Exploratory demo - Attrition dataset
Anh Vu Le
14 12 2020
First view on data
## Rows: 1,470
## Columns: 35
## $ Age <int> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35...
## $ Attrition <fct> Yes, No, Yes, No, No, No, No, No, No, No, ...
## $ BusinessTravel <fct> Travel_Rarely, Travel_Frequently, Travel_R...
## $ DailyRate <int> 1102, 279, 1373, 1392, 591, 1005, 1324, 13...
## $ Department <fct> Sales, Research & Development, Research & ...
## $ DistanceFromHome <int> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 2...
## $ Education <int> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, ...
## $ EducationField <fct> Life Sciences, Life Sciences, Other, Life ...
## $ EmployeeCount <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
## $ EmployeeNumber <int> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, ...
## $ EnvironmentSatisfaction <int> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, ...
## $ Gender <fct> Female, Male, Male, Female, Male, Male, Fe...
## $ HourlyRate <int> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84...
## $ JobInvolvement <int> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, ...
## $ JobLevel <int> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, ...
## $ JobRole <fct> Sales Executive, Research Scientist, Labor...
## $ JobSatisfaction <int> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, ...
## $ MaritalStatus <fct> Single, Married, Single, Married, Married,...
## $ MonthlyIncome <int> 5993, 5130, 2090, 2909, 3468, 3068, 2670, ...
## $ MonthlyRate <int> 19479, 24907, 2396, 23159, 16632, 11864, 9...
## $ NumCompaniesWorked <int> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, ...
## $ Over18 <fct> Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, Y, ...
## $ OverTime <fct> Yes, No, Yes, Yes, No, No, Yes, No, No, No...
## $ PercentSalaryHike <int> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13...
## $ PerformanceRating <int> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, ...
## $ RelationshipSatisfaction <int> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, ...
## $ StandardHours <int> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80...
## $ StockOptionLevel <int> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, ...
## $ TotalWorkingYears <int> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5...
## $ TrainingTimesLastYear <int> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, ...
## $ WorkLifeBalance <int> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, ...
## $ YearsAtCompany <int> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2,...
## $ YearsInCurrentRole <int> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, ...
## $ YearsSinceLastPromotion <int> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, ...
## $ YearsWithCurrManager <int> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, ...Correlation plots
## Age DailyRate DistanceFromHome Education
## Age 1.00000000 0.010660943 -0.001686120 0.20803373
## DailyRate 0.01066094 1.000000000 -0.004985337 -0.01680643
## DistanceFromHome -0.00168612 -0.004985337 1.000000000 0.02104183
## Education 0.20803373 -0.016806433 0.021041826 1.00000000
## EnvironmentSatisfaction 0.01014643 0.018354854 -0.016075327 -0.02712831
## HourlyRate 0.02428654 0.023381422 0.031130586 0.01677483
## EnvironmentSatisfaction HourlyRate JobInvolvement
## Age 0.01014643 0.02428654 0.029819959
## DailyRate 0.01835485 0.02338142 0.046134874
## DistanceFromHome -0.01607533 0.03113059 0.008783280
## Education -0.02712831 0.01677483 0.042437634
## EnvironmentSatisfaction 1.00000000 -0.04985696 -0.008277598
## HourlyRate -0.04985696 1.00000000 0.042860641
## JobLevel JobSatisfaction MonthlyIncome MonthlyRate
## Age 0.509604228 -0.004891877 0.497854567 0.02805117
## DailyRate 0.002966335 0.030571008 0.007707059 -0.03218160
## DistanceFromHome 0.005302731 -0.003668839 -0.017014445 0.02747286
## Education 0.101588886 -0.011296117 0.094960677 -0.02608420
## EnvironmentSatisfaction 0.001211699 -0.006784353 -0.006259088 0.03759962
## HourlyRate -0.027853486 -0.071334624 -0.015794304 -0.01529675
## NumCompaniesWorked PercentSalaryHike PerformanceRating
## Age 0.29963476 0.003633585 0.0019038955
## DailyRate 0.03815343 0.022703677 0.0004732963
## DistanceFromHome -0.02925080 0.040235377 0.0271096185
## Education 0.12631656 -0.011110941 -0.0245387912
## EnvironmentSatisfaction 0.01259432 -0.031701195 -0.0295479523
## HourlyRate 0.02215688 -0.009061986 -0.0021716974
## RelationshipSatisfaction StockOptionLevel
## Age 0.053534720 0.037509712
## DailyRate 0.007846031 0.042142796
## DistanceFromHome 0.006557475 0.044871999
## Education -0.009118377 0.018422220
## EnvironmentSatisfaction 0.007665384 0.003432158
## HourlyRate 0.001330453 0.050263399
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## Age 0.680380536 -0.019620819 -0.021490028
## DailyRate 0.014514739 0.002452543 -0.037848051
## DistanceFromHome 0.004628426 -0.036942234 -0.026556004
## Education 0.148279697 -0.025100241 0.009819189
## EnvironmentSatisfaction -0.002693070 -0.019359308 0.027627295
## HourlyRate -0.002333682 -0.008547685 -0.004607234
## YearsAtCompany YearsInCurrentRole
## Age 0.311308770 0.212901056
## DailyRate -0.034054768 0.009932015
## DistanceFromHome 0.009507720 0.018844999
## Education 0.069113696 0.060235554
## EnvironmentSatisfaction 0.001457549 0.018007460
## HourlyRate -0.019581616 -0.024106220
## YearsSinceLastPromotion YearsWithCurrManager
## Age 0.21651337 0.202088602
## DailyRate -0.03322898 -0.026363178
## DistanceFromHome 0.01002884 0.014406048
## Education 0.05425433 0.069065378
## EnvironmentSatisfaction 0.01619361 -0.004998723
## HourlyRate -0.02671559 -0.020123200
Libraries
- ggcorrplot, corrplot
Advanced exploratory
Libraries
- GGally
Categorical variables
Libraries
- gridExtra
Line graphs
Parallel plots
Parallel coordinates - continuous variable
Parallel coordinates - categorical variables
Independence test of categorical variables
##
## No Yes
## Non-Travel 138 12
## Travel_Frequently 208 69
## Travel_Rarely 887 156Here we see, that Attrition is not independent of both OverTime and JobInvolvement. In particular (comparing expected and observed tables), attrition people tend to spend less time over time and are less involved. Surprisingly however, they travel more, than we would expect.
Dimensionality reduction
PCA
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
TSNE
UMAP
Libraries
- Rtsne, umap, dbscan