R Dataset / Package datasets / sunspot.year

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<iframe src="https://embed.picostat.com/r-dataset-package-datasets-sunspotyear.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-29113.csv 2.68 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the sunspot.year data set which pertains to Yearly Sunspot Data, 1700–1988. The sunspot.year data set is found in the datasets R package. You can load the sunspot.year data set in R by issuing the following command at the console data("sunspot.year"). This will load the data into a variable called sunspot.year. If R says the sunspot.year data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the sunspot.year R data set. The size of this file is about 2,746 bytes.

Documentation

Yearly Sunspot Data, 1700–1988

Description

Yearly numbers of sunspots from 1700 to 1988 (rounded to one digit).

Note that monthly numbers are available as sunspot.month, though starting slightly later.

Usage

sunspot.year

Format

The univariate time series sunspot.year contains 289 observations, and is of class "ts".

Source

H. Tong (1996) Non-Linear Time Series. Clarendon Press, Oxford, p. 471.

See Also

For monthly sunspot numbers, see sunspot.month and sunspots.

Regularly updated yearly sunspot numbers are available from WDC-SILSO, Royal Observatory of Belgium, at http://www.sidc.be/silso/datafiles

Examples

utils::str(sm <- sunspots)# the monthly version we keep unchanged
utils::str(sy <- sunspot.year)
## The common time interval
(t1 <- c(max(start(sm), start(sy)),     1)) # Jan 1749
(t2 <- c(min(  end(sm)[1],end(sy)[1]), 12)) # Dec 1983
s.m <- window(sm, start=t1, end=t2)
s.y <- window(sy, start=t1, end=t2[1]) # {irrelevant warning}
stopifnot(length(s.y) * 12 == length(s.m),
          ## The yearly series *is* close to the averages of the monthly one:
          all.equal(s.y, aggregate(s.m, FUN = mean), tol = 0.0020))
## NOTE: Strangely, correctly weighting the number of days per month
##       (using 28.25 for February) is *not* closer than the simple mean:
ndays <- c(31, 28.25, rep(c(31,30, 31,30, 31), 2))
all.equal(s.y, aggregate(s.m, FUN = mean))                     # 0.0013
all.equal(s.y, aggregate(s.m, FUN = weighted.mean, w = ndays)) # 0.0017
--

Dataset imported from https://www.r-project.org.

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