I have recently looked for advice on how to suppress all but the first occurrences of a value within a group using dplyr (dplyr override all but the first occurrences of a value within a group).
The solution was a really clever one and now I am struggling with finding something equally efficient in case I need to suppress only n next values.
For example, in the code below I create a new "tag" column:
library('dplyr')
data(iris)
set.seed(1)
iris$tag <- sample(c(0,1), 150, replace=TRUE, prob = c(0.7, 0.3))
giris <- iris %>% group_by(Species)
# Source: local data frame [150 x 6]
# Groups: Species [3]
#
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species tag
# (dbl) (dbl) (dbl) (dbl) (fctr) (dbl)
# 1 5.1 3.5 1.4 0.2 setosa 0
# 2 4.9 3.0 1.4 0.2 setosa 0
# 3 4.7 3.2 1.3 0.2 setosa 0
# 4 4.6 3.1 1.5 0.2 setosa 1
# 5 5.0 3.6 1.4 0.2 setosa 0
# 6 5.4 3.9 1.7 0.4 setosa 1
# 7 4.6 3.4 1.4 0.3 setosa 1
# 8 5.0 3.4 1.5 0.2 setosa 0
# 9 4.4 2.9 1.4 0.2 setosa 0
# 10 4.9 3.1 1.5 0.1 setosa 0
# .. ... ... ... ... ... ...
In the setosa group rows: 4, 6, 7, ... are tagged as "1"s. I am trying to suppress "1"s (i.e. convert them to "0"s) in the next two rows after any occurrence of a "1". In other words, rows #5 and #6 should be set to "0" but #7 should remain unaffected. In this case, row #7 happens to be a "1", so rows #8 and #9 should be set to "0"s and so on...
Any hint on how to do this in dplyr? This package is really powerful but for a reason it is a mental challenge for me to master all the subtleties...
Some more examples: in case of: 0 0 1 1, the output should be 0 0 1 0 in case of: 0 0 1 1 1 1 1, the output should be 0 0 1 0 0 1 0