1. 摘要：基於鳶尾花卉(iris)數據集來練習運用ggplot2進行數據可視化。
2. 數據來源：R語言內置數據集
3. 練習
3.1 基於原數據集以及整合數據集
# Aggregate the first four column by Species and calculate the mean iris.summary <- aggregate(iris[1:4], list(iris$Species), mean) # Change the name of the first column to 'Species' names(iris.summary)[1] <- 'Species'
library(ggplot2) # load the ggplot2 package ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point() + geom_point(data = iris.summary, shape = 15, size = 5)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, col = Species)) + geom_point() + geom_vline(
data = iris.summary, linetype = 2, aes(xintercept = Sepal.Length, col = Species)) + geom_hline(
data = iris.summary, linetype = 2, aes(yintercept = Sepal.Width, col = Species))
3.2 基於清洗的數據集
library(tidyr) # load tidyr package
#iris.tidy----data frome format
iris_tidy <- iris %>%
gather(key, value, -Species) %>%
separate(key, c('Part', 'Measure'), '\\.')
head(iris_tidy)
str(iris_tidy)
ggplot(iris_tidy, aes(x = Species, y = value, col = Part)) + geom_jitter() + facet_grid(. ~ Measure)
#iris.wide
iris_wide <- iris %>%
gather(key, value, -Species, -id) %>%
separate(key, c('Part', 'Measure'), '\\.') %>%
spread(Measure, value)
head(iris_wide)
str(iris_wide)
ggplot(iris_wide, aes(x = Length, y = Width, color = Part)) + geom_jitter() + facet_grid(. ~ Species)
