adding rows with values of '0' for missing years - r

Question

I have a question that is somewhat similar to others that have been posted, but after looking thoroughly at several posts, I can't get the code to work. Any help would be much appreciated.
My data frame looks like, this:
'data.frame': 501 obs. of 5 variables:
$ Tattoo.MUM : Factor w/ 250 levels "1004","1007",..: 76 76 76 81 81 81 85 85 85 85 ...
$ OffspringMUMs: int 4 4 4 4 4 4 11 11 11 11 ...
$ YearBIRTH.CUB: int 1988 1990 1991 1988 1991 2007 1989 1991 1992 1993 ...
$ YearBIRTH.MUM: int 1991 1991 NA NA NA NA 1987 1987 1987 1987 ...
$ OFFSpYR : int 2 1 1 1 2 1 1 4 3 3 ...
A few lines here:
structure(list(Tattoo.MUM = structure(c(6L, 6L, 6L, 6L, 7L, 7L,
7L, 8L, 9L, 11L, 11L, 11L, 11L, 5L, 1L, 4L, 2L, 3L, 3L, 10L,
10L, 10L, 10L, 10L), .Label = c("10454", "1045A", "1045X", "12392",
"1601", "22", "27", "29", "41", "424X", "60"), class = "factor"),
OffspringMUMs = c(11L, 11L, 11L, 11L, 5L, 5L, 5L, 1L, 3L,
7L, 7L, 7L, 7L, 1L, 2L, 1L, 1L, 4L, 4L, 6L, 6L, 6L, 6L, 6L
), YearBIRTH.CUB = c(1989L, 1991L, 1992L, 1993L, 1990L, 1991L,
1993L, 1989L, 1988L, 1988L, 1989L, 1991L, 1994L, 2015L, 2012L,
2015L, 2005L, 2009L, 2010L, 1996L, 1998L, 2000L, 2001L, 2006L
), YearBIRTH.MUM = c(1987L, 1987L, 1987L, 1987L, NA, NA,
NA, NA, NA, 1987L, 1987L, 1987L, 1987L, NA, NA, NA, NA, 2005L,
2005L, 1994L, 1994L, 1994L, 1994L, 1994L), OFFSpYR = c(1L,
4L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L)), .Names = c("Tattoo.MUM",
"OffspringMUMs", "YearBIRTH.CUB", "YearBIRTH.MUM", "OFFSpYR"), class = "data.frame", row.names = c(NA,
-24L))
I want to add new rows for all missing years (YearBIRTH.CUB) in Tattoo.MUM keeping the rest of the values the same and adding '0' to OFFSpYR.
Like so:
structure(list(Tattoo.MUM = structure(c(6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 8L, 9L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 5L, 1L,
4L, 2L, 3L, 3L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L), .Label = c("10454", "1045A", "1045X", "12392", "1601",
"22", "27", "29", "41", "424X", "60"), class = "factor"), OffspringMUMs = c(11L,
11L, 11L, 11L, 11L, 5L, 5L, 5L, 5L, 1L, 3L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 1L, 2L, 1L, 1L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L), YearBIRTH.CUB = c(1989L, 1990L, 1991L, 1992L, 1993L,
1990L, 1991L, 1992L, 1993L, 1989L, 1988L, 1988L, 1989L, 1990L,
1991L, 1992L, 1993L, 1994L, 2015L, 2012L, 2015L, 2005L, 2009L,
2010L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L), YearBIRTH.MUM = c(1987L, 1987L, 1987L,
1987L, 1987L, NA, NA, NA, NA, NA, NA, 1987L, 1987L, 1987L, 1987L,
1987L, 1987L, 1987L, NA, NA, NA, NA, 2005L, 2005L, 1994L, 1994L,
1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L
), OFFSpYR = c(1L, 0L, 4L, 3L, 3L, 1L, 1L, 0L, 3L, 1L, 3L, 3L,
1L, 0L, 2L, 0L, 0L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 0L, 1L, 0L,
1L, 2L, 0L, 0L, 0L, 0L, 1L)), .Names = c("Tattoo.MUM", "OffspringMUMs",
"YearBIRTH.CUB", "YearBIRTH.MUM", "OFFSpYR"), class = "data.frame", row.names = c(NA,
-35L))
I've tried:
library(tidyr)
library(dplyr)
df1 <- pedMUM %>% group_by(Tattoo.MUM, OffspringMUMs) %>% complete(YearBIRTH.CUB = full_seq(YearBIRTH.CUB,1)) %>% fill(OFFSpYR=0)
library(data.table)
df1 <- setDT(pedMUM)[CJ(Tattoo.MUM=Tattoo.MUM, OffspringMUMs=OffspringMUMs, YearBIRTH.MUM=YearBIRTH.MUM, YearBIRTH.CUB=seq(min(YearBIRTH.CUB), max(YearBIRTH.CUB)), unique=TRUE),
on=.(Tattoo.MUM, OffspringMUMs, YearBIRTH.CUB), roll=T]
I am obviously using tidyr, dplyr, and data.table wrongly because none have given me the results I want.
I've had a look at the following posts:
Add rows with missing years by group
Adding rows with values of "0" to a dataframe with missing data
Find missing month after grouping with dplyr
And even tried loops:
R code - clever loop to add rows
but I get confused when I try to determine the year sequence for each Tattoo.MUM within the loop.
Would anyone be able to point me in the right direction?

