将数据帧转换为时间序列 R

Question

I have the following data

data_sample
        date         Sum 
1  Feb 2015      3322.01 
2  Mar 2015      6652.77 
3  Apr 2015      3311.12 
etc

I need to convert to time series for forecasting

 > data <- xts(data_sample[,-1], order.by=as.Date(data_sample[,1], "%Y %m"))
Error in 1 - frac : non-numeric argument to binary operator
> data <- xts(data_sample[,-1], order.by=as.Date(data_sample[,1], "%m %Y"))
Error in 1 - frac : non-numeric argument to binary operator



> ts_ts(ts_long(data_sample))
Error in guess_time(x) : 
  No [time] column detected. To be explict, name time column as 'time'.

Answer 1

If you want to use as.Date(), you have to specify full dates. Simply add 01 at the end of each entry.

date <- c("Feb 2015", "Mar 2015", "Apr 2015")
date <- as.Date(paste(date, "01"), format="%b %Y %d")

You can convert them back as follows,

format(date, "%b %Y")

or use as.yearmon from zoo library,

library("zoo")
as.yearmon(date)

Some examples here: Converting Date formats in R

Answer 2

R has multiple ways of representing time series. Since you are working with only Date and Sum, I have created a sample time series for you. I choose random dates and numbers.

Call for Packages

library(xts)

Create a Data Frame

data_sample <- data.frame(
        date = as.Date(c("2012-01-01","2013-01-01","2014-01-01", )),  
        sum1 = c(3322.01, 6652.77, 3311.12))

head(data_sample)

Convert the date as in a format which R understands.

rdate<- as.Date(data_sample$date, "%m/%d/%y")  
fix(rdate)

Plot the graph

plot(data_sample$sum1~rdate,type="l",col="red")

Execution of above code will gives below output.

Answer 3

Assuming data_sample is as shown reproducibly in the Note at the end, convert to a time series of class zoo using read.zoo and then either use it in that form or convert it to some other class such as xts or ts using the appropriate as.* function. Here we used yearmon class to represent the index as that directly represents year and month without day. This class will be used in zoo and xts and when converting to ts it will be converted appropriately.

library(xts) # this also loads zoo

z <- read.zoo(data_sample, FUN = as.yearmon, format = "%b %Y")

as.xts(z)
as.ts(z)

Date

It is also possible to use Date class for the index in zoo and xts but that does not work well with ts class. Using Date class implies that the distance between consecutive points varies according to the number of days per month as opposed to being a regularly spaced series so using Date for monthly data is normally not useful for forecasting.

zd <- aggregate(z, as.Date, c)
xd <- as.xts(zd)

Note

Input in reproducible form

Lines <- "date,Sum 
1,Feb 2015,3322.01 
2,Mar 2015,6652.77 
3,Apr 2015,3311.12 "
data_sample <- read.csv(text = Lines)

Answer 4

air1 <- type.convert(.preformat.ts(AirPassengers))
airpassengers <- as.data.frame(air1)
 
 View(airpassengers)
 class(airpassengers)
[1] "data.frame"

It converts time series data to dataframe.