ggplot2(14)-ggplot2练习

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| 字数统计: 3,891 | 阅读时长 ≈ 20

前言

这一篇笔记主要是翻译国外的一个网站的教程，它对ggplot2的绘图做了很好的汇总，原文地址：R: basic graphs 2 (with ggplot2)，原始数据在原文上有。

第一部分：导入数据

原始数据文件有7个，我已经下载到本地硬盘，如下所示：

mark

直接导入即可，如下所示：

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library(ggplot2)
phdfig21a <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\PhD_fig21A.csv",header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
# header=TRUE表示要读取标题
# sep=","表示数据文件PhD_fig21A.csv中的数据是以逗号分割的，csv文件默认都是以逗号分割的，如果是以空格分割的，则用sep=" "（中间空一格）。
# na.strings="NA"是将空白的数据用NA替代
# dec="."用来表示小数点
# strip.white=TRUE 表示当指定了sep时，则将字符元素前后的空格去掉（而数值元素则默认是去掉前后的空格）
phdfig37b <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\PhD_fig37B.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
phdfig22d <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\PhD_fig22D.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
phdfig23 <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\PhD_fig23.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
phdfig28c <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\PhD_fig28C.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
mdfig30a <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\MD_fig30A.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)
mdfig30b <- read.table("D:\\Personal\\R_code\\data\\ggplot2\\MD_fig30B.csv", header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE)

查看一下其中的某个数据，如下所示：

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> head(phdfig37b) # 先查看一下phdfig37b的数据集
  session       sham ACCX  SEM_sham  SEM_accx
1       1  0.0000000 -0.2 0.7881701 0.4163332
2       2  0.6666667 -0.3 0.4819992 0.3666667
3       3 -0.3333333 -0.2 0.7212503 0.5333333
4       4  1.5833333  1.0 0.5833333 0.4714045
5       5 -0.2500000 -0.3 0.7699370 0.5174725
6       6  1.7500000 -0.8 0.6410810 0.4898979
> tail(phdfig37b)
   session     sham ACCX  SEM_sham  SEM_accx
7        7 1.000000 -1.1 0.8438727 0.7951240
8        8 4.083333  0.2 1.0405297 0.6463573
9        9 4.666667  1.6 0.6999278 0.5617433
10      10 3.333333  3.3 1.0249415 1.0005554
11      11 4.500000  5.3 0.7229988 1.0857665
12      12 4.416667  3.9 0.6331539 0.7063207

现在构建ggplot2能够识别的数据框，如下所示：

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phdfig37b_long = with(phdfig37b,
                      rbind( data.frame( lesion = "sham", session = session, diffscore = sham, sem = SEM_sham ),
                             data.frame( lesion = "ACCX", session = session, diffscore = ACCX, sem = SEM_accx ) )
)
# 建立一个数据框，第1列为lesion，并且为"sham"
# 第2列为session，值等于phdfig37b中的session
# 第3列为diffscore，值等于sham,第4列为sem，值等于SEM_sham

查看一下数据框，如下所示：

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> head(phdfig37b_long)
  lesion session  diffscore       sem
1   sham       1  0.0000000 0.7881701
2   sham       2  0.6666667 0.4819992
3   sham       3 -0.3333333 0.7212503
4   sham       4  1.5833333 0.5833333
5   sham       5 -0.2500000 0.7699370
6   sham       6  1.7500000 0.6410810

第二部分 qplot快速制图

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p<-qplot(session, diffscore, colour=lesion, data=phdfig37b_long) 
p<-p+ geom_line() 
p<-p+ geom_errorbar(aes(ymin = diffscore - sem, ymax = diffscore + sem))
p
# 绘制数据集phdfig37b_long的图形，使x轴=session,y轴=diffscore,颜色属性=lesion
# geom_line() 添加折线图
# geom_errorbar 添加误差线

