Pyear
September 15, 2018
> setwd("C:/Users/batagelj/work/Python/WoS/SocNet/2018/WoS")
> Y <- read.csv("./yearN.clu",header=FALSE,skip=2)$V1
> t <- table(Y)
> t
Y
0 1 2 3 4 6 1008 1010 1020 1030 1050 1082 1145 1195
28602 5 80 250 1 2 1 1 1 1 1 1 2 1
...
1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873
22 21 25 33 23 25 25 27 15 18 31 27 27 30
1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887
27 34 25 22 23 32 34 22 37 39 29 38 37 54
1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901
30 35 54 41 40 38 56 53 61 56 56 54 56 57
1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915
73 63 77 74 87 75 114 105 109 97 97 97 85 83
1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929
73 69 65 80 107 92 128 95 111 128 140 157 172 153
1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943
146 174 202 189 210 169 208 210 198 221 204 152 158 174
1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957
162 210 265 302 326 397 492 454 457 481 538 589 619 707
1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971
712 765 869 906 1005 1062 1164 1360 1456 1740 1785 1973 2077 2256
1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985
2553 2851 3081 3206 3695 4104 4397 4909 5208 5697 6143 6361 6821 7792
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
8644 9430 10313 11288 13293 13496 15262 15908 17595 19383 20966 22967 25507 28105
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
33562 35231 38869 42759 46466 50918 56072 60829 63349 67726 70907 70865 69502 66780
2014 2015 2016 2017 2018 2019 2020 2100
59080 49009 33397 17087 3097 2 2 2
> years <- as.numeric(names(t))
> year <- years[375:496]
> freq <- t[375:496]
> plot(year,freq,cex=0.75,main="Publications per year")
> model <- nls(freq~c*dlnorm(2019-year,a,b),start=list(c=1e6,a=2,b=0.7))
> model
Nonlinear regression model
model: freq ~ c * dlnorm(2019 - year, a, b)
data: parent.frame()
c a b
1.278e+06 2.543e+00 7.206e-01
residual sum-of-squares: 59507589
Number of iterations to convergence: 6
Achieved convergence tolerance: 8.922e-06
> lines(year,predict(model,list(x=2019-year)),col="red",lw=2)
Vladimir Batagelj, Patrick Doreian, Anuška Ferligoj and Nataša Kejžar: Understanding Large Temporal Networks and Spatial Networks: Exploration, Pattern Searching, Visualization and Network Evolution. Wiley Series in Computational and Quantitative Social Science. Wiley, 2014. p 35-37.
January 28, 2019
In the above picture the y-axis has a single tick. To improve the picture we have to set our own ticks to the y-axis. The recipe is as follows
> wdir <- "C:/Users/batagelj/work/Python/WoS/SocNet/2018/WoS"
> setwd(wdir)
> Y <- read.csv("./yearN.clu",header=FALSE,skip=2)$V1
> t <- table(Y)
> years <- as.numeric(names(t))
> year <- years[375:496]
> freq <- t[375:496]
> max(freq)
[1] 70907
> yt <- c(0,10000,30000,50000,70000); yl <- c("0","10k","30k","50k","70k")
> plot(year,freq,cex=0.75,main="Publications per year",yaxt="n")
> axis(side=2,at=yt, labels=yl)
October, 18, 2018 CiteN, YearN, DCn
DCn:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
0 1226341 94.5424 1226341 94.5424 M NEEDS LATINO YOU 2
1 70792 5.4576 1297133 100.0000 BAUMEIST_R(1995)117:497
