dplyr和add_count + slice_max的dplyr解决方案：

library(dplyr)

test_data %>%
  tidyr::pivot_longer(starts_with('week')) %>%
  add_count(Participant, value) %>%
  slice_max(n, by = Participant, with_ties = FALSE)

# # A tibble: 6 × 4
#   Participant name   value     n
#   <chr>       <chr>  <dbl> <int>
# 1 Lion        week_4     1     5
# 2 Cat         week_0     0     9
# 3 Dog         week_1     4     7
# 4 Snake       week_0     0     9
# 5 Tiger       week_2     8     4
# 6 Mouse       week_3     3     5

如果存在“连接”，并且您希望在输出中包括所有连接：

test_data %>%
  tidyr::pivot_longer(starts_with('week')) %>%
  add_count(Participant, value) %>%
  slice_max(n, by = c(Participant, value), with_ties = FALSE) %>%
  slice_max(n, by = Participant)

尝试使用collapse中的fmode

library(collapse)
names(test_data)[-1][max.col(test_data[-1] == dapply(test_data[-1], 
    MARGIN = 1, fmode), "first")]

• 输出

[1] "week_4" "week_0" "week_1" "week_0" "week_2" "week_3"

您的代码是很好的第一步，您可以使用结果match()它在行中的第一个位置，然后使用此位置索引列名称：

apply(test_data[, -1], 1, function(x) {
  val <- names(which.max(table(x)))
  names(test_data)[-1][[match(val, x)]]
})
# "week_4" "week_0" "week_1" "week_0" "week_2" "week_3"

注意我使用test_data[, -1]来排除Participant列;否则，如果没有出现多次的值，代码将返回参与者名称，这可能不是您想要的。

首先定义一个函数来求向量的众数：

Mode <- \(x) names(sort(-table(x)))[1]

Reference
困难的部分已经完成，现在使用dplyr的rowwise()和c_across()：

library(dplyr)

test_data %>%
  rowwise() %>%
  mutate(
    m = {
      x <- c_across(week_0:week_10) # get row as a vector
      names(x) <- names(test_data)[-1]
      index <- which(x == Mode(x))[1] # first occurence of mode in 'x'
      names(x)[index]
    }
  )