tidyplots——简化代码，比ggplot2更高级！

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背景介绍

今天给大家介绍一个用于简化绘图代码的R包——tidyplots，相比较ggplot2，更加高级和实用。

我们首先看一下这个tidyplots包可以什么图吧，就是下面这些！常用的基本都能满足，优点在于绘图过程代码简单，更加方便快捷！

R包安装

1.直接从cran进行安装即可

install.packages("tidyplots")

绘图数据

1.tidyplots内置了非常多的数据，你可以用来进行练习

> study treatment group dose participant age sex score 1 A placebo high p01 23 female 2 2 A placebo high p02 45 male 4 3 A placebo high p03 32 female 5 4 A placebo high p04 37 male 4 5 A placebo high p05 24 female 6 6 B placebo low p06 23 female 9 7 B placebo low p07 45 male 8 8 B placebo low p08 32 female 12 9 B placebo low p09 37 male 15 10 B placebo low p10 24 female 16 11 C treatment high p01 23 female 32 12 C treatment high p02 45 male 35 13 C treatment high p03 32 female 24 14 C treatment high p04 37 male 45 15 C treatment high p05 24 female 56 16 D treatment low p06 23 female 23 17 D treatment low p07 45 male 25 18 D treatment low p08 32 female 21 19 D treatment low p09 37 male 22 20 D treatment low p10 24 female 23

> str(animals) Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 60 obs. of 14 variables: $ animal : chr "Honeybee" "Monarch Butterfly" "Dragonfly" "Firefly" ... $ size : num 1.5 10 10 2.5 17 1 4 0.8 7.5 9 ... $ size_unit : chr "cm" "cm" "cm" "cm" ... $ weight : num 1.2e-04 4.5e-04 2.0e-04 2.0e-05 3.0e-03 2.5e-06 5.0e-06 1.0e-05 2.0e-04 4.0e-04 ... $ weight_unit : chr "kg" "kg" "kg" "kg" ... $ speed : num 0.72 0.432 2.088 0.0432 0.144 ... $ speed_unit : chr "km/h" "km/h" "km/h" "km/h" ... $ habitat : chr "Gardens, meadows, hives" "Gardens, fields, forests" "Wetlands, near water" "Grasslands, forests, wetlands" ... $ activity : chr "Diurnal" "Diurnal" "Diurnal" "Nocturnal" ... $ family : chr "Insect" "Insect" "Insect" "Insect" ... $ color : chr "Yellow/Black" "Orange/Black" "Various" "Light Brown" ... $ number_of_legs : Factor w/ 4 levels "0","2","4","6": 4 4 4 4 4 4 4 4 4 4 ... $ body_temperature: chr "Cold-blooded" "Cold-blooded" "Cold-blooded" "Cold-blooded" ... $ diet : chr "Herbivore" "Herbivore" "Carnivore" "Carnivore" ...

> str(energy) Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 242 obs. of 5 variables: $ year : num 2002 2002 2002 2002 2002 ... $ energy_source: Factor w/ 11 levels "Biomass","Fossil brown coal / lignite",..: 7 2 4 3 5 8 6 1 11 10 ... $ energy_type : Factor w/ 4 levels "Fossil","Nuclear",..: 2 1 1 1 1 3 4 4 4 4 ... $ power : num 23.5 20.3 28.3 20.3 5.3 ... $ power_unit : chr "GW" "GW" "GW" "GW" ...

绘图教程

1.绘制一幅常见的柱形散点图，使用ggplot2的朋友可能知道，如果绘制这样的柱形＋散点的图形，需要写很多代码，而使用tidyplots只需简单的几行

library(tidyplots) study %>% tidyplot(x = treatment, y = score, color = treatment) %>% # 选择映射数据 add_mean_bar(alpha = 0.4) %>% # 添加平均数的柱状图 add_sem_errorbar() %>% # 添加标准误 add_data_points_beeswarm() # 添加数据点

2.如果要绘制累积柱状图，也只需要两行代码

energy %>% tidyplot(x = year, y = power, color = energy_source) %>% # 选择映射的数据 add_barstack_absolute() # 添加累计柱状图的代码

3.如果想按照某个分类进行分图，其实也比较简单

energy %>% dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) %>% tidyplot(y = power, color = energy_source) %>% add_donut() %>% split_plot(by = year) # 按照year进行分图

energy %>% dplyr::filter(year %in% c(2005, 2010, 2015, 2020)) %>% tidyplot(y = power, color = energy_source) %>% add_donut(width = 2) %>% # 设置圈图中间的面积，数值越小，白圈越大 split_plot(by = year,ncol = 3) # 设置行列的数量

4.面积叠加图

energy_week %>% tidyplot(x = date, y = power, color = energy_source) %>% add_areastack_absolute()

5.想把数值加在柱子上也很容易

study %>% tidyplot(x = group, y = score, color = dose) %>% add_mean_bar(alpha = 0.4) %>% add_mean_dash() %>% add_mean_value()

6.含有置信区间的图

time_course %>% tidyplot(x = day, y = score, color = treatment) %>% add_mean_line() %>% # 平均值的线 add_mean_dot() %>% # 平均值的点 add_sem_ribbon() # 置信区间

7. 也可以一键绘制热图

climate %>% tidyplot(x = month, y = year, color = max_temperature) %>% add_heatmap()

8.添加显著性，这个可能最常用了

study %>% tidyplot(x = treatment, y = score, color = treatment) %>% add_boxplot() %>% add_test_pvalue(ref.group = 1)

