数据呈现丨R语言可视化学习笔记之ggridges包
The following article is from 表哥有话讲 Author taoyan
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
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