We could use melt from the devel version of data.table which can take multiple patterns for the measure columns. Instructions to install the devel version of 'data.table' is here
We convert the 'data.frame' to 'data.table' (setDT(df)), melt, and specify the regex in the patterns of measure argument. Remove the rows that are NA for the 'names' and 'address' column.
library(data.table)#v1.9.5+
dM <- melt(setDT(df), measure=patterns(c('^name', '^adress')),
value.name=c('names', 'address') )
dM[!(is.na(names) & is.na(address))]
# id variable names address
#1: 1 1 John street a
#2: 2 1 Jack street b
#3: 3 1 Joey NA
#4: 1 2 Burt street d
#5: 2 2 Ben street e
#6: 3 2 Bob street f
#7: 1 3 chris street 1
#8: 2 3 connor street 2
Or we can use reshape from base R.
dM2 <- reshape(df, idvar='id', varying=list(grep('name', names(df)),
grep('adress', names(df))), direction='long')
The NA rows can be removed as in the data.table solution by using standard 'data.frame' indexing after we create the logical index with is.na.
