生成一个图表,每个列都有不同的图表,标题和副标题,以及每个图表的垂直线:
我使用直方图绘制了一条垂直线的列。
library(ggplot2)
library(gridExtra)
library(tidyr)
actualIris <- data.frame(Sepal.Length=6.1, Sepal.Width=3.1, Petal.Length=5.0, Petal.Width=1.7)
# Sepal Length
oneTailed <- sum(actualIris$Sepal.Length < iris$Sepal.Length)/nrow(iris)
plot1SL <- ggplot(iris, aes(x=Sepal.Length)) + geom_histogram() +
geom_vline(xintercept = actualIris$Sepal.Length, col = "blue", lwd = 2) +
labs(title='Distribution of Sepal Length', x='Sepal Length', y='Frequency',
subtitle=paste('one-tailed test=', oneTailed, sep='')) + theme_bw()
以下代码只是重复其他三列。 (你可以忽略它。)
# Sepal Width
oneTailed <- sum(actualIris$Sepal.Width < iris$Sepal.Width)/nrow(iris)
plot1SW <- ggplot(iris, aes(x=Sepal.Width)) + geom_histogram() +
geom_vline(xintercept = actualIris$Sepal.Width, col = "blue", lwd = 2) +
labs(title='Distribution of Sepal Width', x='Sepal Width', y='Frequency',
subtitle=paste('one-tailed test=', oneTailed, sep='')) + theme_bw()
# Petal Length
oneTailed <- sum(actualIris$Petal.Length < iris$Petal.Length)/nrow(iris)
plot1PL <- ggplot(iris, aes(x=Petal.Length)) + geom_histogram() +
geom_vline(xintercept = actualIris$Petal.Length, col = "blue", lwd = 2) +
labs(title='Distribution of Petal Length', x='Petal Length', y='Frequency',
subtitle=paste('one-tailed test=', oneTailed, sep='')) + theme_bw()
# Petal Width
oneTailed <- sum(actualIris$Petal.Width < iris$Petal.Width)/nrow(iris)
plot1PW <- ggplot(iris, aes(x=Petal.Width)) + geom_histogram() +
geom_vline(xintercept = actualIris$Petal.Width, col = "blue", lwd = 2) +
labs(title='Distribution of Petal Width', x='Petal Width', y='Frequency',
subtitle=paste('one-tailed test=', oneTailed, sep='')) + theme_bw()
# Combine the plots
grid.arrange(plot1SL, plot1SW, plot1PL, plot1PW, nrow=1)
结果如下:
在创建长数据后,我尝试使用facet_wrap
创建单个图,而不是组合多个单图。
tmp <- iris[,-5] %>% gather(Type, value)
#actualIris <- data.frame(Sepal.Length=6.1, Sepal.Width=3.1, Petal.Length=5.0, Petal.Width=1.7)
actuals <- data.frame(col1=colnames(actualIris), col2=as.numeric(actualIris[1,]))
tmp$Actual <- actuals$col2[match(tmp$Type, actuals$col1)]
tmp$Type <- factor(tmp$Type, levels = c('Petal.Length', 'Petal.Width', 'Sepal.Length', 'Sepal.Width'),
labels = c('Petal Length', 'Petal Width', 'Sepal Length', 'Sepal Width'))
ggplot(tmp, aes(value)) + facet_wrap(~Type, scales="free", nrow = 1) + geom_histogram() +
geom_vline(aes(xintercept=Actual), colour="blue", lwd=2)
我尝试使用labeller
选项更改构面标签,但它不起作用。 (但是,这不是主要问题。)
ggplot(tmp, aes(value)) + geom_histogram() +
facet_wrap(~Type, scales="free", nrow = 1,
labeller = as_labeller(paste('Distribution of ', levels(~Type), sep=''))) +
geom_vline(aes(xintercept=Actual), colour="blue", lwd=2)
如何使用创建的长数据tmp
创建类似于第一个图的绘图?
答案 0 :(得分:3)
您可以制作定制的两行标签:
labels <- c(paste('Petal Length\none-tailed test=', round(sum(actualIris$Sepal.Length < iris$Sepal.Length)/nrow(iris), 2)),
paste('Petal Width\none-tailed test=', round(sum(actualIris$Sepal.Width < iris$Sepal.Width)/nrow(iris), 2)),
paste('Sepal Length\none-tailed test=', round(sum(actualIris$Petal.Length < iris$Petal.Length)/nrow(iris), 2)),
paste('Sepal Width\none-tailed test=', round(sum(actualIris$Petal.Width < iris$Petal.Width)/nrow(iris), 2)))
tmp <- iris[,-5] %>% gather(Type, value)
actuals <- data.frame(col1=colnames(actualIris), col2=as.numeric(actualIris[1,]))
tmp$Actual <- actuals$col2[match(tmp$Type, actuals$col1)]
tmp$Type <- factor(tmp$Type, levels = c('Petal.Length', 'Petal.Width', 'Sepal.Length', 'Sepal.Width'),
labels = labels)
ggplot(tmp, aes(value)) + facet_wrap(~Type, scales="free", nrow = 1) + geom_histogram() +
geom_vline(aes(xintercept=Actual), colour="blue", lwd=2)
得到这个: