数据呈现丨R语言可视化学习笔记之ggridges包

简介

ggridges包主要用来绘制山峦图。尤其是针对时间或者空间分布可视化具有十分好的效果。ggridges主要提供两个几何图像函数：

geom_ridgeline()：主要绘制山脊线图

geom_density_ridges()：主要根据密度绘制山脊线图

具体用法可以参考官方文档：

https://cran.r-project.org/web/packages/ggridges/vignettes/introduction.html

geom_ridgeline()

library(ggridges)library(tidyverse)library(gridExtra)my_data <- data.frame(x=1:5, y=rep(1,5), height=c(0,1,-1,3,2))plot_base <- ggplot(my_data, aes(x, y, height=height))grid.arrange(plot_base+geom_ridgeline(), plot_base+geom_ridgeline(min_height=-2), ncol=2)左右滑动查看更多

geom_density_ridges()

geom_density_ridges()函数首先会根据数据计算密度然后绘图，此时美学映射height没有必要写入函数中。下面使用lincoln_weather数据集。

library(viridis)head(lincoln_weather[ ,1:4])左右滑动查看更多

## # A tibble: 6 x 4## CST `Max Temperature [F]` `Mean Temperature [F]` `Min Temperature ~## <chr> <int> <int> <int>## 1 2016-1-1 37 24 11## 2 2016-1-2 41 23 5## 3 2016-1-3 37 23 8## 4 2016-1-4 30 17 4## 5 2016-1-5 38 29 19## 6 2016-1-6 34 33 32左右滑动查看更多

ggplot(lincoln_weather, aes(x=`Mean Temperature [F]`, y=`Month`, fill=..x..))+ geom_density_ridges_gradient(scale=3, rel_min_height=0.01, gradient_lwd = 1.)+ scale_x_continuous(expand = c(0.01, 0))+ scale_y_discrete(expand = c(0.01,0))+ scale_fill_viridis(name="Temp. [F]", option = "C")+ labs(title="Temperature in Lincoln NE",subtitle="Mean temperature (Fahrenheit) by month for 2016\nData:Orogin CSV from the Weather Underground ")+ theme_ridges(font_size = 13, grid = FALSE)+ theme(axis.title.y = element_blank())左右滑动查看更多

cyclinal scales

为了使得ggridges绘制的图形可视化效果最好，同时为了减少用户对颜色设置的困难，作者提供了cyclinal scales用于颜色轮转映射。

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+ geom_density_ridges(scale=4)+ scale_fill_cyclical(values = c("blue", "green"))+ theme_ridges(grid = FALSE)左右滑动查看更多默认的，cyclinal scales为了防止误解是不绘制图例的，但是可以通过选项guide="legend"添加图例。

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+ geom_density_ridges(scale=4)+ scale_fill_cyclical(values = c("blue", "green"), guide="legend")+ theme_ridges(grid = FALSE)左右滑动查看更多   

跟ggplot2一样，图例是可以修改的,其他参数比如大小、透明度、形状等都是可以通过cyclinal scales修改。

ggplot(diamonds, aes(x=price, y=cut, fill=cut))+ geom_density_ridges(scale=4)+ scale_fill_cyclical(values = c("blue", "green"), guide="legend")+ theme_ridges(grid = FALSE)左右滑动查看更多

还有很多用法有兴趣的可以查看官方文档继续学习。

SessionInfo

sessionInfo()## R version 3.4.3 (2017-11-30)## Platform: x86_64-w64-mingw32/x64 (64-bit)## Running under: Windows 10 x64 (build 16299)#### Matrix products: default#### locale:## [1] LC_COLLATE=Chinese (Simplified)_China.936## [2] LC_CTYPE=Chinese (Simplified)_China.936## [3] LC_MONETARY=Chinese (Simplified)_China.936## [4] LC_NUMERIC=C## [5] LC_TIME=Chinese (Simplified)_China.936#### attached base packages:## [1] stats graphics grDevices utils datasets methods base#### other attached packages:## [1] viridis_0.5.0 viridisLite_0.3.0 gridExtra_2.3## [4] forcats_0.2.0 stringr_1.2.0 dplyr_0.7.4## [7] purrr_0.2.4 readr_1.1.1 tidyr_0.8.0## [10] tibble_1.4.2 tidyverse_1.2.1 ggridges_0.4.1.9990## [13] ggplot2_2.2.1.9000#### loaded via a namespace (and not attached):## [1] reshape2_1.4.3 haven_1.1.1 lattice_0.20-35## [4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.1.16## [7] utf8_1.1.3 rlang_0.1.6 pillar_1.1.0## [10] foreign_0.8-69 glue_1.2.0 modelr_0.1.1## [13] readxl_1.0.0 bindrcpp_0.2 bindr_0.1## [16] plyr_1.8.4 munsell_0.4.3 gtable_0.2.0## [19] cellranger_1.1.0 rvest_0.3.2 psych_1.7.8## [22] evaluate_0.10.1 labeling_0.3 knitr_1.19## [25] parallel_3.4.3 broom_0.4.3 Rcpp_0.12.15## [28] scales_0.5.0.9000 backports_1.1.2 jsonlite_1.5## [31] mnormt_1.5-5 hms_0.4.1 digest_0.6.15## [34] stringi_1.1.6 grid_3.4.3 rprojroot_1.3-2## [37] cli_1.0.0 tools_3.4.3 magrittr_1.5## [40] lazyeval_0.2.1 crayon_1.3.4 pkgconfig_2.0.1## [43] xml2_1.2.0 lubridate_1.7.1 assertthat_0.2.0## [46] rmarkdown_1.8 httr_1.3.1 rstudioapi_0.7## [49] R6_2.2.2 nlme_3.1-131 compiler_3.4.3左右滑动查看更多

►一周热文

数据Seminar

这里是大数据、分析技术与学术研究的三叉路口

作者：taoyan出处：表哥有话讲推荐：简华（何年华）编辑：青酱

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