How to apply RLE() function in R with multiple conditions?

Question

I have the following data:

dat <- structure(list(year = c(1979L, 1979L, 1979L, 1979L, 1979L, 1979L, 
       1979L, 1979L, 1979L, 1979L, 1979L, 1980L, 1980L, 1980L, 1980L, 
       1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1981L, 
       1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 
       1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 1981L, 
       1981L, 1981L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 
       1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 
       1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 
       1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 
       1983L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 
       1984L, 1984L, 1984L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 
       1985L, 1986L, 1986L, 1986L, 1987L, 1987L, 1987L, 1987L, 1987L, 
       1987L, 1987L, 1987L, 1987L, 1987L, 1987L, 1988L, 1988L, 1988L, 
       1988L, 1988L, 1988L, 1988L, 1988L, 1988L, 1988L, 1988L, 1988L, 
       1988L, 1989L, 1989L, 1989L, 1989L, 1989L, 1989L, 1989L, 1990L, 
       1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 
       1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1991L, 1991L, 
       1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 
       1991L, 1991L, 1991L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 
       1992L, 1992L, 1992L, 1993L, 1993L, 1993L, 1993L, 1993L, 1994L, 
       1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 
       1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 
       1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 
       1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1995L, 
       1995L, 1995L, 1995L, 1996L, 1996L, 1996L, 1996L, 1996L), mon = c(5L, 
       5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 7L, 7L, 7L, 7L, 7L, 7L, 
       7L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 
       7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
       7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 
       5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 
       7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
       7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 5L, 5L, 5L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
       5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
       6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
       5L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L), day = c(16L, 17L, 18L, 19L, 
       20L, 21L, 22L, 23L, 24L, 25L, 26L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
       11L, 12L, 13L, 14L, 15L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
       17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
       27L, 28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
       11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 7L, 8L, 
       9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 7L, 8L, 9L, 10L, 11L, 
       12L, 13L, 14L, 15L, 16L, 17L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
       17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
       30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
       14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
       27L, 28L, 29L, 30L, 31L, 18L, 19L, 20L, 12L, 13L, 14L, 15L, 16L, 
       17L, 18L, 19L, 20L, 21L, 22L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 
       5L, 6L, 7L, 8L, 9L, 10L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 24L, 
