R中每组的条件累积平均值 [英] Conditional cumulative mean for each group in R

问题描述

我有一个数据集，如下所示：

I have a data set that looks like this:

id   a   b
1    AA  2
1    AB  5
1    AA  1
2    AB  2
2    AB  4
3    AB  4
3    AB  3
3    AA  1

我需要计算每个组中每个记录的累积平均值，不包括 a = ='AA'，所以示例输出应该是：

I need to calculate the cumulative mean for each record within each group and excluding the case where a == 'AA', So sample output should be:

id   a   b  mean
1    AA  2   -
1    AB  5   5
1    AA  1   5
2    AB  2   2
2    AB  4   (4+2)/2
3    AB  4   4
3    AB  3   (4+3)/2
3    AA  1   (4+3)/2
3    AA  4   (4+3)/2

我尝试使用dplyr和cummean获取错误。

I tried to achieve it using dplyr and cummean by getting an error.

df <- df %>%
       group_by(id) %>%
       mutate(mean = cummean(b[a != 'AA']))

错误：不兼容的大小（123），期望147（组大小）o r 1

Error: incompatible size (123), expecting 147 (the group size) or 1

你可以建议一个更好的方式在R中实现相同吗？

Can you suggest a better way to achieve the same in R ?

推荐答案

这里的诀窍是通过将调整后的 cumsum cummean c>按调整数。作为单行：

The trick here is to reconstruct the cummean by dividing the adjusted cumsum by the adjusted count. As a one-liner:

df %>% group_by(id) %>% mutate(cumsum(b * (a != 'AA')) / cumsum(a != 'AA'))

使这个更好一点（乘以 a！='AA' - 魔术！是我心中的丑陋），取出 a！='AA'作为列

We can make this a little nicer (the "multiply by a!='AA' - magic!" is the ugliness in my mind) by taking out the a != 'AA' as a column

df %>%
    group_by(id) %>%
    mutate(relevance = 0+(a!='AA'), 
           mean = cumsum(relevance * b) / cumsum(relevance))

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