a10 dataset in fpp2 package

General
#1

The fpp2 package comes with the a10 dataset, which is supposed to represent monthly anti-diabetic drug subsidy in Australia from 1991 to 2008. The plot given in the corresponding text matches the description and is highly seasonal. But this is what I am getting from the fpp2 package:

a10
Jan Feb Mar Apr May Jun Jul
1991 0.000000
1992 7.920884 9.219959 10.301813 11.613195 12.539031 14.087503 15.346418
1993 26.276099 29.068844 30.516766 32.776821 34.304273 36.672523 38.683060
1994 56.234311 60.176240 62.437087 65.692643 69.161984 72.807822 75.394568
1995 108.989384 115.955064 122.033019 130.895367 144.777168 158.121564 184.613574
Aug Sep Oct Nov Dec
1991 1.295276 2.269658 4.038592 5.514931 6.602400
1992 17.528669 19.089345 20.537184 22.334591 23.980902
1993 41.142395 43.590946 47.928078 50.264135 53.740141
1994 78.482364 82.562048 87.406545 93.068720 100.630282
1995 229.003120

The data runs only till 1995, and is non-seasonal.

Referred here by Forecasting: Principles and Practice, by Rob J Hyndman and George Athanasopoulos

#2

What happens when you use str(a10) and tail(a10)?

> str(a10)
 Time-Series [1:204] from 1992 to 2008: 3.53 3.18 3.25 3.61 3.57 ...
> tail(a10)
          Jan      Feb      Mar      Apr      May      Jun
2008 29.66536 21.65429 18.26495 23.10768 22.91251 19.43174

Perhaps you could uninstall and reinstall the package?

#3

Use it or lose it artifact creates 12-month seasonality

suppressPackageStartupMessages({
  library(fpp2)
})

# Monthly government expenditure (millions of dollars) as part of the Pharmaceutical Benefit Scheme for products falling under ATC code A10 as recorded by the Australian Health Insurance Commission. July 1991 - June 2008

a10
#>            Jan       Feb       Mar       Apr       May       Jun       Jul
#> 1991                                                              3.526591
#> 1992  5.088335  2.814520  2.985811  3.204780  3.127578  3.270523  3.737851
#> 1993  6.192068  3.450857  3.772307  3.734303  3.905399  4.049687  4.315566
#> 1994  6.731473  3.841278  4.394076  4.075341  4.540645  4.645615  4.752607
#> 1995  6.749484  4.216067  4.949349  4.823045  5.194754  5.170787  5.256742
#> 1996  8.329452  5.069796  5.262557  5.597126  6.110296  5.689161  6.486849
#> 1997  8.524471  5.277918  5.714303  6.214529  6.411929  6.667716  7.050831
#> 1998  8.798513  5.918261  6.534493  6.675736  7.064201  7.383381  7.813496
#> 1999 10.391416  6.421535  8.062619  7.297739  7.936916  8.165323  8.717420
#> 2000 12.511462  7.457199  8.591191  8.474000  9.386803  9.560399 10.834295
#> 2001 14.497581  8.049275 10.312891  9.753358 10.850382  9.961719 11.443601
#> 2002 16.300269  9.053485 10.002449 10.788750 12.106705 10.954101 12.844566
#> 2003 16.828350  9.800215 10.816994 10.654223 12.512323 12.161210 12.998046
#> 2004 18.003768 11.938030 12.997900 12.882645 13.943447 13.989472 15.339097
#> 2005 20.778723 12.154552 13.402392 14.459239 14.795102 15.705248 15.829550
#> 2006 23.486694 12.536987 15.467018 14.233539 17.783058 16.291602 16.980282
#> 2007 28.038383 16.763869 19.792754 16.427305 21.000742 20.681002 21.834890
#> 2008 29.665356 21.654285 18.264945 23.107677 22.912510 19.431740          
#>            Aug       Sep       Oct       Nov       Dec
#> 1991  3.180891  3.252221  3.611003  3.565869  4.306371
#> 1992  3.558776  3.777202  3.924490  4.386531  5.810549
#> 1993  4.562185  4.608662  4.667851  5.093841  7.179962
#> 1994  5.350605  5.204455  5.301651  5.773742  6.204593
#> 1995  5.855277  5.490729  6.115293  6.088473  7.416598
#> 1996  6.300569  6.467476  6.828629  6.649078  8.606937
#> 1997  6.704919  7.250988  7.819733  7.398101 10.096233
#> 1998  7.431892  8.275117  8.260441  8.596156 10.558939
#> 1999  9.070964  9.177113  9.251887  9.933136 11.532974
#> 2000 10.643751  9.908162 11.710041 11.340151 12.079132
#> 2001 11.659239 10.647060 12.652134 13.674466 12.965735
#> 2002 12.196500 12.854748 13.542004 13.287640 15.134918
#> 2003 12.517276 13.268658 14.733622 13.669382 16.503966
#> 2004 15.370764 16.142005 16.685754 17.636728 18.869325
#> 2005 17.554701 18.100864 17.496668 19.347265 20.031291
#> 2006 18.612189 16.623343 21.430241 23.575517 23.334206
#> 2007 23.930204 22.930357 23.263340 25.250030 25.806090
#> 2008

autoplot(a10) + theme_minimal()


ggseasonplot(a10) + theme_minimal()


ggsubseriesplot(a10) + theme_minimal()


cbind("Sales ($million)" = a10,
      "Monthly log sales" = log(a10),
      "Annual change in log sales" = diff(log(a10),12)) %>%
  autoplot(facets=TRUE) +
  xlab("Year") + ylab("") +
  ggtitle("Antidiabetic drug sales") + theme_minimal()


nsdiffs(a10)
#> [1] 1