       25L, 26L, 27L, 28L, 29L, 30L, 31L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
       8L, 9L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
       28L, 29L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 28L, 29L, 
       30L, 1L, 2L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
       17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
       30L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 17L, 18L, 
       19L, 20L, 28L, 29L, 30L, 1L, 2L), phase = c(6L, 6L, 6L, 6L, 7L, 
       7L, 7L, 7L, 7L, 8L, 8L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 
       8L, 8L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 7L, 
       7L, 6L, 5L, 5L, 5L, 5L, 5L, 7L, 8L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 
       1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 
       1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
       1L, 1L, 1L, 2L, 2L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
       7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 
       8L, 8L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 
       4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 
       6L, 7L, 7L, 7L, 8L, 8L, 1L, 7L, 7L, 8L, 8L, 8L, 8L, 7L, 7L, 7L, 
       7L, 7L, 7L, 7L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 7L, 7L, 7L, 
       8L, 8L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 8L, 8L, 
       8L, 8L, 8L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
       3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 
       5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 1L, 1L, 1L, 
       1L, 1L, 2L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 
       8L, 1L, 1L, 1L, 1L), Rainfall = c(23.2, 35.575, 37.4, 6.425, 
       10.275, 3.05, 50.075, 23.05, 2, 1.4, 3.325, 12.84, 0.68, 7.78, 
       0, 12.88, 91.48, 41.08, 4.48, 0, 0.26, 0, 2.32, 13.25, 64.5, 
       21.55, 82.175, 33.725, 48.95, 3.8, 16.875, 4.7, 7.7, 48.7, 25.275, 
       3.625, 0.075, 2.5, 0, 3.525, 0.725, 0.2, 0.625, 0.25, 2.85, 6.15, 
       10.675, 41.975, 24.975, 127.775, 86.225, 19.95, 1.725, 11.125, 
       0.075, 1.775, 5.825, 5.975, 18.125, 6.425, 3.725, 11.75, 13.975, 
       0.1, 1, 0.725, 4.775, 0.225, 2.625, 0.575, 13.375, 0, 0, 0, 0, 
       0, 0, 0, 0.825, 0, 0, 0.45, 2.2, 4.5, 0, 0, 0.05, 0, 0, 0.225, 
       0.975, 5.375, 9.1, 27.3, 47.7, 31.475, 4.8, 11.45, 3.15, 2.3, 
       14.975, 77.25, 112.225, 69.675, 27.625, 43.65, 34.85, 47.325, 
       65.725, 83.825, 29.525, 29.95, 12.575, 0, 3.2, 30.95, 26.25, 
       4.15, 0.025, 0.5, 0.375, 5.15, 3.525, 0, 0.55, 2.025, 10.525, 
       0.4, 0.05, 9.225, 0.2, 5.25, 0, 0.5, 1.3, 3.175, 7.825, 1.15, 
       3.475, 0.4, 0.1, 0.2, 4.275, 3.45, 0.075, 3.95, 23.525, 6.2, 
       5.7, 6.1, 4.975, 2.7, 0.95, 0, 0, 1.55, 37.525, 53.8, 26.275, 
       101.25, 81.825, 26.05, 6.4, 6.75, 0.65, 2.475, 2.7, 1.45, 0.775, 
       0.2, 5.8, 0.36, 0.02, 0.8, 2.64, 3.44, 26.8, 17.98, 3.88, 33.48, 
       8.08, 15.8, 11.52, 21.44, 31.18, 13.06, 12.92, 0.24, 4.48, 9.4, 
       4.24, 4.36, 2.34, 5.72, 16.56, 10.96, 24.12, 2.96, 28.48, 14.72, 
       6.32, 0, 0.3, 3.46, 0.62, 0.76, 0.46, 17.22, 10.92, 1.96, 2.92, 
       0, 0, 3.44, 3.86, 2.88, 0.72, 0, 0, 0.06, 1.62, 28.74, 0.64, 
       0, 0, 1.18, 0.42, 5.46, 3.56, 0.44, 0.48, 4.9, 1.48, 2.7, 19.94, 
       0.4, 7.28, 29.56, 8.72, 1.5, 2.32, 2.42, 4.62, 1.2, 13.88, 9.76, 
       26.32, 11, 23.8, 10.08, 17.04, 47.6, 15.22, 4.06, 60.3, 71.2, 
       16.54, 0.44, 0.68, 0, 0.88, 0.1, 0.04, 0.34, 0, 0.36)), row.names = c(NA, 
       266L), class = "data.frame")

