1. What is reshape2 and why would you use it?

reshape2 is an R package that let’s you change the shape of any dataframe, i.e. to pivot and to “unpivot”.

Keep in mind that if your favourite R package for dataframes manipulation is data.table, functions dcast and melt are already in this package and work exactly the same as those in reshape2.

2. A “Hello World” example

In fact there are only two functions worth mentioning: dcast, which is equivalent to MS Excel pivot table, and melt, which does the opposite or unpivots a table.

An example dataframe:

d <- data.frame(
  account_no = paste(rep(7, 5), 1:5, sep=""),
  Jan = rnorm(5, 10, 1),
  Feb = rnorm(5, 10, 2),
  Mar = rnorm(5, 10, 3)
)
print(d)

##   account_no       Jan       Feb       Mar
## 1         71  8.849874  9.052444  5.995447
## 2         72  8.153276 10.052375  9.850486
## 3         73 11.067459 12.403149  9.730900
## 4         74 11.506958 10.222173 14.384810
## 5         75 10.826462  9.549385 11.896931

Transormation into a normalized table (unpivot):

dn <- reshape2::melt(
  data = d, 
  id.vars = "account_no", 
  variable.name = "month", 
  value.name = "revenue"
)
print(dn)

##    account_no month   revenue
## 1          71   Jan  8.849874
## 2          72   Jan  8.153276
## 3          73   Jan 11.067459
## 4          74   Jan 11.506958
## 5          75   Jan 10.826462
## 6          71   Feb  9.052444
## 7          72   Feb 10.052375
## 8          73   Feb 12.403149
## 9          74   Feb 10.222173
## 10         75   Feb  9.549385
## 11         71   Mar  5.995447
## 12         72   Mar  9.850486
## 13         73   Mar  9.730900
## 14         74   Mar 14.384810
## 15         75   Mar 11.896931

And back to the previous format using a pivot:

reshape2::dcast(
  data = dn, 
  formula = account_no ~ month, 
  value.var = "revenue"
)

##   account_no       Jan       Feb       Mar
## 1         71  8.849874  9.052444  5.995447
## 2         72  8.153276 10.052375  9.850486
## 3         73 11.067459 12.403149  9.730900
## 4         74 11.506958 10.222173 14.384810
## 5         75 10.826462  9.549385 11.896931

3. Links

A pretty nice and much longer tutorial is available here.