---
title: "PallaviSaitu_Project"
author: "Pallavi Saitu"
date: "4/23/2019"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    source_code: embed
---

### The HR Dashboard {data-commentary-width=400}

```{r}
library(tidyverse)
library(knitr)
library(gridExtra)
library(ggpubr)
knitr::include_graphics("/Users/pallavisaitu/Downloads/HR.jpg")
```

*** 
Data description:
The data set is an HR Employee Performance data set.It includes 35 different variables about employees’ demographics, evaluations about the company and attrition result. It has 1470 rows of records. 

Variables:
Age, Attrition, BusinessTravel, DailyRate, Department, DistanceFromHome, Education, EducationField, EmployeeCount, EmployeeNumber, EnvironmentSatisfaction, Gender, HourlyRate, obInvolvement, JobLevel, JobRole, JobSatisfaction, MaritalStatus, MonthlyIncome, MonthlyRate, NumCompaniesWorked, Over18, OverTime, PercentSalaryHike, PerformanceRating, RelationshipSatisfaction, StandardHours, StockOptionLevel, TotalWorkingYears, TrainingTimesLastYear, WorkLifeBalance, YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager
The four major objectives are:
1.Provide summary statistics about employees
2.Get understanding about how the company’s employees think about their works
3.Explore relationships between employee attributes and monthly income
4.Investigate possible factors affect attrition

### Summary Statistics

```{r}
HR <- read.csv("/Users/pallavisaitu/Downloads/WA_Fn-UseC_-HR-Employee-Attrition.csv")
names(HR)[1] <- "Age"   #Rename the column name to "Age" for consistence purpose. 

# Employees' education 
HR$Education[HR$Education=="1"] <- "Below College"
HR$Education[HR$Education=="2"] <- "College"
HR$Education[HR$Education=="3"] <- "Bachelor"
HR$Education[HR$Education=="4"] <- "Master"
HR$Education[HR$Education=="5"] <- "Doctor"


# Employees' environment satisfaction
HR$EnvironmentSatisfaction[HR$EnvironmentSatisfaction=="1"] <- "Low"
HR$EnvironmentSatisfaction[HR$EnvironmentSatisfaction=="2"] <- "Medium"
HR$EnvironmentSatisfaction[HR$EnvironmentSatisfaction=="3"] <- "High"
HR$EnvironmentSatisfaction[HR$EnvironmentSatisfaction=="4"] <- "Very High"


# Employees' job involvement
HR$JobInvolvement[HR$JobInvolvement=="1"] <- "Low"
HR$JobInvolvement[HR$JobInvolvement=="2"] <- "Medium"
HR$JobInvolvement[HR$JobInvolvement=="3"] <- "High"
HR$JobInvolvement[HR$JobInvolvement=="4"] <- "Very High"


# Employees' job satisfaction
HR$JobSatisfaction[HR$JobSatisfaction=="1"] <- "Low"
HR$JobSatisfaction[HR$JobSatisfaction=="2"] <- "Medium"
HR$JobSatisfaction[HR$JobSatisfaction=="3"] <- "High"
HR$JobSatisfaction[HR$JobSatisfaction=="4"] <- "Very High"

# Employees' performance rating
HR$PerformanceRating[HR$PerformanceRating=="1"] <- "Low"
HR$PerformanceRating[HR$PerformanceRating=="2"] <- "Good"
HR$PerformanceRating[HR$PerformanceRating=="3"] <- "Excellent"
HR$PerformanceRating[HR$PerformanceRating=="4"] <- "Outstanding"


# Employees' relationship satisfaction
HR$RelationshipSatisfaction[HR$RelationshipSatisfaction=="1"] <- "Low"
HR$RelationshipSatisfaction[HR$RelationshipSatisfaction=="2"] <- "Medium"
HR$RelationshipSatisfaction[HR$RelationshipSatisfaction=="3"] <- "High"
HR$RelationshipSatisfaction[HR$RelationshipSatisfaction=="4"] <- "Very High"


# Employees' life balance
HR$WorkLifeBalance[HR$WorkLifeBalance=="1"] <- "Bad"
HR$WorkLifeBalance[HR$WorkLifeBalance=="2"] <- "Good"
HR$WorkLifeBalance[HR$WorkLifeBalance=="3"] <- "Better"
HR$WorkLifeBalance[HR$WorkLifeBalance=="4"] <- "Best"

summary(HR)

HR$Attrition <- factor(HR$Attrition,levels=c("Yes","No"))
attrition <- data.frame(table(HR$Attrition))
names(attrition)[1] <- "Status"
names(attrition)[2] <- "Counts"
kable(attrition)

department <-data.frame(table(HR$Department))
kable(department,col.names = c("Department","Count"))

```

*** 
In the original data set provided, variables "Education", "EnvironmentSatisfaction", "JobInvolvement", "JobSatisfaction", "PerformanceRating", "RelationshipSatisfaction", and "WorkLifeBalance" are dummy coded using 1-5 or 1-4 scale. For better understanding and visualizing purpose, these variables are reversed  back to actual categories based on the explanations given by the data provider.

To begin the analysis, it is important to get some summary statstics about the 1,470 employees.  
In 1470 employee records, 237 people left the company, which accounts for 16.1% of the total population.  