----------------------------------------------------------------
Sum 1297133 100.0000
Year.clu DC.clu Partitions - extarct SubPartition (2nd from 1st)
==============================================================================
3. Extracting from C1 vertices determined by C2 [1] (70792)
==============================================================================
Dimension: 70792
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1894 1 0.0014 1 0.0014 70541
1901 1 0.0014 2 0.0028 1598
1934 1 0.0014 3 0.0042 30
1939 1 0.0014 4 0.0057 1155
1941 1 0.0014 5 0.0071 1153
1946 1 0.0014 6 0.0085 1160
1948 1 0.0014 7 0.0099 412
1950 2 0.0028 9 0.0127 614
1951 1 0.0014 10 0.0141 1087
1953 2 0.0028 12 0.0170 1424
1954 4 0.0057 16 0.0226 641
1955 1 0.0014 17 0.0240 598
1956 1 0.0014 18 0.0254 1157
1957 3 0.0042 21 0.0297 1329
1958 1 0.0014 22 0.0311 1602
1959 2 0.0028 24 0.0339 580
1960 2 0.0028 26 0.0367 3214
1961 1 0.0014 27 0.0381 1962
1962 2 0.0028 29 0.0410 594
1963 2 0.0028 31 0.0438 826
1964 2 0.0028 33 0.0466 726
1965 4 0.0057 37 0.0523 148
1966 2 0.0028 39 0.0551 31
1967 5 0.0071 44 0.0622 115
1968 1 0.0014 45 0.0636 1192
1969 5 0.0071 50 0.0706 116
1970 21 0.0297 71 0.1003 1027
1971 7 0.0099 78 0.1102 256
1972 6 0.0085 84 0.1187 147
1973 12 0.0170 96 0.1356 21
1974 14 0.0198 110 0.1554 181
1975 20 0.0283 130 0.1836 441
1976 24 0.0339 154 0.2175 184
1977 34 0.0480 188 0.2656 28
1978 36 0.0509 224 0.3164 245
1979 39 0.0551 263 0.3715 29
1980 51 0.0720 314 0.4436 379
1981 62 0.0876 376 0.5311 113
1982 65 0.0918 441 0.6230 84
1983 80 0.1130 521 0.7360 20
1984 77 0.1088 598 0.8447 122
1985 92 0.1300 690 0.9747 3
1986 70 0.0989 760 1.0736 65
1987 77 0.1088 837 1.1823 38
1988 77 0.1088 914 1.2911 5
1989 69 0.0975 983 1.3886 182
1990 91 0.1285 1074 1.5171 90
1991 148 0.2091 1222 1.7262 15
1992 188 0.2656 1410 1.9918 27
1993 210 0.2966 1620 2.2884 126
1994 229 0.3235 1849 2.6119 33
1995 250 0.3531 2099 2.9650 1
1996 310 0.4379 2409 3.4029 154
1997 313 0.4421 2722 3.8451 23
1998 336 0.4746 3058 4.3197 7
1999 370 0.5227 3428 4.8424 52
2000 377 0.5325 3805 5.3749 8
2001 427 0.6032 4232 5.9781 2
2002 466 0.6583 4698 6.6363 25
2003 562 0.7939 5260 7.4302 6
2004 603 0.8518 5863 8.2820 4
2005 707 0.9987 6570 9.2807 11
2006 915 1.2925 7485 10.5732 58
2007 1576 2.2262 9061 12.7995 9
2008 2119 2.9933 11180 15.7927 418
2009 2955 4.1742 14135 19.9669 8260
2010 3564 5.0345 17699 25.0014 8252
2011 4333 6.1207 22032 31.1222 8261
2012 5035 7.1124 27067 38.2345 8242
2013 6081 8.5900 33148 46.8245 8251
2014 7006 9.8966 40154 56.7211 8243
2015 9285 13.1159 49439 69.8370 8249
2016 9693 13.6922 59132 83.5292 8244
2017 9042 12.7726 68174 96.3018 8241
2018 2618 3.6982 70792 100.0000 8247
----------------------------------------------------------------
Sum 70792 100.0000
R code
> Y <- read.csv("./yearN_DC=1.clu",header=FALSE,skip=2)$V1
> t <- table(Y)
> t
Y
1894 1901 1934 1939 1941 1946 1948 1950 1951 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963
1 1 1 1 1 1 1 2 1 2 4 1 1 3 1 2 2 1 2 2
1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983
2 4 2 5 1 5 21 7 6 12 14 20 24 34 36 39 51 62 65 80
