数据分析--用R语言预测离职（上）
数据分析–用R语言预测离职（上）
本文参考的文章：
1.R语言-离职率分析
2.R语言-逻辑回归+主成分分析-员工离职预测训练赛
3.Kaggle十大经典案例—员工离职预测
4.Kaggle十大案例精讲（免费）
5.数据分析实例：员工流失建模与预测
数据来源：
SAMPLE DATA: HR Employee Attrition and Performance
数据可以直接下载，字段都是英文的，部分字段描述如下：
| 变量类型 | 变量名 | 描述 | 取值范围 |
|---|---|---|---|
| 结果变量 | Attrition | 员工是否流失 | Yes, No |
| 自变量 | Age | 年龄 | 数值 |
| BusinessTravel | 出差 | 1.Non-Travel， 2.Travel_Rarely 3.Travel_Frequently | |
| Department | 部门 | 1.Sales 2.Research & Development 3.Human Resources | |
| DistanceFromHome | 公司到家的距离 | 数值 | |
| Education | 学历 | 1 ‘Below College’ 2 ‘College’ 3 ‘Bachelor’ 4 ‘Master’ 5 ‘Doctor’ | |
| EducationField | 学历领域 | ||
| EnvironmentSatisfaction | 环境满意度 | 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’ | |
| Gender | 性别 | 1.Male 2.Female | |
| JobInvolvement | 工作投入 | 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’ | |
| JobLevel | 职位等级 | ||
| JobRole | 职位 | ||
| JobSatisfaction | 工作满意度 | 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’ | |
| MaritalStatus | 是否结婚 | 1.Single 2.Married 3.Divorced | |
| MonthlyIncome | 月收入 | 数值 | |
| NumCompaniesWorked | 任职过的企业数量 | 数值 | |
| OverTime | 是否加班 | Yes, No | |
| PercentSalaryHike | 涨薪百分比 | 数值 | |
| PerformanceRating | 绩效评分 | 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’ | |
| RelationshipSatisfaction | 关系满意度 | 1 ‘Low’ 2 ‘Medium’ 3 ‘High’ 4 ‘Very High’ | |
| StockOptionLevel | 员工优先认股权 | 数值 | |
| TotalWorkingYears | 工龄 | 数值 | |
| TrainingTimesLastYear | 上一年培训次数 | 数值 | |
| WorkLifeBalance | 工作与生活平衡情况 | 1 ‘Bad’ 2 ‘Good’ 3 ‘Better’ 4 ‘Best’ | |
| YearsAtCompany | 在公司工作时间 | 数值 | |
| YearsInCurrentRole | 当前职位的工作时间 | 数值 | |
| YearsSinceLastPromotion | 距离上次升职的时间 | 数值 | |
| YearsWithCurrManager | 与当前经理工作的时间 | 数值 |
数据读取
读取数据之后，summary一下，观察变量
(注意一点：在读取数据的时候，stringsAsFactors = T，因为数据里面有字符串的变量)
> attr.df <- read.csv("HR-Employee-Attrition.csv",header = T,stringsAsFactors = T)
> summary(attr.df)
Age Attrition BusinessTravel DailyRate Department DistanceFromHome
Min. :18.00 No :1233 Non-Travel : 150 Min. : 102.0 Human Resources : 63 Min. : 1.000
1st Qu.:30.00 Yes: 237 Travel_Frequently: 277 1st Qu.: 465.0 Research & Development:961 1st Qu.: 2.000
Median :36.00 Travel_Rarely :1043 Median : 802.0 Sales :446 Median : 7.000
Mean :36.92 Mean : 802.5 Mean : 9.193
3rd Qu.:43.00 3rd Qu.:1157.0 3rd Qu.:14.000
Max. :60.00 Max. :1499.0 Max. :29.000
Education EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction Gender
Min. :1.000 Human Resources : 27 Min. :1 Min. : 1.0 Min. :1.000 Female:588
1st Qu.:2.000 Life Sciences :606 1st Qu.:1 1st Qu.: 491.2 1st Qu.:2.000 Male :882
Median :3.000 Marketing :159 Median :1 Median :1020.5 Median :3.000
Mean :2.913 Medical :464 Mean :1 Mean :1024.9 Mean :2.722
3rd Qu.:4.000 Other : 82 3rd Qu.:1 3rd Qu.:1555.8 3rd Qu.:4.000
Max. :5.000 Technical Degree:132 Max. :1 Max. :2068.0 Max. :4.000
HourlyRate JobInvolvement JobLevel JobRole JobSatisfaction MaritalStatus
Min. : 30.00 Min. :1.00 Min. :1.000 Sales Executive :326 Min. :1.000 Divorced:327
