由于实验需要，帮师姐画了一张折线图。
特此感谢师姐提供数据给我锻炼的机会！
成品图如下
image.png
直接上代码
1. 读入数据
library(ggplot2)
library(Rmisc)
library(cowplot)
# 读入数据
W1 <- read.csv("W1.txt", sep = "\t", header = T)
W2 <- read.csv("W2.txt", sep = "\t", header = T)
W3 <- read.csv("W3.txt", sep = "\t", header = T)
> head(W1)
# Subject Value Time
# 1 A 0.00000 1
# 2 A 0.00000 1
# 3 A 0.00000 1
# 4 A 0.00000 1
# 5 B 236.82228 1
# 6 B 97.07076 1
2. 计算标准差
# summarySE 计算标准差和标准误差以及95%的置信区间.
tgc1 <- summarySE(W1, measurevar="Value", groupvars=c("Subject","Time"))
tgc2 <- summarySE(W2, measurevar="Value", groupvars=c("Subject","Time"))
tgc3 <- summarySE(W3, measurevar="Value", groupvars=c("Subject","Time"))
> head(tgc1)
Subject Time N Value sd se ci
1 A 1 4 0.00000 0.00000 0.00000 0.0000
2 A 2 4 0.00000 0.00000 0.00000 0.0000
3 A 3 4 69.62475 63.42117 31.71059 100.9172
4 A 4 4 0.00000 0.00000 0.00000 0.0000
5 A 5 4 0.00000 0.00000 0.00000 0.0000
6 B 1 4 252.48276 123.24463 61.62232 196.1097
3. 绘制折线图
P1 <- ggplot(tgc1, aes(x=Time, y=Value, colour=Subject)) +
geom_errorbar(aes(ymin=Value-se, ymax=Value+se), width=.1) +
geom_line() +
geom_point()+
theme_bw()
P2 <- ggplot(tgc2, aes(x=Time, y=Value, colour=Subject)) +
geom_errorbar(aes(ymin=Value-se, ymax=Value+se), width=.1) +
geom_line() +
geom_point()+
theme_bw()
P3 <- ggplot(tgc3, aes(x=Time, y=Value, colour=Subject)) +
geom_errorbar(aes(ymin=Value-se, ymax=Value+se), width=.1) +
geom_line() +
geom_point()+
theme_bw()
plot_grid(P1,P2,P3, nrow = 1, labels = LETTERS[1:3], align = c("v", "h"))
结果如图所示，从图中可以看出，不能太方便得进行横向和纵向比较。
image.png
所以计划采用分面绘图
facet_grid()。
4. 分面
横纵向比较时统一了Y轴范围（0～1100）。
P4 <- P1+
coord_cartesian(ylim = c(0, 1100))+
labs(x= 'Time (d)', y = 'OD')+
facet_grid(.~Subject)+
theme_bw()
P5 <- P2+
coord_cartesian(ylim = c(0, 1100))+
labs(x= 'Time (d)', y = 'OD')+
facet_grid(.~Subject)+
theme_bw()
P6 <- P3+
coord_cartesian(ylim = c(0, 1100))+
labs(x= 'Time (d)', y = 'OD')+
facet_grid(.~Subject)+
theme_bw()
plot_grid(P4,P5,P6, ncol = 1, labels = LETTERS[1:3], align = c("v", "h"))
image.png
