参考链接：
https://github.com/gkaramanis/tidytuesday/blob/master/week-42/big-epa-cars.R
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学习到的技能（需要掌握的函数）
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-
pivot_longer()函数: 宽数据变长数据。 -
forcats包对于处理factor相关操作非常方便，绘图排序经常用到 -
row_number()函数：通用排名，并列的名次结果按先后顺序不一样，靠前出现的元素排名在前
画图思路
数据清洗就不说了，一如既往的结合
filter() + group_by() + summarize() + ungroup() + mutate()等常用函数
数据清洗
提一下:在只看到这里数据清洗的视化并未理解为啥要产生lane这个变量，直到，看了后面可视化geom_image(data = city_race, aes(x = lane, y = city_mpg_median, image = car), size= .05, asp = 0.6)后，才发现原来是给图中的小车子标识位置，一种公路分两列，又一次跪拜。
# 没有这些包的先自行安装。
library(tidyverse)
library(here)
library(ggimage)
big_epa_cars <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-15/big_epa_cars.csv")
city_race <- big_epa_cars %>%
# pivot_longer() 函数将宽数据变为长数据，参数意思都是字面意思
pivot_longer(cols = c("city08", "cityA08"), names_to = "city_fuel", values_to = "city_mpg") %>%
filter(city_mpg > 0) %>% # 筛选 city_mpg 数值大于 1 的行
group_by(make) %>% # 按照 make 列变量进行分组
summarize(city_mpg_median = median(city_mpg)) %>%
# 然后分别求出 make 列对应的每一组变量的 city_mpg 的平局值，这里其实就可以运用到我们的将候选基因分 bin 啊，然后在求出每个 bin 对应的候选基因的值的平均值、最大值、最小值等等。
ungroup() %>% # 取消分组
mutate(make = fct_reorder(make, city_mpg_median)) %>%
# 将 make 对应的因子即变量按照 city_map_median 重新进行排序
# 详细见 R|数据处理|因子型数据
# https://ask.hellobi.com/blog/R_shequ/15408
top_n(10, city_mpg_median) %>% # 取 city_mpg_median 前十个
arrange(city_mpg_median) %>% # 按照 city_mpg_median 大小进行重新排序
mutate(lane = row_number() %% 2 * 2)
# row_number 通用排名，并列的名次结果按先后顺序不一样，靠前出现的元素排名在前
highway_race <- big_epa_cars %>%
pivot_longer(cols = c("highway08", "highwayA08"), names_to = "highway_fuel", values_to = "highway_mpg") %>%
filter(highway_mpg > 0) %>%
group_by(make) %>%
summarize(highway_mpg_median = median(highway_mpg)) %>%
ungroup() %>%
mutate(make = fct_reorder(make, highway_mpg_median)) %>%
top_n(10, highway_mpg_median) %>%
arrange(highway_mpg_median) %>%
mutate(lane = row_number() %% 2 * 2 + 13) %>%
group_by(highway_mpg_median) %>%
mutate(make = paste0(make, collapse = ", "))
产生的数据格式是这样的
导入图中的小车 png 图片
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注意：这里先将图片保存在自己当前环境的路径下
# Icon by mynamepong, flaticon.com
# here() 函数上一次介绍过就不介绍了，就是当前路径
car = here::here("car.png")
绘图
大致思路：用geom_rect()绘制一个大的灰色背景幕布 →geom_rect()绘制城市公路，哈哈（就是途中的黑色）→ 再绘制公路中间的虚线（白色虚线）→ 同城市公路绘制高速公路 → 通过geom_image()把小车图标加上去，哈哈（）萌死我了 → 使用geom_text()加上车子类型注释，注意其中的参数hjust参数一左一右 → 使用geom_segment加上刻度尺，这里太机智了，我的妈欸（分了左右，而不是在一条线上）→xlim()函数标注 x 轴的范围 →labs()加上标题啊、小标题啊、脚注啊等等 → 主题优化
总之逻辑特别清晰，一步紧跟一步，绘图代码部分，一步一步运行就好，简单粗暴易理解，真的是学习的好案例。（ 作者脑洞真的大，也可能是我脑子太小了 ）
ggplot() +
# city background
geom_rect(aes(xmin = -2, ymin = 0, xmax = 7, ymax = 105), fill = "grey60") +
# city street
geom_rect(aes(xmin = -0.75, ymin = 0, xmax = 2.75, ymax = 105), fill = "grey20") +
# city street line
geom_segment(aes(x = 1, y = 0, xend = 1, yend = 105), size = 2, color = "grey90", linetype = 2) +
