使用dplyr按组进行累计计数 [英] Using dplyr to get cumulative count by group

问题描述

提前感谢。我有以下数据：

Thanks in advance. I have the following data:

df <- data.frame(person=c(1,1,1,1,2,2,2,2,3,3,3,3), 
             neighborhood=c("A","A","A","A","B","B","C","C","D","D","E","F"))

我想生成一个新的列，给出每个人随着面板进度而移动的社区的累积计数。像这样：

I would like to generate a new column that gives the cumulative count of neighborhoods that each person moves through as the panel progresses. Like such:

df2 <- data.frame(person=c(1,1,1,1,2,2,2,2,3,3,3,3), 
             neighborhood=c("A","A","A","A","B","B","C","C","D","D","E","F"),
             moved=c(0,0,0,0,0,0,1,1,0,0,1,2)
             )

再次感谢

推荐答案

我们可以使用group by'person'，然后通过匹配创建'移动'使用独特的值来获取索引并减1。

We can use group by 'person', then create the 'moved' by matching the 'neighborhood' with its unique values to get the index and subtract 1.

df %>%
   group_by(person) %>% 
   mutate(moved = match(neighborhood, unique(neighborhood))-1)
#   person neighborhood moved
#    <dbl>       <fctr> <dbl>
#1       1            A     0
#2       1            A     0
#3       1            A     0
#4       1            A     0
#5       2            B     0
#6       2            B     0
#7       2            C     1
#8       2            C     1
#9       3            D     0
#10      3            D     0
#11      3            E     1
#12      3            F     2

或使用因素 with levels 指定为邻居中的独特值，强制为整数，并减1。

or use factor with levels specified as the unique values in 'neighborhood', coerce to 'integer' and subtract 1.

df %>%
   group_by(person) %>% 
   mutate(moved = as.integer(factor(neighborhood, levels = unique(neighborhood)))-1)
#   person neighborhood moved
#    <dbl>       <fctr> <dbl>
#1       1            A     0
#2       1            A     0
#3       1            A     0
#4       1            A     0
#5       2            B     0
#6       2            B     0
#7       2            C     1
#8       2            C     1
#9       3            D     0
#10      3            D     0
#11      3            E     1
#12      3            F     2

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