加載數據 繪制莖葉圖 繪制直方圖 繪制概率密度曲線 繪制小提琴圖 繪制箱線圖 繪制小提琴圖箱線圖 集中趨勢統計 分散程度 apply的使用
加載數據
模擬數據下載
library(tidyverse)
cjb <- read.csv("/home/wy/Downloads/cjb.csv",header = TRUE,stringsAsFactors = FALSE,fileEncoding = "UTF-8")繪制莖葉圖
cjb %>% filter(bj == '1101') %>%select(sx) %>%as_vector() %>%stem()
5 | 57996 | 00146 | 557897 | 0000111223344447 | 7888998 | 1112223344448 | 5899 | 224繪制直方圖
sx_hist_result = hist(cjb$sx,plot = FALSE)
typeof(sx_hist_result)
names(sx_hist_result)
# 使用ggplot繪制與hist相同的直方圖
ggplot(data = cjb,mapping = aes(sx))+geom_histogram(breaks = sx_hist_result$breaks,color = "darkgray",fill = "white")+stat_bin(breaks = sx_hist_result$breaks,geom = "text",aes(label = ..count..))+coord_flip()
繪制概率密度曲線
ggplot(data = cjb,mapping = aes(sx))+geom_histogram(breaks = sx_hist_result$breaks,color = "darkgray",fill = "white",aes(y = ..density.. ))+geom_density(color = 'blue')
繪制小提琴圖
ggplot(cjb,aes(x=factor(0),y=sx))+geom_violin(fill="orange",alpha=0.2)+coord_flip()
繪制箱線圖
cjb %>%ggplot(aes(x=factor(0),y=sx))+geom_boxplot(width=0.25,fill = "#E69F00",outlier.colour = "red",outlier.shape = 1,outlier.size = .2)+geom_rug(position = "jitter",size=0.1,sides = "l")+coord_flip()
boxplot.stats(cjb$sx)
# $stats 下邊界 一分位距 中位數 三分位距 上邊界
# [1] 60 81 89 95 100
# $n 數據記錄數
# [1] 775
# $conf
# [1] 88.20543 89.79457
# $out 異常點
# [1] 55 59 57 59 58 51 56 55 59 26 58 46 0 59 59繪制小提琴圖+箱線圖
cjb %>%ggplot(aes(x=factor(0),y=sx)) +geom_violin(fill="#56B4E9",width=0.75) +geom_boxplot(width=0.25,fill = "#E69F00",outlier.colour = "red",outlier.shape = 1,outlier.size = 2)+geom_rug(position = "jitter",size=0.1,sides = "l")+coord_flip()
集中趨勢統計
cjb %>%group_by(wlfk) %>% # 按文理分科分組統計summarise(count = n(), # 各組人數sx_median = median(sx), # 中位數sx_mean = mean(sx) # 均值)
# A tibble: 2 x 4
# wlfk count sx_median sx_mean
# <chr> <int> <dbl> <dbl>
# 1 文科 394 84 82.7
# 2 理科 381 93 89.5分散程度
cjb %>%group_by(wlfk) %>% # 按文理分科分組統計summarise(sx_max = max(sx), # 最大值sx_min = min(sx), # 最小值sx_range = max(sx) - min(sx) # 極差)# A tibble: 2 x 4
# wlfk sx_max sx_min sx_range
# <chr> <int> <int> <int>
# 1 文科 100 26 74
# 2 理科 100 0 100
cjb %>%group_by(wlfk) %>% # 按文理分科分組統計summarise(sx_O3 = quantile(sx,3/4), # 第三分位數sx_min = quantile(sx,1/4), # 第一分位數sx_iqr = IQR(sx) # 四分位距)
# A tibble: 2 x 4
# wlfk sx_O3 sx_min sx_iqr
# <chr> <dbl> <dbl> <dbl>
# 1 文科 92 75 17
# 2 理科 96 86 10apply的使用
round(apply(cjb[,4:12], 2, function(x){c(mean = mean(x),median = median(x),range = diff(range(x)),IQR = IQR(x))
}))
# yw sx wy zz ls dl wl hx sw
# mean 87 86 87 92 89 93 81 92 86
# median 88 89 88 93 90 94 83 94 88
# range 96 100 99 100 100 100 100 100 100
# IQR 6 14 8 5 10 6 17 10 12
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