Question

Suppose that we have a data frame that looks like

set.seed(7302012)

county         <- rep(letters[1:4], each=2)
state          <- rep(LETTERS[1], times=8)
industry       <- rep(c("construction", "manufacturing"), 4)
employment     <- round(rnorm(8, 100, 50), 0)
establishments <- round(rnorm(8, 20, 5), 0)

data <- data.frame(state, county, industry, employment, establishments)

  state county      industry employment establishments
1     A      a  construction        146             19
2     A      a manufacturing        110             20
3     A      b  construction        121             10
4     A      b manufacturing         90             27
5     A      c  construction        197             18
6     A      c manufacturing         73             29
7     A      d  construction         98             30
8     A      d manufacturing        102             19

We’d like to reshape this so that each row represents a (state and) county, rather than a county-industry, with columns construction.employment, construction.establishments, and analogous versions for manufacturing. What is an efficient way to do this?

One way is to subset

construction <- data[data$industry == "construction", ]
names(construction)[4:5] <- c("construction.employment", "construction.establishments")

And similarly for manufacturing, then do a merge. This isn’t so bad if there are only two industries, but imagine that there are 14; this process would become tedious (though made less so by using a for loop over the levels of industry).

Any other ideas?

Answer 1

This can be done in base R reshape, if I understand your question correctly:

reshape(data, direction="wide", idvar=c("state", "county"), timevar="industry")
#   state county employment.construction establishments.construction
# 1     A      a                     146                          19
# 3     A      b                     121                          10
# 5     A      c                     197                          18
# 7     A      d                      98                          30
#   employment.manufacturing establishments.manufacturing
# 1                      110                           20
# 3                       90                           27
# 5                       73                           29
# 7                      102                           19