This project is analyzing the detriminants of wages dataset from the year 1985. The dataset was collected from the United States Census Bureau.The analysis will project the summary statistics of the dataset. The analysis will also measure the trend if the Union workers earn more than the non union workers.

setwd("C:/Users/malia/OneDrive/Desktop/MSDS R Bridge")
library(tidyverse)
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library(ggplot2)
dat<-read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/CPS1985.csv")

Analyzing the mean,median,mode and quartiles of wages in 1985 in the United States for the purpose of data exploration.

summary(dat$wage)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   5.250   7.780   9.024  11.250  44.500
boxplot(dat$wage)

histogram(dat$wag,emain = "Histogram 1.1",xlab = "Wage exploration",col = "green")

### Created a new variable named social class using years of education for the Data Wragling purpose.
dat$socclass<- cut(dat$education, c(0,11,12,99), labels=c("high school dropout","highschool graduate","at least some college"))

corelation between wage and experience of the workers.

corelation between wage and education of the workers.

The corelation coefficient analysis projects that there is a positive corelation between wage and education.Positive corelation between wage and experiance has also been observed at a 99% confidence interval.

correlate<-data.frame(dat$experience,dat$wage)
correlate2<-rcorr(as.matrix(correlate))
correlate2
##                dat.experience dat.wage
## dat.experience           1.00     0.09
## dat.wage                 0.09     1.00
## 
## n= 534 
## 
## 
## P
##                dat.experience dat.wage
## dat.experience                0.0443  
## dat.wage       0.0443
correlate<-data.frame(dat$education,dat$wage)
correlate2<-rcorr(as.matrix(correlate))
correlate2
##               dat.education dat.wage
## dat.education          1.00     0.38
## dat.wage               0.38     1.00
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## n= 534 
## 
## 
## P
##               dat.education dat.wage
## dat.education                0      
## dat.wage       0
### number has been assigned to categorical variable for the purpose of analysis. 1 = "yes"(If the partcipant belongs to a union),2 = "no"(if the participant does not belong to a union.)
dat$union= factor(dat$union,
levels = c('yes','no'),
labels = c(1,2))

##ANOVA analysis has been conducted to see if the partcipants who belong to a union earn more than those who do not. The result portrays union members earn more than those who do not belong to a union.

one.way <- aov(wage ~ union, data = dat)
summary(one.way)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## union         1    368   368.4    14.3 0.000174 ***
## Residuals   532  13708    25.8                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Aggregate function: calculating means by group

aggregate(x = dat$wage,                      
          by = list(dat$union),               
          FUN = mean,                          
          na.rm=TRUE) 
##   Group.1         x
## 1       1 10.798125
## 2       2  8.635228

Eventhough the Union members tend to earn more. Majority of the participants are not union members. 1 = “yes”, 2 = “no”.

histogram(dat$union,main = "Histogram 1.2",xlab = "Trends for union members",col = "blue")