Data606 Project
Joe Rovalino
12/8/2019
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Part 1 - Introduction
What is your research question?
Which majors/Major categories should a young female choose to increase earning potential and face less competition as a female in the field.
Why do you care?
The project was prompted from reading the following article “The Economic Guide to Picking A College Major” Ths was interesting to me on a personal level due to the fact that my niece entered college this year and chose to go to a 2 year college to reduce the loans that she would incur. She did come to me for advice on her next steps and choosing a major. I wasn’t really sure what would be the best major she should chose. This was a very interesting article that prompted my curiousity to get some data driven answers to her. I have shared the article with her and will hopefully find similiar conclusions from the article.
Why should others care?
There are numerous articles in the papers of our young population having huge debt in the pursuit of a good paying career via education. I feel that if you are going to take away 4-8 years away from your job earning years-it should have a great return on investment (ROI). As a society, having our young people in debt for something that just gives a very poor ROI is a recipe for a weak and unhapy country in the future.
Part 2 - Data
The data found on fivethirtyeight.com article: https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/ Data is found here: https://github.com/fivethirtyeight/data/tree/master/college-majors
Three main data files: - all-ages.csv - recent-grads.csv (ages <28) - grad-students.csv (ages 25+)
All contain basic earnings and labor force information. recent-grads.csv contains a more detailed breakdown, including by sex and by the type of job they got. grad-students.csv contains details on graduate school attendees. The data set most intereting to my need to get back some info to my niece is the recent-grads.csv.
For the data in datafile - recent grads under the age of 28, there are 173 majors in the data set. The response variable is the major code, major and the major category. It is qualitative. The quantitative independet variables are total, Sample_size, ShareWomen, Employed, Full-time, Part-time, Full_time_year_round, Unemployed, Unemployment_rate, Median, P25th, P75th, College, jobs, Non-college_jobs, Low_wage_job
library(tidyr)
library(dplyr)
library(tidyverse)
library(ggplot2)# load data
majors <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv")## Parsed with column specification:
## cols(
## Major_code = col_double(),
## Major = col_character(),
## Major_category = col_character(),
## Total = col_double(),
## Employed = col_double(),
## Employed_full_time_year_round = col_double(),
## Unemployed = col_double(),
## Unemployment_rate = col_double(),
## Median = col_double(),
## P25th = col_double(),
## P75th = col_double()
## )head(majors)## # A tibble: 6 x 11
## Major_code Major Major_category Total Employed Employed_full_t… Unemployed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1100 GENE… Agriculture &… 128148 90245 74078 2423
## 2 1101 AGRI… Agriculture &… 95326 76865 64240 2266
## 3 1102 AGRI… Agriculture &… 33955 26321 22810 821
## 4 1103 ANIM… Agriculture &… 103549 81177 64937 3619
## 5 1104 FOOD… Agriculture &… 24280 17281 12722 894
## 6 1105 PLAN… Agriculture &… 79409 63043 51077 2070
## # … with 4 more variables: Unemployment_rate <dbl>, Median <dbl>, P25th <dbl>,
## # P75th <dbl>#women_stem <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/women-stem.csv")
#head(women_stem)
recentgrads <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## Major = col_character(),
## Major_category = col_character()
## )## See spec(...) for full column specifications.head(recentgrads)## # A tibble: 6 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2419 PETR… 2339 2057 282 Engineering 0.121 36
## 2 2 2416 MINI… 756 679 77 Engineering 0.102 7
## 3 3 2415 META… 856 725 131 Engineering 0.153 3
## 4 4 2417 NAVA… 1258 1123 135 Engineering 0.107 16
## 5 5 2405 CHEM… 32260 21239 11021 Engineering 0.342 289
## 6 6 2418 NUCL… 2573 2200 373 Engineering 0.145 17
## # … with 12 more variables: Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>Part 3 - Exploratory data analysis
summary(recentgrads)## Rank Major_code Major Total
