Get column name for first occurence of most frequent value in a row

Question

I have a data frame that looks like the following:

week_0 <- c(5,0,1,0,0,1)
week_1 <- c(5,0,4,0,2,1)
week_2 <- c(5,0,4,0,8,1)
week_3 <- c(5,0,4,0,8,3)
week_4 <- c(1,0,4,0,8,3)
week_5 <- c(1,0,4,0,8,3)
week_6 <- c(1,0,4,0,1,3)
week_7 <- c(1,0,4,0,1,3)
week_8 <- c(1,0,6,0,3,4)
week_9 <- c(2,4,6,7,3,4)
week_10 <- c(2,4,6,7,3,4)
Participant <- c("Lion","Cat","Dog","Snake","Tiger","Mouse")
test_data <- data.frame(Participant,week_0,week_1,week_2,week_3,week_4,week_5,week_6,week_7,week_8,week_9,week_10)

> test_data

    Participant week_0 week_1 week_2 week_3 week_4 week_5 week_6 week_7 week_8 week_9 week_10
1        Lion      5      5      5      5      1      1      1      1      1      2       2
2         Cat      0      0      0      0      0      0      0      0      0      4       4
3         Dog      1      4      4      4      4      4      4      4      6      6       6
4       Snake      0      0      0      0      0      0      0      0      0      7       7
5       Tiger      0      2      8      8      8      8      1      1      3      3       3
6       Mouse      1      1      1      3      3      3      3      3      4      4       4

I would like to identify the value in a row that appears more than other value. For example, for the first row the value is 1. And the output I want to return is week_4 for the first row. For the second row the value that appears more than other is 0. And the output I want to return is week_0, etc. So the end result should be: week_4, week_0, week_1, week_0, week_2, week_3. I have to use:

apply(test_data, 1, function(x) names(which.max(table(x))))

but I do not get the result that I'm searching for. Any suggestions on how to do this?

Answer 1

A dplyr solution with add_count + slice_max:

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

---

If there are "ties" and you want to include all ties in the output:

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)

Answer 2

Try with fmode from collapse

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

-output

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