How to convert dataframe into time series

Question

I have one csv file in which I have 2 closing prices of stock on daily basis   Dates   Bajaj close Hero close 3 14 2013   1854 8  1669 1 3 15 2013   1850 3  1684 45 3 18 2013   1812 1  1690 5 3 19 2013   1835 9  1645 6 3 20 2013   1840    1651 15 3 21 2013   1755 3  1623 3 3 22 2013   1820 65 1659 6 3 25 2013   1802 5  1617 7 3 26 2013   1801 25 1571 85 3 28 2013   1799 55 1542   I want to convert above data into time series format   start date is 3 14 2013 and end date is 3 13 2015  I have tried this but its giving me some weird output  values  lt - bajaj hero   -1    excluded first column i e date in real dataset  bajaj hero timeseries  lt - ts values start c 2013 1  end c 2015 3  frequency 365    Output is              Bajaj close Hero close 2013 000     1854 80    1669 10 2013 003     1850 30    1684 45 2013 005     1812 10    1690 50 2013 008     1835 90    1645 60 2013 011     1840 00    1651 15 2013 014     1755 30    1623 30 2013 016     1820 65    1659 60 2013 019     1802 50    1617 70 2013 022     1801 25    1571 85

User · Accepted Answer

R has multiple ways of represeting time series. Since you're working with daily prices of stocks, you may wish to consider that financial markets are closed on weekends and business holidays so that trading days and calendar days are not the same. However, you may need to work with your times series in terms of both trading days and calendar days. For example, daily returns are calculated from sequential daily closing prices regardless of whether a weekend intervenes. But you may also want to do calendar-based reporting such as weekly price summaries. For these reasons the xts package, an extension of zoo, is commonly used with financial data in R. An example of how it could be used with your data follows.

Assuming the data shown in your example is in the dataframe df

  library(xts)
  stocks <- xts(df[,-1], order.by=as.Date(df[,1], "%m/%d/%Y"))
#
#  daily returns
#
   returns <- diff(stocks, arithmetic=FALSE ) - 1
#
#  weekly open, high, low, close reports
#
   to.weekly(stocks$Hero_close, name="Hero")

which gives the output

           Hero.Open Hero.High Hero.Low Hero.Close
2013-03-15    1669.1   1684.45   1669.1    1684.45
2013-03-22    1690.5   1690.50   1623.3    1659.60
2013-03-28    1617.7   1617.70   1542.0    1542.00

User · Answer

With library fpp  you can easily create time series with date format      time ser ts data frequency 4 start c 1954 2    here we start at the 2nd quarter of 1954 with quarter fequency

User · Answer

Late to the party  but the tsbox package is designed to perform conversions like this  To convert your data into a ts-object  you can do   dta  lt - data frame    Dates   c  3 14 2013    3 15 2013    3 18 2013    3 19 2013      Bajaj close   c 1854 8  1850 3  1812 1  1835 9     Hero close   c 1669 1  1684 45  1690 5  1645 6     dta   gt        Dates Bajaj close Hero close   gt  1 3 14 2013      1854 8    1669 10   gt  2 3 15 2013      1850 3    1684 45   gt  3 3 18 2013      1812 1    1690 50   gt  4 3 19 2013      1835 9    1645 60  library tsbox  ts ts ts long dta     gt  Time Series    gt  Start   2013 1971293045    gt  End   2013 21081883954    gt  Frequency   365 2425    gt           Bajaj close Hero close   gt  2013 197      1854 8    1669 10   gt  2013 200      1850 3    1684 45   gt  2013 203          NA         NA   gt  2013 205          NA         NA   gt  2013 208      1812 1    1690 50   gt  2013 211      1835 9    1645 60   It automatically parses the dates  detects the frequency and makes the missing values at the weekends explicit  With ts  lt class gt   you can convert the data to any other time series class

User · Answer

See this question  Converting data frame to xts order by requires an appropriate time-based object  which suggests looking at argument to order by      Currently acceptable classes include     Date        POSIXct        timeDate       as well as    yearmon    and    yearqtr    where the index values remain   unique    And further suggests an explicit conversion using order by   as POSIXct   df Date  lt - as POSIXct strptime df Date format  tz  UTC   xts df   -1   order by as POSIXct df Date     Where your format is assigned elswhere   format  lt -   m  d  Y   see strptime for details

User · Answer

Input  We will start with the text of the input shown in the question since the question did not provide the csv input   Lines  lt -  Dates   Bajaj close Hero close 3 14 2013   1854 8  1669 1 3 15 2013   1850 3  1684 45 3 18 2013   1812 1  1690 5 3 19 2013   1835 9  1645 6 3 20 2013   1840    1651 15 3 21 2013   1755 3  1623 3 3 22 2013   1820 65 1659 6 3 25 2013   1802 5  1617 7 3 26 2013   1801 25 1571 85 3 28 2013   1799 55 1542    zoo   ts  class series normally do not represent date indexes but we can create a zoo series that does  see zoo package    library zoo  z  lt - read zoo text   Lines  header   TRUE  format     m  d  Y     Alternately  if you have already read this into a data frame DF then it could be converted to zoo as shown on the second line below   DF  lt - read table text   Lines  header   TRUE  z  lt - read zoo DF  format     m  d  Y     In either case above z ia a zoo series with a  Date  class time index   One could also create the zoo series  zz  which uses 1  2  3      as the time index   zz  lt - z time zz   lt - seq along time zz     ts  Either of these could be converted to a  ts  class series   as ts z  as ts zz    The first has a time index which is the number of days since the Epoch  January 1  1970  and will have NAs for missing days and the second will have 1  2  3      as the time index and no NAs   Monthly series  Typically  ts  series are used for monthly  quarterly or yearly series   Thus if we were to aggregate the input into months we could reasonably represent it as a  ts  series   z m  lt - as zooreg aggregate z  as yearmon  mean   freq   12  as ts z m

[r] How to convert dataframe into time series?

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