之前分享过云雨图的小例子，现在分析一个进阶版的云雨图，喜欢的小伙伴可以关注个人公众号R语言数据分析指南持续分享更多优质案例，在此先行拜谢了！！
加载R包
library(tidyverse)
library(colorspace)
library(ggtext)
library(tidytuesdayR)
library(ggdist)
设置主题
theme_set(theme_minimal())
theme_update(
panel.grid.major = element_line(color = "grey92", size = .4),
panel.grid.minor = element_blank(),
axis.title.x = element_text(color = "grey30",
margin = margin(t = 7)),
axis.title.y = element_text(color = "grey30",
margin = margin(r = 7)),
axis.text = element_text(color = "grey50"),
axis.ticks = element_line(color = "grey92", size = .4),
axis.ticks.length = unit(.6, "lines"),
plot.title = element_text(hjust = 0,
color = "black",size = 21, margin = margin(t = 10, b = 35)),
plot.subtitle = element_text(hjust = 0,
face = "bold", color = "grey30",size = 14,
margin = margin(0, 0, 25, 0)),
plot.title.position = "plot",
plot.caption = element_text(color = "grey50",
size = 10, hjust = 1,lineheight = 1.05,
margin = margin(30, 0, 0, 0)),
plot.caption.position = "plot",
plot.margin = margin(rep(20, 4)))
加载数据
通过tidytuesdayR包可以轻松的访问每周的TidyTuesday项目数据集，建议小伙伴多多了解TidyTuesday
pal <- c("#FF8C00", "#A034F0", "#159090")
tuesdata <- tidytuesdayR::tt_load('2020-07-28')
df_penguins <- tuesdata$penguins
数据清洗
df_rect <- tibble(xmin = c(-Inf, 2.46, 3.27),
xmax = c(Inf, Inf, Inf),ymin = c(3, 2, 1),
ymax = c(Inf, Inf, Inf))
df_peng_iqr <-
df_penguins %>%
mutate(bill_ratio = bill_length_mm / bill_depth_mm) %>%
filter(!is.na(bill_ratio)) %>% group_by(species) %>%
mutate(median = median(bill_ratio),
q25 = quantile(bill_ratio, probs = .25),
q75 = quantile(bill_ratio, probs = .75),
n = n()) %>% ungroup() %>%
mutate(species_num = as.numeric(fct_rev(species)))
ggplot2数据可视化
p <- ggplot(df_peng_iqr,aes(bill_ratio,species_num -.2)) +
geom_rect(data = df_rect,aes(xmin = xmin, xmax = xmax,
ymin = ymin, ymax = ymax),inherit.aes = F,fill = "white")+
geom_linerange(data = df_peng_iqr %>%
group_by(species, species_num) %>%
summarize(m = unique(median)),
aes(xmin = -Inf, xmax = m, y = species_num,
color = species),
inherit.aes = F,linetype = "dotted",size = .7)+
geom_boxplot(aes(color = species),
width = 0,size = .9)+
geom_rect(aes(xmin = q25,
xmax = median,ymin = species_num - .05,
ymax = species_num - .35),fill = "grey89") +
geom_rect(aes(xmin = q75,xmax = median,
ymin = species_num - .05,ymax = species_num - .35),
fill = "grey79")+
geom_segment(aes(x = q25, xend = q25,y = species_num - .05,
yend = species_num - .35,
color = species),
size = .25)+
geom_segment(aes(x = q75, xend = q75,y = species_num - .05,
yend = species_num - .35,color = species),
size = .25)+
geom_point(aes(color = species),
shape = "|",size = 5,alpha = .33)
pp <- p + ggdist::stat_halfeye(aes(y = species_num,
color = species,fill = after_scale(lighten(color, .5))),
shape = 18,point_size = 3,interval_size = 1.8,
adjust = .5,.width = c(0, 1))+
geom_text(data = df_peng_iqr %>%
group_by(species, species_num) %>%
summarize(m = unique(median)),
aes(x = m, y = species_num + .12,
label = format(round(m, 2), nsmall = 2)),
inherit.aes = F,color = "white",size = 3.5) +
geom_text(data = df_peng_iqr %>%
group_by(species, species_num) %>%
summarize(n = unique(n), max = max(bill_ratio, na.rm = T)),
aes(x = max + .01, y = species_num + .02,
label = glue::glue("n = {n}"),
color = species),inherit.aes = F,
size = 3.5,hjust = 0) +coord_cartesian(clip = "off") +
scale_x_continuous(limits = c(1.57, 3.7),
breaks = seq(1.6, 3.6, by = .2),
expand = c(.001, .001)) +
xlab(NULL)+ylab(NULL)+
scale_y_continuous(limits = c(.55, NA),
breaks = 1:3,
labels = c("Gentoo","Chinstrap", "Adelie"),
expand = c(0, 0))
pp + scale_color_manual(values = pal,guide = F) +
scale_fill_manual(values = pal,guide = F) +
theme(panel.grid.major.y = element_blank(),
axis.text.y = element_text(color = rev(pal),
size = 14, lineheight = .9),
axis.ticks.length = unit(0, "lines"))
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