Bind two data frames into a multivariate data frame
Usually data frames represent one set of variables and one needs to
bind/join them for multivariate analysis. When merge is not
the approriate solution, bindData might perform an appropriate binding
for two data frames. This is especially usefull when some variables are
measured once, while others are repeated.
bindData(x, y, common)
x |
data.frame |
y |
data.frame |
common |
character, list of column names that are common to both input data frames |
Data frames are joined in a such a way, that the new data frame has c + (n_1 - c) + (n_2 - c) columns, where c is the number of common columns, and n_1 and n_2 are the number of columns in the first and in the second data frame, respectively.
A data frame.
Gregor Grojanc
n1 <- 6
n2 <- 12
n3 <- 4
## Single trait 1
num <- c(5:n1, 10:13)
(tmp1 <- data.frame(y1=rnorm(n=n1),
f1=factor(rep(c("A", "B"), n1/2)),
ch=letters[num],
fa=factor(letters[num]),
nu=(num) + 0.5,
id=factor(num), stringsAsFactors=FALSE))
## Single trait 2 with repeated records, some subjects also in tmp1
num <- 4:9
(tmp2 <- data.frame(y2=rnorm(n=n2),
f2=factor(rep(c("C", "D"), n2/2)),
ch=letters[rep(num, times=2)],
fa=factor(letters[rep(c(num), times=2)]),
nu=c((num) + 0.5, (num) + 0.25),
id=factor(rep(num, times=2)), stringsAsFactors=FALSE))
## Single trait 3 with completely distinct set of subjects
num <- 1:4
(tmp3 <- data.frame(y3=rnorm(n=n3),
f3=factor(rep(c("E", "F"), n3/2)),
ch=letters[num],
fa=factor(letters[num]),
nu=(num) + 0.5,
id=factor(num), stringsAsFactors=FALSE))
## Combine all datasets
(tmp12 <- bindData(x=tmp1, y=tmp2, common=c("id", "nu", "ch", "fa")))
(tmp123 <- bindData(x=tmp12, y=tmp3, common=c("id", "nu", "ch", "fa")))
## Sort by subject
tmp123[order(tmp123$ch), ]Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.