dplyr在mutate中使用rowwise和df-wise值

Question

如何执行rowwise使用其他行的值的操作(以dplyr/tidy样式)？假设我有这个df:

df <- data_frame(value = c(5,6,7,3,4),
                 group = c(1,2,2,3,3),
                 group.to.use = c(2,3,3,1,1))

我想创建一个新变量new.value,它等于每行的当前值加上"group"等于该行的"group.to.use"的行的最大值.所以对于第一行

new.value = 5 + (max(value[group === 2])) = 5 + 7 = 12

期望的输出:

# A tibble: 5 x 4
  value group group.to.use new.value
  <dbl> <dbl>        <dbl>     <dbl>
1    5.    1.           2.       12.
2    6.    2.           3.       10.
3    7.    2.           3.       11.
4    3.    3.           1.        8.
5    4.    3.           1.        9.

伪代码:

df %<>%
  mutate(new.value = value + max(value[group.to.use == <group.for.this.row>]))

Answer 1

在横行操作,你可以参考整个data.frame用.,并与正常语法data.frame一整列.$colname或.[['col.name']]:

df %>%
    rowwise() %>%
    mutate(new.value = value + max(.$value[.$group == group.to.use])) %>%
    ungroup()

# # A tibble: 5 x 4
#   value group group.to.use new.value
#   <dbl> <dbl>        <dbl>    <dbl>
# 1     5     1            2       12
# 2     6     2            3       10
# 3     7     2            3       11
# 4     3     3            1        8
# 5     4     3            1        9

或者,您可以预先计算每个组的最大值,然后执行左连接:

df.max <- df %>% group_by(group) %>% summarise(max.value = max(value))

df %>%
    left_join(df.max, by = c('group.to.use' = 'group')) %>%
    mutate(new.value = value + max.value) %>%
    select(-max.value)
# # A tibble: 5 x 4
#   value group group.to.use new.value
#   <dbl> <dbl>        <dbl>     <dbl>
# 1     5     1            2        12
# 2     6     2            3        10
# 3     7     2            3        11
# 4     3     3            1         8
# 5     4     3            1         9