第2章  数据分析

#machine learing for heckers

#chapter 2

library(ggplot2)heights.weights <- read.csv("ML_for_Hackers/02-Exploration/data/01_heights_weights_genders.csv",                             header = TRUE, sep = ",")

　　

#不同区间宽度的直方图

ggplot(heights.weights, aes(x = Height)) + geom_histogram(binwidth = 1)ggplot(heights.weights, aes(x = Height)) + geom_histogram(binwidth = 5)ggplot(heights.weights, aes(x = Height)) + geom_histogram(binwidth = 0.001)

　　

      

#密度曲线图

ggplot(heights.weights, aes(x = Height)) + geom_density()

　　

 

#峰值处平坦，考虑图像有结构缺失，根据性别分别绘制密度曲线图

ggplot(heights.weights, aes(x = Height, fill = Gender)) + geom_density()ggplot(heights.weights, aes(x = Weight, fill = Gender)) + geom_density()ggplot(heights.weights, aes(x = Weight, fill = Gender)) + geom_density() + facet_grid(Gender ~ .)

　　

           

 

#正态分布：钟形的窄尾分布，单峰对称

#柯西分布：钟形的重尾分布，单峰对称

set.seed(1)normal.values <- rnorm(250, 0, 1)cauchy.values <- rcauchy(250, 0, 1)ggplot(data.frame(X = normal.values), aes(x = X)) + geom_density()ggplot(data.frame(X = cauchy.values), aes(x = X)) + geom_density()

　　

        

 

#gamma分布

#gamma分布只有正值

gamma.values <- rgamma(100000, 1, 0.001)ggplot(data.frame(X = gamma.values), aes(x = X)) + geom_density()

　　

 

#从身高体重预测性别（分类器）

#书中代码画图命令有"stat_abline"完成添加直线操作，而package: ggplot2(version 2.1.0)中"stat"族函数已经没有"abline"，只能用"geom"族完成

heights.weights <- transform(heights.weights, Male = ifelse(Gender == 'Male', 1, 0))logit.model <- glm(Male ~ Weight + Height, data = heights.weights,                    family = binomial(link = 'logit'))ggplot(heights.weights, aes(x = Height, y = Weight, color = Gender)) + geom_point() +   geom_abline(intercept = -coef(logit.model)[1]/coef(logit.model)[2],               slope = -coef(logit.model)[3]/coef(logit.model)[2],               color = 'black')