mark

第三部分：ggplot制图

目标：

  1. 绘制折线图；
  2. 添加误差线；
  3. 分类；
  4. 添加参考线。
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library(grid) # 后面要用到unit函数
f1 <- ggplot(data = phdfig37b_long, aes(x = session, y = diffscore, group = lesion) ) + # 绘图phdfig37b_long数据集的图形，使x轴=session,y轴=diffscore,group在这里的意思是，desion中的数据不属于一组
  geom_errorbar(aes(ymin = diffscore - sem, ymax = diffscore + sem), width=0.3) +
  # 添加误差线，指定宽度为0.4
  geom_line() + # 添加折线
  geom_point(aes(shape=lesion, fill=lesion), size=5) + # 添加数据点，将lesion映射到形状属性上，将将lesion映射到填充属性上（即数据点内部的颜色，非轮廓）
  scale_x_continuous("Session", breaks=1:12) + # 对x轴上的sesssion指定刻度，从1到12
  scale_y_continuous("Difference score", limits = c(-4, 8), breaks=seq(-4, 8, by = 2)) + # 指定y轴的刻度
  scale_shape_manual(values=c(24,21)) + # 设置数据点的形状，一个是24,一个是21,24表示正三角，21表示圆形
  scale_fill_manual(values=c("white","black")) + # 指定数据点的颜色，一个是白色，一个是黑色
  geom_abline(intercept=0, slope=0, linetype="dotted") + # 添加参考线，截距是0，斜率是0，点的类型是点状
  annotate("text", x=11, y=-0.25, label="chance") + # 添加注释，坐标为(11,-0.25)，文字为"chance"
  theme_bw() + # 将绘图的主题改为白色
  ggtitle("Two-stimulus discriminated approach task")+ # 添加图形的标题
  theme(
    plot.title = element_text(face="bold", size=14), # 修改标题文本的属性
    axis.title.x = element_text(face="bold", size=12), # 修改x轴标签文本的属性
    axis.title.y = element_text(face="bold", size=12, angle=90),# 修改y轴标签文本的属性
    axis.text.x=element_text(size=15), # 修改x轴坐标轴刻度的大小
    panel.grid.major = element_blank(), # 移除绘图区域的主要网格线
    panel.grid.minor = element_blank(), # 移除绘图区域的次要网格线
    legend.position = c(0.2,0.8), # 图例的位置坐标
    legend.title = element_blank(), # 图例标题为空
    legend.text = element_text(size=12), # 图例中项目文本的大小
    legend.key.size = unit(1.5, "lines"), # 图例中项目的间隔，需要grid包
    legend.key = element_blank() # 移除图例中项目的边框
  )
print(f1)

mark

第四部分：输出图形

保存图形命令如下所示：

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ggsave(filename="d:\\我的图形.pdf",plot=f1)形为f1
## Saving 5 x 4 in image
# 另外一些保存图形的方法，如下所示：
ggsave("d:\\f1.pdf")
## Saving 5 x 4 in image
ggsave("d:\\f1.png",dpi=300) # 保存f1，分辨率为300dpi
## Saving 5 x 4 in image

第五部分：自由浮动的SED

A.构建数据框

如下所示：

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phdfig21a_long <- with(phdfig21a,
                       rbind( data.frame( lesion = "sham", lever = "CRf", dose = dose, sqrtpresses = shamCR, sem = SEMshamCR, halfSED = halfSED[1] ),
                              data.frame( lesion = "sham", lever = "NCRf", dose = dose, sqrtpresses = shamNCR, sem = SEMshamNCR, halfSED = halfSED[1] ),
                              data.frame( lesion = "ACCX", lever = "CRf", dose = dose, sqrtpresses = ACCXCR, sem = SEMACCXCR, halfSED = halfSED[1] ),
                              data.frame( lesion = "ACCX", lever = "NCRf", dose = dose, sqrtpresses = ACCXNCR, sem = SEMACCXNCR, halfSED = halfSED[1] ) )
)
head(phdfig21a_long)

数据如下所示：

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> head(phdfig21a_long)
  lesion lever dose sqrtpresses       sem halfSED
1   sham   CRf    0    5.112528 0.5539816  0.9396
2   sham   CRf    3    6.829842 1.0673567  0.9396
3   sham   CRf   10    9.230089 0.9947341  0.9396
4   sham   CRf   20    8.563017 0.8215663  0.9396
5   sham  NCRf    0    3.685772 0.4961057  0.9396
6   sham  NCRf    3    4.024871 0.6831637  0.9396

