Employee Attrition
sileshi
3/15/2022
#The term attrition to the rate at which employees of an organization leave the workforce over a given period of time
#install.packages("tidyverse")
library(tidyverse) # you will use the readr package in tidyverse to read in this data## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#install.packages('rpart')
#install.packages('caret')
#install.packages('dplyr')
#install.packages('ggplot2')
library(rpart)
library(caret)## Warning: package 'caret' was built under R version 4.1.3## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftlibrary(ggplot2)
library(scales)##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorlibrary(dplyr)#Importing the dataset from from datasets of data source folder.
HREmployeeAttrition <- read.csv(file = ('/Users/Sileshi Boru/Downloads/HR_EmployeeAttrition.csv'))#install the data HREmployeeAttrition to excute the program for processing the data.
#install.packages('HREmployeeAttrition')#Viewing and Understanding the dataset and its structure
str(HREmployeeAttrition)## 'data.frame': 1470 obs. of 27 variables:
## $ Age : int 41 49 37 33 27 32 59 30 38 36 ...
## $ Attrition : logi TRUE FALSE TRUE FALSE FALSE FALSE ...
## $ BusinessTravel : chr "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" "Travel_Frequently" ...
## $ Department : chr "Sales" "Research & Development" "Research & Development" "Research & Development" ...
## $ DistanceFromHome : int 1 8 2 3 2 2 3 24 23 27 ...
## $ Education : int 2 1 2 4 1 2 3 1 3 3 ...
## $ EducationField : chr "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EnvironmentSatisfaction : int 2 3 4 4 1 4 3 4 4 3 ...
## $ Gender : chr "Female" "Male" "Male" "Female" ...
## $ JobInvolvement : int 3 2 2 3 3 3 4 3 2 3 ...
## $ JobLevel : int 2 2 1 1 1 1 1 1 3 2 ...
## $ JobRole : chr "Sales Executive" "Research Scientist" "Laboratory Technician" "Research Scientist" ...
## $ JobSatisfaction : int 4 2 3 3 2 4 1 3 3 3 ...
## $ MaritalStatus : chr "Single" "Married" "Single" "Married" ...
## $ MonthlyIncome : int 5993 5130 2090 2909 3468 3068 2670 2693 9526 5237 ...
## $ NumCompaniesWorked : int 8 1 6 1 9 0 4 1 0 6 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ OverTime : chr "Yes" "No" "Yes" "Yes" ...
## $ PercentSalaryHike : int 11 23 15 11 12 13 20 22 21 13 ...
## $ PerformanceRating : int 3 4 3 3 3 3 4 4 4 3 ...
## $ RelationshipSatisfaction: int 1 4 2 3 4 3 1 2 2 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 0 1 0 0 1 0 3 1 0 2 ...
