Question

我想根据除行以外的所有行来汇总多个列中的数据，这些行在单独的分组变量列中具有一定的值。例如，在下面的df中，我想基于未分配给与给定行匹配的群集的行中的值来获取A，B，C，D和E的中值。

df = data.frame(cluster = c(1:5, 1:3, 1:2),
                    A = rnorm(10, 2),
                    B = rnorm(10, 5),
                    C = rnorm(10, 0.4),
                    D = rnorm(10, 3),
                    E = rnorm(10, 1))

df %>%
group_by(cluster) %>%
summarise_at(toupper(letters[1:5]), funs(m = fun_i_need_help_with(.)))

fun_i_need_help_with将等于：

    first row: median(df[which(df$cluster != 1), "A"])
    second row: median(df[which(df$cluster != 2), "A"])
    and so on...

我可以使用嵌套的for循环来做到这一点，但是它运行起来很慢，而且似乎不是一个像R一样好的解决方案。

for(col in toupper(letters[1:5])){
    for(clust in unique(df$cluster)){
        df[which(df$cluster == clust), col] <-
           median(df[which(df$cluster != clust), col])
     }
    }

Answer 1

使用tidyverse的解决方案。

set.seed(123)

df = data.frame(cluster = c(1:5, 1:3, 1:2),
                A = rnorm(10, 2),
                B = rnorm(10, 5),
                C = rnorm(10, 0.4),
                D = rnorm(10, 3),
                E = rnorm(10, 1))

library(tidyverse)

df2 <- map_dfr(unique(df$cluster),
        ~df %>%
          filter(cluster != .x) %>%
          summarize_at(vars(-cluster), funs(median(.))) %>%
          # Add a label to show the content of this row is not from a certain cluster number
          mutate(not_cluster = .x))
df2
#          A        B          C        D         E not_cluster
# 1 2.070508 5.110683  0.1820251 3.553918 0.7920827           1
# 2 2.070508 5.400771 -0.6260044 3.688640 0.5333446           2
# 3 1.920165 5.428832 -0.2769652 3.490191 0.8543568           3
# 4 1.769823 5.400771 -0.2250393 3.426464 0.5971152           4
# 5 1.769823 5.400771 -0.3288912 3.426464 0.5971152           5

R汇总dplyr分组数据，其中某些行基于另一列被排除

1 个答案: