In drake-smu/unintCS02:

Enviornment Setup

library(readxl)
library(pander)
library(ggthemes)
library(ggplot2)
library(grid)
library(gridExtra)

Reading in Data and Cleaning

#Reading in hr data file
raw<-readRDS("data/hr-data.rds")
pander(head(raw))

#Turning Variables into Factors
names <- c('WorkLifeBalance' ,'StockOptionLevel','PerformanceRating','JobSatisfaction',
           'RelationshipSatisfaction','JobLevel','JobInvolvement','EnvironmentSatisfaction','Education')
raw[,names] <- lapply(raw[,names] , factor)

#Looking at dataset structure
pander(str(raw))

Initial Variable Plot Objects

ggplot(raw,aes(Attrition,fill=Attrition))+geom_bar()+theme_tufte()+scale_fill_few("Dark")+ggtitle("Attrition Frequencies")+ylab("Frequency of Employees")+ theme(plot.title = element_text(hjust = 0.5))

pander(prop.table(table(raw$Attrition)))

#We see that there is a 16% attrition rate.

Variable Density Plots

# Age
agePlot <- ggplot(raw,aes(Age,fill=Attrition))+
    geom_density()+
    facet_grid(~Attrition)+
    theme_tufte()+
    scale_fill_few("Dark")

# Travel
travelPlot <-ggplot(raw, aes(BusinessTravel, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark");

ratePlot <- ggplot(raw, aes(DailyRate, Attrition)) +
    geom_point(size = 4, alpha = 0.05) +
    theme_tufte() +
    scale_fill_few("Dark")

    depPlot <-
    ggplot(raw, aes(Department, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    distPlot <-
    ggplot(raw, aes(DistanceFromHome, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    eduPlot <-
    ggplot(raw, aes(Education, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    edufieldPlot <-
    ggplot(raw, aes(EducationField, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    envPlot <-
    ggplot(raw, aes(EnvironmentSatisfaction, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")
    genPlot <-
    ggplot(raw, aes(Gender, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    hourlyPlot <-
    ggplot(raw, aes(HourlyRate, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    jobInvPlot <-
    ggplot(raw, aes(JobInvolvement, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    jobLevelPlot <-
    ggplot(raw, aes(JobLevel, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    jobSatPlot <-
    ggplot(raw, aes(JobSatisfaction, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    marPlot <-
    ggplot(raw, aes(MaritalStatus, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    monthlyIncPlot <-
    ggplot(raw, aes(MonthlyIncome, fill = Attrition)) + geom_density() + theme_tufte() +
    scale_fill_few("Dark")
    monthlyRatePlot <-
    ggplot(raw, aes(MonthlyRate, fill = Attrition)) + geom_density() + theme_tufte() +
    scale_fill_few("Dark")
    numCompPlot <-
    ggplot(raw, aes(NumCompaniesWorked, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    overTimePlot <-
    ggplot(raw, aes(OverTime, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    hikePlot <-
    ggplot(raw, aes(PercentSalaryHike, Attrition)) + geom_point(size = 4, alpha = 0.01) +
    theme_tufte() + scale_fill_few("Dark")
    perfPlot <-
    ggplot(raw, aes(PerformanceRating, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    RelSatPlot <-
    ggplot(raw, aes(RelationshipSatisfaction, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")
    overTimePlot <-
    ggplot(raw, aes(OverTime, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    hikePlot <-
    ggplot(raw, aes(PercentSalaryHike, Attrition)) + geom_point(size = 4, alpha = 0.01) +
    theme_tufte() + scale_fill_few("Dark")
    perfPlot <-
    ggplot(raw, aes(PerformanceRating, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    RelSatPlot <-
    ggplot(raw, aes(RelationshipSatisfaction, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")
    StockPlot <-
    ggplot(raw, aes(StockOptionLevel, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    workingYearsPlot <-
    ggplot(raw, aes(TotalWorkingYears, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    TrainTimesPlot <-
    ggplot(raw, aes(TrainingTimesLastYear, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")
    WLBPlot <-
    ggplot(raw, aes(WorkLifeBalance, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    YearAtComPlot <-
    ggplot(raw, aes(YearsAtCompany, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    YearInCurrPlot <-
    ggplot(raw, aes(YearsInCurrentRole, fill = Attrition)) + geom_bar() + theme_tufte() +
    scale_fill_few("Dark")
    YearsSinceProm <-
    ggplot(raw, aes(YearsSinceLastPromotion, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")
    YearsCurrManPlot <-
    ggplot(raw, aes(YearsWithCurrManager, fill = Attrition)) + geom_bar() +
    theme_tufte() + scale_fill_few("Dark")

Consolidated Plot of All Variables with Respect to Attrition

grid.arrange(marPlot,monthlyIncPlot,monthlyRatePlot,numCompPlot,hourlyPlot,jobInvPlot,jobLevelPlot,jobSatPlot,overTimePlot,hikePlot,perfPlot,RelSatPlot,StockPlot,workingYearsPlot,TrainTimesPlot,WLBPlot,YearAtComPlot,YearInCurrPlot,YearsSinceProm,YearsCurrManPlot,ncol=3,top = "Analysis of Attrition in Comparison to All Dataset Variables")

Consolidated Plot of All Variables with Respect to Attrition

grid.arrange(agePlot,travelPlot,ratePlot,depPlot,distPlot,eduPlot,edufieldPlot,envPlot,genPlot,ncol=3,top="plots")

Missing Value Analysis

na_count <- data.frame(sapply(raw, function(y) sum(length(which(is.na(y))))))
pander(na_count)

#No nulls, it's a miracle!

Data Refactorization

#We did all of our refactorization in another data file. It is read in here and used for further analysis. We leveraged the data description file to redine these new feature. 

# fd <-read(factored-definitions)