图片alt
library(tidyverse) library(treemap) # install.packages("treemap") cjb <- read.csv("/home/wy/Downloads/cjb.csv", header = TRUE, stringsAsFactors = FALSE, fileEncoding = "UTF-8")
cjb %>% group_by(wlfk,bj,xb) %>% summarise(count=n()) %>% as.data.frame() %>% treemap( index = c("wlfk","bj","xb"), vSize = "count", vColor="count", type = "value" )
ggplot(cjb,aes( x=sx, y=sw, shape = wlfk, color =wlfk ))+ geom_point(size = 2)+ labs(x= "数学", y="生物", color="文理分科", shape="文理分科")
GGally::ggpairs(cjb,columns =4:12 )
cor_coef <- cor(cjb[,4:12]) cor_coef <- round(cor_coef,2) cor_coef %>% as.data.frame()
cor_coef %>% as.data.frame() %>% rownames_to_column(var="km1")
cor_coef %>% as.data.frame() %>% rownames_to_column(var="km1") %>% gather(key = km2,value = cor_num,-km1)
cor_coef %>% as.data.frame() %>% rownames_to_column(var="km1") %>% gather(key = km2,value = cor_num,-km1) %>% mutate(cor_level = cut(cor_num, breaks = c(0,0.3,0.5,0.8,1), right = FALSE))
cor_coef %>% as.data.frame() %>% rownames_to_column(var="km1") %>% gather(key = km2,value = cor_num,-km1) %>% mutate(cor_level = cut(cor_num, breaks = c(0,0.3,0.5,0.8,1), right = FALSE)) %>% ggplot(aes(x=km1,y=km2,fill=cor_level))+ geom_tile(color="white",size=1.5)+ geom_text(aes(label=format(cor_num,digits = 2)))+ scale_fill_brewer(palette = "YlGn",name="相关系数区间")
看看不同班级数学成绩分布
cjb$bj <- factor(cjb$bj) ggplot(cjb,aes(x=bj,y=sx,fill=bj))+ geom_boxplot(outlier.colour = "red", outlier.shape = 3, outlier.size = 1)+ labs(x="班级",y="数学成绩")+ theme(legend.position = "none")
library(ggridges) library(viridis) ggplot(cjb,aes(x=sx,y=bj,fill = ..x..))+ geom_density_ridges_gradient(scale=2, rel_min_height=0.01, gradient_lwd = 1)+ scale_fill_viridis(name="数学成绩", option = "C")+ labs(x="数学",y="班级")