我正在绘制两个群体中每个个体测量的两个同位素的相关系数(值= 0.0:1.0)。我希望我的散点图具有固定的宽高比,这样无论图形设备如何,x轴和y轴的大小都完全相同。建议?
这是我在R中的第一个情节,对我的代码的改进的任何评论都表示赞赏?最后,是否值得投资学习基本的绘图技术,还是应该直接跳到ggplot2或格子?
我的剧情剧本:
## Create dataset
WW_corr <-
structure(list(South_N15 = c(0.7976495, 0.1796725, 0.5338347,
0.4103769, 0.7447027, 0.5080296, 0.7566544, 0.7432026, 0.8927161
), South_C13 = c(0.76706752, 0.02320767, 0.88429902, 0.36648357,
0.73840937, 0.0523504, 0.52145159, 0.50707858, 0.51874445), North_N15 = c(0.7483608,
0.4294148, 0.9283554, 0.8831571, 0.5056481, 0.1945943, 0.8492716,
0.5759033, 0.7483608), North_C13 = c(0.08114805, 0.47268136,
0.94975596, 0.06023815, 0.33652839, 0.53055943, 0.30228833, 0.8864435,
0.08114805)), .Names = c("South_N15", "South_C13", "North_N15",
"North_C13"), row.names = c(NA, -9L), class = "data.frame")
opar <- par()
## Plot results
par(oma = c(1, 0, 0, 0), mar = c(4, 5, 2, 2))
plot(1,1,xlim=c(0:1.0), ylim=c(0:1.0), type="n", las=1, bty="n", main = NULL,
ylab=expression(paste("Correlation Coefficient (r) for ", delta ^{15},"N ",
"\u0028","\u2030","\u0029")),
xlab=expression(paste("Correlation Coefficient (r) for ", delta ^{13},"C ",
"\u0028","\u2030","\u0029")))
points(WW_corr$South_N15, WW_corr$South_C13, pch = 23, cex = 1.25,
bg ="antiquewhite4", col = "antiquewhite4")
points(WW_corr$North_N15, WW_corr$North_C13, pch = 15, cex = 1.25,
bg ="black")
axis(1, at = seq(0, 1.0, by = 0.1), labels = F, tick = TRUE, tck = -0.01)
axis(2, at = seq(0, 1.0, by = 0.1), labels = F, tick = TRUE, tck = -0.01)
abline(h=.86, v=.86, col = "gray60", lty = 2)
legend("topleft", c("North", "South"), pch = c(15, 23),
col = c("black", "antiquewhite4"), pt.bg = c("black", "antiquewhite4"),
horiz=TRUE, bty = "n")
par(opar)
答案 0 :(得分:43)
par(pty="s")
plot(...)
将绘图类型设置为方形,这将完成您的工作(我认为),因为您的x和y范围是相同的。相当好的隐藏选项记录在?par。
答案 1 :(得分:40)
使用asp=1
作为参数进行绘图将由低级plot.window调用解释,并且应该为您提供单一的宽高比。使用ylim和xlim的调用可能与宽高比scpecification冲突,而asp
应该“占优势”。这是一个非常令人印象深刻的第一个R图,由外出。和一个很好的问题建设。高分。
一个不和谐的注释是你对结构xlim=c(0:1.0)
的使用。由于xlim需要一个两元素向量,我希望xlim = c(0,1)。如果您更改为一组不同的限制,将来击键次数会减少,并且将来会更容易出错,因为如果您尝试使用“0:2.5”,“:”运算符会给您带来意想不到的结果。