Monthly Sunspot Data, from 1749 to "Present"

Description

Monthly numbers of sunspots, as from the World Data Center, aka SIDC. This is the version of the data that will occasionally be updated when new counts become available.

Usage

sunspot.month

Format

The univariate time series sunspot.year and sunspot.month contain 289 and 2988 observations, respectively. The objects are of class "ts".

Author(s)

R

Source

WDC-SILSO, Solar Influences Data Analysis Center (SIDC), Royal Observatory of Belgium, Av. Circulaire, 3, B-1180 BRUSSELS Currently at http://www.sidc.be/silso/datafiles

See Also

sunspot.month is a longer version of sunspots; the latter runs until 1983 and is kept fixed (for reproducibility as example dataset).

Examples

require(stats); require(graphics)
## Compare the monthly series
plot (sunspot.month,
      main="sunspot.month & sunspots [package'datasets']", col=2)
lines(sunspots) # -> faint differences where they overlap

## Now look at the difference :
all(tsp(sunspots)     [c(1,3)] ==
    tsp(sunspot.month)[c(1,3)]) ## Start & Periodicity are the same
n1 <- length(sunspots)
table(eq <- sunspots == sunspot.month[1:n1]) #>  132  are different !
i <- which(!eq)
rug(time(eq)[i])
s1 <- sunspots[i] ; s2 <- sunspot.month[i]
cbind(i = i, time = time(sunspots)[i], sunspots = s1, ss.month = s2,
      perc.diff = round(100*2*abs(s1-s2)/(s1+s2), 1))

## How to recreate the "old" sunspot.month (R <= 3.0.3):
.sunspot.diff <- cbind(
    i = c(1202L, 1256L, 1258L, 1301L, 1407L, 1429L, 1452L, 1455L,
          1663L, 2151L, 2329L, 2498L, 2594L, 2694L, 2819L),
    res10 = c(1L, 1L, 1L, -1L, -1L, -1L, 1L, -1L,
          1L, 1L, 1L, 1L, 1L, 20L, 1L))
ssm0 <- sunspot.month[1:2988]
with(as.data.frame(.sunspot.diff), ssm0[i] <<- ssm0[i] - res10/10)
sunspot.month.0 <- ts(ssm0, start = 1749, frequency = 12)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(datasets)
> png(filename="/home/ddbj/snapshot/RGM3/R_rel/result/datasets/sunspot.month.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sunspot.month
> ### Title: Monthly Sunspot Data, from 1749 to "Present"
> ### Aliases: sunspot.month
> ### Keywords: datasets
> 
> ### ** Examples
> 
> require(stats); require(graphics)
> ## Compare the monthly series
> plot (sunspot.month,
+       main="sunspot.month & sunspots [package'datasets']", col=2)
> lines(sunspots) # -> faint differences where they overlap
> 
> ## Now look at the difference :
> all(tsp(sunspots)     [c(1,3)] ==
+     tsp(sunspot.month)[c(1,3)]) ## Start & Periodicity are the same
[1] TRUE
> n1 <- length(sunspots)
> table(eq <- sunspots == sunspot.month[1:n1]) #>  132  are different !