B. 再添加一个新的变量，并查看数据，如下所示：

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phdfig21a_long <- within(phdfig21a_long, {
  # 加入一列新的变量 
  lesion_lever <- do.call(paste, c(phdfig21a_long[c("lesion", "lever")], sep = " "))
  # do.call表示，运行函数paste,将phdfig21a_long中的lession与lever用空格连接起来，并生成一列新的变量 
  lesion_lever <- factor(lesion_lever, levels=unique(lesion_lever))
  # unique(lesion_lever)返回phdfig21a_long数据集中的lesion_lever中的重复元素，即提取出互不相同的元素（如果有相同的几个则取一个，只有一个的，也取一个）
  dose <- factor(dose)
})
head(phdfig21a_long)

修改后的数据框如下所示：

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> head(phdfig21a_long)
  lesion lever dose sqrtpresses       sem halfSED lesion_lever
1   sham   CRf    0    5.112528 0.5539816  0.9396     sham CRf
2   sham   CRf    3    6.829842 1.0673567  0.9396     sham CRf
3   sham   CRf   10    9.230089 0.9947341  0.9396     sham CRf
4   sham   CRf   20    8.563017 0.8215663  0.9396     sham CRf
5   sham  NCRf    0    3.685772 0.4961057  0.9396    sham NCRf
6   sham  NCRf    3    4.024871 0.6831637  0.9396    sham NCRf

C.绘图

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f2 <- ggplot(data = phdfig21a_long, aes(x = dose, y = sqrtpresses, group = lesion_lever ) ) +
  geom_line() +
  geom_point(aes(shape=lesion_lever, fill=lesion_lever), size=5) + # 将变量lesion_lever映射到shape上，将lesion_lever映射到填充色上
  geom_errorbar(aes(x=1, ymin=10 - phdfig21a$halfSED[1], ymax=10 + phdfig21a$halfSED[1], width = 0.2)) + # 绘制一个误差棒，位置是x=1，宽度为0.2
  scale_shape_manual(values=c(21,24,21,24)) + # 指定shamCR, shamNCR, ACCXCR, ACCXNCR这四种类型的形状，分别为圆、三角、圆、三角
  scale_fill_manual(values=c("white","white","black","black")) + # 指定shamCR, shamNCR, ACCXCR, ACCXNCR这四种类型的颜色，分别为白、白、黑、黑
  xlab(substitute(paste("Intra-Acb amphetamine (",mu,"g)"))) + #添加x轴标签，substitute用来显示公式，
  scale_y_continuous(expression(sqrt("lever presses")), expand=c(0,0), limits = c(0, 14), breaks=seq(0, 14, by = 2)) + # the "expand" bit prevents ggplot2 from expanding the axis a little beyond what you specify
  annotate("text", x=1.4, y=10, label="SED") +
  theme_bw() +
  ggtitle("Conditioned reinforcement")+ # 添加图形的标题
  theme(plot.title = element_text(face="bold", size=14),
        axis.title.x = element_text(face="bold", size=12),
        axis.title.y = element_text(face="bold", size=12, angle=90),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border=element_blank(), # 取消绘图区域四周的边框
        axis.line=element_line(colour="black"), # 添加坐标轴（即x轴与y轴）的直线
        legend.position = c(0.8,0.2),
        legend.title = element_blank(),
        legend.text = element_text(size=10),
        legend.key.size = unit(1, "lines"),
        legend.key = element_blank()
  )
print(f2)

mark

第六部分 绘制双变量的条形图及误差棒

A.整理数据

查看数据，如下所示：

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> phdfig22d
  group CSpluslatency CSminuslatency CSplusSEM CSminusSEM
1  sham      3.884335       4.212531 0.2248404  0.2593122
2  ACCX      4.672486       3.619434 0.3476289  0.4902273

整理数据，如下所示：

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phdfig22d_long = with(phdfig22d,
                      rbind( data.frame( lesion=group, stimulus="CS+", latency=CSpluslatency, sem=CSplusSEM ),
                             data.frame( lesion=group, stimulus="CS-", latency=CSminuslatency, sem=CSminusSEM ) )
)
phdfig22d_long$lesion = factor(phdfig22d_long$lesion, levels=unique(phdfig22d_long$lesion))

上述代码是生成phdfig22d_long数据框，这个数据框有四列变量，分别为lesion,stimulus,lantency，sem，查看数据，如下所示：

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> phdfig22d_long
  lesion stimulus  latency       sem
1   sham      CS+ 3.884335 0.2248404
2   ACCX      CS+ 4.672486 0.3476289
3   sham      CS- 4.212531 0.2593122
4   ACCX      CS- 3.619434 0.4902273

