Week 5: Apply 4

Import data

# excel file
data <- readxl::read_excel("Salaries.xlsx")
data

## # A tibble: 397 × 6
##    rank      discipline yrs.since.phd yrs.service sex    salary
##    <chr>     <chr>              <dbl>       <dbl> <chr>   <dbl>
##  1 Prof      B                     19          18 Male   139750
##  2 Prof      B                     20          16 Male   173200
##  3 AsstProf  B                      4           3 Male    79750
##  4 Prof      B                     45          39 Male   115000
##  5 Prof      B                     40          41 Male   141500
##  6 AssocProf B                      6           6 Male    97000
##  7 Prof      B                     30          23 Male   175000
##  8 Prof      B                     45          45 Male   147765
##  9 Prof      B                     21          20 Male   119250
## 10 Prof      B                     18          18 Female 129000
## # … with 387 more rows

State one question

What occupational industry makes up the majority of those who are considered wealthy?

Plot data

data %>%
    
    ggplot(aes(yrs.since.phd)) + 
    geom_point(mapping = aes(x = yrs.since.phd, y = salary))

Analysis

This graph show that those who are considered wealthy are also more likely to have graduated from college with a Master’s degree. This graph shows a good representation about how by going back to college to further your education to not only to help further your learning, but it also helps those who want to be considered successful. The more learning you bring to your company about what you know, then the more you are able to show your company why you deserved that job and why you stand out from the rest.