Answer 1

I haven't used complete() before, but the following seems to work. nesting() allows you to keep two variables together, =full_seq() allows you to expand the values of a variable, fill=list() allows you to fill in blanks.
pedMUM <- structure(list(Tattoo.MUM = structure(c(6L, 6L, 6L, 6L, 7L, 7L,
7L, 8L, 9L, 11L, 11L, 11L, 11L, 5L, 1L, 4L, 2L, 3L, 3L, 10L,
10L, 10L, 10L, 10L), .Label = c("10454", "1045A", "1045X", "12392",
"1601", "22", "27", "29", "41", "424X", "60"), class = "factor"),
OffspringMUMs = c(11L, 11L, 11L, 11L, 5L, 5L, 5L, 1L, 3L,
7L, 7L, 7L, 7L, 1L, 2L, 1L, 1L, 4L, 4L, 6L, 6L, 6L, 6L, 6L
), YearBIRTH.CUB = c(1989L, 1991L, 1992L, 1993L, 1990L, 1991L,
1993L, 1989L, 1988L, 1988L, 1989L, 1991L, 1994L, 2015L, 2012L,
2015L, 2005L, 2009L, 2010L, 1996L, 1998L, 2000L, 2001L, 2006L
), YearBIRTH.MUM = c(1987L, 1987L, 1987L, 1987L, NA, NA,
NA, NA, NA, 1987L, 1987L, 1987L, 1987L, NA, NA, NA, NA, 2005L,
2005L, 1994L, 1994L, 1994L, 1994L, 1994L), OFFSpYR = c(1L,
4L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L)), .Names = c("Tattoo.MUM",
"OffspringMUMs", "YearBIRTH.CUB", "YearBIRTH.MUM", "OFFSpYR"), class = "data.frame", row.names = c(NA,
-24L))
library(tidyr)
library(dplyr)
df1 <- pedMUM %>%
group_by(Tattoo.MUM) %>% # find min and max year for each mum
mutate(
minyear=min(YearBIRTH.CUB, na.rm=TRUE),
maxyear=max(YearBIRTH.CUB, na.rm=TRUE)
) %>%
complete( # complete table
nesting(Tattoo.MUM, minyear, maxyear, OffspringMUMs, YearBIRTH.MUM),
YearBIRTH.CUB=full_seq(YearBIRTH.CUB, 1),
fill=list(OFFSpYR=0)
) %>%
filter(YearBIRTH.CUB>=minyear & YearBIRTH.CUB<=maxyear) %>% # remove unwanted years
select(names(pedMUM)) # return original column order