基于R dplyr中的条件折叠一个热编码列

Question

我有一张表，其中包含热编码的变量。我想将这些变量折叠到一列中。例如，任何具有“high”、“med”或“low”的列，我希望成为具有 high = 0、med = 1 和 low = 2 的数字编码的一列。我如何在 R 中执行此dplyr操作？我怀疑转向会有所帮助，但我不知道从哪里开始。生成的列名称应包含三列的名称，不带 high、med、low 名称。例如，我会将 columns d-high_cm1、d-med_cm1、转换d-low_cm1为d-cm1数字编码。

输入：

sex age    cost_cm  d-high_cm1 d-med_cm1 d-low_cm1 c-high_cm1 c-med_cm1 c-low_cm1
f   old    1        1           0         0           1           0         0
m   young  0        1           0         0           1           0         0
m   old    0        0           1         0           0           1         0
f   young  0        1           0         0           0           0         1
m   old    1        0           0         1           0           0         1

预期输出：

sex age    cost_cm  d-cm1 c-cm1 
f   old    1        0     0
m   young  0        0     0
m   old    0        1     1
f   young  0        0     2
m   old    1        2     2

Answer 1

我们可以做

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library(stringr)\nlibrary(dplyr)\nlibrary(tidyr)\ndf1 %>% \n   mutate(across(contains("-"), ~ case_when(str_detect(cur_column(),\n      'low') ~ . * 2, str_detect(cur_column(), 'med')  ~ . * 1,\n        TRUE ~ .* 0))) %>%\n   rename_with(~ str_replace(., "-(\\\\w+)_(\\\\w+)", "-\\\\2_\\\\1"), contains('-')) %>% \n   pivot_longer(cols = contains('-'), names_to = c(".value"), \n      names_pattern = "^([^_]+)_.*")%>% \n   group_by(sex, age, cost_cm) %>% \n   summarise(across(everything(), max), .groups = 'drop')\n

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-输出

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# A tibble: 5 \xc3\x97 5\n  sex   age   cost_cm `d-cm1` `c-cm1`\n  <chr> <chr>   <int>   <dbl>   <dbl>\n1 f     old         1       0       0\n2 f     young       0       0       2\n3 m     old         0       1       1\n4 m     old         1       2       2\n5 m     young       0       0       0\n

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或者使用base R

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lst1 <- lapply(split.default(df1[-c(1:3)], sub("-[^_]+", "", \n    names(df1)[-(1:3)])), function(x) do.call(pmax, x *  (0:2)[col(x)]))\ncbind(df1[1:3], lst1)\n

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-输出

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   sex   age cost_cm c_cm1 d_cm1\n1   f   old       1     0     0\n2   m young       0     0     0\n3   m   old       0     1     1\n4   f young       0     2     0\n5   m   old       1     2     2\n

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数据

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df1 <- structure(list(sex = c("f", "m", "m", "f", "m"), age = c("old", \n"young", "old", "young", "old"), cost_cm = c(1L, 0L, 0L, 0L, \n1L), `d-high_cm1` = c(1L, 1L, 0L, 1L, 0L), `d-med_cm1` = c(0L, \n0L, 1L, 0L, 0L), `d-low_cm1` = c(0L, 0L, 0L, 0L, 1L), `c-high_cm1` = c(1L, \n1L, 0L, 0L, 0L), `c-med_cm1` = c(0L, 0L, 1L, 0L, 0L), `c-low_cm1` = c(0L, \n0L, 0L, 1L, 1L)), class = "data.frame", row.names = c(NA, -5L\n))\n

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