控制ggplot图例中的线条颜色和线条types
背景
德国有16个联邦州，其中10个属于西德，其中6个属于东德。 在某些方面，例如某些癌症的死亡率，十个原西方国家与六个原东方国家之间存在长期的差异。 各组内各州之间也有差异。
为了显示不同国家之间的差异，从每个国家绘制数据，例如年龄标准化乳腺癌死亡率，可以有一定的意义。 有16行的情节并不总是一个好的select，我不想就此进行讨论。 有时候，权力就是这样说的。
问题
区分情节中的16条线可能是困难的。 为此，我通常使用RColorBrewer软件包中的颜色组合（ Set3的前十种颜色再加上该调色板的前六种颜色，对应于十个前西和前六个东州）和线型（一行键入东部，西部一个）。 使用lattice包装，从1998年到2010年的年龄标准化的乳腺癌死亡率的情节可能是这样的：
这个问题
我想用ggplot做一个类似的情节，但我还没有想出如何将图例中的颜色和线条types结合起来。 到目前为止，我已经得到了很多：
如果可以在ggplot传说中结合颜色和线条types，怎么去做呢？
以下是创build数据框和图的代码：
mort3 <- structure(list(State = structure(c(8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L, 8L, 9L, 11L, 12L, 4L, 2L, 6L, 13L, 3L, 5L, 7L, 10L, 14L, 15L, 1L, 16L), class = "factor", .Label = c("SH", "HH", "NI", "HB", "NW", "HE", "RP", "BW", "BY", "SL", "BE", "BB", "MV", "SN", "ST", "TH")), BCmort = c(16.5, 16.6, 15, 14.4, 13.5, 17.1, 15.8, 16.3, 18.3, 16.8, 17, 18.1, 13.1, 15.1, 18.8, 13.1, 16.4, 16.1, 15.8, 12.8, 16.3, 19.2, 16.8, 13, 17.9, 17, 19.4, 19.4, 13.1, 13.8, 18.1, 13.8, 15.9, 17.3, 17.5, 13.7, 17.4, 17.5, 16.7, 15.5, 18.1, 18, 20.1, 19.1, 11.8, 14.6, 18.2, 13.4, 16.8, 17.5, 15.6, 14.1, 13.9, 18.2, 17.1, 15.2, 18.1, 16.6, 19.3, 18.6, 13.1, 14.6, 19.6, 12.4, 16.6, 17.8, 17.5, 14.3, 20.5, 19.2, 19, 12.6, 19.5, 17.8, 19.2, 21, 14.4, 13.4, 19.8, 14, 17.5, 18.9, 16.4, 14.7, 17.7, 20.1, 18.5, 14.5, 19.1, 19.2, 20.1, 19.7, 14.2, 16.2, 17.9, 12.6, 18, 18.7, 17.7, 16.5, 16.6, 20.3, 18.1, 15.2, 19, 20, 19.8, 21.3, 13.8, 14.8, 20.4, 14.8, 18.2, 18.7, 16.9, 16.2, 20.2, 20.4, 18.5, 14, 20.2, 18.7, 20.3, 17.7, 14.4, 14.5, 21.7, 13.7, 18.3, 19.7, 17.8, 16.5, 20.2, 21.7, 18.8, 16.7, 20.4, 20, 19.6, 22.9, 15.2, 14.9, 21.7, 14.6, 18.3, 19.7, 17, 16.7, 22.9, 16.2, 19.6, 15.9, 20.3, 19.9, 18.9, 21.8, 14.9, 18, 21.4, 16.1, 19.6, 19.2, 19.1, 16.7, 20, 18.2, 20.5, 15.5, 20.5, 21.1, 21.3, 23.8, 15.8, 15.3, 21.3, 15.7, 19.6, 20.3, 19.2, 17.4, 18.1, 23.1, 20.6, 16.2, 21.5, 20.3, 21.4, 20.8, 16.1, 15.8, 22.1, 14.5, 20, 20.2, 19, 18.7, 23.1, 21.8, 19.4, 17.4, 20.9, 20.5, 20.4, 23.2, 16.3, 17.6, 23.1, 16.5), year = c(2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2004, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2002, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2001, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998), eastWest = structure(c(1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L), .Label = c("west", "east"), class = "factor")), .Names = c("State", "BCmort", "year", "eastWest"), class = "data.frame", row.names = c(NA, -208L)) colVec<-c(brewer.pal(10,"Set3"),brewer.pal(6,"Set3")) ltyVec<-rep(c("solid","dashed"),c(10,6)) ggplot(mort3, aes(x = year, y = BCmort, col = State, lty = eastWest)) + geom_line(lwd = 1) + scale_linetype_manual(values = c(west = "solid", east = "dashed")) + scale_color_manual(values = c(brewer.pal(10, "Set3"), brewer.pal(6, "Set3"))) + opts(title = "BC mortality") xyplot(BCmort ~ year, data = mort3, groups = State, lty = ltyVec, type = "l", col = colVec, lwd = 2, key = list(lines = list(lty = ltyVec, col = colVec, lwd = 2), text = list(levels(mort3$State)), columns = 1, space = "right", title = "State"), grid = TRUE, main = "BC mortality")
诀窍是将colour和linetype映射到State ，然后用16个级别定义scale_linetype_manual ：
ggplot(mort3, aes(x = year, y = BCmort, col = State, linetype = State)) + geom_line(lwd = 1) + scale_linetype_manual(values = c(rep("solid", 10), rep("dashed", 6))) + scale_color_manual(values = c(brewer.pal(10, "Set3"), brewer.pal(6, "Set3"))) + opts(title = "BC mortality") + theme_bw()