1st Qu.: 48.00 1st Qu.:2.00 1st Qu.:1.000 Research Scientist :292 1st Qu.:2.000 Married :673
Median : 66.00 Median :3.00 Median :2.000 Laboratory Technician :259 Median :3.000 Single :470
Mean : 65.89 Mean :2.73 Mean :2.064 Manufacturing Director :145 Mean :2.729
3rd Qu.: 83.75 3rd Qu.:3.00 3rd Qu.:3.000 Healthcare Representative:131 3rd Qu.:4.000
Max. :100.00 Max. :4.00 Max. :5.000 Manager :102 Max. :4.000
(Other) :215
MonthlyIncome MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating
Min. : 1009 Min. : 2094 Min. :0.000 Y:1470 No :1054 Min. :11.00 Min. :3.000
1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000 Yes: 416 1st Qu.:12.00 1st Qu.:3.000
Median : 4919 Median :14236 Median :2.000 Median :14.00 Median :3.000
Mean : 6503 Mean :14313 Mean :2.693 Mean :15.21 Mean :3.154
3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000 3rd Qu.:18.00 3rd Qu.:3.000
Max. :19999 Max. :26999 Max. :9.000 Max. :25.00 Max. :4.000
RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
Min. :1.000 Min. :80 Min. :0.0000 Min. : 0.00 Min. :0.000 Min. :1.000
1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000
Median :3.000 Median :80 Median :1.0000 Median :10.00 Median :3.000 Median :3.000
Mean :2.712 Mean :80 Mean :0.7939 Mean :11.28 Mean :2.799 Mean :2.761
3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000
Max. :4.000 Max. :80 Max. :3.0000 Max. :40.00 Max. :6.000 Max. :4.000
YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.000
Median : 5.000 Median : 3.000 Median : 1.000 Median : 3.000
Mean : 7.008 Mean : 4.229 Mean : 2.188 Mean : 4.123
3rd Qu.: 9.000 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.000
Max. :40.000 Max. :18.000 Max. :15.000 Max. :17.000 我们的数据总共有1470行，35列
上面Attrition是我们研究的变量：代表是否离职的意思
从上面我们可以看出：
1.离职的人数占总人数的 16%左右；
2.月收入平均为：6503，中值为：4919，其中中值更能代表薪资水平
3.加班的人数占总人数的28%（Overtime字段）
数据分析及可视化
下面我们来看下离职的人和各个变量之间的关系：
> library(ggplot2)
> library(gridExtra)
> g1 <- ggplot(attr.df, aes(x=Age,fill=Attrition))+
+ geom_density(alpha = 0.7)
> g2 <- ggplot(attr.df, aes(x=DistanceFromHome, fill=Attrition))+
+ geom_density(alpha = 0.7)
> g3 <- ggplot(attr.df, aes(x=MonthlyIncome, fill=Attrition))+
+ geom_density(alpha = 0.7)
> g4 <- ggplot(attr.df, aes(x=NumCompaniesWorked, fill= Attrition))+
+ geom_density(alpha = 0.7)
> g5 <- ggplot(attr.df, aes(x=TotalWorkingYears, fill= Attrition))+
+ geom_density(alpha = 0.7)
> g6 <- ggplot(attr.df, aes(x=TrainingTimesLastYear, fill= Attrition))+
+ geom_density(alpha = 0.7)
> g7 <- ggplot(attr.df, aes(x=YearsAtCompany, fill= Attrition))+
+ geom_density(alpha = 0.7)
> g8 <- ggplot(attr.df, aes(x=YearsInCurrentRole, fill= Attrition))+
+ geom_density(alpha = 0.7)
> g9 <- ggplot(attr.df, aes(x=YearsWithCurrManager, fill= Attrition))+
+ geom_density(alpha = 0.7)
> grid.arrange(g1,g2,g3,g4,g5,g6,g7,g8,g9, ncol = 3, nrow = 3)
这里选择的9个变量，来做核密度曲线：
其中我们可以看出
1.从年龄上面看30岁左右的人员是离职的高峰，
2.从离家距离来看，10英里意外的人员离职的概率会比较大
3.低收入的人员离职概率较大
4.在任职公司超过5个的离职概率较大
5.工龄在5年以下的离职率要高
其可能的原因在于年轻的员工更倾向于多尝试，且对未来目标相对迷茫，高流失率也意味着此类员工难以在短期形成对企业价值观的长期认同。