tempAnalysis.Rmd

---
title: "Initial_Analysis"
author: "Samantha Sprague"
date: "12/2/2018"
output: html_document
---

#Enviornment Setup
```{r setup, include=FALSE}
library(readxl)
library(pander)
library(ggthemes)
library(ggplot2)
library(grid)
library(gridExtra)
```

#Reading in Data and Cleaning
```{r}
#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
```{r}
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
```{r}
# 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
```{r}
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
```{r}
grid.arrange(agePlot,travelPlot,ratePlot,depPlot,distPlot,eduPlot,edufieldPlot,envPlot,genPlot,ncol=3,top="plots")
```

#Missing Value Analysis
```{r}
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
```{r}
#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)
```


```{r}
distPlot
```



#Feature Engineering
```{r}

```