Human Resource Analytics
Pavithra A N
December 29, 2017
Our example concerns a big company that wants to understand why some of their best and most experienced employees are leaving prematurely. The company also wishes to predict which valuable employees will leave next.
Reading the data into a dataframe
hresource <- read.csv(paste("human.csv", sep=""))
attach(hresource)
dim(hresource)## [1] 14999 10library(psych)
describe(hresource)## vars n mean sd median trimmed mad min
## satisfaction_level 1 14999 0.61 0.25 0.64 0.63 0.28 0.09
## last_evaluation 2 14999 0.72 0.17 0.72 0.72 0.22 0.36
## number_project 3 14999 3.80 1.23 4.00 3.74 1.48 2.00
## average_montly_hours 4 14999 201.05 49.94 200.00 200.64 65.23 96.00
## time_spend_company 5 14999 3.50 1.46 3.00 3.28 1.48 2.00
## Work_accident 6 14999 0.14 0.35 0.00 0.06 0.00 0.00
## left 7 14999 0.24 0.43 0.00 0.17 0.00 0.00
## promotion_last_5years 8 14999 0.02 0.14 0.00 0.00 0.00 0.00
## sales* 9 14999 6.94 2.75 8.00 7.23 2.97 1.00
## salary* 10 14999 2.35 0.63 2.00 2.41 1.48 1.00
## max range skew kurtosis se
## satisfaction_level 1 0.91 -0.48 -0.67 0.00
## last_evaluation 1 0.64 -0.03 -1.24 0.00
## number_project 7 5.00 0.34 -0.50 0.01
## average_montly_hours 310 214.00 0.05 -1.14 0.41
## time_spend_company 10 8.00 1.85 4.77 0.01
## Work_accident 1 1.00 2.02 2.08 0.00
## left 1 1.00 1.23 -0.49 0.00
## promotion_last_5years 1 1.00 6.64 42.03 0.00
## sales* 10 9.00 -0.79 -0.62 0.02
## salary* 3 2.00 -0.42 -0.67 0.01View(hresource)Visualization of catagorical values
one way contigency table
mytable <- with(hresource,table(left))
mytable## left
## 0 1
## 11428 3571prop.table(mytable)*100## left
## 0 1
## 76.19175 23.80825mytable1 <- with(hresource,table(promotion_last_5years))
mytable1## promotion_last_5years
## 0 1
## 14680 319prop.table(mytable1)*100## promotion_last_5years
## 0 1
## 97.873192 2.126808mytable2 <- with(hresource,table(salary))
mytable2## salary
## high low medium
## 1237 7316 6446prop.table(mytable2)*100## salary
## high low medium
## 8.247216 48.776585 42.976198mytable3 <- with(hresource,table(Work_accident))
mytable3## Work_accident
## 0 1
## 12830 2169prop.table(mytable3)*100## Work_accident
## 0 1
## 85.53904 14.46096Two way contingency table
mytable4 <- xtabs(~ left+promotion_last_5years, data=hresource)
mytable4## promotion_last_5years
## left 0 1
## 0 11128 300
## 1 3552 19margin.table(mytable4,1)## left
## 0 1
## 11428 3571prop.table(mytable4, 1)*100## promotion_last_5years
## left 0 1
## 0 97.3748687 2.6251313
## 1 99.4679362 0.5320638mytable5 <-xtabs(~left+salary,data=hresource)
mytable5## salary
## left high low medium
## 0 1155 5144 5129
## 1 82 2172 1317margin.table(mytable5,2) ## salary
## high low medium
## 1237 7316 6446prop.table(mytable5, 2)## salary
## left high low medium
## 0 0.93371059 0.70311646 0.79568725
## 1 0.06628941 0.29688354 0.20431275mytable6 <- xtabs(~left+Work_accident,data=hresource)
mytable6## Work_accident
## left 0 1
## 0 9428 2000
## 1 3402 169margin.table(mytable6,1)## left
## 0 1
## 11428 3571prop.table(mytable6, 1)## Work_accident
## left 0 1
## 0 0.82499125 0.17500875
## 1 0.95267432 0.04732568library(gmodels)
CrossTable(hresource$left,hresource$salary)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 14999
##
##
## | hresource$salary
## hresource$left | high | low | medium | Row Total |
## ---------------|-----------|-----------|-----------|-----------|
## 0 | 1155 | 5144 | 5129 | 11428 |
## | 47.915 | 33.200 | 9.648 | |
## | 0.101 | 0.450 | 0.449 | 0.762 |
## | 0.934 | 0.703 | 0.796 | |
## | 0.077 | 0.343 | 0.342 | |
## ---------------|-----------|-----------|-----------|-----------|
## 1 | 82 | 2172 | 1317 | 3571 |
## | 153.339 | 106.247 | 30.876 | |
## | 0.023 | 0.608 | 0.369 | 0.238 |
## | 0.066 | 0.297 | 0.204 | |
## | 0.005 | 0.145 | 0.088 | |
## ---------------|-----------|-----------|-----------|-----------|
## Column Total | 1237 | 7316 | 6446 | 14999 |
## | 0.082 | 0.488 | 0.430 | |
## ---------------|-----------|-----------|-----------|-----------|
##
## CrossTable(hresource$left,hresource$promotion_last_5years)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 14999
##
##
## | hresource$promotion_last_5years
## hresource$left | 0 | 1 | Row Total |
## ---------------|-----------|-----------|-----------|
## 0 | 11128 | 300 | 11428 |
## | 0.290 | 13.343 | |
## | 0.974 | 0.026 | 0.762 |
## | 0.758 | 0.940 | |
## | 0.742 | 0.020 | |
## ---------------|-----------|-----------|-----------|
## 1 | 3552 | 19 | 3571 |
## | 0.928 | 42.702 | |
## | 0.995 | 0.005 | 0.238 |
## | 0.242 | 0.060 | |
## | 0.237 | 0.001 | |
## ---------------|-----------|-----------|-----------|
## Column Total | 14680 | 319 | 14999 |
## | 0.979 | 0.021 | |
## ---------------|-----------|-----------|-----------|
##
## Boxplot
par(mfrow=c(1,2))
