统计、可视化两不误,多达19种可视化技能你一定要掌握~~
今日小编继续给大家推荐优质绘图工具,帮助小伙伴们更好的是实现不同领域中可视化作品的快速绘制。今天的主角为R-grafify包,其包含5大类共19种可视化图表,舒适和符合出版要求的配色更是为这个可视化包填色,下面就通过以下两个方面介绍下整个优质可视化工具。
R-grafify介绍 R-grafify样例介绍
R-grafify介绍
这一部分小编重点放在R-grafify可绘制的5大类可视化图表和颜色搭配上,内容如下:
5大类可视化图表
R-grafify包可绘制的19种图表主要可分为以下5个小类:
Two variables、 Three or four variables、 Numeric X-Y Plots、 Before-after Plots Data distributions
下面的样例介绍部分,小编将进行具体介绍~~
颜色搭配
R-grafify 包有其自己独有的颜色搭配设计,这里直接列出其所有的颜色条,如下图所示:
![](https://filescdn.proginn.com/20f546a4154cdfb484c7302f5eea4fa7/657e13e646ee425afe0dcc50570ad7ec.webp)
你可以像使用ggplot2种的scale_fill... 和scale_colour... 函数一样使用R-grafify包的颜色名称进行颜色选择和设置。
R-grafify样例介绍
这一部分小编将对上述列举过的5大类图表进行一一介绍(有的类样例较多,将介绍几个典型的图表类型),详细内容如下:
Two variables
这一小类所含图表类型较多,这里小编简单列举3个样例,如下:
plot_scatterbar_sd()
plot_scatterbar_sd(data = data_1w_death, #data table
xcol = Genotype, #X variable
ycol = Death)+ #Y variable
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
labs(
title = "Example of grafify::plot_scatterbar_sd function",
subtitle = "processed charts with plot_scatterbar_sd()",
caption = "Visualization by DataCharm") +
theme(
plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
size = 20, margin = margin(t = 1, b = 12)),
plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
plot.caption = element_markdown(hjust = 1,face = 'bold',size = 12))
![](https://filescdn.proginn.com/1e8e886388c7ca58e8f422baa6548b6a/8cf9ce1dd6d369cccef6c755416a3ba1.webp)
plot_scatterbox()
plot_scatterbox(data_1w_death, #data table
Genotype, #X variable
Death, #Y variable
symsize = 3, #larger symbols
jitter = 0.2)
![](https://filescdn.proginn.com/7b0958a91e4d5471b8f38857f6f54c7f/d258631e4ca8fb170df651f468512062.webp)
plot_point_sd()
plot_point_sd(data_1w_death,
Genotype,
Death,
symsize = 6, #larger symbols
ewid = 0.2, #narrower error bars
ColPal = "bright", #"bright" palette
ColRev = F) +
![](https://filescdn.proginn.com/b6be90c0494f1bbc41cd34dfcb72b368/3ee37b9acb476eda441ffd4e8184a80f.webp)
「注意」:这里更改了颜色条:ColPal = "bright",且ColRev设置成False。
Three or four variables
这类图表包含plot_3d_scatterbar(), plot_3d_scatterbox(), plot_4d_scatterbar(), plot_4d_scatterbox() 函数,具体如下:
plot_3d_scatterbar()
plot_3d_scatterbar(data_2w_Festing, #data table
Strain, #X variable
GST, #Y variable
shapes = Treatment,
symsize = 3, #grouping factor for shapes
ColPal = "vibrant") + #"vibrant" palette
hrbrthemes::theme_ipsum(base_family = "Roboto Condensed") +
labs(
title = "Example of grafify::plot_3d_scatterbar function",
subtitle = "processed charts with plot_3d_scatterbar()",
caption = "Visualization by DataCharm") +
theme(
