using dplyr to calculate consecutive days with a particular value

Question

I'm trying to calculate an index of winter severity, and one of the components of the index requires calculating the consecutive number of days < 0 degrees C, prior to and including that particular date. For example:

Day 1 = 2 degrees C  
Day 2 = -2 degrees C  
Day 3 = -5 degrees C

So the value that I'm trying to calculate (called tempdays) is equal to 0 for Day 1; 1 for Day 2; and 2 for Day 3.

Here's an example showing what the data looks like:

dat <- tibble(
  date = seq(as.Date('2010-01-01'), as.Date('2010-01-10'), 1),
  temp = c(4.2, 3.35, -0.6, -0.25, 0.8, 0.8, -2.5, -5.25, -0.5, 3.35)
)
dat

 date        temp
   <date>     <dbl>
 1 2010-01-01  4.2 
 2 2010-01-02  3.35
 3 2010-01-03 -0.6 
 4 2010-01-04 -0.25
 5 2010-01-05  0.8 
 6 2010-01-06  0.8 
 7 2010-01-07 -2.5 
 8 2010-01-08 -5.25
 9 2010-01-09 -0.5 
10 2010-01-10  3.35

Here's another data set starting with a temp value less than zero since that seemed to cause an issue:

dat2 <- tibble(
  date = seq(as.Date('2010-01-01'), as.Date('2010-01-10'), 1),
  temp = c(-1.95, -1.1, -2.8, -6.7, 1.4, 4.45, 6.1, 4.7, -1.7, -3.9)
)
dat2

So dat2 should look like this:

date        temp tempdays
   <date>     <dbl>    <dbl>
 1 2010-01-01 -1.95        1
 2 2010-01-02 -1.1         2
 3 2010-01-03 -2.8         3
 4 2010-01-04 -6.7         4
 5 2010-01-05  1.4         0
 6 2010-01-06  4.45        0
 7 2010-01-07  6.1         0
 8 2010-01-08  4.7         0
 9 2010-01-09 -1.7         1
10 2010-01-10 -3.9         2

I'm guessing lag() can be used to do this?

Answer 1

You could create a grouping variable using cumsum and then use row_number to generate the consecutive days when the temperature was less than 0.

library(dplyr)

dat %>%
  group_by(group = cumsum(temp > 0)) %>%
  mutate(tempdays = row_number() - 1) %>%
  ungroup() %>%
  select(-group)


#    date      temp tempdays
#   <date>     <dbl>    <dbl>
# 1 2010-01-01  4.2         0
# 2 2010-01-02  3.35        0
# 3 2010-01-03 -0.6         1
# 4 2010-01-04 -0.25        2
# 5 2010-01-05  0.8         0
# 6 2010-01-06  0.8         0
# 7 2010-01-07 -2.5         1
# 8 2010-01-08 -5.25        2
# 9 2010-01-09 -0.5         3
#10 2010-01-10  3.35        0

and using base R that would be with ave

with(dat, ave(temp, cumsum(temp > 0), FUN = seq_along) - 1)

EDIT

This doesn't work as expected if first group is negative. Here is an updated version using rle which works with dat as well as dat2

dat2 %>%
  mutate(tempdays = with(rle(temp < 0), rep(values, lengths))) %>%
  group_by(group = cumsum(temp > 0)) %>%
  mutate(tempdays = cumsum(tempdays)) %>%
  ungroup() %>%
  select(-group)


#      date    temp   tempdays
#     <date>   <dbl>    <int>
# 1 2010-01-01 -1.95        1
# 2 2010-01-02 -1.1         2
# 3 2010-01-03 -2.8         3
# 4 2010-01-04 -6.7         4
# 5 2010-01-05  1.4         0
# 6 2010-01-06  4.45        0
# 7 2010-01-07  6.1         0
# 8 2010-01-08  4.7         0
# 9 2010-01-09 -1.7         1
#10 2010-01-10 -3.9         2

Answer 2

We can use data.table

library(data.table)
setDT(dat)[, tempdays := seq_len(.N) -1 , cumsum(temp > 0)]
dat
#          date  temp tempdays
# 1: 2010-01-01  4.20        0
# 2: 2010-01-02  3.35        0
# 3: 2010-01-03 -0.60        1
# 4: 2010-01-04 -0.25        2
# 5: 2010-01-05  0.80        0
# 6: 2010-01-06  0.80        0
# 7: 2010-01-07 -2.50        1
# 8: 2010-01-08 -5.25        2
# 9: 2010-01-09 -0.50        3
#10: 2010-01-10  3.35        0