r - 控制ggplot图例中的线条颜色和线条类型

Question

背景

德国有16个联邦州，其中10个属于西德，6个属于东德。在某些方面，例如某些癌症的死亡率，前西部十个州和前东部六个州之间存在持续差异。各个组内的州之间也存在差异。

为了显示各州之间的差异，绘制每个州的数据(例如按年份划分的年龄标准化乳腺癌死亡率)具有一定的意义。 16 行情节并不总是一个好的选择，我不想对此展开讨论。有时，当权者会说事情必须如此。

问题

区分绘图上的 16 条线可能很困难。为此，我通常使用 RColorBrewer 包中的颜色组合(Set3 的前十种颜色加上该调色板的前六种颜色，对应于十种颜色)前西州和六个前东州)和线路类型(东线一种，西线一种)。使用lattice包，1998-2010年各州年龄标准化乳腺癌死亡率图可能如下所示:

问题

我想使用 ggplot 做一个类似的绘图，但我还没有弄清楚如何组合图例中的颜色和线型。到目前为止，我已经做到了这一点:

如果可以在ggplot图例中组合颜色和线型，那么如何去做呢？

这是创建数据框和绘图的代码:

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")

Answer 1

诀窍是将colour和linetype映射到State，然后用16定义scale_linetype_manual级别:

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()