There are four columns in this data: year, month, day, phase, Rainfall

I would like to:

(1) Count the number of cases when rainfall is below 5 mm/day for at least 3 consecutive days (length >= 3)

(2) and the start (first occurrence) of these consecutive days should have a phase equal to 1.

I have the following incorrect script:

 dat2<-subset(dat, phase == 1)
 countruns = function(x){
    rainfall_rle_df <- data.frame(unclass(rle(x$Rainfall < 5)))
    nrow(subset(rainfall_rle_df, lengths >= 3 & values == TRUE))
    }

 countruns(dat2)

This script gives me a value of 5, but this is incorrect. The correct answer for the whole data should only be 4.

So the problem is that this script ignores the starting phase of the consecutive days.

For example the year 1996 in the data above has the following values:

 year   month day phase Rainfall
 1996   6  28     8    0.100
 1996   6  29     1    0.040
 1996   6  30     1    0.340
 1996   7   1     1    0.000
 1996   7   2     1    0.360

The starting phase is phase 8 so this should not be counted.

The years that satisfied the above conditions should be 1983, 1984, 1991, and 1993.

Any suggestions on how I can do this in R? Or is there a better way to do this in R?

I have to put the script inside a function countruns().

I'll appreciate any help!

no @akrun.. I solved the one yesterday. I deleted the post from yesterday. — Lyndz, Aug 14 '20 at 21:50
@akrun, the expected output should be 4. My script above gives 5 — Lyndz, Aug 14 '20 at 21:53

akrun · Accepted Answer · 2020-08-14T22:37:37.690

2

We create grouping column with rleid (from data.table) after grouping by 'year', then keep the groups where all values of 'Rainfall' are less than 5, filter the groups where the first value of 'phase' is 1, and do the frequency count (n()) in summarise

library(dplyr)
library(data.table)
dat %>% 
  # // create a Date column as it is easier to check for consecutive days
  mutate(Date = as.Date(paste(year, mon, day, sep="-"))) %>% 
  # // create a group with year
  group_by(year) %>%
  # // add more groups with rleid on logical vector
  group_by(grp = rleid(Rainfall < 5), 
           # // checks for difference between adjacent Date
           # // if the difference is greater than 1, cumsum increments by 1
           grp2 = cumsum(c(0, abs(diff(Date))) > 1), .add = TRUE) %>% 
  # // filter groups where all Rainfall is less than 5 and number of rows >= 3
  filter(all(Rainfall < 5), n() >= 3) %>% 
  # // filter the groups where the first value of phase is 1
  filter(first(phase) == 1) %>%
  # // get the frequency count
  summarise(n = n(), .groups = 'drop') %>%
  # // remove the columns that are not needed
  select(-grp, -grp2)
# A tibble: 4 x 2
#   year     n
#  <int> <int>
#1  1983     8
#2  1984    11
#3  1991     6
#4  1993     5

If we want to wrap it in a function, the {{}} does the enquo + !! (when we pass unquoted variable names as arguments to function esp for the column names)

f1 <- function(data, year, month, day, rainfall) {
        data %>%
           mutate(Date = as.Date(str_c({{year}}, {{month}}, 
                {{day}}, sep="-"))) %>%
           group_by({{year}}) %>%
           group_by(grp = rleid({{rainfall}} < 5),
                    grp2 = cumsum(c(0, abs(diff(Date))) > 1),
                .add = TRUE) %>%
           filter(all({{rainfall}} < 5), n() >=3 ) %>%
           filter(first(phase) == 1) %>%
           summarise(n = n(), .groups = 'drop') %>%
           select(-grp, -grp2)
     }

-apply the function

f1(dat, year, mon, day, Rainfall)
# A tibble: 4 x 2
#   year     n
#  <int> <int>
#1  1983     8
#2  1984    11
#3  1991     6
#4  1993     5

edited Aug 14 '20 at 22:37

answered Aug 14 '20 at 21:58

akrun

874,273
37
540
662

the output should only be 4? the "at least 3 consecutive days" condition is missing here. – Lyndz Aug 14 '20 at 22:05
sorry...4 is the correct answer. The output should be a number since I will put this inside a function countruns() – Lyndz Aug 14 '20 at 22:07
@Lyndz I added the condition, but the now the number of rows would be 2 for 1996 – akrun Aug 14 '20 at 22:09
hmm..the years that satisfied the conditions should be 1983,1984,1991, and 1993. I just counted manually. – Lyndz Aug 14 '20 at 22:12
@Lyndz I think i got the issue – akrun Aug 14 '20 at 22:14
@Lyndz then what about 1996, which you showed as expected 4? – akrun Aug 14 '20 at 22:16
@Lyndz From the example showed, there are no gaps in the dates – akrun Aug 14 '20 at 22:17
It should not be counted since the start (June 28) of the consecutive days is phase 8 not phase 1. – Lyndz Aug 14 '20 at 22:17
@Lyndz In your post, it says `For example the year 1996 in the data above has the following values:` – akrun Aug 14 '20 at 22:18
Let us [continue this discussion in chat](https://chat.stackoverflow.com/rooms/219842/discussion-between-lyndz-and-akrun). – Lyndz Aug 14 '20 at 22:18
nice. How do I put this inside a function countruns()? – Lyndz Aug 14 '20 at 22:24

How to apply RLE() function in R with multiple conditions?

1 Answers1