## Min. : 1 Min. :1100 Length:173 Min. : 124
## 1st Qu.: 44 1st Qu.:2403 Class :character 1st Qu.: 4550
## Median : 87 Median :3608 Mode :character Median : 15104
## Mean : 87 Mean :3880 Mean : 39370
## 3rd Qu.:130 3rd Qu.:5503 3rd Qu.: 38910
## Max. :173 Max. :6403 Max. :393735
## NA's :1
## Men Women Major_category ShareWomen
## Min. : 119 Min. : 0 Length:173 Min. :0.0000
## 1st Qu.: 2178 1st Qu.: 1778 Class :character 1st Qu.:0.3360
## Median : 5434 Median : 8386 Mode :character Median :0.5340
## Mean : 16723 Mean : 22647 Mean :0.5222
## 3rd Qu.: 14631 3rd Qu.: 22554 3rd Qu.:0.7033
## Max. :173809 Max. :307087 Max. :0.9690
## NA's :1 NA's :1 NA's :1
## Sample_size Employed Full_time Part_time
## Min. : 2.0 Min. : 0 Min. : 111 Min. : 0
## 1st Qu.: 39.0 1st Qu.: 3608 1st Qu.: 3154 1st Qu.: 1030
## Median : 130.0 Median : 11797 Median : 10048 Median : 3299
## Mean : 356.1 Mean : 31193 Mean : 26029 Mean : 8832
## 3rd Qu.: 338.0 3rd Qu.: 31433 3rd Qu.: 25147 3rd Qu.: 9948
## Max. :4212.0 Max. :307933 Max. :251540 Max. :115172
##
## Full_time_year_round Unemployed Unemployment_rate Median
## Min. : 111 Min. : 0 Min. :0.00000 Min. : 22000
## 1st Qu.: 2453 1st Qu.: 304 1st Qu.:0.05031 1st Qu.: 33000
## Median : 7413 Median : 893 Median :0.06796 Median : 36000
## Mean : 19694 Mean : 2416 Mean :0.06819 Mean : 40151
## 3rd Qu.: 16891 3rd Qu.: 2393 3rd Qu.:0.08756 3rd Qu.: 45000
## Max. :199897 Max. :28169 Max. :0.17723 Max. :110000
##
## P25th P75th College_jobs Non_college_jobs
## Min. :18500 Min. : 22000 Min. : 0 Min. : 0
## 1st Qu.:24000 1st Qu.: 42000 1st Qu.: 1675 1st Qu.: 1591
## Median :27000 Median : 47000 Median : 4390 Median : 4595
## Mean :29501 Mean : 51494 Mean : 12323 Mean : 13284
## 3rd Qu.:33000 3rd Qu.: 60000 3rd Qu.: 14444 3rd Qu.: 11783
## Max. :95000 Max. :125000 Max. :151643 Max. :148395
##
## Low_wage_jobs
## Min. : 0
## 1st Qu.: 340
## Median : 1231
## Mean : 3859
## 3rd Qu.: 3466
## Max. :48207
## top5majorsbyPay<- top_n(recentgrads, 5, Median)
top5majorsbyPay## # A tibble: 6 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2419 PETR… 2339 2057 282 Engineering 0.121 36
## 2 2 2416 MINI… 756 679 77 Engineering 0.102 7
## 3 3 2415 META… 856 725 131 Engineering 0.153 3
## 4 4 2417 NAVA… 1258 1123 135 Engineering 0.107 16
## 5 5 2405 CHEM… 32260 21239 11021 Engineering 0.342 289
## 6 6 2418 NUCL… 2573 2200 373 Engineering 0.145 17
## # … with 12 more variables: Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>top5majorswhighunempl <- top_n(recentgrads, 5, Unemployment_rate)
top5majorswhighunempl## # A tibble: 5 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 6 2418 NUCL… 2573 2200 373 Engineering 0.145 17
## 2 30 5402 PUBL… 5978 2639 3339 Law & Public … 0.559 55
## 3 85 2107 COMP… 7613 5291 2322 Computers & M… 0.305 97
## 4 90 5401 PUBL… 5629 2947 2682 Law & Public … 0.476 46
## 5 171 5202 CLIN… 2838 568 2270 Psychology & … 0.800 13
## # … with 12 more variables: Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl># using the sammary function on recent grads. Will create a data frame to only include the majors with a median pay greater than the mean of 40,151
overmeanpayingmajors <- filter(recentgrads, Median > 40151)
#reduces the number of majors to focus on from 173 down to 56
top5wMedian <- top_n(overmeanpayingmajors, 5, Median)
top5wMedian## # A tibble: 6 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2419 PETR… 2339 2057 282 Engineering 0.121 36
## 2 2 2416 MINI… 756 679 77 Engineering 0.102 7
## 3 3 2415 META… 856 725 131 Engineering 0.153 3
## 4 4 2417 NAVA… 1258 1123 135 Engineering 0.107 16
## 5 5 2405 CHEM… 32260 21239 11021 Engineering 0.342 289
## 6 6 2418 NUCL… 2573 2200 373 Engineering 0.145 17
## # … with 12 more variables: Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>It looks like median unemployement is approximately 20% less for the top 5 majors compared to overall recent grads (0.05561/0.6819).
