Transforming a time-series into a data frame and back
Try the package "tsbox"
ts = ts(rnorm(12*5, 17, 8), start=c(1981,1), frequency = 12)
df = ts_df(ts)
str(df)
data.frame: 60 obs. of 2 variables:
time : Date, format: "1981-01-01" "1981-02-01"
value: num 23.15 22.77 5.1 1.05 13.87
Here are two ways. The first way creates dimnames for the matrix about to be created and then strings out the data into a matrix, transposes it and converts it to data frame. The second way creates a by list consisting of year and month variables and uses tapply on that later converting to data frame and adding names.
# create test data
set.seed(123)
tt <- ts(rnorm(12*5, 17, 8), start=c(1981,1), frequency = 12)
1) matrix. This solution requires that we have whole consecutive years
dmn <- list(month.abb, unique(floor(time(tt))))
as.data.frame(t(matrix(tt, 12, dimnames = dmn)))
If we don't care about the nice names it is just as.data.frame(t(matrix(tt, 12)))
.
We could replace the dmn<-
line with the following simpler line using @thelatemail's comment:
dmn <- dimnames(.preformat.ts(tt))
2) tapply. A more general solution using tapply
is the following:
Month <- factor(cycle(tt), levels = 1:12, labels = month.abb)
tapply(tt, list(year = floor(time(tt)), month = Month), c)
Note: To invert this suppose X
is any of the solutions above. Then try:
ts(c(t(X)), start = 1981, freq = 12)
Update
Improvement motivated by comments of @latemail below.
Example with the AirPassengers dataset:
Make the data available and check its type:
data(AirPassengers)
class(AirPassengers)
Convert Time-Series into a data frame:
df <- data.frame(AirPassengers, year = trunc(time(AirPassengers)),
month = month.abb[cycle(AirPassengers)])
Redo the creation of the Time-Series object:
tsData = ts(df$AirPassengers, start = c(1949,1), end = c(1960,12), frequency = 12)
Plot the results to ensure correct execution:
components.ts = decompose(tsData)
plot(components.ts)