11.14 Chapter 11 Review Questions

This data frame is called collegegrads. It is a subset of a data set called college_recent_grads put out by FiveThirtyEight. These data were used to write the story, "The Economic Guide To Picking A College Major". FiveThirtyEight originally culled this data from the American Community Survey, a detailed data collection effort by the US Census Bureau.

A data frame with 173 rows representing college majors:

  • major - Major description
  • major_category - Category of major
  • engineering - TRUE/FALSE, is this an engineering major?
  • STEM - TRUE/FALSE, is this a STEM (science, technology, engineering, math) major?
  • totalgrads - Total number of people with this major in thousands
  • employed_fulltime - Employed 35 hours or more
  • unemployment_rate - Unemployed / (Unemployed + Employed)
  • p25th_income - 25th percentile of earnings in thousands of dollars (salary in a year)
  • median_income - Median earnings of full-time, year-round workers in thousands of dollars (salary in a year)
  • p75th_income - 75th percentile of earnings in thousands of dollars (salary in a year)

require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run the following code gf_histogram(~median_income, data = collegegrads) %>% gf_facet_grid(STEM ~ .) %>% gf_labs(x = "Median Income (in thousands of dollars)")
require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run your code here
require(coursekata) # data prep - rename college_recent_grads$totalgrads <- college_recent_grads$total college_recent_grads$p25th_income <- college_recent_grads$p25th college_recent_grads$median_income <- college_recent_grads$median college_recent_grads$p75th_income <- college_recent_grads$p75th # create new engineering variable college_recent_grads <- mutate(college_recent_grads, engineering = ifelse(major_category == "Engineering", "TRUE", "FALSE")) # create STEM variable college_recent_grads <- mutate(college_recent_grads, STEM = ifelse(major_category == "Engineering" | major_category == "Biology & Life Science" | major_category == "Computers & Mathematics" |major_category == "Physical Sciences", "TRUE", "FALSE")) # data prep collegegrads <- select(college_recent_grads, major, major_category, engineering, STEM, totalgrads, employed_fulltime, unemployment_rate, p25th_income, median_income, p75th_income) collegegrads$median_income <- collegegrads$median_income/1000 # Run your code here

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