Python-joypy和 R-ggridges 峰峦图制作

DataCharm

发布于 2021-02-22 15:15:09

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发布于 2021-02-22 15:15:09

文章被收录于专栏：数据学术商业新闻数据学术商业新闻

01. 引言

有时写较多的处理过程反倒让读者没法耐着性子读完，本期推文及以后，关于可视化的推文，我直接明了给出教程和关键步骤解释下，若想详细了解，大家可进群讨论和在文末读者讨论中进行。本期推文为峰峦图的Python和R绘制方法。

02. Python-joypy 制作

Python 制作峰峦图有直接的第三方库joypy进行绘制，该库可以直接通过pip安装。可视化代码如下：

import matplotlib.pyplot as plt

plt.rcParams['font.family'] = ['Times New Roman']

colors = ['#791E94','#58C9B9','#519D9E','#D1B6E1']
fig,axs = joypy.joyplot(data_ed, by="source",fill=True, legend=True,alpha=.8,
                         range_style='own',xlabelsize=22,ylabelsize=22,
                         grid='both', linewidth=.8,linecolor='k', figsize=(12,6),color=colors,
                       )
ax = plt.gca()
#设置x刻度为时间形式
x = np.arange(6)
xlabel=['8-21','8-28','9-4','9-11','9-18','9-25']
ax.set_xlim(left=-.5,right=5.5)
ax.set_xticks(x)
ax.set_xticklabels(xlabel)
ax.text(.47,1.1,"Joyplot plots of media shares (TV, Online News and Google Trends)",
        transform = ax.transAxes,ha='center', va='center',fontsize = 25,color='black')
ax.text(.5,1.03,"Python Joyplot Test",
        transform = ax.transAxes,ha='center', va='center',fontsize = 15,color='black')
ax.text(.90,-.11,'\nVisualization by DataCharm',transform = ax.transAxes,
        ha='center', va='center',fontsize = 12,color='black')
plt.savefig(r'F:\DataCharm\Artist_charts_make_python_R\joyplots\Joyplot_python.png',
            width=7,height=5,dpi=900,bbox_inches='tight')

可视化结果如下：

关于 joypy库其他详细的参数设置，可以去官网(https://github.com/sbebo/joypy) 下载 Joyplot.ipynb 文件查看，最好查看所绘制数据的格式，有助于更好绘制峰峦图。

03. R-ggridges 绘制

借助于R语言丰富且强大的第三方绘图包，在应对不同类型图表时，机会都会有对应的包进行绘制。本次就使用ggridges包（https://wilkelab.org/ggridges/）进行峰峦图的绘制。官网的例子如下：

ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = Month, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01, gradient_lwd = 1.) +
  scale_x_continuous(expand = c(0, 0)) +
  scale_y_discrete(expand = expand_scale(mult = c(0.01, 0.25))) +
  scale_fill_viridis_c(name = "Temp. [F]", option = "C") +
  labs(
    title = 'Temperatures in Lincoln NE',
    subtitle = 'Mean temperatures (Fahrenheit) by month for 2016'
  ) +
  theme_ridges(font_size = 13, grid = TRUE) + 
  theme(axis.title.y = element_blank())

结果如下：

这里我们没有使用 geom_density_ridges_gradient()进行绘制，使用了 geom_ridgeline() 进行类似于山脊线图的绘制。

绘制代码如下：

library(ggthemes)
library(hrbrthemes)
plot <- ggplot(all_data, aes(x = date, y = source)) +
  geom_ridgeline(aes(height = value, fill = factor(hurricane)), size = 0.1, scale = 0.8, alpha = 0.8) +
  labs(title = "Ridgeline plots of media shares (TV, Online News and Google Trends)",
       subtitle = "ggridges ridgeline plot test",
       caption = "Visualization by DataCharm",
       y = NULL,
       x = NULL) +
  scale_x_date(expand = c(0,0)) +
  scale_fill_manual(values = c('#791E94','#58C9B9','#D1B6E1','#519D9E'),name="Hurricane")+
  theme_ipsum()+
  theme(text = element_text(family = 'Poppins',face = 'bold'),
        axis.text.y = element_text(vjust = -2))
plot

可视化结果如下：