## $ TotalWorkingYears : int 8 10 7 8 6 8 12 1 10 17 ...
## $ TrainingTimesLastYear : int 0 3 3 3 3 2 3 2 2 3 ...
## $ WorkLifeBalance : int 1 3 3 3 3 2 2 3 3 2 ...#names(HREmployeeAtREmployeeAttritiontrition)
glimpse(HREmployeeAttrition)## Rows: 1,470
## Columns: 27
## $ Age <int> 41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2~
## $ Attrition <lgl> TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE~
## $ BusinessTravel <chr> "Travel_Rarely", "Travel_Frequently", "Travel~
## $ Department <chr> "Sales", "Research & Development", "Research ~
## $ DistanceFromHome <int> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, ~
## $ Education <int> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, ~
## $ EducationField <chr> "Life Sciences", "Life Sciences", "Other", "L~
## $ EmployeeCount <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ EnvironmentSatisfaction <int> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, ~
## $ Gender <chr> "Female", "Male", "Male", "Female", "Male", "~
## $ JobInvolvement <int> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, ~
## $ JobLevel <int> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, ~
## $ JobRole <chr> "Sales Executive", "Research Scientist", "Lab~
## $ JobSatisfaction <int> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, ~
## $ MaritalStatus <chr> "Single", "Married", "Single", "Married", "Ma~
## $ MonthlyIncome <int> 5993, 5130, 2090, 2909, 3468, 3068, 2670, 269~
## $ NumCompaniesWorked <int> 8, 1, 6, 1, 9, 0, 4, 1, 0, 6, 0, 0, 1, 0, 5, ~
## $ Over18 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", ~
## $ OverTime <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",~
## $ PercentSalaryHike <int> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1~
## $ PerformanceRating <int> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, ~
## $ RelationshipSatisfaction <int> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, ~
## $ StandardHours <int> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8~
## $ StockOptionLevel <int> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, ~
## $ TotalWorkingYears <int> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3~
## $ TrainingTimesLastYear <int> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, ~
## $ WorkLifeBalance <int> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, ~#summary(HREmployeeAttrition)#Changing name of column Age
names(HREmployeeAttrition)[1] <- "Age"#Checking for Missing Values
#
apply(is.na(HREmployeeAttrition),2, sum)## Age Attrition BusinessTravel
## 0 0 0
## Department DistanceFromHome Education
## 0 0 0
## EducationField EmployeeCount EnvironmentSatisfaction
## 0 0 0
## Gender JobInvolvement JobLevel
## 0 0 0
## JobRole JobSatisfaction MaritalStatus
## 0 0 0
## MonthlyIncome NumCompaniesWorked Over18
## 0 0 0
## OverTime PercentSalaryHike PerformanceRating
## 0 0 0
## RelationshipSatisfaction StandardHours StockOptionLevel
## 0 0 0
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## 0 0 0#Checking for Null values
#
sum(is.null(HREmployeeAttrition))## [1] 0#Checking for duplicate records
sum(is.na(duplicated(HREmployeeAttrition)))## [1] 0#Removing non-significant columns because all of the have the same value for below observation
HREmployeeAttrition$EmployeeCount <- NULL
HREmployeeAttrition$EmployeeNumber <- NULL
HREmployeeAttrition$StandardHours <- NULL
HREmployeeAttrition$Over18 <- NULL#Changing categorical attributes to factor which are specified integer in data set
HREmployeeAttrition$Education <- factor(HREmployeeAttrition$Education)
HREmployeeAttrition$EnvironmentSatisfaction <- factor(HREmployeeAttrition$EnvironmentSatisfaction)
HREmployeeAttrition$JobInvolvement <- factor(HREmployeeAttrition$JobInvolvement)
HREmployeeAttrition$JobLevel <- factor(HREmployeeAttrition$JobLevel)
HREmployeeAttrition$JobSatisfaction <- factor(HREmployeeAttrition$JobSatisfaction)
HREmployeeAttrition$PerformanceRating <- factor(HREmployeeAttrition$PerformanceRating)
HREmployeeAttrition$RelationshipSatisfaction <- factor(HREmployeeAttrition$RelationshipSatisfaction)
HREmployeeAttrition$StockOptionLevel <- factor(HREmployeeAttrition$StockOptionLevel)
HREmployeeAttrition$WorkLifeBalance <- factor(HREmployeeAttrition$WorkLifeBalance)#HREmployeeAttrition$Attritiontable(c(Attrition)
attritions_number <- HREmployeeAttrition %>% group_by(Attrition) %>% summarise(Count=n())
print(attritions_number)## # A tibble: 2 x 2
## Attrition Count
## <lgl> <int>
## 1 FALSE 1233
## 2 TRUE 237Fig1: Attrition VS Overtime
HREmployeeAttrition %>%
ggplot(aes(x = OverTime, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "over time") +
