I would like to use tapply to group the outcome of a function according to a variable. The function that I think I need to pass to tapplyis, I believe, apply. I want this function to assign a value depending on the presence of another value in a different variable (var1), however if the value of var1 is something else on the next row (within the grouping) then I would like to assign a different value. Here is what I have attempted, but it doesn't work. I'm not sure if that's because there's something wrong with my approach or because the function I apply is incorrect.
#example data
df.examp <- tibble(id = c(rep(1, 4), rep(2, 4), rep(3, 4)),
var1 = c('a','a','b','b','a','a','a','a','b','b','b','b'))
#my attempt
df.examp$var2 <- tapply(df.examp$var1, df.examp$id,
apply(df.examp$var1, 1, function(j)
if (j == 'a'){
'foo'
} else if (j == 'a' & j + 1 == 'b'){
'bar'
} else {
'other'
}
)
)
#hoped for outcome
df.examp <- mutate(df.examp, var2 = c(rep('bar', 4), rep('foo', 4), rep('other', 4)))
Does anyone have any ideas where this is going wrong?
We could case_when after grouping by 'id'
library(dplyr)
df.examp %>%
group_by(id) %>%
mutate(var2 = case_when(any(var1 == 'a' & lead(var1) == 'b') ~ 'bar',
var1 == 'a' ~ 'foo',
TRUE ~ 'other'))
# A tibble: 12 x 3
# Groups: id [3]
# id var1 var2
# <dbl> <chr> <chr>
# 1 1 a bar
# 2 1 a bar
# 3 1 b bar
# 4 1 b bar
# 5 2 a foo
# 6 2 a foo
# 7 2 a foo
# 8 2 a foo
# 9 3 b other
#10 3 b other
#11 3 b other
#12 3 b other