boxplot(hresource$satisfaction_level,col="yellow",main="Satisfaction level")
boxplot(hresource$last_evaluation,col="blue",main="Last Evaluation")
par(mfrow=c(1,2))
boxplot(hresource$number_project,col="green",main="Number of project")
boxplot(hresource$average_montly_hours,col="red",main="Average monthly hours")
par(mfrow=c(1,2))
boxplot(satisfaction_level ~left ,data=hresource, main="satisfaction of left", ylab="satisfaction level", xlab="left",col= "lightblue",vertical=TRUE)
boxplot(satisfaction_level ~promotion_last_5years ,data=hresource, main= "satisfaction with promotion", ylab="satisfaction level", xlab="promotion in last 5 years",col= "peachpuff",vertical=TRUE)
par(mfrow=c(1,2))
boxplot(number_project ~left ,data=hresource, main="Distribution of number of projects", ylab="number of projects", xlab="left",col= "lightblue",vertical=TRUE)
boxplot(satisfaction_level ~salary ,data=hresource, main="Distribution of satisfaction with salary", ylab="satisfaction level", xlab=" salary",col= "blue",vertical=TRUE)
Histogram
par(mfrow=c(1,2))
library(lattice)
histogram(~left, data = hresource,
main = "HR leaving the company", xlab="left", col='lightgreen' ) 
histogram(~satisfaction_level,data=hresource,main="satisfaction level",col="lightblue")
histogram(~last_evaluation,data=hresource,main="frequency of last evalution",col="yellow")
histogram(~salary,data=hresource,main="frequency of salary",col="green")
Scatterplot
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(satisfaction_level~number_project, data=hresource,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter plot of satisfaction level vs number of project",
xlab="number of project",
ylab="satisfaction level")
scatterplot(satisfaction_level~average_montly_hours,data=hresource,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter plot of satisfaction level vs average working hours",
xlab="average working hours",
ylab="satisfaction level")
library(ggplot2)##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphaggplot(hresource, aes(satisfaction_level, average_montly_hours)) + geom_point(aes(color = left)) + scale_x_continuous("satisfaction level ") + scale_y_continuous("average monthly hours")+ labs(title="satisfaction of people leaving company")
ggplot(hresource, aes(satisfaction_level, average_montly_hours)) + geom_point(aes(color = left)) + scale_x_continuous("satisfaction level ") + scale_y_continuous("average monthly hours")+ labs(title="satisfaction of people leaving company with salary")+facet_wrap( ~ salary)
Corrgram
library(corrgram)
corrgram(hresource, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="human_resource analysis ")
Correlation test
cor.test(hresource$satisfaction_level,hresource$average_montly_hours)##
## Pearson's product-moment correlation
##
## data: hresource$satisfaction_level and hresource$average_montly_hours
## t = -2.4556, df = 14997, p-value = 0.01408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.036040356 -0.004045605
## sample estimates:
## cor
## -0.02004811cor.test(hresource$satisfaction_level,hresource$number_project)##
## Pearson's product-moment correlation
##
## data: hresource$satisfaction_level and hresource$number_project
## t = -17.69, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1586105 -0.1272570
## sample estimates:
## cor
## -0.1429696T-test
t.test(hresource$satisfaction_level,hresource$average_montly_hours)##
## Welch Two Sample t-test
##
## data: hresource$satisfaction_level and hresource$average_montly_hours
## t = -491.51, df = 14999, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -201.2368 -199.6382
## sample estimates:
## mean of x mean of y
## 0.6128335 201.0503367t.test(hresource$satisfaction_level,hresource$number_project)##
## Welch Two Sample t-test
##
## data: hresource$satisfaction_level and hresource$number_project
## t = -310.72, df = 16216, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -3.210345 -3.170095
## sample estimates:
## mean of x mean of y
## 0.6128335 3.8030535Visualization
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionfirst <- hresource %>% select(satisfaction_level:promotion_last_5years)
M <- cor(first)
library(corrplot)## corrplot 0.84 loadedcorrplot(M, method="circle")
On average people who leave have a low satisfaction level, they work more and didn’t get promoted within the past five years.
