数据主题：Rap Artists

数据源：
https://github.com/gkaramanis/tidytuesday/tree/master/2020-week16/data

依然是第十六周的主题:

前置知识

- futurevisions包

https://github.com/JoeyStanley/futurevisions

devtools::install_github("JoeyStanley/futurevisions")

基于NASA航天局visions-of-the-future的调色包

个人感觉用色鲜明，饱和度高，还挺好看的

# 两个核心函数
futurevisions("mars")
# [1] "#DB3A2F" "#EAB33A" "#275D8E" "#902A57" "#F7EBD3" "#0B0C0B"
show_palette("mars")

ggplot(mpg, aes(cty, hwy, color = factor(cyl))) +
  geom_jitter() +
  scale_color_manual(values = futurevisions("mars"))

含梯度颜色5类，离散颜色4类，分类颜色11类

- rev函数

逆转向量顺序

x <- c(1,2,3)
rev(x)
# [1] 3 2 1
# 等同于
x[length(x):1]

futurevisions("cancri")[1:5]
# [1] "#343854" "#8C384D" "#CF2438" "#D95E31" "#F0C742"
rev(futurevisions("cancri")[1:5])
# [1] "#F0C742" "#D95E31" "#CF2438" "#8C384D" "#343854"

- 关于内置主题

参考：
https://dataxujing.github.io/ggplot2%E7%9A%84%E4%B8%BB%E9%A2%98%E6%A8%A1%E6%9D%BF/

# ggplot2内置主题主要包括
theme_gray() # 默认
theme_bw()
theme_linedraw()
theme_light()
theme_dark()
theme_minimal()
theme_classic()
theme_void()

library(ggplot2)
library(gridExtra)
p <- ggplot(mtcars) + geom_point(aes(x = wt, y = mpg))

grid.arrange(p + theme_gray() + ggtitle("theme_gray"),
             p + theme_bw() + ggtitle("theme_bw"),
             p + theme_linedraw() + ggtitle("theme_linedraw"), 
             p + theme_light() + ggtitle("theme_light"),
             p + theme_dark() + ggtitle("theme_dark"), 
             p + theme_minimal() + ggtitle("theme_minimal"),
             p + theme_classic() + ggtitle("theme_classic"),
             p + theme_void() + ggtitle("theme_void"),ncol=2)

本例用的是theme_void，基本只保留数据图本身(点线条)

原图复现

- 导入包和数据

library(tidyverse)
library(magrittr)
library(janitor)
library(ggimage)
# remotes::install_github("wilkelab/ggtext")
# https://github.com/wilkelab/ggtext
library(ggtext)
# devtools::install_github("JoeyStanley/futurevisions")
library(futurevisions)

windowsFonts(HEL=windowsFont("Helvetica CE 55 Roman"),
             RMN=windowsFont("Times New Roman"),
             ARL=windowsFont("Arial"),
             ARLB=windowsFont("Arial Bold"),
             JBM=windowsFont("JetBrains Mono"))

# 使用rio包对于文件编码的处理不智能 
?rio::import
rankings <- readr::read_csv('https://raw.githubusercontent.com/gkaramanis/tidytuesday/master/2020-week16/data/data-kOlSQ.csv')

- 数据处理

# 新学的操作，不过其实Hadley大佬不建议用%<>% 
rankings %<>% 
  # 可以将重复列区分开
  janitor::clean_names() %>% 
  # 选择列的时候可以直接rename
  select(title_artist = song_artist, img = spotify_thumb_sm, 3:15) %>% 
  filter(n > 4) %>% 
  mutate(
    rank = row_number(),
    title_artist = str_remove(title_artist, "!\\[\\].+"),
    title_artist = str_replace(title_artist, "\\*\\* ", "\\*\\*"),
    img = ifelse(str_detect(img, "data"), NA, img),
    img = ifelse(str_detect(title, "Shook"), "https://i.scdn.co/image/ab67616d00004851a2203fa0656cede30f879b97", img), # fix image for this track
    # 因子重排序默认降序
    title = fct_reorder(title, critic_rating),  
    n_1 = n1,
    n_2_5 = n2 + n3 + n4 + n5
  ) %>% 
  pivot_longer(cols = n1:n4, names_to = "votes", values_to = "votes_n")

- 画图

p_img <- ggplot(rankings) +
  geom_bar(data = subset(rankings, votes == "n1"), 
           aes(votes_n, title,fill = votes), 
           # 决定柱形图方向
           orientation = "y", 
           # position = "stack", # 柱形纵向堆叠，dodge是横向并列
           # 本质下一行代码一样，实际上这里不需要写
           position = position_stack(reverse = FALSE),
           stat = "identity", 
           width = 0.8) +
  geom_bar(data = subset(rankings, votes != "n1"), 
           aes(-votes_n, title, fill = votes), 
           orientation = "y", 
           position = position_stack(reverse = TRUE), 
           stat = "identity", 
           width = 0.8) +
  # n_1 + 1.5 明确了图片和右侧柱形的相对映射位置
  # image通过url将图片载入图层
  # asp是横纵比
  geom_image(aes(n_1 + 1.5, title, image = img), size = 0.029, asp = 1.375)

p <- p_img +
  geom_richtext(aes(n_1 + 2.5, title, label = title_artist), 
                size = 7, family = "ARL", 
                hjust = 0, vjust = 0.5, fill = NA, label.color = NA, lineheight = 1.1) +
  annotate("text",  x = 35, y = 13, 
           label = "BBC Music’s\ngreatest hip-hop\nsongs of all time", 
           hjust = 1, vjust = 0, colour = "black", family = "JBMB", size = 18, lineheight = 1) +
  annotate("text",  x = 35, y = 10.9, 
           label = "Distribution of critics' votes\nfor songs with 5 votes or more", 
           hjust = 1, vjust = 0, colour = "black", family = "ARL", size = 12, lineheight = 1) +
  # 添加中白线，美化
  geom_vline(xintercept = 0, color = "white") +
  # 控制x轴范围
  scale_x_continuous(limits = c(-12, 35)) +
  
  scale_fill_manual(values = rev(futurevisions("cancri")[1:5]), 
                    labels = c("1st fav.", "2nd fav.", "3rd fav.", "4th fav.", "5th fav."), 
                    guide = guide_legend(direction = "horizontal", 
                                         title = "", 
                                         title.position = "top", 
                                         title.hjust = 0.5, 
                                         label.position = "bottom")) +
  labs(
    caption = "Source: BBC Music/Datawrapper | Graphic: Georgios Karamanis"
  ) +
  theme_void() +
  theme(
    legend.position = c(0.815, 0.45),
    legend.spacing.x = unit(0, "pt"),
    legend.key.width = unit(90, "pt"),
    legend.text = element_text(family = "IBM Plex Sans Bold", size = 10),
    # legend.title = element_text(family = "IBM Plex Serif Bold", size = 15),
    # 填充画面背景色
    plot.background = element_rect(fill = "#a7a6ba", colour = NA),
    plot.caption = element_text(family = "IBM Plex Sans", size = 12, hjust = 0.96),
    # 调节画面边缘间距，很实用
    plot.margin = margin(20, 20, 20, 20)
  ) +
  ggsave(here::here(".",paste0("rap-artists-likert-", format(Sys.time(), "%Y%m%d"), ".png")), 
         dpi = 320, width = 22, height = 16)