I have a Google stock data. It has two columns Date(Daily Data) and Close i.e. Google closing index.
Date Close
10/11/2013 871.99
10/10/2013 868.24
10/9/2013 855.86
10/8/2013 853.67
10/7/2013 865.74
10/4/2013 872.35
10/3/2013 876.09
10/2/2013 887.99
10/1/2013 887
9/30/2013 875.91
9/27/2013 876.39
9/26/2013 878.17
9/25/2013 877.23
9/24/2013 886.84
and its in csv format and I read it through read.csv which return data frame object. When I tried to transform it into timeseries / ts() object, it returns unwanted numbers.
Please help me to convert data frame into ts() object.
Thanks in advance.
To convert the given dataframe with the date column to the time series object, the user first needs to import and load the xts package. The user then needs to call the xts() function with the required parameters the main need to call this function is to create the time-series object in R language and at the end use is.
as. data. frame() function in R Programming Language is used to convert an object to data frame. These objects can be Vectors, Lists, Matrices, and Factors.
The easiest way import data in . csv files into R is to use the R function read. csv(). Now do the same for the Microsoft data.
Convert data from a string to a timestamp: if we have a list of string data that resembles DateTime, we can first convert it to a dataframe using pd. DataFrame() method and convert it to DateTime column using pd. to_datetime() method.
I suggest using xts
instead of ts
as it has lot of functions especially for financial time series.
If your data is in data.frame DF
then you can convert it to xts
as follows
xts(DF$Close, as.Date(DF$Date, format='%m/%d/%Y')
Here's an approach using zoo
from zoo package and then coercing the result to be ts
> library(zoo)
> ZOO <- zoo(df$Close, order.by=as.Date(as.character(df$Date), format='%m/%d/%Y'))
> ZOO
2013-09-24 2013-09-25 2013-09-26 2013-09-27 2013-09-30 2013-10-01 2013-10-02 2013-10-03 2013-10-04
886.84 877.23 878.17 876.39 875.91 887.00 887.99 876.09 872.35
2013-10-07 2013-10-08 2013-10-09 2013-10-10 2013-10-11
865.74 853.67 855.86 868.24 871.99
> ts(ZOO) # coercing to be `ts`
Time Series:
Start = 1
End = 14
Frequency = 1
[1] 886.84 877.23 878.17 876.39 875.91 887.00 887.99 876.09 872.35 865.74 853.67 855.86 868.24
[14] 871.99
attr(,"index")
[1] "2013-09-24" "2013-09-25" "2013-09-26" "2013-09-27" "2013-09-30" "2013-10-01" "2013-10-02"
[8] "2013-10-03" "2013-10-04" "2013-10-07" "2013-10-08" "2013-10-09" "2013-10-10" "2013-10-11"
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