B.绘图

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f3 <- ggplot(data = phdfig22d_long, aes(x=lesion, y=latency, fill=stimulus) ) +
  geom_errorbar(aes(ymin=latency, ymax=latency+sem, width = 0.2),position=position_dodge(width=0.90)) + # 误差棒只添加上部分，因此误差棒的最低部是latency，最上部是lantency + sem，宽度是0.2，最低部与最上部进行堆叠(position_dodge)
  geom_bar(stat="identity", position="dodge") +
  geom_bar(stat="identity", position="dodge", colour="black") +
  scale_fill_manual(values=c("grey80", "white")) + # 对两个变量进行颜色填充，分别为灰度80与白色
  xlab("") + # x轴不添加标签
  scale_y_continuous("Latency to approach (s)", expand=c(0,0), limits = c(0, 5.5)) + # expand=c(0,0) 表示将y轴从0开始
  theme_bw() +
  ggtitle("Autoshaping approach latencies")+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.line=element_line(colour="black"),
    axis.text.y=element_text(angle=90, hjust=0.5),
    legend.position = c(0.25,0.85),
    legend.key = element_rect()
  )
print(f3)

mark

第七部分：带有注释线的误差棒

A.查看数据

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B.整理数据

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phdfig23_long = with(phdfig23,
rbind( data.frame( lesion=group, stimulus="CS+", approaches=CSplus, sem=SEMCSplus ),
data.frame( lesion=group, stimulus="CS-", approaches=CSminus, sem=SEMCSminus ) )
)

查看数据，如下所示：

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> phdfig23_long
  lesion stimulus approaches       sem
1   sham      CS+  14.000000 0.9309493
2   ACCX      CS+   9.857143 1.0785478
3   sham      CS-   4.500000 0.8595865
4   ACCX      CS-   6.428571 1.0658585

C.绘图

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f4 <- ggplot(data = phdfig23_long, aes(x=lesion, y=approaches, fill=stimulus, width=0.5) ) +
geom_errorbar(aes(ymin=approaches, ymax=approaches+sem, width = 0.2), position=position_dodge(width=0.8)) +
geom_bar(stat="identity", position=position_dodge(width=0.8)) +
geom_bar(stat="identity", position=position_dodge(width=0.8), colour="black") +
scale_fill_manual(values=c("grey80", "white")) +
scale_x_discrete("Group", limits=c("sham", "ACCX")) + # 设置x轴上的分类变量，顺序为sham,ACCX
scale_y_continuous("Number of approaches", expand=c(0,0), limits = c(0, 17), breaks=seq(0, 16, by=2)) +
geom_segment(aes(x=0.8, y=15.3, xend=0.8, yend=16)) +
geom_segment(aes(x=0.8, y=16, xend=1.2, yend=16)) +
geom_segment(aes(x=1.2, y=6, xend=1.2, yend=16)) + # 添加注释线，注意坐标
annotate("text", x=1, y=15.5, label="***") +
theme_bw() +
ggtitle("Autoshaping probe test")+
theme( plot.title = element_text(face="bold", size=14),
axis.title.x = element_text(face="bold", size=12),
axis.title.y = element_text(face="bold", size=12, angle=90),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line=element_line(colour="black"),
axis.text.y=element_text(angle=90, hjust=0.5),
legend.title = element_blank(),
legend.position = c(0.85,0.85),
legend.key.size = unit(1.5, "lines"),
legend.key = element_rect()
)
print(f4)

mark

代码解释：

上述代码中有一部分是添加注释线的，就是与三个星号在一起的一段线。代码为： 

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geom_segment(aes(x=0.8, y=15.3, xend=0.8, yend=16)) +
geom_segment(aes(x=0.8, y=16, xend=1.2, yend=16)) +
geom_segment(aes(x=1.2, y=6, xend=1.2, yend=16)) + # 添加注释线，注意坐标
annotate("text", x=1, y=15.5, label="***") +

