Correct previous year by id within R - r

Question

I have data something like this:
df <- data.frame(Id=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,9,9,9,9),Date=c("2013-04","2013-12","2013-01","2013-12","2013-11",
"2013-12","2012-04","2013-12","2012-08","2014-12","2013-08","2014-12","2013-08","2014-12","2011-01","2013-11","2013-12","2014-01","2014-04"))
To get the correct format:
df$Date <- paste0(df$Date,"-01")
I would need to obtain only years, so that each id contains 2 dates following on each other.
I if do on the existing data something like this:
require(lubridate)
df$Date <- year(as.Date(df$Date)-days(1))
I get sometimes same date for given id.
The desired output for the column Date is this:
2012 2013 2012 2013 2012 2013 2012 2013 2013 2014 2013 2014 2013 2014 2011 2013 2014
Please note that the last date for given id is always correct, so just the preceding year have to be corrected based on the last date. The date have to be in format that can be converted to years only as shown.
EDIT Here is the case:
Id Date
1 2013-11-01
1 2013-12-01
1 2014-01-01
1 2014-04-01
Now I'm getting this: 2012,2013,2013,2013
I would need: 2012,2013,2013,2014

Answer 1

This is how I would solve this using data.table package (though it looks over complicated to me)
library(data.table)
setDT(df)[, year := year(Date)][,
year := if(.N == 2) (year[2] - 1):year[2] else year,
Id][]
# Id Date year indx
# 1: 1 2013-04-01 2012 2
# 2: 1 2013-12-01 2013 2
# 3: 2 2013-01-01 2012 2
# 4: 2 2013-12-01 2013 2
# 5: 3 2013-11-01 2012 2
# 6: 3 2013-12-01 2013 2
# 7: 4 2012-04-01 2012 2
# 8: 4 2013-12-01 2013 2
# 9: 5 2012-08-01 2013 2
# 10: 5 2014-12-01 2014 2
# 11: 6 2013-08-01 2013 2
# 12: 6 2014-12-01 2014 2
# 13: 7 2013-08-01 2013 2
# 14: 7 2014-12-01 2014 2
# 15: 8 2011-01-01 2011 1
Or all in one step (thanks to #Arun for providing this):
setDT(df)[, year := {tmp = year(Date);
if (.N == 2L) (tmp[2]-1L):tmp[2] else tmp},
Id]
Edit:
Per OPs new data, we can modify the code by adding additional index
setDT(df)[, indx := if(.N > 2) rep(seq_len(.N/2), each = 2) + 1L else .N, Id]
df[, year := {tmp = year(Date); if (.N > 1L) (tmp[2] - 1L):tmp[2] else tmp},
list(Id, indx)][]
# Id Date indx year
# 1: 1 2013-04-01 2 2012
# 2: 1 2013-12-01 2 2013
# 3: 2 2013-01-01 2 2012
# 4: 2 2013-12-01 2 2013
# 5: 3 2013-11-01 2 2012
# 6: 3 2013-12-01 2 2013
# 7: 4 2012-04-01 2 2012
# 8: 4 2013-12-01 2 2013
# 9: 5 2012-08-01 2 2013
# 10: 5 2014-12-01 2 2014
# 11: 6 2013-08-01 2 2013
# 12: 6 2014-12-01 2 2014
# 13: 7 2013-08-01 2 2013
# 14: 7 2014-12-01 2 2014
# 15: 8 2011-01-01 1 2011
# 16: 9 2013-11-01 2 2012
# 17: 9 2013-12-01 2 2013
# 18: 9 2014-01-01 3 2013
# 19: 9 2014-04-01 3 2014
Or another possible solution provided by #akrun
setDT(df)[, `:=`(year = year(Date), indx = .N, indx2 = as.numeric(gl(.N,2, .N))), Id]
df[indx > 1, year:=(year[2]-1):year[2], list(Id, indx2)][]

Answer 2

Using dplyr using similar approach as #David Arenburg's
library(dplyr)
df %>%
group_by(Id) %>%
mutate(year=as.numeric(sub('-.*', '', Date)),
year=replace(year, n()>1, c(year[2]-1, year[2])))
# Id Date year
#1 1 2013-04 2012
#2 1 2013-12 2013
#3 2 2013-01 2012
#4 2 2013-12 2013
#5 3 2013-11 2012
#6 3 2013-12 2013
#7 4 2012-04 2012
#8 4 2013-12 2013
#9 5 2012-08 2013
#10 5 2014-12 2014
#11 6 2013-08 2013
#12 6 2014-12 2014
#13 7 2013-08 2013
#14 7 2014-12 2014
#15 8 2011-01 2011
Or using base R
with(df, ave(as.numeric(sub('-.*', '', Date)), Id,
FUN=function(x) if(length(x)>1)(x[2]-1):x[2] else x))
#[1] 2012 2013 2012 2013 2012 2013 2012 2013 2013 2014 2013 2014 2013 2014 2011
Update
You can try
df$indx <- with(df, ave(Id, Id, FUN=function(x) (seq_along(x)-1)%/%2+1))
with(df, ave(as.numeric(sub('-.*', '', Date)), Id, indx,
FUN=function(x) if(length(x)>1)(x[2]-1):x[2] else x))
#[1] 2012 2013 2012 2013 2012 2013 2012 2013 2013 2014 2013 2014 2013 2014 2011
#[16] 2012 2013 2013 2014
Or
df %>%
group_by(Id) %>%
mutate(year=as.numeric(sub('-.*', '', Date))) %>%
group_by(indx=cumsum(rep(c(TRUE,FALSE), length.out=n())), add=TRUE) %>%
mutate(year=replace(year, n()>1, c(year[2]-1, year[2])))

Answer 3

Here's a dplyr solution. You can remove the intermediate fields last_year and year2, but I left them here for clarity:
library(stringr)
library(dplyr)
df %>%
group_by(Id) %>%
mutate(
last_year = last(as.integer(str_sub(Date, 1, 4))),
year2 = row_number() - n(),
year = last_year + year2
)