cor(first)## satisfaction_level last_evaluation number_project
## satisfaction_level 1.00000000 0.105021214 -0.142969586
## last_evaluation 0.10502121 1.000000000 0.349332589
## number_project -0.14296959 0.349332589 1.000000000
## average_montly_hours -0.02004811 0.339741800 0.417210634
## time_spend_company -0.10086607 0.131590722 0.196785891
## Work_accident 0.05869724 -0.007104289 -0.004740548
## left -0.38837498 0.006567120 0.023787185
## promotion_last_5years 0.02560519 -0.008683768 -0.006063958
## average_montly_hours time_spend_company
## satisfaction_level -0.020048113 -0.100866073
## last_evaluation 0.339741800 0.131590722
## number_project 0.417210634 0.196785891
## average_montly_hours 1.000000000 0.127754910
## time_spend_company 0.127754910 1.000000000
## Work_accident -0.010142888 0.002120418
## left 0.071287179 0.144822175
## promotion_last_5years -0.003544414 0.067432925
## Work_accident left promotion_last_5years
## satisfaction_level 0.058697241 -0.38837498 0.025605186
## last_evaluation -0.007104289 0.00656712 -0.008683768
## number_project -0.004740548 0.02378719 -0.006063958
## average_montly_hours -0.010142888 0.07128718 -0.003544414
## time_spend_company 0.002120418 0.14482217 0.067432925
## Work_accident 1.000000000 -0.15462163 0.039245435
## left -0.154621634 1.00000000 -0.061788107
## promotion_last_5years 0.039245435 -0.06178811 1.000000000Who is leaving ?
library(lattice)
leaving <- hresource[ which(hresource$left=='1'), ]
par(mfrow=c(1,3))
histogram(leaving$satisfaction_level,col="green",xlab="Degree of Satisfaction", main="Satisfaction distribution of left")
histogram(leaving$promotion_last_5years,col="blue",xlab="promotion of last 5 years", main="promotion over 5 years of left")
histogram(leaving$last_evaluation,col="yellow",xlab="last evaluation", main="evaluation of left ")
par( mfrow= c(1,2) )
histogram(leaving$Work_accident,col="lightblue", main = "Work accident",xlab ="workaccident")
histogram(leaving$salary,col="lightblue", main = "Salary",xlab="salary")
In the total of 15 000 employees that compose our database, here are the people that have left:
nrow(leaving)## [1] 3571Why good people leave?
goodleaving <- leaving %>% filter(last_evaluation >= 0.70 | time_spend_company >= 4 | number_project > 5)
nrow(goodleaving)## [1] 2014goodleaving2<- hresource %>% filter(last_evaluation >= 0.70 | time_spend_company >= 4 | number_project > 5)
goodleavingselected <- goodleaving2 %>% select(satisfaction_level:promotion_last_5years)
M <- cor(goodleavingselected)
corrplot(M, method="circle")
Here it’s much clearer. On average valuable employees that leave are not satisfied, work on many projects, spend many hours in the company each month and aren’t promoted.