在添加注释线的时候，如果要精确地绘制，需要知道x轴上条形图中心的坐标，可以用str(print(f4))查看，str(print(f4))，前5行的数字为： 

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List of 3
$ data :List of 7
..$ :'data.frame': 4 obs. of 9 variables:
.. ..$ fill : chr [1:4] "grey80" "white" "grey80" "white"
.. ..$ x : num [1:4] 0.8 1.2 1.8 2.2

其中第5行是： 

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.. ..$ x : num [1:4] 0.8 1.2 1.8 2.2

可以发现，条形图第一个条带的中心处，对应的x轴坐标为0.8，因此在绘制注释线的时候，x的起点为是0.8。

第八部分：含有双向x轴的绘图

A.查看原始数据

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> phdfig28c
  block      sham      ACCX    SEMsham    SEMACCX
1    -3 0.8905724 0.8648725 0.04328269 0.05561053
2    -2 0.9212121 0.8789751 0.03030303 0.03002131
3    -1 0.9237374 0.9204775 0.02654952 0.02897810
4     0        NA        NA         NA         NA
5     1 0.7755162 0.6273393 0.04621826 0.05090232
6     2 0.8674289 0.6453398 0.05521676 0.05800651
7     3 0.8327228 0.6989440 0.07589090 0.06084011
8     4 0.9145688 0.8424770 0.03463735 0.03901640
9     5 0.8874644 0.8592287 0.03880296 0.04260364

B.整理数据

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phdfig28c_long <- with(phdfig28c,
rbind( data.frame( block=block, lesion="sham", diffscore=sham, sem=SEMsham ),
data.frame( block=block, lesion="ACCX", diffscore=ACCX, sem=SEMACCX ) ) )
phdfig28c_long # 查看整理后的数据

整理后的数据如下所示：

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> phdfig28c_long # 查看整理后的数据
   block lesion diffscore        sem
1     -3   sham 0.8905724 0.04328269
2     -2   sham 0.9212121 0.03030303
3     -1   sham 0.9237374 0.02654952
4      0   sham        NA         NA
5      1   sham 0.7755162 0.04621826
6      2   sham 0.8674289 0.05521676
7      3   sham 0.8327228 0.07589090
8      4   sham 0.9145688 0.03463735
9      5   sham 0.8874644 0.03880296
10    -3   ACCX 0.8648725 0.05561053
11    -2   ACCX 0.8789751 0.03002131
12    -1   ACCX 0.9204775 0.02897810
13     0   ACCX        NA         NA
14     1   ACCX 0.6273393 0.05090232
15     2   ACCX 0.6453398 0.05800651
16     3   ACCX 0.6989440 0.06084011
17     4   ACCX 0.8424770 0.03901640
18     5   ACCX 0.8592287 0.04260364

C.绘图

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f5 <- ggplot(data = phdfig28c_long, aes(x = block, y = diffscore, group = lesion) ) +
geom_errorbar(aes(ymin = diffscore - sem, ymax = diffscore + sem), width=0.3) +
geom_line() +
geom_point(aes(shape=lesion, fill=lesion), size=5) +
scale_x_continuous("Trial block", expand=c(0,0), limits=c(-3.5,5.5), breaks=-3:5, labels=c("-3","-2","-1","surgery","1","2","3","4","5")) +
scale_y_continuous("Difference score", expand=c(0,0), limits = c(0.4, 1), breaks=seq(0.4, 1, by = 0.1)) +
scale_shape_manual(values=c(24,21)) +
scale_fill_manual(values=c("white","black")) +
geom_abline(intercept=0.5, slope=0, linetype="dotted") + # 截距为0.5，斜率为0的虚线
annotate("text", x=-2, y=0.48, label="chance") +
theme_bw() +
theme(plot.margin=unit(c(1,1,1.5,1.5), "lines"), # 分别指的是整个绘图区域（包括x轴与y轴的标签）距离顶边，右边，底边，左边的距离（默认是1，1，0.5，0.5）
axis.ticks.margin=unit(0.25,"lines"), # 坐标轴刻度的值距离刻度线的距离
axis.title.x = element_text(face="bold", size=12, vjust=-1), # use vjust to move text away from the axis
axis.title.y = element_text(face="bold", size=12, angle=90, vjust=0), # likewise
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.2,0.4),
legend.title = element_blank(),
legend.text = element_text(size=12),
legend.key.size = unit(1.5, "lines"),
legend.key = element_blank() # switch off the rectangle around symbols in the legend
)
print(f5)

mark

第九部分：分面、折线、星号

A.查看原始数据

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> mdfig30a
  delay     sham    SEMsham     AcbC    SEMAcbC
1     0 2.640708 0.09574035 3.015727 0.07431359
2    10 2.276907 0.07223806 1.977225 0.12287066
3    20 1.956931 0.07676898 1.400463 0.11313513

