R语言可视化学习笔记之ggridges包绘制山峦图

library(ggridges)
library(tidyverse)
# grid.arrange来自于gridExtra包，可以同时拼图多个ggplot2对象
library(gridExtra)


my_data <- data.frame(x=1:5, y=rep(1,5), height=c(0,1,-1,3,2))
plot_base <- ggplot(my_data, aes(x, y, height=height))

# 默认负值不显示，除非指定min_height参数

grid.arrange(plot_base+geom_ridgeline(),
             plot_base+geom_ridgeline(min_height=-2), ncol=2)

# creates a vector of n equally spaced colors along the
# Matplolib 'viridis' color map

# also designed to be perceived by readers with the most common form of color blindness
#  scale_fill_viridis函数来源于此包，
# 其参数 option用于设置颜色 "magma" (or "A"), "inferno" (or "B"), "plasma" (or "C"),
and "viridis" (or "D", the default option).
# ?viridis可以查看其具体含义

library(viridis)
head(lincoln_weather[ ,1:4])


## # A tibble: 6 x 4
##   CST      `Max Temperature [F]` `Mean Temperature [F]` `Min Temperature ~
##   <chr>                    <int>                  <int>              <int>
## 1 2016-1-1                    37                     24                 11
## 2 2016-1-2                    41                     23                  5
## 3 2016-1-3                    37                     23                  8
## 4 2016-1-4                    30                     17                  4
## 5 2016-1-5                    38                     29                 19
## 6 2016-1-6                    34                     33                 32

# x后的值用 ` (反引号)括起，是因为列名字中存在空格和特殊字符，需要特殊对待
# fill = ..x..， double dots是ggplot2的一种特殊识别符，用来区分定义的和计算的美学参数
# 这里指用横轴的数据着色

ggplot(lincoln_weather, aes(x=`Mean Temperature [F]`, y=`Month`, fill=..x..))+
  geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd = 1.)+
  scale_x_continuous(expand = c(0.01, 0))+ # 扩展下横轴和纵轴
  scale_y_discrete(expand = c(0.01,0))+
  scale_fill_viridis(name="Temp. [F]", option = "C")+
  labs(title="Temperature in Lincoln NE",
       subtitle="Mean temperature (Fahrenheit) by month for 2016nData:Orogin CSV from the Weather Underground ")+
  theme_ridges(font_size = 13, grid = FALSE)+
  theme(axis.title.y = element_blank())

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+
  geom_density_ridges(scale=4)+
  scale_fill_cyclical(values = c("blue", "green"))+
  theme_ridges(grid = FALSE)

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+
  geom_density_ridges(scale=4)+
  scale_fill_cyclical(values = c("blue", "green"), guide="legend")+
  theme_ridges(grid = FALSE)

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+
  geom_density_ridges(scale=4)+
  scale_fill_cyclical(values = c("blue", "green"), guide="legend",
                      labels=c("Fair"="blue", "Good"="green"),
                      name="Fill colors")+
  theme_ridges(grid = FALSE)

library(dplyr)
library(forcats)
Catalan_elections %>%
  mutate(YearFct = fct_rev(as.factor(Year))) %>%
  ggplot(aes(y = YearFct)) +
  geom_density_ridges(
    aes(x = Percent, fill = paste(YearFct, Option)),
    alpha = .8, color = "white", from = 0, to = 100
  ) +
  labs(
    x = "Vote (%)",
    y = "Election Year",
    title = "Indy vs Unionist vote in Catalan elections",
    subtitle = "Analysis unit: municipalities (n = 949)",
    caption = "Marc Belzunces (@marcbeldata) | Source: Idescat"
  ) +
  scale_y_discrete(expand = c(0.01, 0)) +
  scale_x_continuous(expand = c(0.01, 0)) +
  scale_fill_cyclical(
    breaks = c("1980 Indy", "1980 Unionist"),
    labels = c(`1980 Indy` = "Indy", `1980 Unionist` = "Unionist"),
    values = c("#ff0000", "#0000ff", "#ff8080", "#8080ff"),
    name = "Option", guide = "legend"
  ) +
  theme_ridges(grid = FALSE)

library(DAAG) # for ais dataset
ais$sport <- factor(
  ais$sport,
  levels = c("B_Ball", "Field", "Gym", "Netball", "Row", "Swim", "T_400m", "T_Sprnt", "Tennis", "W_Polo"),
  labels = c("Basketball", "Field", "Gym", "Netball", "Row", "Swim", "Track 400m", "Track Sprint", "Tennis", "Water Polo")
)

ggplot(ais, aes(x=ht, y=sport, color=sex, point_color=sex, fill=sex)) +
  geom_density_ridges(
    jittered_points=TRUE, scale = .95, rel_min_height = .01,
    point_shape = "|", point_size = 3, size = 0.25,
    position = position_points_jitter(height = 0)
  ) +
  scale_y_discrete(expand = c(.01, 0)) +
  scale_x_continuous(expand = c(0, 0), name = "height [cm]") +
  scale_fill_manual(values = c("#D55E0050", "#0072B250"), labels = c("female", "male")) +
  scale_color_manual(values = c("#D55E00", "#0072B2"), guide = "none") +
  scale_discrete_manual("point_color", values = c("#D55E00", "#0072B2"), guide = "none") +
  guides(fill = guide_legend(
    override.aes = list(
      fill = c("#D55E00A0", "#0072B2A0"),
      color = NA, point_color = NA))
  ) +
  ggtitle("Height in Australian athletes") +
  theme_ridges(center = TRUE)