barplot(apply(data,1,mean))#按行做均值条形图
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
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=1:8) #按列做彩色均值图条形图
去掉食品列后的数据按列做均值条形图
barplot(apply(data[,2:8],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
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(data) #具有图例的360度星相图
增加 key.loc=c()用于指定星相图图例的位置
stars(data,key.loc=c(17,7)) #具有图例的360度星相图
draw.segments=T在stars( ) 函数中尤其有用，表示允许绘制线段,允许为不同线段指定颜色
stars(data,draw.segments=T,key.loc=c(17,7))#具有图例的360度彩色圆形星相图
full=F指定星相图为半图（即180度）；draw.segments=T,key.loc=c(17,7))表示星相图为彩色并指定图例位置
stars(data,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
aplpack::faces(data) #根据数据画出脸谱图
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"data[,2:8]表示去除第一个变量，ncol.plot=7指定了每行的个数（即指定了所画之图的列数）
aplpack::faces(data[,2:8],ncol.plot=7)#去掉第一个变量按每行7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"data[c(1,9,19,28,29,30),]表示专门指定了某几个变量
aplpack::faces(data[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"在TeachingDemos安装包下，绘制出不同样式的黑白脸谱图
TeachingDemos::faces2(data,ncols=7) #TeachingDemos::faces(d3.1)
各消费项目雷达图
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
clr=5指定使用第5种颜色来绘制曲线, ymax=6设定了y轴最大值
andrews::andrews(data,clr=5,ymax=6) #使用data数据作调和曲线图
data[c(1,9,19,28,29,30)表示指定用第1,9,19,28,29,30个观测的多元数据做调和曲线图
andrews::andrews(data[c(1,9,19,28,29,30),],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
改进调和曲线图，且用不同的颜色和线段表示不同数据并增加了图例
msa.andrews(data) #绘制调和曲线图
选择第1,9,19,28,29,30个观测的多元数据作改进后的调和曲线图，图中用不同的颜色和线段表达了不同变量，使数据更清晰易读
msa.andrews(data[c(1,9,19,28,29,30),])