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
77 92 70 77 77 69 91 148 188 210 229 249 310 313 336 370 377 427 466 562
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
603 707 915 1576 2119 2955 3564 4333 5035 6081 7006 9285 9693 9042 2618
> years <- as.integer(names(t))
> freq <- as.vector(t[1984<=year & year<=2018])
> y <- 1984:2018
> plot(y,freq,cex=0.75,main="Hits per year")
> model <- nls(freq~c*dlnorm(2018-y,a,b),start=list(c=1e6,a=2,b=0.7))
> model
Nonlinear regression model
model: freq ~ c * dlnorm(2018 - y, a, b)
data: parent.frame()
c a b
7.110e+04 1.501e+00 9.587e-01
residual sum-of-squares: 9618707
Number of iterations to convergence: 8
Achieved convergence tolerance: 6.763e-06
> lines(y,predict(model,list(x=2018-year)),col="red",lw=2)
yearN_DC=1.clu
Y <- read.csv("./yearN_DC=1.clu",header=FALSE,skip=2)$V1
t <- table(Y)
t
values for t
1894 1901 1934 1939 1941 1946 1948 1950 1951 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964
1 1 1 1 1 1 1 2 1 2 4 1 1 3 1 2 2 1 2 2 2
1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985
4 2 5 1 5 21 7 6 12 14 20 24 34 36 39 51 62 65 80 77 92
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
70 77 77 69 91 148 188 210 229 249 310 313 336 370 377 427 466 562 603 707 915
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
1576 2119 2955 3564 4333 5035 6081 7006 9285 9693 9042 2618
Y
years <- as.integer(names(t))
length(years) [75]
year <- years[22:75] #take only those from 1965 to 2018
freq <- t[22:75]
plot(year,freq,cex=0.75,main="Hits per year")
Decided to reduce only for those from 1965 to 2015 (there is max of works)
yearn <- year[1:51]
freqn <- freq[1:51]
model <- nls(freqn~c*a^(yearn-1965),start=list(c=5,a=1.2)) #Vlado proposed these values for c and a
model
The values for the model are
Nonlinear regression model
model: freqn ~ c * a^(yearn - 1965)
data: parent.frame()
c a
0.2526 1.2338
residual sum-of-squares: 1306602
To make the picture:
plot(yearn,freqn,cex=0.75,main="Hits per year", xlab = "Years", ylab = "Freq")
lines(yearn,predict(model,list(x=yearn-1965)),col="red",lw=2)
How much the field is growing - it dounles in almost 3 years
> log(2)/log(1.2338)
[1] 3.299148
setwd("C:/Mail.Ru Cloud/ANR HSE/ANR Projects/SNA Vlado Batagelj/Final/September 2018/Sept 14/Sn17new")
Y <- read.csv("./yearN_DC=0.clu",header=FALSE,skip=2)$V1
t <- table(Y)
t
Y
years <- as.integer(names(t))
length(years)
years[377:496]
year <- years[377:496]
freq <- t[377:496]
plot(year,freq,cex=0.75,main="Cited only works per year", xlab = "Years", ylab = "Freq")
model <- nls(freq~c*dlnorm(2019-year,a,b),start=list(c=1e6,a=2,b=0.7))
model
lines(year,predict(model,list(x=2019-year)),col="red",lw=2)
Model
> model
Nonlinear regression model
model: freq ~ c * dlnorm(2019 - year, a, b)
data: parent.frame()
c a b
1.198e+06 2.570e+00 6.831e-01
residual sum-of-squares: 18264224
Number of iterations to convergence: 6
Achieved convergence tolerance: 1.284e-07
Y
0 1 2 3 4 6 1008 1010 1020 1030 1050 1082 1145 1195 1202 1290 1295
28602 5 80 250 1 2 1 1 1 1 1 1 2 1 1 1 1
1306 1309 1335 1339 1340 1347 1350 1351 1352 1354 1358 1361 1365 1366 1372 1377 1382
