21. 雷达图绘制
清除当前环境中的变量
rm(list=ls())
设置工作目录
setwd("C:/Users/Dell/Desktop/R_Plots/21radar/")
使用fmsb包绘制雷达图
# 安装并加载所需的R包
#install.packages("fmsb")
library(fmsb)
# 构建数据集
# Data must be given as the data frame, where the first cases show maximum.
# 用于生成雷达图的最大最小值，第一行为最大值，第二行为最小值
maxmin <- data.frame(
total=c(5, 1),
phys=c(15, 3),
psycho=c(3, 0),
social=c(5, 1),
env=c(5, 1))
head(maxmin)
## total phys psycho social env
## 1 5 15 3 5 5
## 2 1 3 0 1 1
set.seed(123)
dat <- data.frame(
total=runif(3, 1, 5),
phys=rnorm(3, 10, 2),
psycho=c(0.5, NA, 3),
social=runif(3, 1, 5),
env=c(5, 2.5, 4))
head(dat)
## total phys psycho social env
## 1 2.150310 12.380413 0.5 2.826459 5.0
## 2 4.153221 6.620889 NA 4.827333 2.5
## 3 2.635908 12.478992 3.0 2.813337 4.0
# combine data
dat <- rbind(maxmin,dat)
head(dat)
## total phys psycho social env
## 1 5.000000 15.000000 3.0 5.000000 5.0
## 2 1.000000 3.000000 0.0 1.000000 1.0
## 3 2.150310 12.380413 0.5 2.826459 5.0
## 4 4.153221 6.620889 NA 4.827333 2.5
## 5 2.635908 12.478992 3.0 2.813337 4.0
# 使用radarchart函数绘制雷达图
radarchart(dat,
axistype=1, #设定axes的类型,1 means center axis label only
seg=5, #设定网格的数目
plty=1, #设定point连线的线型
vlabels=c("Total\nQOL", "Physical\naspects",
"Phychological\naspects", "Social\naspects",
"Environmental\naspects"),
title="(axis=1, 5 segments, with specified vlabels)",
vlcex=1 #设置标签的字体粗细大小
)
image.png
radarchart(dat,
axistype=2,
pcol=topo.colors(3),
plty=1, pdensity=c(5, 10, 30),
pangle=c(10, 45, 120),
pfcol=topo.colors(3),
title="(topo.colors, fill, axis=2)")
image.png
radarchart(dat,
axistype=3, pty=16, plty=2,
axislabcol="grey", na.itp=FALSE,
title="(no points, axis=3, na.itp=FALSE)")
image.png
radarchart(dat,
axistype=1, plwd=1:5, pcol=1, centerzero=TRUE,
seg=4, caxislabels=c("worst", "", "", "", "best"),
title="(use lty and lwd but b/w, axis=1,\n centerzero=TRUE, with centerlabels)")
image.png
使用ggradar包绘制雷达图
# 安装并加载所需的R包
#devtools::install_github("ricardo-bion/ggradar", dependencies=TRUE)
library(ggradar)
# 构建示例数据
library(dplyr)
library(scales)
library(tibble)
mtcars_radar <- mtcars %>%
as_tibble(rownames = "group") %>%
mutate_at(vars(-group), rescale) %>%
tail(4) %>%
select(1:10)
# 查看示例数据
mtcars_radar
## # A tibble: 4 x 10
## group mpg cyl disp hp drat wt qsec vs am
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Ford Pantera L 0.230 1 0.698 0.749 0.673 0.424 0 0 1
## 2 Ferrari Dino 0.396 0.5 0.184 0.435 0.396 0.321 0.119 0 1
## 3 Maserati Bora 0.196 1 0.573 1 0.359 0.526 0.0119 0 1
## 4 Volvo 142E 0.468 0 0.124 0.201 0.622 0.324 0.488 1 1
# 使用ggradar函数绘制雷达图
ggradar(mtcars_radar)
image.png
ggradar(mtcars_radar,base.size = 12,
values.radar = c("0%","25%","50%","75%","100%"),
legend.title = "group",legend.text.size = 12,
legend.position = "right")
image.png
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## 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] tibble_2.1.3 scales_1.0.0 dplyr_0.8.3 ggradar_0.2 fmsb_0.7.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 pillar_1.4.2 compiler_3.6.0 tools_3.6.0
## [5] digest_0.6.20 evaluate_0.14 gtable_0.3.0 pkgconfig_2.0.2
## [9] rlang_0.4.7 cli_1.1.0 yaml_2.2.0 xfun_0.8
## [13] stringr_1.4.0 knitr_1.23 vctrs_0.3.2 grid_3.6.0
## [17] tidyselect_0.2.5 glue_1.3.1 R6_2.4.0 fansi_0.4.0
## [21] rmarkdown_1.13 ggplot2_3.2.0 purrr_0.3.2 magrittr_1.5
## [25] htmltools_0.3.6 assertthat_0.2.1 colorspace_1.4-1 labeling_0.3
## [29] utf8_1.1.4 stringi_1.4.3 lazyeval_0.2.2 munsell_0.5.0
## [33] crayon_1.3.4
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