B.整理数据

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mdfig30_long <- rbind( data.frame( type="programmed", delay=mdfig30a$delay, lesion="sham", sqrtpresses = mdfig30a$sham, ysem = mdfig30a$SEMsham, xsem = 0 ),
data.frame( type="programmed", delay=mdfig30a$delay, lesion="AcbC", sqrtpresses = mdfig30a$AcbC, ysem = mdfig30a$SEMAcbC, xsem = 0 ),
data.frame( type="experienced", delay=mdfig30b$shamdelay, lesion="sham", sqrtpresses = mdfig30b$shamresponses, ysem = mdfig30b$SEMshamresponses, xsem = mdfig30b$SEMshamdelay ),
data.frame( type="experienced", delay=mdfig30b$AcbCdelay, lesion="AcbC", sqrtpresses = mdfig30b$AcbCresponses, ysem = mdfig30b$SEMAcbCresponses, xsem = mdfig30b$SEMAcbCdelay ) )
mdfig30_annotations <- data.frame(
x = c(0,10,20,10),
y = c(3.2,1.7,1.2,1.4),
type = c("programmed", "programmed", "programmed", "experienced"),
label = c("*", "*", "***", "###"),
lesion = c("sham", "sham", "sham", "sham") )

查看整整后的数据，如下所示：

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> mdfig30_long
          type     delay lesion sqrtpresses       ysem      xsem
1   programmed  0.000000   sham    2.640708 0.09574035 0.0000000
2   programmed 10.000000   sham    2.276907 0.07223806 0.0000000
3   programmed 20.000000   sham    1.956931 0.07676898 0.0000000
4   programmed  0.000000   AcbC    3.015727 0.07431359 0.0000000
5   programmed 10.000000   AcbC    1.977225 0.12287066 0.0000000
6   programmed 20.000000   AcbC    1.400463 0.11313513 0.0000000
7  experienced  2.127342   sham    2.640708 0.09574035 0.3954407
8  experienced  9.483373   sham    2.276907 0.07223806 0.4826215
9  experienced 15.812267   sham    1.956931 0.07676898 1.0514280
10 experienced  2.307076   AcbC    3.015727 0.07431359 0.3887686
11 experienced 11.680260   AcbC    1.977225 0.12287066 0.5989249
12 experienced 20.936766   AcbC    1.400463 0.11313513 1.8152600
> mdfig30_annotations
   x   y        type label lesion
1  0 3.2  programmed     *   sham
2 10 1.7  programmed     *   sham
3 20 1.2  programmed   ***   sham
4 10 1.4 experienced   ###   sham

C.绘图

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f6 <- ggplot(data = mdfig30_long, aes(x = delay, y = sqrtpresses, fill = lesion) ) +
facet_grid(. ~ type) +
geom_line() +
geom_errorbar(aes(ymin=sqrtpresses - ysem, ymax=sqrtpresses + ysem), width=0.8) +
geom_errorbarh(aes(xmin=delay - xsem, xmax=delay + xsem), height=0.05) +
geom_point(shape=21, colour="black", size=5) +
scale_fill_manual(values=c("white","black")) +
scale_x_continuous("Delay from response to reinforcer (s)") +
scale_y_continuous(expression(sqrt("active lever presses per minute in session 10")), expand=c(0,0), limits = c(0, 3.5), breaks=0:3) +
geom_text(data = mdfig30_annotations, aes(x = x, y = y, label = label) ) +
theme_bw() +
theme(plot.margin=unit(c(1,1,1.5,2), "lines"),
axis.title.x = element_text(size=12, vjust=-1),
axis.title.y = element_text(size=12, angle=90, vjust=0), # likewise
legend.title = element_blank(),
legend.key.size = unit(1.5, "lines"),
legend.key = element_blank()
)
print(f6)

mark

参考资料

http://egret.psychol.cam.ac.uk/statistics/R/graphs2.html