FALSE  TRUE 
  143  2677 
> i <- which(!eq)
> rug(time(eq)[i])
> s1 <- sunspots[i] ; s2 <- sunspot.month[i]
> cbind(i = i, time = time(sunspots)[i], sunspots = s1, ss.month = s2,
+       perc.diff = round(100*2*abs(s1-s2)/(s1+s2), 1))
          i     time sunspots ss.month perc.diff
  [1,]   55 1753.500     22.2     22.0       0.9
  [2,]  838 1818.750     31.7     31.6       0.3
  [3,]  841 1819.000     32.5     32.8       0.9
  [4,]  862 1820.750      9.0      8.9       1.1
  [5,]  864 1820.917      9.7      9.1       6.4
  [6,]  866 1821.083      4.3      4.2       2.4
  [7,]  876 1821.917      0.0      0.2     200.0
  [8,]  901 1824.000     21.6     21.7       0.5
  [9,]  917 1825.333     15.4     15.5       0.6
 [10,]  920 1825.583     25.4     25.7       1.2
 [11,]  943 1827.500     42.9     42.3       1.4
 [12,]  946 1827.750     57.2     56.1       1.9
 [13,]  955 1828.500     54.3     54.2       0.2
 [14,]  960 1828.917     46.6     46.9       0.6
 [15,]  965 1829.333     67.5     67.4       0.1
 [16,]  968 1829.583     78.3     77.6       0.9
 [17,]  976 1830.250    107.1    106.3       0.7
 [18,]  988 1831.250     54.6     54.5       0.2
 [19,]  992 1831.583     54.9     55.0       0.2
 [20,]  994 1831.750     46.2     46.3       0.2
 [21,]  998 1832.083     55.5     55.6       0.2
 [22,] 1003 1832.500     13.9     14.0       0.7
 [23,] 1047 1836.167     98.1     98.2       0.1
 [24,] 1061 1837.333    111.3    111.7       0.4
 [25,] 1081 1839.000    107.6    105.6       1.9
 [26,] 1087 1839.500     84.7     84.8       0.1
 [27,] 1090 1839.750     90.8     90.9       0.1
 [28,] 1092 1839.917     63.6     63.7       0.2
 [29,] 1095 1840.167     55.5     67.8      20.0
 [30,] 1102 1840.750     49.8     55.0       9.9
 [31,] 1105 1841.000     24.0     24.1       0.4
 [32,] 1108 1841.250     42.6     40.2       5.8
 [33,] 1109 1841.333     67.4     67.5       0.1
 [34,] 1113 1841.667     35.1     36.5       3.9
 [35,] 1124 1842.583     26.5     26.6       0.4
 [36,] 1125 1842.667     18.5     18.4       0.5
 [37,] 1132 1843.250      8.8      9.5       7.7
 [38,] 1145 1844.333     12.0     11.6       3.4
 [39,] 1149 1844.667      6.9      7.0       1.4
 [40,] 1156 1845.250     56.9     57.0       0.2
 [41,] 1168 1846.250     69.2     69.3       0.1
 [42,] 1185 1847.667    161.2    160.9       0.2
 [43,] 1191 1848.167    108.9    108.6       0.3
 [44,] 1194 1848.417    123.8    129.0       4.1
 [45,] 1196 1848.583    132.5    132.6       0.1
 [46,] 1200 1848.917    159.9    159.5       0.3
 [47,] 1201 1849.000    156.7    157.0       0.2
 [48,] 1202 1849.083    131.7    131.8       0.1
 [49,] 1203 1849.167     96.5     96.2       0.3
 [50,] 1206 1849.417     81.2     81.1       0.1
 [51,] 1208 1849.583     61.3     67.7       9.9
 [52,] 1211 1849.833     99.7     99.0       0.7
 [53,] 1224 1850.917     60.0     61.0       1.7
 [54,] 1235 1851.833     50.9     51.0       0.2
 [55,] 1238 1852.083     67.5     66.4       1.6
 [56,] 1243 1852.500     42.0     42.1       0.2
 [57,] 1256 1853.583     50.4     50.5       0.2
 [58,] 1258 1853.750     42.3     42.4       0.2
 [59,] 1264 1854.250     26.4     26.5       0.4
 [60,] 1270 1854.750     12.7     12.6       0.8
 [61,] 1272 1854.917     21.4     21.6       0.9
 [62,] 1282 1855.750      9.7      9.6       1.0
 [63,] 1283 1855.833      4.3      4.2       2.4
 [64,] 1290 1856.417      5.0      5.2       3.9
 [65,] 1301 1857.333     29.2     28.5       2.4
 [66,] 1333 1860.000     81.5     82.4       1.1
 [67,] 1334 1860.083     88.0     88.3       0.3
 [68,] 1346 1861.083     77.8     77.7       0.1
 [69,] 1350 1861.417     87.8     88.1       0.3
 [70,] 1366 1862.750     42.0     41.9       0.2
 [71,] 1407 1866.167     24.6     24.5       0.4
 [72,] 1424 1867.583      4.9      4.8       2.1
 [73,] 1427 1867.833      9.3      9.6       3.2
 [74,] 1429 1868.000     15.6     15.5       0.6
