# NOT RUN {
summary(Indometh)
wide <- reshape(Indometh, v.names = "conc", idvar = "Subject",
timevar = "time", direction = "wide")
wide
reshape(wide, direction = "long")
reshape(wide, idvar = "Subject", varying = list(2:12),
v.names = "conc", direction = "long")
## times need not be numeric
df <- data.frame(id = rep(1:4, rep(2,4)),
visit = I(rep(c("Before","After"), 4)),
x = rnorm(4), y = runif(4))
df
reshape(df, timevar = "visit", idvar = "id", direction = "wide")
## warns that y is really varying
reshape(df, timevar = "visit", idvar = "id", direction = "wide", v.names = "x")
## unbalanced 'long' data leads to NA fill in 'wide' form
df2 <- df[1:7, ]
df2
reshape(df2, timevar = "visit", idvar = "id", direction = "wide")
## Alternative regular expressions for guessing names
df3 <- data.frame(id = 1:4, age = c(40,50,60,50), dose1 = c(1,2,1,2),
dose2 = c(2,1,2,1), dose4 = c(3,3,3,3))
reshape(df3, direction = "long", varying = 3:5, sep = "")
## an example that isn't longitudinal data
state.x77 <- as.data.frame(state.x77)
long <- reshape(state.x77, idvar = "state", ids = row.names(state.x77),
times = names(state.x77), timevar = "Characteristic",
varying = list(names(state.x77)), direction = "long")
reshape(long, direction = "wide")
reshape(long, direction = "wide", new.row.names = unique(long$state))
## multiple id variables
df3 <- data.frame(school = rep(1:3, each = 4), class = rep(9:10, 6),
time = rep(c(1,1,2,2), 3), score = rnorm(12))
wide <- reshape(df3, idvar = c("school","class"), direction = "wide")
wide
## transform back
reshape(wide)
# }
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