There is 3 parts for the assignment 4. The data we will use for the assignment is available at https://www.kaggle.com/ludobenistant/hr-analytics. The data is about Humam resources from a company, each row corresponds to an employee. Here is the list of library we are going to use.

library(dplyr)
library(tibble)
library(xtable)
library(ggplot2)
library(reshape2)
mydf <- read.csv("C:/Users/tresz/Desktop/DNSC6211/ass04/HR_comma_sep.csv")

PART A

Get row and column

dim.data.frame(mydf)
## [1] 14999    10

Explore the data structure

str(mydf)
## 'data.frame':    14999 obs. of  10 variables:
##  $ satisfaction_level   : num  0.38 0.8 0.11 0.72 0.37 0.41 0.1 0.92 0.89 0.42 ...
##  $ last_evaluation      : num  0.53 0.86 0.88 0.87 0.52 0.5 0.77 0.85 1 0.53 ...
##  $ number_project       : int  2 5 7 5 2 2 6 5 5 2 ...
##  $ average_montly_hours : int  157 262 272 223 159 153 247 259 224 142 ...
##  $ time_spend_company   : int  3 6 4 5 3 3 4 5 5 3 ...
##  $ Work_accident        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ left                 : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ promotion_last_5years: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ sales                : Factor w/ 10 levels "accounting","hr",..: 8 8 8 8 8 8 8 8 8 8 ...
##  $ salary               : Factor w/ 3 levels "high","low","medium": 2 3 3 2 2 2 2 2 2 2 ...

Create a correlation heat map

melted_cormat <- melt(cor(subset(mydf, select = -c(sales, salary))))
head(melted_cormat)
##                   Var1               Var2       value
## 1   satisfaction_level satisfaction_level  1.00000000
## 2      last_evaluation satisfaction_level  0.10502121
## 3       number_project satisfaction_level -0.14296959
## 4 average_montly_hours satisfaction_level -0.02004811
## 5   time_spend_company satisfaction_level -0.10086607
## 6        Work_accident satisfaction_level  0.05869724
g <- ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) + 
  geom_raster()
g <- g + theme(axis.text.x = element_text(angle = 90, hjust = 1))
g

# Develop a 3-way table
#create column
mydf$status <- ifelse(mydf$left == 1, "Left", "Stay")
#table(x,y,z)
table(mydf$status,  mydf$salary, mydf$sales)
## , ,  = accounting
## 
##       
##        high  low medium
##   Left    5   99    100
##   Stay   69  259    235
## 
## , ,  = hr
## 
##       
##        high  low medium
##   Left    6   92    117
##   Stay   39  243    242
## 
## , ,  = IT
## 
##       
##        high  low medium
##   Left    4  172     97
##   Stay   79  437    438
## 
## , ,  = management
## 
##       
##        high  low medium
##   Left    1   59     31
##   Stay  224  121    194
## 
## , ,  = marketing
## 
##       
##        high  low medium
##   Left    9  126     68
##   Stay   71  276    308
## 
## , ,  = product_mng
## 
##       
##        high  low medium
##   Left    6  105     87
##   Stay   62  346    296
## 
## , ,  = RandD
## 
##       
##        high  low medium
##   Left    4   55     62
##   Stay   47  309    310
## 
## , ,  = sales
## 
##       
##        high  low medium
##   Left   14  697    303
##   Stay  255 1402   1469
## 
## , ,  = support
## 
##       
##        high  low medium
##   Left    8  389    158
##   Stay  133  757    784
## 
## , ,  = technical
## 
##       
##        high  low medium
##   Left   25  378    294
##   Stay  176  994    853

Example:Employees who leave by department

ggplot(mydf[ which(mydf$status=='Left'),], 
       aes(x = sales, fill = status)) + geom_bar(aes(fill=sales)) + guides(fill=FALSE) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Complete this for employees who stay in the company by department

ggplot(mydf[ which(mydf$status=='Stay'),], 
       aes(x = sales, fill = status)) + geom_bar(aes(fill=sales)) + guides(fill=FALSE) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

PART B

1. Complete this for employees who leave the company by salary level

ggplot(mydf[ which(mydf$status=='Left'),], 
       aes(x = salary, fill = status)) + geom_bar(aes(fill=salary)) + guides(fill=FALSE) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

2.Complete this for employees who stay in the company by salary level

ggplot(mydf[ which(mydf$status=='Stay'),], 
       aes(x = salary, fill = status)) + geom_bar(aes(fill=salary)) + guides(fill=FALSE) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

3.Make a chart for number of employees who leave the company by time spent in the company, faceted by salary level AND department

ggplot(mydf[ which(mydf$status=='Left'),], 
  aes(x = time_spend_company, fill = status)) + 
  geom_bar(aes(fill=sales)) + guides(fill=FALSE) + 
  facet_grid(salary~ sales) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

4. CMake a chart for number of employees who who stay in the company by time spent in the company, faceted by salary level AND department

ggplot(mydf[ which(mydf$status=='Stay'),], 
       aes(x = time_spend_company, fill = status)) + 
  geom_bar(aes(fill=sales)) + guides(fill=FALSE) + 
  facet_grid(salary~ sales) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

PART C

1. Draw a bar plot of how many years did employees stay in the company before leaving

Year stayed in the company is the x axis

ggplot(mydf[ which(mydf$status=='Left'),], 
       aes(x = time_spend_company, fill = status)) + geom_bar() + guides(fill=FALSE)

2.Draw a bar plot of how many years did employees stay in the company before leaving

by department

Year stayed in the company is the x axis

ggplot(mydf[ which(mydf$status=='Left'),], 
       aes(x = time_spend_company, fill = status)) + 
  geom_bar(aes(fill=sales)) + guides(fill=FALSE) + 
  facet_grid(.~ sales)

3.Draw a bar plot of how many years did employees stay in the company before leaving

by salary level

Year stayed in the company is the x axis

ggplot(mydf[ which(mydf$status=='Left'),], 
       aes(x = time_spend_company, fill = status)) + 
  geom_bar(aes(fill=salary)) + guides(fill=FALSE) + 
  facet_grid(.~ salary)

Summary

From the analysis by R, we can see that high level salary employees leave the company least among 3 salary levels in all department. Employees from sales department has the most number in both employee leave and stay in the company. Employees with high salary tend to stay at the company most, and employees with low salary leave the company most. Employees tend to leave the company after worked in the company for 3 years for all salary level and all departments. The more year employees work for he company, the more likely they are stay in the company because we can see that the number of employees who work more than 6 years don’t leave the company.