绘图流程

下面是tidyplots的绘图流程，从tidyplot→add→remove→adjust→theme→split_plot→save_plot，熟悉这个流程，然后选择对应的函数即可

功能函数

1.创建图形函数

tidyplot()

2.添加图形元素函数

a.数据点和数量

# 数据点 add_data_points() add_data_points_jitter() add_data_points_beeswarm() # 数值 add_count_bar() add_count_dash() add_count_dot() add_count_value() add_count_line() add_count_area() # 数值总结 add_sum_bar() add_sum_dash() add_sum_dot() add_sum_value() add_sum_line() add_sum_area() # 热图 add_heatmap() # 线或面积 add_line() add_area()

b.添加集中趋势

# 添加均值 add_mean_bar() add_mean_dash() add_mean_dot() add_mean_value() add_mean_line() add_mean_area() # 添加中位数 add_median_bar() add_median_dash() add_median_dot() add_median_value() add_median_line() add_median_area() # 拟合曲线 add_curve_fit()

c.添加数值分布

# 直方图 add_histogram() # 箱线图 add_boxplot() # 小提琴图 add_violin() # 误差线 add_sem_errorbar() add_range_errorbar() add_sd_errorbar() add_ci95_errorbar() # 置信区间 add_sem_ribbon() add_range_ribbon() add_sd_ribbon() add_ci95_ribbon()

d.比例

# 条形堆叠 add_barstack_absolute() add_barstack_relative() # 面积堆叠 add_areastack_absolute() add_areastack_relative() # 饼图或圆环图 add_pie() add_donut()

e.统计测试

# 统计结果 add_test_pvalue() add_test_asterisks()

f.注释

# 标题或说明 add_title() add_caption() # 数据标签 add_data_labels() add_data_labels_repel() # 参考线 add_reference_lines()

3.移除函数

用来移除绘图的元素

# 移除图例或标题 remove_legend() remove_legend_title() # 删除绘图区域填充 remove_padding() # 删除标题或说明 remove_title() remove_caption() # 删除 x 轴或其部分 remove_x_axis() remove_x_axis_line() remove_x_axis_ticks() remove_x_axis_labels() remove_x_axis_title() # 删除 y 轴或其部分 remove_y_axis() remove_y_axis_line() remove_y_axis_ticks() remove_y_axis_labels() remove_y_axis_title()

4.调整函数

a. 用来调整绘图组件、属性和数据标签

# 调整颜色 adjust_colors() # 调整字体 adjust_font() # 调整图例 adjust_legend_title() adjust_legend_position() # 调整标题和注释 adjust_title() adjust_x_axis_title() adjust_y_axis_title() adjust_caption() # 调整绘图大小 adjust_size() # 调整绘图区域填充 adjust_padding() # 调整x和y轴 adjust_x_axis() adjust_y_axis()

b. 坐标轴和颜色标签

# 重命名轴和颜色标签 rename_x_axis_labels() rename_y_axis_labels() rename_color_labels() # 重排序轴和颜色标签 reorder_x_axis_labels() reorder_y_axis_labels() reorder_color_labels() # 对轴或颜色标签进行排序 sort_x_axis_labels() sort_y_axis_labels() sort_color_labels() # 翻转轴或颜色标签 reverse_x_axis_labels() reverse_y_axis_labels() reverse_color_labels()

5.主题调整

用于主题的调整

# 主题 theme_tidyplot() theme_ggplot2() theme_minimal_xy() theme_minimal_x() theme_minimal_y() # 调节主题细节 adjust_theme_details()

6.颜色主题

有多种配色方案可供选择

# 离散配色方案 colors_discrete_friendly colors_discrete_seaside colors_discrete_apple colors_discrete_friendly_long colors_discrete_okabeito colors_discrete_ibm colors_discrete_metro colors_discrete_candy colors_discrete_alger colors_discrete_rainbow # 连续配色方案 colors_continuous_viridis colors_continuous_magma colors_continuous_inferno colors_continuous_plasma colors_continuous_cividis colors_continuous_rocket colors_continuous_mako colors_continuous_turbo colors_continuous_bluepinkyellow # 不同的配色方案 colors_diverging_blue2red colors_diverging_blue2brown colors_diverging_BuRd colors_diverging_BuYlRd colors_diverging_spectral colors_diverging_icefire # 新的配色方案 new_color_scheme()

7.分割函数

将主图分成多个子图

# 将图形分割为多个子图 split_plot()

8.输出

用于保存图片

# 在屏幕上查看图表 view_plot() # 保存图形文件 save_plot()

9.一些小技巧

# 子集数据行 all_rows() filter_rows() max_rows() min_rows() first_rows() last_rows() sample_rows() # 添加ggplot2代码到tidyplot add() # 转换ggplot2到tidyplot as_tidyplot() # 翻转 x 轴和 y 轴 flip_plot() # 格式化数字或 p 值 format_number() format_p_value()

10.内置数据集

# 动物数据 animals # 气候数据 climate # 恐龙数据 dinosaurs # 分布数据 distributions # 能源数据 energy # 能源周数据 energy_week # 欧盟国家数据 eu_countries # RNA-Seq 表达数据 gene_expression # 支出数据 spendings # 研究数据 study # 时间进程数据 time_course

参考链接：

[1] https://tidyplots.org/

[2] https://jbengler.github.io/tidyplots/reference/index.html