reshaping time series data in R -

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i have quarterly time series economic data imf ifs need long form.

right now, rows variables per country , columns time, looks this. 

     country  variable    q1  q2  [1,] "usa"   "inflation" "1" "5" [2,] "usa"   "gdppc"     "2" "6" [3,] "uk"    "inflation" "3" "7" [4,] "uk"    "gdppc"     "4" "8"

i need long form:

    country  time  inflation    gdppc [1,] "usa"   "q1" "1"          "2"   [2,] "usa"   "q2" "5"          "6"   [3,] "uk"    "q1" "3"          "4"   [4,] "uk"    "q2" "7"          "8"

i haven't been able find advice on using reshape when id variable , measurement variables both in rows.

it partial melt followed dcast in reshape2 package:

d = data.table(country = c("usa","usa","uk","uk"), variable = c("inflation","gdppc","inflation","gdppc"),q1=as.character(1:4),q2=as.character(5:8)) require(reshape2) d2 = melt(d, id=c("country", "variable")) colnames(d2)[3] = "time"  rr=dcast(d2, country +time ~ variable) rr = rr[order(rr$country,decreasing=t),c(1:2,4,3)]

gives:

> rr   country time inflation gdppc 3     usa   q1         1     2 4     usa   q2         5     6 1      uk   q1         3     4 2      uk   q2         7     8

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