I need to aggregate my data Kg
Data Kg
1 2013-03-01 271
2 2013-03-06 374
3 2013-03-07 51
4 2013-03-12 210
5 2013-03-13 698
6 2013-03-15 328
by week or month. I have found this answer here in stackoverflow, but I really don't understand the answer. Who can show me how can I do this case. Thanx
The answer mentioned suggest that you should to use xts package.
library(xts)
## create you zoo objects using your data
## you replace text argument by read.zoo(yourfile, header = TRUE)
x.zoo <- read.zoo(text=' Data Kg
+ 1 2013-03-01 271
+ 2 2013-03-06 374
+ 3 2013-03-07 51
+ 4 2013-03-12 210
+ 5 2013-03-13 698
+ 6 2013-03-15 328',header=TRUE)
### then aggregate
apply.weekly(x.zoo, mean) ## per week
apply.monthly(x.zoo, mean) ## per month
see ??apply.xxxly:
Essentially a wrapper to the xts functions endpoints and period.apply, mainly as a convenience.
Or, you could use tapply to apply by group of weeks. Here I am using lubridate package to extract week part from a date.
# fake data
df <- structure(list(Datechar = c("2013-03-01", "2013-03-06", "2013-03-07",
"2013-03-12", "2013-03-13", "2013-03-15"), Kg = c(271L, 374L,
51L, 210L, 698L, 328L)), .Names = c("Datechar", "Kg"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6"))
# convert character to date
df$Date <- as.Date(df$Datechar)
# calculate mean kg for each week
library(lubridate)
tapply(df$Kg, week(df$Date), mean)
tapply(df$Kg, month(df$Date), mean)
