barplot(apply(data,1,mean))#按行做均值条形图apply对矩阵案列或行操作，barplot画条形图
多元数据直观表示
各省消费项目均值条形图
省份过多，各省的名称均不能全部显示
将横轴左边旋转90度，各省的名称均可显示
barplot(apply(data,1,mean),las=3)#按行做均值条形图las = 0：轴标签平行于轴（默认值）。
#las = 1：轴标签水平显示。
#las = 2：轴标签垂直显示，与轴方向成90度角。
#las = 3：轴标签垂直显示，与轴方向成90度，但是相对 las=2 反转方向。利用ggplot2包作图较为美观
data %>%
mutate(Average_Consumption = rowMeans(select(., -1), na.rm = TRUE)) %>%
ggplot(aes(x = reorder(row.names(data), -Average_Consumption), y = Average_Consumption)) +
geom_bar(stat = "identity", position = position_dodge(), colour = "black", fill = "steelblue") +
labs(title = "各省消费项目均值条形图", x = "", y = "均值") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) 
各消费项目均值条形图
按消费项目做均值图条形图
barplot(apply(data,2,mean))#按列做均值图条形图
对不同项目的条形添加不同颜色
barplot(apply(data,2,mean),col=c("blue", "red", "green", "yellow")) #按列做彩色均值图条形图，col设定颜色
去掉衣着列后的数据按列做均值条形图
barplot(apply(data[,-2],2,mean))
按消费项目做中位数条形图
barplot(apply(data,2,median))
利用ggplot作均值条形图
data %>% summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Consumption_Type", values_to = "Average") %>%
mutate(
Consumption_Type=factor(Consumption_Type,level=c('食品','衣着','设备','医疗','交通','教育','居住','杂项')),
) %>%
ggplot(aes(x = Consumption_Type, y = Average, fill = Consumption_Type)) +
geom_bar(stat = "identity", position = position_dodge(), colour = "black") +
theme_minimal() +
labs(title = "各消费项目均值条形图", x = "类别", y = "均值",fill = "消费种类")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `across(everything(), mean, na.rm = TRUE)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.
# Previously
across(a:b, mean, na.rm = TRUE)
# Now
across(a:b, \(x) mean(x, na.rm = TRUE))
使各条形的颜色相同
data %>% summarise(across(everything(), mean, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Consumption_Type", values_to = "Average") %>%
mutate(
Consumption_Type=factor(Consumption_Type,level=c('食品','衣着','设备','医疗','交通','教育','居住','杂项')),
) %>%
ggplot(aes(x = Consumption_Type, y = Average)) +
geom_bar(stat = "identity", position = position_dodge(), colour = "black", fill = "steelblue") +
theme_minimal() +
labs(title = "各消费项目均值条形图", x = "类别", y = "均值")
各消费项目箱线图
boxplot函数直接作箱线图，默认每个变量（列）作一个箱线，并将全部变量的箱线在同一个图中展示。
boxplot(data)#按列做箱线图
boxplot(data,horizontal=T,las=1)#箱线图中图形按水平放置，horizontal
利用ggplot函数作箱线图，需要对数据转化为长结果数据
data %>% pivot_longer(cols = 1:8, names_to = "Consumption_Type", values_to = "Value") %>%
mutate(
Consumption_Type=factor(Consumption_Type,level=c('食品','衣着','设备','医疗','交通','教育','居住','杂项')),
) %>%
ggplot(aes(x = Consumption_Type, y = Value)) +
geom_boxplot() +
labs(title = "各消费项目箱线图", x = "", y = "消费水平") +
theme_minimal() # + coord_flip() 
各消费项目星相图
stars(d3.1) 
stars(d3.1,key.loc=c(-3,7)) #具有图例的360度星相图，key设定图例，loc图例位置
stars(d3.1,full=F,key.loc=c(17,7)) #full=F半圆