1 2 1 1 2 1 2 1 2 1 1 1 1 1 1 1 1
1383 1384 1385 1386 1387 1388 1389 1391 1392 1393 1415 1417 1419 1422 1424 1427 1429
2 2 3 1 4 1 2 1 3 1 1 1 1 5 1 2 1
1430 1435 1440 1462 1466 1468 1470 1502 1516 1520 1522 1524 1526 1530 1531 1532 1534
2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
1536 1546 1549 1551 1553 1555 1556 1557 1558 1559 1562 1563 1564 1565 1566 1567 1571
1 1 1 1 1 1 2 1 2 2 1 1 1 2 2 1 1
1572 1573 1574 1578 1579 1581 1591 1597 1598 1599 1600 1601 1605 1606 1609 1610 1612
1 1 1 2 2 1 1 1 1 1 1 2 2 2 3 1 4
1613 1614 1616 1617 1620 1621 1622 1623 1625 1627 1630 1631 1634 1635 1636 1637 1638
4 2 3 2 2 4 1 2 2 2 1 3 3 2 2 5 2
1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1653 1654 1655 1656 1657
2 1 1 4 5 3 4 2 3 6 8 5 1 1 3 1 1
1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1671 1672 1673 1674 1675
2 2 1 2 5 6 5 8 5 3 5 5 4 2 3 3 3
1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692
3 4 3 1 2 4 2 5 2 7 3 7 6 6 2 2 3
1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1706 1707 1708 1709 1710
1 2 2 1 3 1 1 3 2 2 2 3 5 2 3 4 7
1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727
5 2 4 4 3 2 2 3 5 2 5 4 1 4 2 3 2
1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
5 4 1 2 4 3 8 7 10 7 4 3 2 4 4 5 4
1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761
12 7 8 7 6 9 6 4 3 6 4 5 3 5 8 6 4
1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778
2 7 4 1 10 7 3 4 6 5 5 5 5 6 11 7 2
1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795
2 7 6 9 3 2 21 5 16 7 10 4 9 9 3 5 8
1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812
9 6 8 23 18 12 22 17 9 6 7 8 12 11 17 11 15
1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829
7 12 8 11 14 11 7 12 14 6 4 20 9 15 7 9 6
1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846
17 11 13 9 8 16 8 8 14 12 16 14 11 14 26 28 22
1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863
18 21 10 15 12 13 10 14 14 14 17 17 17 22 21 25 33
1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880
23 25 25 27 15 18 31 27 27 30 27 34 25 22 23 32 34
1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897
22 37 39 29 38 37 54 30 35 54 41 40 38 55 53 61 56
1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914
56 54 56 56 73 63 77 74 87 75 114 105 109 97 97 97 85
1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931
83 73 69 65 80 107 92 128 95 111 128 140 157 172 153 146 174
1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948
202 189 209 169 208 210 198 220 204 151 158 174 162 210 264 302 325
1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965
397 490 453 457 479 534 588 618 704 711 763 867 905 1003 1060 1162 1356
1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982
1454 1735 1784 1968 2056 2249 2547 2839 3067 3186 3671 4070 4361 4870 5157 5635 6078
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
6281 6744 7700 8574 9353 10236 11219 13202 13348 15074 15698 17366 19133 20656 22654 25171 27735
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
33185 34804 38403 42197 45863 50211 55157 59253 61230 64771 67343 66532 64467 60699 52074 39724 23704
2017 2018 2019 2020 2100
8045 479 2 2 2
January 29, 2019
