[R] Use grafify for statistical plotting, ANOVA, intervention comparisons, and more!

This suggests a high qualityR包grafify,grafifyFunction displays in the following aspects：

• A few lines of code to draw the high level of appearance statistical chart,支持6大类,19种图表 (见上图);

• The palette that contains high identification,可与ggplot2良好兼容;

• Support the variance analysis (ANOVA),Including randomized block and repeated measurement design;

• Support the changes before and after the intervention more (post-hoc comparisons);

• 模拟1-way和2-way ANOVA设计.

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grafify调色盘

Support the following palette,Contains qualitative color scheme（Qualitative colormaps）如okabe_ito、r4Such as color scheme and order（Sequential colormaps）如grey_conti等.使用方法类似ggplot2,参考 R可视化07|ggplot2图层-标度图层（scale layer）-颜色盘篇

plot_grafify_palette(palette = "kelly") #查看kellyAll color palette number value

grafify:::graf_palettes #输出grafifyAll the palette color value

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grafify绘制统计图

Statistical figure correspondingRealization method are below the red box里了,Need the class diagram,Use the figure above functions,下面简单举例,说明grafify的使用：

• demo0

plot_4d_scatterbar()实现3或4个分类变量bar图,

plot_4d_scatterbar(data_2w_Tdeath,           
                   Genotype,                  
                   PI,                        
                   Time,                      
                   Experiment,                
                   b_alpha = 0.7,             
                   ColPal = "vibrant")+     
  scale_colour_manual(values = c("black", "grey20"))+   
  labs(title = "2way/RM, bars/SD",
       subtitle = "(shapes = randomised blocks)")

• demo1

Map a combination：bar图+散点图+SD,plot_scatterbar_sd()可轻松搞定

library('grafify') # 导入包
plot_scatterbar_sd(data = data_doubling_time,#使用内置数据集data_doubling_time
                   xcol = Student,           # 指定X轴变量
                   ycol = Doubling_time,     # 指定Y轴变量
                   SingleColour = "r4_1")+ # 指定颜色,Take child palette herer4
                   labs(title = "Scatter+bar+SD") # 设置标题

• demo2

QQ图：使用plot_qqline()即可,

plot_qqline(data_t_pratio, 
            log(Cytokine),  
            Genotype)+         
            labs(title = "QQ plot with plot_qqline()")

• demo3

Clinical a chart：SCIThe article as well as a chart comparing the changes before and after the intervention,grafify中plot_befafter_colours()可轻松实现,

plot_befafter_colours(data = data_t_pdiff,
                      xcol = Condition,
                      ycol = Mass,
                      match = Subject,
                      symsize = 5,
                      ColPal = "r4",
                      ColRev = T)+
  labs(title = "TBefore-after with plot_befafter_colours")

• demo4

plot_xy_NumGroup()

plot_xy_NumGroup(dplyr::filter(diamonds, cut == "Premium" & clarity == "SI1"), 
                 depth, 
                 price, 
                 carat, 
                 s_alpha = .5, 
                 ColPal = "grey_conti")+ #colschem
  labs(title = "plot_xy_NumGroup()")

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grafify方差分析

This part is not in detail,The categories of main supportANOVA：

• Simple linear models: One-way ANOVA, Two-way ANOVA

• Mixed effects models: One-way ANOVA & randomised blocks,Two-way ANOVA & randomised blocks,Two-way ANOVA, repeated-measures & randomised blocks

• Posthoc comparisons：posthoc_Pariwise, posthoc_Levelwise, posthoc_vsRef

参考：https://github.com/ashenoy-cmbi/grafify

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