facet_grid(~Attrition) +
theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")
## Fig2: BusinessTravel VS Attrition
HREmployeeAttrition %>%
ggplot(aes(x = BusinessTravel, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "business travel") +
facet_grid(~Attrition) +
theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")
Fig3: Marital status VS Attrition
#V3: Marital status VS Attrition
HREmployeeAttrition %>%
ggplot(aes(x = MaritalStatus, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "marital status") +
facet_grid(~Attrition) +
theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")
Figr4:JobInvolvement vs Attrition
HREmployeeAttrition %>%
ggplot(aes(x = JobInvolvement, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "JobInvolvement") +
facet_grid(~Attrition) +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")
Figr5:JobSatisfaction vs Attrition
HREmployeeAttrition %>%
ggplot(aes(x = JobSatisfaction, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "JobSatisfaction") +
facet_grid(~Attrition) +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")
## Figr6:JobRole vs Attrition
levels(HREmployeeAttrition$JobRole) <- c("HC Rep", "HR", "LT", "Man", "MD", "RD", "RsScientist", "SalesEx", "SalesRep")
HREmployeeAttrition %>%
ggplot(aes(x = JobRole, group = Attrition)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)),
stat="count",
alpha = 0.7) +
geom_text(aes(label = scales::percent(..prop..), y = ..prop.. ),
stat= "count",
vjust = -.5) +
labs(y = "Percentage", fill= "job role") +
facet_grid(~Attrition) +
theme(legend.position = "right", plot.title = element_text(hjust = 0.5)) +
ggtitle("Attrition")+
theme(axis.text.x = element_text(angle = 90))
Figr7:MonthlyIncome,Gender vs Attrition
box.attrition <-HREmployeeAttrition %>% select(Attrition, MonthlyIncome, Gender) %>%
ggplot(aes(x=Attrition, y=MonthlyIncome, fill=Attrition)) + geom_boxplot(color="black") + theme_minimal() + facet_wrap(~Gender) +
scale_fill_manual(values=c("#FA5858", "#9FF781"))
print(box.attrition)
Figr8:DistanceFromHome,Gender vs Attrition
box.attrition <-HREmployeeAttrition %>% select(Attrition, DistanceFromHome, Gender) %>%
ggplot(aes(x=Attrition, y=DistanceFromHome, fill=Attrition)) + geom_boxplot(color="black") + theme_minimal() + facet_wrap(~Gender) +
scale_fill_manual(values=c("#FA5858", "#9FF781"))
print(box.attrition)
## Figr8:Age, Gender vs Attrition
box.attrition <-HREmployeeAttrition %>% select(Attrition, Age, Gender) %>%
ggplot(aes(x=Attrition, y=Age, fill=Attrition)) + geom_boxplot(color="black") + theme_minimal() + facet_wrap(~Gender) +
scale_fill_manual(values=c("#FA5858", "#9FF781"))
print(box.attrition)
`
INtroduction
The data set used for this analysis is based on from data source of the course document under the name of HR-EmployeeAttritiona . This data set contains information on 1470 employees of IBM with 35 variables representing specific characteristics of each employee. This includes their Attrition status (if they left the company or not). This data consists of both numerical and categorical variables. The dependent variable here is Attrition which is a binomial categorical variable with an outcome of “TRUE”, which means an employee has left the company or if “FALSE”, it means the employee is still currently employed with the company.
To clean the data I checked the following:
also we did glimpse() Checking for duplicate records Checking for Missing Values Checking for Null values Removing non-significant columns because all of the have the same value for all observation Changing categorical attributes to factor which are specified integer in data set
code used
The code used to plot the function for each variable is “ggplot2()”
Area of Interest
1.Perform a data analysis to identify factors that lead to attrition using visualizations graphs. 2.Top factors that contribute to turnover based by evidence obtained from the data analyses. 3.Any other interesting trends and observations from the data analysis.
conculussion Findings
The top “5”variables which impacted attrition were
Overtime Travel frequently job roles Job satisfaction singles (Martial status)
*people who have to work overtime show higher proportion of leaveres compared to their counter parts
*people who travel frequently show higher proportion of leavers compared to their counterpartes
*People from some specific Job roles like sales eccutive,research scientist,and laboratory technician have higher attrition rates
*people with low satisfaction of the work enviroment had higher attrition rates.
* Single employees show the largest proportion of leavers,compared to married and Diverorced counterpartes.