number<-read.table(file="yearN_DC=1.clu", sep=",", header=FALSE, skip=2)$V1
t<-table(number)
head(t)
t
years <- as.integer(names(t))
length(years)
years
year <- years[22:75]
freq <- t[22:75]
plot(year,freq,cex=0.75,main="Hits per year")
yearn <- year[1:51]
freqn <- freq[1:51]
model <- nls(freqn~c*a^(yearn-1965),start=list(c=5,a=1.2)) #Vlado proposed these values for c and a
model
max(freqn)
yt <- c(0,1000,3000,5000,7000,9000); yl <- c("0","1k","3k","5k","7k","9k")
plot(yearn,freqn,cex=0.75,main="Hits per year",yaxt="n", xlab = "Years", ylab = "Freq")
axis(side=2,at=yt, labels=yl)
lines(yearn,predict(model,list(x=yearn-1965)),col="red",lw=2)
Y <- read.csv("./yearN_DC=0.clu",header=FALSE,skip=2)$V1
t <- table(Y)
t
Y
years <- as.integer(names(t))
length(years)
years[377:496]
year <- years[377:496]
freq <- t[377:496]
model <- nls(freq~c*dlnorm(2019-year,a,b),start=list(c=1e6,a=2,b=0.7))
model
max(freq)
yt <- c(0,10000,30000,50000,70000); yl <- c("0","10k","30k","50k","70k")
plot(year,freq,cex=0.75,main="Cited only works per year", yaxt="n", xlab = "Years", ylab = "Freq")
axis(side=2,at=yt, labels=yl)
lines(year,predict(model,list(x=2019-year)),col="red",lw=2)
In Pajek:
- CiteB (Bounded net)
Number of vertices (n): 222086
- Boundary.clu
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
0 1075047 82.8787 1075047 82.8787 1
1 222086 17.1213 1297133 100.0000 2
----------------------------------------------------------------
Sum 1297133 100.0000
- YearN.clu
Extract second (Boundary) from first (YearN)
4. Extracting from C2 vertices determined by C1 [1-*] (222086)
Binarize this partition -- chose the years 1965-2018
==============================================================================
Binarizing Partition
==============================================================================
Time spent: 0:00:00
==============================================================================
5. Binarized C4 [1965-2018] (222086)
==============================================================================
Dimension: 222086
The lowest value: 0
The highest value: 1
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
0 2963 1.3342 2963 1.3342 ALEXANDE_C(1964):
1 219123 98.6658 222086 100.0000 *CDCP(2002):
----------------------------------------------------------------
Sum 222086 100.0000
Extract subpartition: Binirized [1965]2018] rom previous obtained partition (222086).
7. Years 1965-2018_Extracting from C4 vertices determined by C5 [1-*] (219123)
Extracting Subnetwork according to Partition (previous one, with 0 and 1 =219123)
==============================================================================
8. Extracting from C5 vertices determined by C5 [1-*] (219123)
==============================================================================
Dimension: 219123
The lowest value: 1
The highest value: 1
Frequency distribution of cluster values:
Cluster Freq Freq% CumFreq CumFreq% Representative
----------------------------------------------------------------
1 219123 100.0000 219123 100.0000 1
----------------------------------------------------------------
Sum 219123 100.0000
Network + Partition - Shrink network