 [75,] 1430 1868.083     15.8     15.7       0.6
 [76,] 1435 1868.500     28.6     29.0       1.4
 [77,] 1437 1868.667     43.8     47.2       7.5
 [78,] 1438 1868.750     61.7     61.6       0.2
 [79,] 1442 1869.083     59.3     59.9       1.0
 [80,] 1445 1869.333    104.0    103.9       0.1
 [81,] 1450 1869.750     59.4     59.3       0.2
 [82,] 1451 1869.833     77.4     78.1       0.9
 [83,] 1452 1869.917    104.3    104.4       0.1
 [84,] 1455 1870.167    159.4    157.5       1.2
 [85,] 1472 1871.583    110.0    110.1       0.1
 [86,] 1476 1871.917     90.3     90.4       0.1
 [87,] 1486 1872.750    103.5    102.6       0.9
 [88,] 1497 1873.667     47.5     47.1       0.8
 [89,] 1498 1873.750     47.4     47.1       0.6
 [90,] 1514 1875.083     22.2     21.5       3.2
 [91,] 1527 1876.167     31.2     30.6       1.9
 [92,] 1539 1877.167     11.7     11.9       1.7
 [93,] 1541 1877.333     21.2     21.6       1.9
 [94,] 1542 1877.417     13.4     14.2       5.8
 [95,] 1543 1877.500      5.9      6.0       1.7
 [96,] 1545 1877.667     16.4     16.9       3.0
 [97,] 1547 1877.833     14.5     14.2       2.1
 [98,] 1548 1877.917      2.3      2.2       4.4
 [99,] 1550 1878.083      6.0      6.6       9.5
[100,] 1553 1878.333      5.8      5.9       1.7
[101,] 1561 1879.000      0.8      1.0      22.2
[102,] 1571 1879.833     12.9     13.1       1.5
[103,] 1572 1879.917      7.2      7.3       1.4
[104,] 1574 1880.083     27.5     27.2       1.1
[105,] 1575 1880.167     19.5     19.3       1.0
[106,] 1576 1880.250     19.3     19.5       1.0
[107,] 1588 1881.250     51.7     51.6       0.2
[108,] 1592 1881.583     58.0     58.4       0.7
[109,] 1594 1881.750     64.0     64.4       0.6
[110,] 1598 1882.083     69.3     69.5       0.3
[111,] 1599 1882.167     67.5     66.8       1.0
[112,] 1613 1883.333     32.1     31.5       1.9
[113,] 1614 1883.417     76.5     76.3       0.3
[114,] 1623 1884.167     86.8     87.5       0.8
[115,] 1643 1885.833     33.3     30.9       7.5
[116,] 1656 1886.917     12.4     13.0       4.7
[117,] 1663 1887.500     23.3     23.4       0.4
[118,] 1683 1889.167      7.0      6.7       4.4
[119,] 1687 1889.500      9.7      9.4       3.1
[120,] 1712 1891.583     33.2     33.0       0.6
[121,] 1716 1891.917     32.3     32.5       0.6
[122,] 1723 1892.500     76.8     76.5       0.4
[123,] 1734 1893.417     88.2     89.9       1.9
[124,] 1735 1893.500     88.8     88.6       0.2
[125,] 1738 1893.750     79.7     80.0       0.4
[126,] 1774 1896.750     28.4     28.7       1.1
[127,] 1837 1902.000      5.2      5.5       5.6
[128,] 2126 1926.083     70.0     69.9       0.1
[129,] 2151 1928.167     85.4     85.5       0.1
[130,] 2153 1928.333     76.9     77.0       0.1
[131,] 2162 1929.083     64.1     62.8       2.0
[132,] 2174 1930.083     49.2     49.9       1.4
[133,] 2233 1935.000     18.9     18.6       1.6
[134,] 2315 1941.833     38.3     38.4       0.3
[135,] 2329 1943.000     12.4     12.5       0.8
[136,] 2378 1947.083    113.4    133.4      16.2
[137,] 2427 1951.167     59.9     55.9       6.9
[138,] 2498 1957.083    130.2    130.3       0.1
[139,] 2552 1961.583     55.9     55.8       0.2
[140,] 2556 1961.917     40.0     39.9       0.3
[141,] 2594 1965.083     14.2     14.3       0.7
[142,] 2790 1981.417     90.0     90.9       1.0
[143,] 2819 1983.833     33.3     33.4       0.3
> 
> ## How to recreate the "old" sunspot.month (R <= 3.0.3):
> .sunspot.diff <- cbind(
+     i = c(1202L, 1256L, 1258L, 1301L, 1407L, 1429L, 1452L, 1455L,
+           1663L, 2151L, 2329L, 2498L, 2594L, 2694L, 2819L),
+     res10 = c(1L, 1L, 1L, -1L, -1L, -1L, 1L, -1L,
+           1L, 1L, 1L, 1L, 1L, 20L, 1L))
> ssm0 <- sunspot.month[1:2988]
> with(as.data.frame(.sunspot.diff), ssm0[i] <<- ssm0[i] - res10/10)
> sunspot.month.0 <- ts(ssm0, start = 1749, frequency = 12)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>