require(datasets)
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(magrittr)
## Loading required package: magrittr
work_profess <- read.csv(file = "http://www.personal.psu.edu/dlp/WFED540/pwces.csv", header = TRUE, sep=",")

glimpse(work_profess)
## Observations: 756
## Variables: 2
## $ gender  (int) 1, 1, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 1, 0, 1, 1, NA, 0...
## $ lifesat (int) 20, 18, 25, 7, 23, 25, 22, NA, 21, 29, 26, 26, 18, 28,...
summary (work_profess)
##      gender          lifesat    
##  Min.   :0.0000   Min.   : 5.0  
##  1st Qu.:0.0000   1st Qu.:18.0  
##  Median :0.0000   Median :23.0  
##  Mean   :0.4111   Mean   :21.5  
##  3rd Qu.:1.0000   3rd Qu.:25.0  
##  Max.   :1.0000   Max.   :30.0  
##  NA's   :70       NA's   :78
t.test(work_profess$lifesat~work_profess$gender)
## 
##  Welch Two Sample t-test
## 
## data:  work_profess$lifesat by work_profess$gender
## t = -1.3392, df = 578.22, p-value = 0.181
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.4771301  0.2794267
## sample estimates:
## mean in group 0 mean in group 1 
##        21.23869        21.83755
p-value = 0.181 is greater than α = .05, so fail to reject the null hypothesis (H0)
The gender differences in mean life satisfaction are not statically evident among working professionals, t = -1.3392, df = 578.22, p-value = 0.181, 95% CI, [-1.4771301, 0.2794267]

some useful calculations

#number of male
Male<-nrow(filter(work_profess, gender == 0))
Male
## [1] 404
#number of female
Female<-nrow(filter(work_profess, gender == 1))
Female
## [1] 282
#calculating the mean of the male
M <- filter(work_profess, gender == 0, lifesat, na.rm=TRUE) 
Male_mean <- (mean(M$lifesat))
Male_mean
## [1] 21.23869
#calculating the mean of the female
Fe <- filter(work_profess, gender == 1, lifesat, na.rm=TRUE) 
Female_mean <- (mean(Fe$lifesat))
Female_mean
## [1] 21.83755