Look at the partition and manually change the labels (leave only years)
Converting Partition to Permutation
Network + Permutations - Reorder Network
Save as matrix
Pictures:
- Natural Logarithm
- Adding constant 1 + Natural Logarithm
- Square root (twice)
install.packages("plot3D")
library("plot3D")
setwd("C:/Mail.Ru Cloud/ANR HSE/ANR Projects/SNA Vlado Batagelj/Final/September 2018/Sept 14/Sn17new")
help("scan")
A <- scan("YearS_1965-2018.mat", skip = 56)
C <- matrix(A, ncol = 54, byrow = TRUE)
C[1:5,1:5]
C[50:54,50:54]
y <- 1965:2018
y
Years <- as.character(y) #added titles
Years
row.names(C) <- colnames(C) <- Years
C[50:54,50:54]
years <- 1965:2018
help("hist3D")
hist3D(x = years, y = years, z = C, scale = FALSE, expand = 0.003, bty = "g", phi = 20,
col = "#0072B2", shade = 0.2, ltheta = 90,
space = 0.3, ticktype = "detailed", d = 2)
hist3D(x = years, y = years, z = C, scale = FALSE, expand = 0.003, bty = "g", phi = 20,
col = "lightblue1", shade = 0.9, ltheta = 90,
space = 0.3, ticktype = "detailed", d = 2, cex.axis = 1.1)
setwd("C:/Mail.Ru Cloud/ANR HSE/ANR Projects/SNA Vlado Batagelj/Final/September 2018/Sept 14/Sn17new")
library("plot3D")
A <- scan("YearS_1900-2018.mat", skip = 121)
C <- matrix(A, ncol = 119, byrow = TRUE)
C[115:119,115:119]
y <- 1900:2018
y
Years <- as.character(y) #added titles
Years
row.names(C) <- colnames(C) <- Years
C[115:119,115:119]
years <- 1900:2018
hist3D(x = years, y = years, z = C, scale = FALSE, expand = 0.003, bty = "g", phi = 20,
col = "lightblue1", shade = 0.9, ltheta = 90,
space = 0.3, ticktype = "detailed", d = 2, cex.axis = 1.1)
N <- t(scale(t(C), center = rep(0,nrow(C)), scale = rowSums(C)))
N[115:119,115:119]
N[is.nan(N)]<-0
P <- rowSums (N)
P
hist3D(x = years, y = years, z = N, scale = FALSE, expand = 50, bty = "g", phi = 20,
col = "lightblue1", shade = 0.9, ltheta = 90,
space = 0.5, ticktype = "detailed", d = 2, cex.axis = 1.1)
NR <- N[91:119,66:119]
length(NR)
yearsR <- years[66:119]
yearsR
yearsP <- years[91:119]
yearsP
hist3D(x = yearsP, y = yearsR, z = NR, scale = FALSE, expand = 250, bty = "g", phi = 20,
col = "lightblue1", shade = 0.9, ltheta = 120,
space = 0.2, ticktype = "detailed", d = 2, cex.axis = 1.1, xlab = "from", ylab = "to", zlab = "prob")
dim(NR)
f <- NR[27,]
f
plot(f,type="h")
plot(f,type="l")
for(y in 1:29){
f <- NR[y,]
if (y>1) lines(f,type="l")
else plot(f,type="l", ylim=c(0,0.1))
}
help(plot)
for(y in 1:29){
f <- N[y+90,(66-y):(120-y)]
if (y>91) lines(f,type="l")
else plot(f,type="l", ylim=c(0,0.1))
}
dim(N)
y <- 1
f <- as.vector (N[(67-y):(120-y), y+90])
x <- 1:54
length(f)
length(x)
plot(x,f)
for(y in 1:29){
f <- N[120-y,(66-y):(120-y)]
if (y>1) lines(f,type="l")
else plot(f,type="l", ylim=c(0,0.1), xlab = "years", ylab = "prob")
}
Year Data Growth WoS
2000 377 347
2001 427 113% 390
2002 466 109% 432
2003 562 121% 564
2004 603 107% 616
2005 707 117% 873
2006 915 129% 1170
2007 1576 172% 1646
2008 2119 134% 2394
2009 2955 139% 3346
2010 3564 121% 4114
2011 4333 122% 5054
2012 5035 116% 5907
2013 6081 121% 7240
2014 7006 115% 8322
2015 9285 133% 9581
2016 9693 104% 10005
2017 9042 93% 9836
2018 2618 29% 8063
Total 70792 82860