# city label
geom_text(aes(x = -1.6, y = 1, label = "CITY"), size = 8, color = "grey90", alpha = 0.4, family = "IBM Plex Sans Medium", angle = 90, hjust = "left") +
# highway background
geom_rect(aes(xmin = 8, ymin = 0, xmax = 17, ymax = 105), fill = "brown4") +
# highway
geom_rect(aes(xmin = 12.25, ymin = 0, xmax = 15.75, ymax = 105), fill = "grey20") +
# highway road line
geom_segment(aes(x = 14, y = 0, xend = 14, yend = 105), size = 2, color = "grey90", linetype = "longdash") +
# highway label
geom_text(aes(x = 16.6, y = 1, label = "HIGHWAY"), size = 8, color = "grey90", alpha = 0.4, family = "IBM Plex Sans Medium", angle = 90, hjust = "left") +
# city cars
geom_image(data = city_race, aes(x = lane, y = city_mpg_median, image = car), size= .05, asp = 0.6) +
# city cars make
geom_text(data = city_race, aes(x = 6.6, y = city_mpg_median, label = make), hjust = "right", family = "IBM Plex Sans Condensed", size = 6, color = "white") +
geom_segment(data = city_race, aes(x = 6.7, y = city_mpg_median, xend = 7, yend = city_mpg_median), color = "white") +
# highway cars
geom_image(data = highway_race, aes(x = lane, y = highway_mpg_median, image = car), size= .05, asp = 0.6) +
# highway cars make
geom_text(data = highway_race, aes(x = 8.4, y = highway_mpg_median, label = make), hjust = "left", family = "IBM Plex Sans Condensed", size = 6, color = "white", check_overlap = TRUE) +
geom_segment(data = highway_race, aes(x = 8, y = highway_mpg_median, xend = 8.3, yend = highway_mpg_median), color = "white") +
# y axis between roads
geom_text(aes(x = 7.5, y = seq(0, 100, 10)), label = seq(0, 100, 10), family = "IBM Plex Mono Light", size = 6, color = "black") +
geom_text(aes(x = 7.5, y = seq(5, 95, 10)), label = seq(5, 95, 10), family = "IBM Plex Mono Light", size = 6, color = "grey50") +
geom_text(aes(x = 7.5, y = -3, label = "MPG/MPGe"), family = "IBM Plex Mono Light", size = 6, color = "black") +
# title and theme
xlim(-2, 17) +
labs(
title = "Tesla is the leading car brand in energy efficiency",
subtitle = "Top 10 most energy efficient brands in city and highway driving.\nRanking is based on calculated median MPG and MPGe of all\nmodels made by every car manufacturer since 1984.",
caption = "Source: EPA | Graphic: Georgios Karamanis\nCar icon by mynamepong, flaticon.com"
) +
theme_void(base_family = "IBM Plex Sans") +
theme(
plot.margin = margin(20, 20, 20, 20),
plot.title = element_text(family = "IBM Plex Serif Bold", size = 28, margin = margin(10, 0, 0, 0)),
plot.subtitle = element_text(size = 24, margin = margin(10, 0, 0, 40)),
plot.caption = element_text(size = 12, color = "grey60", margin = margin(0, 40, 0, 0)),
) +
ggsave(
here::here(paste0("big-epa-cars-", format(Sys.time(), "%Y%m%d_%H%M%S"), ".png")),
height = 19, width = 12, dpi = 320
) # 出图到保存一步到位
