也许你只是这个意思。

data %>%
  mutate(across(starts_with("act1"), ~ paste0(.x, Day)))
#   Day act1_001 act1_002 act1_003 act1_004 act1_005 act1_006 act1_007
# 1   1       a1       b1       d1       a1       a1       b1       b1
# 2   1       a1       a1       b1       a1       d1       a1       b1
# 3   2       b2       b2       a2       b2       d2       b2       b2
# 4   2       d2       b2       d2       d2       b2       d2       d2
# 5   1       d1       d1       a1       d1       a1       b1       d1

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数据

data <- structure(list(Day = c(1, 1, 2, 2, 1), act1_001 = c("a", "a", "b", "d", "d"), act1_002 = c("b", "a", "b", "b", "d"), act1_003 = c("d", "b", "a", "d", "a"), act1_004 = c("a", "a", "b", "d", "d"), act1_005 = c("a", "d", "d", "b", "a"), act1_006 = c("b", "a", "b", "d", "b"), act1_007 = c("b", "b", "b", "d", "d")), class = "data.frame", row.names = c(NA, -5L))

型

从你的逻辑判断，你想在每一列中折叠与Day相关的数字和字母。要在基数R中做到这一点，你可以这样做：

act_cols <- grep("act1", names(data)) # identify columns of interest

data[act_cols] <- lapply(data[act_cols], \(x) paste0(unlist(x), unlist(data[1])))

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输出量：

#   Day act1_001 act1_002 act1_003 act1_004 act1_005 act1_006 act1_007
# 1   1       a1       b1       d1       a1       a1       b1       b1
# 2   1       a1       a1       b1       a1       d1       a1       b1
# 3   2       b2       b2       a2       b2       d2       b2       b2
# 4   2       d2       b2       d2       d2       b2       d2       d2
# 5   1       d1       d1       a1       d1       a1       b1       d1

型

您使用的across不正确。下面是tidyverse中使用case_when()的解决方案：

library(dplyr)
library(tidyr)

data %>%
  pivot_longer(cols = starts_with("act1"), names_to = "activity", values_to = "value") %>% 
  mutate(result = case_when(
    Day == 1 & value == "a" ~ "a1",
    Day == 2 & value == "a" ~ "a2",
    Day == 1 & value == "b" ~ "b1",
    Day == 2 & value == "b" ~ "b2",
    Day == 1 & value == "d" ~ "d1",
    .default =  "d2"
  ))

A tibble: 35 × 4
     Day activity value result
   <dbl> <chr>    <chr> <chr> 
 1     1 act1_001 a     a1    
 2     1 act1_002 b     b1    
 3     1 act1_003 d     d1    
 4     1 act1_004 a     a1    
 5     1 act1_005 a     a1    
 6     1 act1_006 b     b1    
 7     1 act1_007 b     b1    
 8     1 act1_001 a     a1    
 9     1 act1_002 a     a1    
10     1 act1_003 b     b1    
# ℹ 25 more rows
# ℹ Use `print(n = ...)` to see more rows

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数据类型：

Day <- c(1,1,2,2,1)
act1_001 <- c("a","a","b","d","d")
act1_002 <- c("b","a","b","b","d")
act1_003 <- c("d","b","a","d","a")
act1_004 <- c("a","a","b","d","d")
act1_005 <- c("a","d","d","b","a")
act1_006 <- c("b","a","b","d","b")
act1_007 <- c("b","b","b","d","d")

data <- data.frame(Day, act1_001, act1_002, act1_003, act1_004, act1_005, act1_006, act1_007)

型