r - Filtering with dataframe leads partially to NAs

Question

I am measuring electric current (µA) over a certain time interval (s) for 4 different channels (chan_n) and this is how my data looks:

dat 

s       µA            chan_n
<dbl>   <dbl>        <chr>
0.00    -0.03167860   1     
0.02    -0.03136610   1     
0.04    -0.03118490   1     
0.06    -0.03094740   1     
0.08    -0.03065360   1     
0.10    -0.03047860   1     
0.12    -0.03012230   1     
0.14    -0.02995980   1     
0.16    -0.02961610   1
...     ...           ...

My end goal is to get the current of a certain time after the peak value. Therefore I first get the time timepoints at which the maximum appears for each channel:

BaslineTime <- dat %>% 
  group_by(chan_n) %>%      
  slice(which.max(µA)) %>%  # get max current values
  transmute(s =  s + 30)    # add 30 to the timepoints at which the max value appears


chan_n s
<chr>  <dbl>
1      539.84           
2      540.00           
3      539.82           
4      539.80    

But if I use BaselineTime to filter for my current values I get two NAs:

BaslineVal <- right_join(dat, BaselineTime, by =c("chan_n","s")) 


s       µA          chan_n
<dbl>   <dbl>       <chr>
540.00  0.00364974  2       
539.80  0.00610948  4       
539.84  NA          1       
539.82  NA          3

I checked if the time values exist for channel 1 and 3 and they do. Also if I create a data frame manualy by hardcoding the time values and use it for filtering, it works just fine. So why isn't it working? I would be very happy for any suggestions or explanations. I think it might have something to do the the decimal places as for channel 2 and 4 there is a 0 on the last decimal place.

Answer 1

Untested as the sample data isn't suitable for testing. I would try something like this:

data %>% 
  group_by(chan_n) %>%
  mutate(
    is_peak = row_number() == which.max(µA),
    post_peak = lag(is_peak, n = 30, default = FALSE)
  )

This will give a TRUE in the new post_peak column 30 rows after the peak, so you can trivially ... %>% filter(post_peak) or do whatever you need to with the result.

If you need more help than this, please share some data that illustrates the problem better, e.g., 10 rows each of 2 chan_n groups with the goal of finding the row 3 after the peak (and that row existing in the data).