I'm working with an unbalanced panel that contains information across various countries for several years. One of the variables I have is dist, which represents the log-deviations from HP-filtered GDP. If dist>1, then dummy=1.
structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L,
10L, 10L, 10L, 10L), .Label = c("Bangladesh", "Barbados", "Benin",
"Burundi", "Cameroon", "Chile", "Cyprus", "Ecuador", "Equatorial Guinea",
"Gabon", "Ghana", "Guatemala", "Guinea", "Guyana", "Haiti", "India",
"Jamaica", "Jordan", "Lebanon", "Liberia", "Madagascar", "Mali",
"Mexico", "Morocco", "Mozambique", "Nepal", "Nicaragua", "Niger",
"Oman", "Pakistan", "Panama", "Papua New Guinea", "Peru", "Rwanda",
"Senegal", "Seychelles", "Sierra Leone", "Singapore", "Sri Lanka",
"Sudan", "Togo", "Trinidad and Tobago", "Tunisia", "Turkey",
"Uganda", "Zambia", "Zimbabwe"), class = c("pseries", "factor"
)), date = structure(c(12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("1965", "1966", "1967",
"1968", "1969", "1970", "1971", "1972", "1973", "1974", "1975",
"1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983",
"1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991",
"1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999",
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015",
"2016", "2017", "2018"), class = c("pseries", "factor")), dist = c(-2.44153863355447,
-0.565497304455015, 1.4222058591902, 1.18583114700364, 0.936095936859405,
-0.40582369098349, 2.08296049774769, -1.59377116576285, 0.316969269882462,
-0.562808458278515, -0.438155688763343, 0.824980940313303, -0.60293744113302,
0.397182279339227, 1.34777053122572, 0.00539983501313633, 0.0528824118570846,
-0.838507946421168, 0.220969370384196, -1.06945294612116, 0.157324834050942,
0.244080457191975, 0.215931770884978, 0.226176571912671, 0.197663973781873,
0.183399695771442, 0.334605869075331, 0.0683789169881243, -0.0432432418415798,
-0.191834311017179, -0.368292801016647, -0.572222256415503, -0.838601784768107,
-1.15213567684887, -1.49844386483361, 2.39265920977342, 1.90573467650352,
1.3362714819111, 0.158391029649629, 1.12699318386612, 2.38996532864524
), dummy = c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 0, 1, 1)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 400L,
401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L, 410L, 411L,
412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L), class = "data.frame")
I would like to identify episodes in the following way based on the values of dist and dummy. An episode is defined as the time spam for which values of dist>0.5, conditioned on dist>= 1 at some point. In the example above, the episode from Bangladesh lasts for 3 years from 1978-1980 and the one for Guatemala lasts for another 3 years from 2006 to 2008 Any help would be much appreciated!
I think this does what you want. I use dplyr for the data manipulation, but also the utility function rleid from the data.table package.
x %>%
group_by(country) %>%
mutate(
potential_episode_grp = (dist > 0.5) * data.table::rleid(dist > 0.5)
) %>%
group_by(country, potential_episode_grp) %>%
mutate(episode = as.integer(potential_episode_grp > 0 & any(dist > 1)))
# # A tibble: 11 × 6
# # Groups: country, potential_episode_grp [4]
# country date dist dummy episode potential_episode_grp
# <chr> <int> <dbl> <int> <int> <int>
# 1 Bangladesh 1976 -2.44 0 0 0
# 2 Bangladesh 1977 -0.565 0 0 0
# 3 Bangladesh 1978 1.42 1 1 2
# 4 Bangladesh 1979 1.19 1 1 2
# 5 Bangladesh 1980 0.936 0 1 2
# 6 Guatemala 2004 -0.390 0 0 0
# 7 Guatemala 2005 -0.267 0 0 0
# 8 Guatemala 2006 1.46 1 1 2
# 9 Guatemala 2007 0.763 0 1 2
# 10 Guatemala 2008 1.11 1 1 2
# 11 Guatemala 2009 0.289 0 0 0
Using this data:
x = read.table(text = ' country date dist dummy episode
1 Bangladesh 1976 -2.4415386 0 0
2 Bangladesh 1977 -0.5654973 0 0
3 Bangladesh 1978 1.4222059 1 1
4 Bangladesh 1979 1.1858311 1 1
5 Bangladesh 1980 0.9360959 0 1
560 Guatemala 2004 -0.3899023 0 0
561 Guatemala 2005 -0.2672316 0 0
562 Guatemala 2006 1.4632532 1 1
563 Guatemala 2007 0.7633748 0 1
564 Guatemala 2008 1.1143216 1 1
565 Guatemala 2009 0.2891909 0 0', header =T)