cor(goodleavingselected)## satisfaction_level last_evaluation number_project
## satisfaction_level 1.00000000 0.07462604 -0.40620511
## last_evaluation 0.07462604 1.00000000 0.07066455
## number_project -0.40620511 0.07066455 1.00000000
## average_montly_hours -0.19065675 0.19647605 0.29820758
## time_spend_company -0.11169444 -0.20566461 0.09466946
## Work_accident 0.04563333 -0.06070303 -0.06768055
## left -0.29268571 0.26396556 0.47224280
## promotion_last_5years 0.02629127 -0.05025390 -0.03627774
## average_montly_hours time_spend_company
## satisfaction_level -0.19065675 -0.1116944420
## last_evaluation 0.19647605 -0.2056646119
## number_project 0.29820758 0.0946694589
## average_montly_hours 1.00000000 0.0469311258
## time_spend_company 0.04693113 1.0000000000
## Work_accident -0.06425132 0.0007404257
## left 0.44804323 0.2014875881
## promotion_last_5years -0.01664902 0.0770800344
## Work_accident left promotion_last_5years
## satisfaction_level 0.0456333312 -0.29268571 0.02629127
## last_evaluation -0.0607030310 0.26396556 -0.05025390
## number_project -0.0676805487 0.47224280 -0.03627774
## average_montly_hours -0.0642513222 0.44804323 -0.01664902
## time_spend_company 0.0007404257 0.20148759 0.07708003
## Work_accident 1.0000000000 -0.14813587 0.04962916
## left -0.1481358726 1.00000000 -0.07299786
## promotion_last_5years 0.0496291604 -0.07299786 1.00000000Relation between left people and satisfaction level
relation1 <- lm(hresource$satisfaction_level ~ hresource$left)
plot(hresource$left, hresource$satisfaction_level,
ylab = "Satisfaction Level",
xlab = "left",
main = "relation between left(0 or 1) and satisfaction level of people",
col = "lightblue")
abline (relation1, col = "black")
summary(relation1)##
## Call:
## lm(formula = hresource$satisfaction_level ~ hresource$left)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5468 -0.1368 -0.0001 0.1732 0.4799
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.666810 0.002143 311.12 <2e-16 ***
## hresource$left -0.226712 0.004393 -51.61 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2291 on 14997 degrees of freedom
## Multiple R-squared: 0.1508, Adjusted R-squared: 0.1508
## F-statistic: 2664 on 1 and 14997 DF, p-value: < 2.2e-16Relation between promotion in last5 years and satisfaction level
relation2 <- lm(hresource$satisfaction_level ~ hresource$promotion_last_5years)
plot(hresource$promotion_last_5years, hresource$satisfaction_level,
ylab = "Satisfaction Level",
xlab = "promotion",
main = "relation between promotion(0 or 1) and satisfaction level ",
col = "lightblue")
abline (relation2, col = "black")
summary(relation2)##
## Call:
## lm(formula = hresource$satisfaction_level ~ hresource$promotion_last_5years)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.5460 -0.1719 0.0281 0.2081 0.3881
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.611895 0.002051 298.273 < 2e-16 ***
## hresource$promotion_last_5years 0.044124 0.014067 3.137 0.00171 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2486 on 14997 degrees of freedom
## Multiple R-squared: 0.0006556, Adjusted R-squared: 0.000589
## F-statistic: 9.839 on 1 and 14997 DF, p-value: 0.001712Relation between number of project and satisfcation level
relation3 <- lm(hresource$satisfaction_level ~ hresource$number_project)
plot(hresource$number_project, hresource$satisfaction_level,
ylab = "Satisfaction Level",
xlab = "number of projects",
main = "relation between number of project and satisfaction level ",
col = "lightblue")
abline (relation3, col = "black")
summary(relation3)##
## Call:
## lm(formula = hresource$satisfaction_level ~ hresource$number_project)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56483 -0.21483 0.03169 0.20401 0.45052
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.722509 0.006517 110.86 <2e-16 ***
## hresource$number_project -0.028839 0.001630 -17.69 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2461 on 14997 degrees of freedom
## Multiple R-squared: 0.02044, Adjusted R-squared: 0.02037
## F-statistic: 312.9 on 1 and 14997 DF, p-value: < 2.2e-16Relation between satisfaction level and average monthly hours
relation4 <- lm(hresource$average_montly_hours ~ hresource$satisfaction_level)
plot(hresource$satisfaction_level, hresource$average_montly_hours,
ylab = "average monthly hours",
xlab = "Satisfaction Level",
main = "relation between Satisfaction Level and average monthly hours",
col = "lightblue")
abline (relation4, col = "black")
summary(relation4)##
## Call:
## lm(formula = hresource$average_montly_hours ~ hresource$satisfaction_level)
##
## Residuals:
## Min 1Q Median 3Q Max
## -106.914 -45.176 -0.619 43.985 109.301
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 203.518 1.085 187.648 <2e-16 ***
## hresource$satisfaction_level -4.027 1.640 -2.456 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.93 on 14997 degrees of freedom
## Multiple R-squared: 0.0004019, Adjusted R-squared: 0.0003353
## F-statistic: 6.03 on 1 and 14997 DF, p-value: 0.01408