使用ggplot2的线图，手动线/填充颜色独立于组审美

Question

我试图使用ggplot2绘制来自线图中两种不同颜色的独立研究数据。这些数据来自两项独立研究，这些研究使用了组内设计（同一家族中的多个兄弟姐妹，但每个都在不同的治疗组中）。因此，我使用了＆＃34; group＆＃34;通过家庭对个体进行分组的美学，从而在同一家庭中的个体之间划清界线。这部分效果很好，现在我想要的是在两个单独的研究中收集的数据用不同的颜色表示。我已经玩过一堆，手动改变颜色等等，我只是无法让它发挥作用。我在网上看到的关于手动改变线条颜色的一切都取决于＆＃34; group＆＃34;在这种情况下，我需要颜色和群体彼此独立。

数据采用长格式，研究是一个因素。然后我使用了summarySEwithin函数，它来自用于绘图的R-cookbook代码（在此处可用：http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/）来构建下面复制的数据框。

Similarity90SUM<- summarySEwithin(Day90, measurevar="Similarity", betweenvars=c("Condition","Study"),withinvars=c("Family"), idvar="Male_Num",na.rm=TRUE)

然后我使用ggplot来构建线图。这段代码完成了我想要的一切，除了以不同的颜色绘制两个研究中的数据。

ggplot(Similarity90SUM, aes(x=Condition, y=Similarity, group=Family),colour=Study) +
  geom_line() +
  geom_point(shape=19, size=2)+
  scale_colour_manual(values=c("blue","darkgreen"))

不幸的是，情节中的所有线都是黑色的，而不是蓝色和绿色（抱歉，我无法发布图像）。

我认为问题来自于用于组和颜色的变量不同的事实，因为如果我在aes（）中包含color = Study，那么我得到错误信息：＆＃34;错误：美学必须要么是长度一，要么与dataProblems相同：条件，相似度，家庭＆＃34;。任何的意见都将会有帮助！我确信有一些相对简单的解决方法，我只是不知道。提前谢谢！

dput(Similarity90SUM)

structure(list(Condition= structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Control", 
"AVT", "MC"), class = "factor"), Study = structure(c(1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L
), .Label = c("Study 1", "Study 2"), class = "factor"), Family = structure(c(7L, 
19L, 20L, 21L, 1L, 4L, 5L, 8L, 12L, 17L, 3L, 6L, 18L, 19L, 22L, 
1L, 4L, 5L, 8L, 12L, 17L, 6L, 7L, 10L, 13L, 14L, 18L, 19L, 20L, 
1L, 4L, 5L, 12L, 17L), .Label = c("1", "150-2", "150-8", "2", 
"3", "34-4", "34-8", "4", "48-1", "48-2", "48-6", "5", "54-1", 
"54-5", "54-8", "6", "7", "79-2", "79-4", "87-4", "87-6", "87-8"
), class = "factor"), N = c(1, 1, 1, 1, 10, 9, 10, 10, 10, 4, 
1, 1, 1, 2, 1, 10, 4, 7, 7, 10, 10, 1, 1, 1, 1, 1, 1, 1, 1, 9, 
10, 10, 10, 9), Similarity = c(73, 89, 80, 91, 93.3, 80.3333333333333, 
46.1, 90.4, 81, 86.25, 62, 74, 75, 76.5, 66, 83.9, 79.75, 92.1428571428571, 
68.7142857142857, 47.9, 91.2, 61, 76, 52, 41, 47, 60, 78, 61, 
64.8888888888889, 49.1, 26.4, 44.3, 75), Similarity_norm = c(69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503, 69.1437125748503, 69.1437125748503, 69.1437125748503, 
69.1437125748503), sd = c(NA, NA, NA, NA, 2.68860027311079, 5.29375193259087, 
8.29349206197309, 3.34980059423901, 5.68856645911677, 4.12599194422499, 
NA, NA, NA, 0, NA, 4.13515065015422, 4.37253331823185, 4.63154911077632, 
5.5344724669591, 4.58880625622207, 3.47264441877645, NA, NA, 
NA, NA, NA, NA, NA, NA, 10.5806302009727, 0.896200091036931, 
7.30470602822113, 12.0629302291892, 3.02765035409749), se = c(NA, 
NA, NA, NA, 0.850210058078086, 1.76458397753029, 2.62263246723613, 
1.05929995851808, 1.79888266320481, 2.0629959721125, NA, NA, 
NA, 0, NA, 1.30764945224135, 2.18626665911592, 1.75056101887093, 
2.09183396935827, 1.4511079510892, 1.09814658672052, NA, NA, 
NA, NA, NA, NA, NA, NA, 3.52687673365756, 0.283403352692695, 
2.3099508687141, 3.81463347799347, 1.00921678469916), ci = c(NA, 
NA, NA, NA, 1.92330877276442, 4.06913794909986, 5.93280682114534, 
2.39630298871351, 4.06935530160228, 6.56537390926458, NA, NA, 
NA, 0, NA, 2.95810857481691, 6.95767625166843, 4.28346850322768, 
5.11853333025038, 3.28263424554985, 2.48418016695222, NA, NA, 
NA, NA, NA, NA, NA, NA, 8.13299233216971, 0.641102924254807, 
5.22547190337353, 8.62930044569276, 2.32725807883372)), .Names = c("Condition", 
"Study", "Family", "N", "Similarity", "Similarity_norm", "sd", 
"se", "ci"), row.names = c(NA, -34L), class = "data.frame")

Answer 1

这是你需要做的，我假设你比较相似性（y轴）和治疗（x轴）。 正如@Gregor所说，你需要在aes调用中传递color = study

您可以在ggplot调用或geom_line（）图层调用中传递它 传递颜色=在ggplot调用的aes内学习 这将改变线条和点的颜色（查看图例）

ggplot(Similarity90SUM, aes(x=Treatment, y=Similarity, group=Family,colour=Study)) +
    geom_line() +
    geom_point(shape=19, size=2)+
    scale_colour_manual(values=c("blue","darkgreen")) +
    xlab("Condition") +theme_bw()

在geom_line调用中传递它只更改线条的颜色（还要注意图例如何更改）

    ggplot(Similarity90SUM, aes(x = Treatment, y = Similarity,
                               group = Family)) +
        geom_line(aes(colour = Study)) +
        geom_point(shape = 19, size = 2)+
        scale_colour_manual(values = c("blue","darkgreen")) +
    xlab("Condition") +theme_bw()

结果图如下所示：