Advanced Features of xts Manipulating Time Series Data in R: Case - - PowerPoint PPT Presentation

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Advanced Features of xts Manipulating Time Series Data in R: Case - - PowerPoint PPT Presentation

Jan 14, 2023 308 likes •440 views

MANIPULATING TIME SERIES DATA IN R: CASE STUDIES Advanced Features of xts Manipulating Time Series Data in R: Case Studies Finding Endpoints endpoints() indexes last observation per interval > years <- endpoints(unemployment, on =

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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Advanced Features of xts

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Manipulating Time Series Data in R: Case Studies

Finding Endpoints

  • endpoints() indexes last observation per interval

> years <- endpoints(unemployment, on = "years") > unemployment[years] us ma Dec 1976 7.650000 8.200000 Dec 1977 6.400000 6.200000 Dec 1978 6.000000 5.700000 Dec 1979 6.000000 4.900000 Dec 1980 7.200000 5.100000

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Manipulating Time Series Data in R: Case Studies

Apply by Period

  • period.apply() extends apply functions to time

> period.apply(unemployment, INDEX = years, FUN = mean) us ma Dec 1976 7.654167 9.633333 Dec 1977 7.016667 7.804167 Dec 1978 6.066667 6.220833 Dec 1979 5.945833 5.516667 Dec 1980 7.200000 5.629167

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Manipulating Time Series Data in R: Case Studies

Sports Data

Source: hps://commons.wikimedia.org/

  • Boston sports games, 2010 through 2015
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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Let’s practice!

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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Indexing Commands in xts

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Manipulating Time Series Data in R: Case Studies

Extracting the Index

  • .index() extracts raw time index

> .index(unemployment) [1] 189302400 191980800 194486400 197164800 [5] 199756800 202435200 205027200 207705600 [9] 210384000 212976000 215654400 218246400 [13] 220924800 223603200 226022400 228700800 [17] 231292800 233971200 236563200 239241600

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Manipulating Time Series Data in R: Case Studies

Weekday Observations

  • .indexwday() gives the weekday of each observation

> .indexwday(sports) [1] 0 2 3 5 6 0 1 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 5 6 [25] 0 1 2 3 4 5 6 0 1 2 3 5 6 0 1 2 3 4 5 6 0 1 2 3 [49] 4 5 6 0 2 3 4 5 6 0 1 2 3 4 5 6 ...

  • Select only Sunday games

> sunday_games <- which(.indexwday(sports) == 0)

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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Let’s practice!

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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Congratulations!

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Manipulating Time Series Data in R: Case Studies

Time Series Data

  • Weather paerns
  • Sports scores
  • Portfolio returns
  • Commodity prices
  • User data

Jan 03 2000 Jan 02 2004 Jan 02 2008 Jan 03 2012 Jan 04 2016 8000 12000 18000

Dow Jones Industrial Average

Source: hps://finance.yahoo.com

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MANIPULATING TIME SERIES DATA IN R: CASE STUDIES

Thank you!