Reputation: 192
a common task in the data I work with is reshaping client data from long to wide. I have a process to do this with Reshape outlined below that basically creates new (but unmodified) columns with a numeric index appended. In my case I do not want to perform any modifications on the data. My question, because I often use reshape2 for other operations, is how this can be accomplished with dcast? It does not seem that the example data need to be melted by id, for example, but I'm not sure how I would go about making it wide. Would anyone be able to provide code in reshape2 to produce a frame comparable to "wide" in the example below?
Thanks.
date_up <- as.numeric(as.Date("1990/01/01"))
date_down <- as.numeric(as.Date("1960/01/01"))
ids <- data.frame(id=rep(1:1000, 3),site=rep(c("NMA", "NMB","NMC"), 1000))
ids <- ids[order(ids$id), ]
dates <- data.frame(datelast=runif(3000, date_down, date_up),
datestart=runif(3000, date_down, date_up),
dateend=runif(3000, date_down, date_up),
datemiddle=runif(3000, date_down, date_up))
dates[] <- lapply(dates[ , c("datestart", "dateend", "datemiddle")],
as.Date.numeric, origin = "1970-01-01")
df <- cbind(ids, dates)
# Make a within group index and reshape df
df$gid <- with(df, ave(rep(1, nrow(df)), df[,"id"], FUN = seq_along))
wide <- reshape(df, idvar = "id", timevar = "gid", direction = "wide")
Upvotes: 1
Views: 546
Reputation: 887481
We can use dcast
from data.table
, which can take multiple value.var
columns. Convert the 'data.frame' to 'data.table' (setDT(df)
), use the dcast
with formula and value.var
specified.
library(data.table)
dcast(setDT(df), id~gid, value.var=names(df)[2:6])
NOTE: The data.table
method would be faster compared to the reshape2
Upvotes: 2