plot.title = element_markdown(hjust = 0.5,vjust = .5,color = "black",
size = 20, margin = margin(t = 1, b = 12)),
plot.subtitle = element_markdown(hjust = 0,vjust = .5,size=15),
plot.caption = element_markdown(hjust = 1,face = 'bold',size = 12))
![](https://filescdn.proginn.com/8303646420fc2b07220cdf2b7f96544f/35dadf1e4951cd6023f0040264e4805f.webp)
plot_3d_scatterbox()
plot_3d_scatterbox(data_2w_Tdeath,
Time,
PI,
Genotype,
b_alpha = 0.5, #reduced opacity
ColPal = "contrast", #"contrast" palette
ColRev = T)
![](https://filescdn.proginn.com/0571a9c712f980aeac6a3f9fd5d27292/20f377d950dd91ee603d7d2239141f84.webp)
plot_4d_scatterbar()
plot_4d_scatterbar(data_2w_Tdeath,
Time,
PI,
Genotype,
Experiment,
b_alpha = 0.7,
ColPal = "okabe_ito")+
scale_colour_manual(values = c("black", "grey20"))
![](https://filescdn.proginn.com/0607d05c701ec8e78dd2b4e9aadb1f9c/4a10a53c8f81fe4224a68d1d4a4f03ec.webp)
plot_4d_scatterbox()
plot_4d_scatterbox(data_2w_Tdeath,
Time,
PI,
Genotype,
Experiment,
b_alpha = 0.7,
ColPal = "okabe_ito")+
scale_colour_manual(values = c("black", "grey20")) #manual colour scale
![](https://filescdn.proginn.com/e4c331da8e02e14d4b58bd2d6e7d96c6/ffa4e60570d62068f7faee9d2462e151.webp)
Numeric X-Y Plots
此类图表包含plot_xy_NumGroup(), plot_xy_CatGroup() 函数,具体如下:
plot_xy_NumGroup()
plot_xy_NumGroup(airquality,
Wind,
Temp,
Ozone,
symsize = 3)
![](https://filescdn.proginn.com/caafd78174f92952f8209aecbd14e511/58161e667e40bcdce9bf31e3a0b418cd.webp)
plot_xy_CatGroup()
plot_xy_CatGroup(neuralgia,
Age,
Duration,
Pain,
symsize = 3,
ColPal = "muted", #palette
ColRev = T)
![](https://filescdn.proginn.com/84332ba0b1c52b5a27753ff1a5577199/56e22c604fc7dd9cabc1bf70d50e1cc6.webp)
Before-after Plots
此类图表包含plot_befafter_colours() , plot_befafter_shapes() 函数,具体如下:
plot_befafter_colours()
plot_befafter_colours(data = data_t_pdiff,
xcol = Condition,
ycol = Mass,
groups = Subject,
symsize = 5,
ColPal = "light",
ColRev = T)
![](https://filescdn.proginn.com/6f26f6866ec53d8e586c774c1702c683/0712f4f3e911de239a081aa416301fb0.webp)
Data distributions
此类图表包含plot_qqline(), plot_density() 和plot_histogram() 函数,具体如下:
lot_qqline()
plot_qqline(data = data_t_pratio,
ycol = Cytokine,
xcol = Genotype)
![](https://filescdn.proginn.com/45d5146ebb70b65b1fe5d4a492b4dac3/764b773148085218757f8d618de78b32.webp)
以上就是小编关于R-grafify包的简单介绍,特别是对每种绘图函数所需数据的数据形式理解。更多关于该包参数和使用方法等介绍可参考R-grafify包官网[1]
总结
今天的推文小编简单介绍了R-grafify包的各种图表类型和颜色搭配,希望感兴趣的小伙伴可以进行相关科研图表 的绘制和对其颜色搭配 的参考~~
参考资料
R-grafify包官网: https://grafify-vignettes.netlify.app/。
E N D
各位伙伴们好,詹帅本帅假期搭建了一个个人博客和小程序,汇集各种干货和资源,也方便大家阅读,感兴趣的小伙伴请移步小程序体验一下哦!(欢迎提建议)
推荐阅读