### Employees by Department

```{r}
inc_1 <- ggplot(HR, aes(x = MonthlyIncome, fill = Attrition)) + 
            geom_histogram(position = "dodge") + labs(x="Monthly Income", y="Number of employees")
inc_2 <- ggplot(HR, aes(x = HourlyRate, fill = Attrition)) + 
            geom_histogram(position = "dodge") + labs(x="Hourly Rate", y="Number of employees")
inc_3 <- ggplot(HR, aes(x = DailyRate, fill = Attrition)) + 
            geom_histogram(position = "dodge") + labs(x="Daily Rate", y="Number of employees")
grid.arrange(inc_1,inc_2,inc_3, ncol = 2, nrow = 2, top = "Income Distribution in company", bottom = "IBM HR Analytics")
```

*** 

Q1. Which departments are these employees come from?

961 employees are from the Research & Development department, accounts for the majority of the data set. Other two departments include in the data set are Sales and Human Resources.  
   

### Employee Income Distribution

```{r}
ggplot(HR) + 
  geom_histogram(mapping=(aes(TotalWorkingYears)),fill="skyblue",col="white",binwidth = 1) + 
  labs(x="Total Working Years", y="Number of employees",caption="IBM HR Analytics", title="Total Working Years") + theme(legend.position="none")

ggplot(HR, aes(x= Department, y=TotalWorkingYears, group = Department, fill = Department)) +
  geom_violin() + theme(legend.position="none") +
  coord_flip() + 
  labs(x="Department",y="Total Working Years",caption="IBM HR Analytics", title="Total Working Years by Attrition") +
  facet_wrap(~ Attrition)

ggplot(HR) + 
  geom_histogram(mapping=(aes(YearsAtCompany)),fill="skyblue",col="white",binwidth = 1) + 
  labs(x="Working Years at the company", y="Number of employees",caption="IBM HR Analytics", title="Working Years at Company") + theme(legend.position="none")

ggplot(HR, aes(x= Department, y=YearsAtCompany, group = Department, fill = Department)) +
  geom_violin() + theme(legend.position="none") +
  coord_flip() + 
  labs(x="Department",y="Working Years at the company",caption="IBM HR Analytics", title="Working Years at Company by Attrition") +
  facet_wrap(~ Attrition)
```

*** 
Q2. How employees' incomes distributed?

Since the data set doesn't explain how "rate" and "income", the analysis assume the "hourly rate" and "daily rate" are equal to "hourly income" and "daily income". It is clear that most employees who left have a relatively low monthly income level. Also, the monthly income is positive skewed, which means the most of employees have lower than $10,000 per month income level.  
The distributions of hourly rate and daily rate don't provide much valuable insights of employees. For employees who choose to leave, their hourly rate and daily rate are not significantly different from people who stay in the company. 

### Work Life Balance 


```{r}
# Filter by department
worklife_sales <-data.frame(table(filter(HR,Department=="Sales")$WorkLifeBalance))
names(worklife_sales)[1] <- "Status"
names(worklife_sales)[2] <- "Counts"
worklife_RD <-data.frame(table(filter(HR,Department=="Research & Development")$WorkLifeBalance))
names(worklife_RD)[1] <- "Status"
names(worklife_RD)[2] <- "Counts"
worklife_HR <-data.frame(table(filter(HR,Department=="Human Resources")$WorkLifeBalance))
names(worklife_HR)[1] <- "Status"
names(worklife_HR)[2] <- "Counts"

a <- ggpie(worklife_HR,"Counts",fill="Status",color="white", label="Counts",lab.pos = "out",lab.font = "white") +
  ggtitle("Human Resources") + theme(legend.position = "right") 
b <- ggpie(worklife_RD,"Counts",fill="Status",color="white", label="Counts",lab.pos = "out",lab.font = "white") +
  ggtitle("Research & Development") + theme(legend.position = "right") 
c <- ggpie(worklife_sales,"Counts",fill="Status",color="white", label="Counts",lab.pos = "out",lab.font = "white") +
  ggtitle("Sales") + theme(legend.position = "right")
grid.arrange(a,b,c,ncol=2,nrow=2,newpage = FALSE)

```

*** 
Q3. Working Years in the company?

In thhe company, majority employees have 0-12 years of work experience. And when taking look at the work experience they gained at the company, 0-10 years is the most common time length for all three departments. Most left employees choose to leave after they work for the company 0-5 years, or after they work 0-10 years in total. There are few employees working for the company over 20 years no matter they choose to leave or stay.  


### Job Satisfaction
```{r}
HR$JobSatisfaction <- factor(HR$JobSatisfaction, 
                                     levels = c("Low", "Medium","High","Very High"))
ggplot(HR,aes(Age,MonthlyIncome)) + geom_point(aes(color=Department)) + 
  geom_smooth(col="black", se=FALSE,method="loess")+ facet_grid(.~Department) + 
  labs(title="Age and Monthly Income", x="Age", y="Monthly Income")

```

*** 

Q4. What is the job satisfaction levels?

The job satisfaction increases when the age and experience of the employee increases. We see that there is a downfall in satisfaction after age of 50 and monthly income of $14,000. There is also a mixed level of satisfaction for employees in the sales department. 


### Possible factors affect attrition
```{r}
ggplot(data=HR)+
  geom_bar(position="dodge",mapping=aes(JobSatisfaction,fill=Attrition)) + labs(title="Job Satisfaction and Attrition", x="Job Satisfaction", y="Number of employees", caption="IBM HR Analytics") 
```


*** 

Q5. What are the possible factors that affect attrition?

It is interesting to notice that the two major groups who left companies are employees who have "low" and "high" job satisfaction. For employees who are highly satisfied with thier job, it is more important for HR to know what cause them left.