stars(d3.1,draw.segments=T,key.loc=c(17,7))#具有图例的360度彩色圆形星相图，draw.segments = T：T 代表TRUE。启用此参数表示将每个变量的数据画成不同颜色的分段，这些分段构成一个完整的圆形。
stars(d3.1,full=F,draw.segments=T,key.loc=c(17,7))#具有图例的180度彩色圆形星相图
各消费项目脸谱图
library(aplpack) #加载aplpack包
aplpack::faces(d3.1)
effect of variables:
modified item Var
"height of face " "食品"
"width of face " "衣着"
"structure of face" "设备"
"height of mouth " "医疗"
"width of mouth " "交通"
"smiling " "教育"
"height of eyes " "居住"
"width of eyes " "杂项"
"height of hair " "食品"
"width of hair " "衣着"
"style of hair " "设备"
"height of nose " "医疗"
"width of nose " "交通"
"width of ear " "教育"
"height of ear " "居住"aplpack::faces(d3.1[,-1],ncol.plot=5)#去掉第一个变量按每行5个做脸谱图
effect of variables:
modified item Var
"height of face " "衣着"
"width of face " "设备"
"structure of face" "医疗"
"height of mouth " "交通"
"width of mouth " "教育"
"smiling " "居住"
"height of eyes " "杂项"
"width of eyes " "衣着"
"height of hair " "设备"
"width of hair " "医疗"
"style of hair " "交通"
"height of nose " "教育"
"width of nose " "居住"
"width of ear " "杂项"
"height of ear " "衣着"aplpack::faces(d3.1[c(1,9,19,28,29,30),])#选择第1,9,19,28,29,30个观测的多元数据做脸谱图
effect of variables:
modified item Var
"height of face " "食品"
"width of face " "衣着"
"structure of face" "设备"
"height of mouth " "医疗"
"width of mouth " "交通"
"smiling " "教育"
"height of eyes " "居住"
"width of eyes " "杂项"
"height of hair " "食品"
"width of hair " "衣着"
"style of hair " "设备"
"height of nose " "医疗"
"width of nose " "交通"
"width of ear " "教育"
"height of ear " "居住"library("TeachingDemos") #install.packages("TeachingDemos")
faces2(d3.1,ncols=7) #TeachingDemos::faces(d3.1),是另一种绘制脸谱图的方式，来自 TeachingDemos 包，每行展示7个脸谱图。
各消费项目雷达图
library("fmsb")#打开包
rddat=d3.1[c(1,9,19,28,29,30),]#赋值数据，第1、9...等省份
maxmin=rbind(apply(rddat,2,max),apply(rddat,2,min))#将最大最小值赋值给新变量
rddat=rbind(maxmin,rddat)#将最值矩阵添加到原矩阵顶部
radarchart(rddat, axistype=2, pcol=topo.colors(6), plty=1, pdensity=seq(5,40,by=5), pangle=seq(0,150,by=30), pfcol=topo.colors(6))#绘图axistype = 2：指定坐标轴的类型，2 表示坐标轴在每个顶点上，适合展示极值。
#pcol = topo.colors(6)：指定绘制线条的颜色，使用 topo.colors(6) #生成6种颜色，适合数据中包含6个观测值。
#plty = 1：设置线条类型，1 表示实线。
#pdensity = seq(5, 40, by=)：设置线条的填充密度，这里设置为从5到40的序列，步长为5。
#pangle = seq(0, 150, by=30)：设置每个变量的角度，默认情况下可能会覆盖到每个顶点。
#pfcol = topo.colors(6)：设置填充颜色，同样使用 topo.colors(6)。ggplot2的扩展包ggiraphExtra能作雷达图
data[c(1,9,19,28,29,30),] %>%
mutate(省份=rownames(.)) %>%
ggRadar(aes(group = 省份)) 
各消费项目调和曲线图
#install.packages("andrews")
library(andrews) See the package vignette with `vignette("andrews")`andrews(d3.1,clr=5,ymax=6)
#选择第1,9,19,28,29,30个观测的多元数据做调和曲线图
andrews(d3.1[c(1,9,19,28,29,30),],clr=5,ymax=8) #clr=5颜色ymax=6坐标最值
source("msaR.R")#加自定义函数
msa.andrews(d3.1)#绘制调和曲线图
msa.andrews(d3.1[c(1,9,19,28,29,30),])#选择特定数据绘制