1写在前面

最近天天下雨，真是下得我没脾气啊，动不动就淋成狗。😭

下雨也就算了，还特别热。🤒

不知道是不是人到中年了，事情也特别的多，能静下心来思考的时间越来越少了。😞

也越来越明白，为什么大家会说希望家里人身体都健健康康的，这个真的是最重要的事情了。🥸

今天分享的是环形barplot，颜值还是蛮高的，大家试试吧！~🥳

配个BGM吧，Leonard Cohen的Hallelujah！~😘

2用到的包

rm(list = ls())
library(tidyverse)	

3示例数据

hike_data <- readr::read_rds("./hike_data.rds")

DT::datatable(hike_data)

4查看数据特征

str(hike_data)

5清洗数据

5.1 提取region信息并因子化

hike_data$region <- as.factor(word(hike_data$location, 1, sep = " -- "))

5.2 提取number of miles

这里大家记得转成number格式，要不然后面会报错的。🤣

hike_data$length_num <- as.numeric(sapply(strsplit(hike_data$length, " "), "[[", 1))

5.3 计算其他画图数据

接着，计算每个region的cumulative length和mean gain，并计算每个region的number of tracks。

plot_df <- hike_data %>%
  group_by(region) %>%
  summarise(
    sum_length = sum(length_num),
    mean_gain = mean(as.numeric(gain)),
    n = n()
  ) %>%
  mutate(mean_gain = round(mean_gain, digits = 0))

DT::datatable(plot_df)

6开始绘图

6.1 初步绘图

p <- ggplot(plot_df) +
  # Make custom panel grid
  geom_hline(
    aes(yintercept = y), 
    data.frame(y = c(0:3) * 1000),
    color = "lightgrey"
  ) + 
  geom_col(
    aes(
      x = reorder(str_wrap(region, 5), sum_length),
      y = sum_length,
      fill = n
    ),
    position = "dodge2",
    show.legend = T,
    alpha = .9
  ) +
  geom_point(
    aes(
      x = reorder(str_wrap(region, 5),sum_length),
      y = mean_gain
    ),
    size = 3,
    color = "gray12"
  ) +
  
  geom_segment(
    aes(
      x = reorder(str_wrap(region, 5), sum_length),
      y = 0,
      xend = reorder(str_wrap(region, 5), sum_length),
      yend = 3000
    ),
    linetype = "dashed",
    color = "gray12"
  ) + 
  coord_polar()

p

6.2 添加注释并修改颜色

p <- p +
  annotate(
    x = 11, 
    y = 1300,
    label = "Mean Elevation Gain\n[FASL]",
    geom = "text",
    angle = -67.5,
    color = "gray12",
    size = 2.5
  ) +
  annotate(
    x = 11, 
    y = 3150,
    label = "Cummulative Length [FT]",
    geom = "text",
    angle = 23,
    color = "gray12",
    size = 2.5
  ) +
  annotate(
    x = 11.7, 
    y = 1100, 
    label = "1000", 
    geom = "text", 
    color = "gray12"
  ) +
  annotate(
    x = 11.7, 
    y = 2100, 
    label = "2000", 
    geom = "text", 
    color = "gray12"
  ) +
  annotate(
    x = 11.7, 
    y =3100, 
    label = "3000", 
    geom = "text", 
    color = "gray12"
  ) +
  scale_y_continuous(
    limits = c(-1500, 3500),
    expand = c(0, 0),
    breaks = c(0, 1000, 2000, 3000)
  ) + 
  scale_fill_gradientn(
    "Amount of Tracks",
     colours = c( "#6C5B7B","#C06C84","#F67280","#F8B195")
  ) +
  guides(
    fill = guide_colorsteps(
      barwidth = 15, barheight = .5, title.position = "top", title.hjust = .5
    )
  ) +
  theme(
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    axis.text.y = element_blank(),
    axis.text.x = element_text(color = "gray12", size = 12),
    legend.position = "bottom",
  )

p

6.3 添加title和其他信息

p <- p +
  labs(
    title = "\nHiking Locations in Washington",
    subtitle = paste(
      "\nThis Visualisation shows the cummulative length of tracks,",
      "the amount of tracks and the mean gain in elevation per location.\n",
      "If you are an experienced hiker, you might want to go",
      "to the North Cascades since there are a lot of tracks,",
      "higher elevations and total length to overcome.",
      sep = "\n"
  ),
    caption = "\n\nData Visualisation by Tobias Stalder\ntobias-stalder.netlify.app\nSource: TidyX Crew (Ellis Hughes, Patrick Ward)\nLink to Data: github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-11-24/readme.md") +
  theme(
    text = element_text(color = "gray12"),
    plot.title = element_text(face = "bold", size = 25, hjust = 0.05),
    plot.subtitle = element_text(size = 14, hjust = 0.05),
    plot.caption = element_text(size = 10, hjust = .5),
    panel.background = element_rect(fill = "white", color = "white"),
    panel.grid = element_blank(),
    panel.grid.major.x = element_blank()
  )
p

点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