如何使用 R dplyr 找到行中第一个非零值的列索引？

Question

I'm working in R. I have a dataset of COVID case totals that looks like this:

Facility	Day_1	Day_2	Day_3
A	0	0	1
B	1	2	5
C	0	2	6
D	0	0	0

I would like to use mutate() to create a new column, first_case, that has the column index of the first non-zero element in each row -- or "NA" if there is no non-zero element. I thought about using where(), but couldn't quite figure out how to get a column index instead of a row index.

Any help is much appreciated!

Answer 1

We can use max.col to get the first instance when the value is non-zero in each zero.

library(dplyr)

df %>%
  mutate(first_case = {
    tmp <- select(., starts_with('Day'))
    ifelse(rowSums(tmp) == 0, NA, max.col(tmp != 0, ties.method = 'first'))
  })

#  Facility Day_1 Day_2 Day_3 first_case
#1        A     0     0     1          3
#2        B     1     2     5          1
#3        C     0     2     6          2
#4        D     0     0     0         NA

first_case has column number of the 'Day' columns, if you need column number in the data you can add + 1 to above output.

Answer 2

This is probably unnecessarily complex, because the data is not in a long ('tidy') format that dplyr etc expect.

datlong <- dat %>%
  pivot_longer(cols=starts_with("Day"), names_to = c("day"), names_pattern="_(\\d+)")

## A tibble: 12 x 3
#   Facility day   value
#   <chr>    <chr> <int>
# 1 A        1         0
# 2 A        2         0
# 3 A        3         1
# 4 B        1         1
# 5 B        2         2
# 6 B        3         5
# 7 C        1         0
# 8 C        2         2
# 9 C        3         6
#10 D        1         0
#11 D        2         0
#12 D        3         0

It's then simple to get the first/second/third/[n]th day above whatever value, as well as to calculate minimums, maximums, means, weekly averages, rolling averages, whatever, because you are now dealing with a plain old vector of values rather than a list of values across multiple columns.

datlong %>%
  group_by(Facility) %>%
  filter(value > 0, .preserve=TRUE) %>%
  summarise(first_day = first(day))

#`summarise()` ungrouping output (override with `.groups` argument)
## A tibble: 4 x 2
#  Facility first_day
#  <chr>    <chr>    
#1 A        3        
#2 B        1        
#3 C        2        
#4 D        <NA>    

Alternative using indexes and stuff, which is less dplyr -like:

datlong %>%
  group_by(Facility) %>%
  summarise(first_day = day[value > 0][1])