How do I use the command for for-loops?

Question

I need to create the variable "peace" that tells me how many years there was peace before the variable conflict==1 (meaning a conflict starts).

Someone recommended to me to do a for -loop with a variable ("peace") that starts with 0, adds 1 if conflict==0 , saves the value if conflict==1 and puts it back to 0 afterwards.

Here is a short example of what the variable "peace" should look like:

Df <- data.frame(country = c("A", "A", "A", "A", "A", "B","B", "B", "B"),
             year = c("1950", "1951", "1952", "1953", "1954", "1950", "1951", "1952", "1953"),
             conflict = c(0, 0, 1, 1, 0, 0, 1, 0, 1), peace = c(0, 0, 2, 2, 0, 0, 1, 0, 1))

What is the best way to achieve "peace"? How would the code look like?

Thank you!

Answer 1

See this link for your basic loops in R: https://www.geeksforgeeks.org/loops-in-r-for-while-repeat/

For your issue, you would want to iterate over each row-index, except the first

# these two lines do two very different things. See if you can spot it.
peace <- 1
Df$peace <- 0

for (i in 2:nrow(Df)) {
  # compare row i-1 to i
  if (Df$country[i] != Df$country[i-1]) {
    peace <- 1
    next
  }
  if (Df$conflict[i] == 0 && Df$conflict[i-1] == 1) {
    peace <- 1
  } else if (Df$conflict[i] == 0) {
    peace <- peace + 1
  } else {
    Df$peace[i] <- peace
  }
}

Answer 2

I have created a new variable called n as output -

library(dplyr)

Df <- Df %>% mutate(grp = data.table::rleid(conflict))

Df %>%
  count(country, grp) %>%
  group_by(country) %>%
  mutate(n = lag(n, default = 0)) %>%
  ungroup %>%
  left_join(Df, by = c('country', 'grp')) %>%
  mutate(n = replace(n, conflict == 0, 0))

#  country   grp     n year  conflict peace
#  <chr>   <int> <dbl> <chr>    <dbl> <dbl>
#1 A           1     0 1950         0     0
#2 A           1     0 1951         0     0
#3 A           2     2 1952         1     2
#4 A           2     2 1953         1     2
#5 A           3     0 1954         0     0
#6 B           3     0 1950         0     0
#7 B           4     1 1951         1     1
#8 B           5     0 1952         0     0
#9 B           6     1 1953         1     1