R语言操作练习2

1. 加载tidyr包，探索table1,table2,table3,table4a, table4b维度和结构

2. 将table4a进行宽转长操作，列名为country,year,population

3. 基于题2，以country为横坐标，population为纵坐标，fill=year，采用dodge形式作柱状图，颜色为#022a99和#fbcd08

4. 基于题2，以country为横坐标，population为纵坐标，fill=year，作堆叠柱状图，颜色为#022a99和#fbcd08

5. 基于题2，以country为横坐标，population为纵坐标，year作为分面对象，作分面柱状图，主题用theme_bw()，采用Pastel3填充country

6. 基于题2，以country为横坐标，population为纵坐标，year作为分面对象，作分面柱状图，主题采用theme()

7. 绘制参考范例中的和弦图

   https://jokergoo.github.io/circlize_book/book/the-chorddiagram-function.html#scaling

8. 绘制参考范例中的峰峦图

   https://r-graph-gallery.com/294-basic-ridgeline-plot.html

install.packages("tidyr")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("RColorBrewer")
install.packages("circlize")
install.packages("ggridges")
library(ggridges)
library(tidyr)
library(ggplot2)
library(dplyr)
library(RColorBrewer)
library(circlize)
str(table1)
str(table2)
str(table3)
str(table4a)
str(table4b)
table4a_long <- table4a %>%
  pivot_longer(cols = -country, names_to = "year", values_to = "population")
ggplot(table4a_long, aes(x = country, y = population, fill = year)) +
  geom_bar(stat = "identity", position = "dodge") +
  scale_fill_manual(values = c("#022a99", "#fbcd08")) +
  theme_minimal()
ggplot(table4a_long, aes(x = country, y = population, fill = year)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#022a99", "#fbcd08")) +
  theme_minimal()
ggplot(table4a_long, aes(x = country, y = population, fill = country)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Pastel3") +
  facet_wrap(~year, scales = "free_y") +
  theme_bw()
ggplot(table4a_long, aes(x = country, y = population, fill = country)) +
  geom_bar(stat = "identity") +
  facet_wrap(~year, scales = "free_y") +
  theme()
chord_data <- data.frame(
  from = c("A", "B", "C"),
  to = c("D", "E", "F"),
  value = c(10, 20, 30)
)
chordDiagram(chord_data, transparency = 0.5)
ridge_data <- data.frame(
  country = rep(c("Country1", "Country2"), each = 100),
  year = rep(rep(2000:2001, each = 50), times = 2),
  population = rnorm(200, mean = 100, sd = 20)
)
ridge_data$year <- as.factor(ridge_data$year)
ggplot(ridge_data, aes(x = population, y = year, fill = country)) +
  geom_density_ridges(alpha = 0.7, position = "identity", scale = 0.9) +
  scale_fill_manual(values = c("#022a99", "#fbcd08")) +
  labs(title = "Population Distribution by Country and Year",
       x = "Population", y = "Year") +
  theme_ridges()