(grp = na.locf(row_number(cond), 
                              fromLast = TRUE, 
                              na.rm = FALSE)) %>%
  mutate(test = cumsum(inc))
#    cond desired   inc   grp  test
#   <chr>   <dbl> <dbl> <int> <dbl>
# 1  <NA>       0     0     1     0
# 2  <NA>       5     5     1     5
# 3     X      10     5     1    10
# 4     X       7     7     2     7
# 5  <NA>      16    16     3    16
# 6  <NA>      21     5     3    21
# 7  <NA>      26     5     3    26
# 8  <NA>      31     5     3    31
# 9     X      37     6     3    37
#10  <NA>       5     5     4     5

【讨论】：