It also looks like median Share of Women is approximately 25% less in the top 5 paid majors then compared to overall recent grads (.1328/.5340)
summary(recentgrads$Unemployment_rate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.05031 0.06796 0.06819 0.08756 0.17723summary(overmeanpayingmajors$Unemployment_rate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.04667 0.06151 0.06562 0.08801 0.17723summary(top5wMedian$Unemployment_rate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.01838 0.03060 0.05561 0.07469 0.10321 0.17723ChanceofUnemployment <- cbind(recentgrads$Unemployment_rate, overmeanpayingmajors$Unemployment_rate, top5wMedian$Unemployment_rate)## Warning in cbind(recentgrads$Unemployment_rate,
## overmeanpayingmajors$Unemployment_rate, : number of rows of result is not a
## multiple of vector length (arg 2)boxplot(ChanceofUnemployment,names = c("All", "Above Median Pay", "Top5Majors"), ylab = "Unemployment Rate")
summary(recentgrads$ShareWomen)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.3360 0.5340 0.5222 0.7033 0.9690 1summary(overmeanpayingmajors$ShareWomen)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.09071 0.21105 0.32078 0.35872 0.43960 0.92781 1summary(top5wMedian$ShareWomen)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1019 0.1106 0.1328 0.1616 0.1510 0.3416ShareofWomen <- cbind(recentgrads$ShareWomen, overmeanpayingmajors$ShareWomen, top5wMedian$ShareWomen)## Warning in cbind(recentgrads$ShareWomen, overmeanpayingmajors$ShareWomen, :
## number of rows of result is not a multiple of vector length (arg 2)boxplot(ShareofWomen,names = c("All", "Above Median Pay", "Top5Majors"), ylab = "ShareofWomen")
It looks like median unemployement is approximately 20% less for the top 5 majors compared to overall recent grads (0.05561/0.6819).
It also looks like median Share of Women is approximately 25% less in the top 5 paid majors then compared to overall recent grads (.1328/.5340)
options(scipen = 999)
gg <- ggplot(overmeanpayingmajors, aes(x=ShareWomen, y=Median)) +
geom_point(aes(col=ShareWomen, size=Median)) +
geom_smooth(method="loess", se=F)
labs(subtitle="SHare of Women by Median pay",
y="Median Pay",
x="Share of Women",
title="Scatterplot",
caption = "Source: DF overmeanpayingmajors")## $y
## [1] "Median Pay"
##
## $x
## [1] "Share of Women"
##
## $title
## [1] "Scatterplot"
##
## $subtitle
## [1] "SHare of Women by Median pay"
##
## $caption
## [1] "Source: DF overmeanpayingmajors"
##
## attr(,"class")
## [1] "labels" require(scales)## Loading required package: scales##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factor gg + scale_y_continuous(labels = comma) ## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
plot(gg)## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
The histogram will show a normal distribution unimodal with some slight right skwe.
hist(recentgrads$Median, main = "Median Income for recent grads < 28", xlab = "Median Income for recent grads < 28", col = " navy")
hist(top5wMedian$Median, main = "Median Income for Top5 majors for grads <28", xlab = "Median for top 5 majors for grads <29", col = "dark green")
Part 4 - Inference
This is an observational study of the college majors and the income. From our textbook, Open Intro Statistics chapter 7.5 Comparing many means wiht ANOVA: "Generally we must check three conditions on the data before performing ANOVA:
- the observations are independent within and across groups,
• the data within each group are nearly normal, and
• the variability across the groups is about equal."
Majorcategory_mean_variance <- summarize(recentgrads %>% group_by(Major_category), mean = mean(Median))
Majorcategory_mean_variance## # A tibble: 16 x 2
## Major_category mean
## <chr> <dbl>
## 1 Agriculture & Natural Resources 36900
## 2 Arts 33062.
## 3 Biology & Life Science 36421.
## 4 Business 43538.
## 5 Communications & Journalism 34500
## 6 Computers & Mathematics 42745.
## 7 Education 32350
## 8 Engineering 57383.
## 9 Health 36825
## 10 Humanities & Liberal Arts 31913.
## 11 Industrial Arts & Consumer Services 36343.
## 12 Interdisciplinary 35000
## 13 Law & Public Policy 42200
## 14 Physical Sciences 41890
## 15 Psychology & Social Work 30100
## 16 Social Science 37344.Looking at the analytis of the group of major category and it’s mean, it appears that the variability across the groups is about equal
- We see from the data that each major is indepedent
- Reviewing the histograms we can see that the distribution of the median salary is a normal distribtion.
- check the variability is about equal using and F-Test
Arts, Social Science & Humanities = 1 Arts = 2 Biology and Life Sciences = 3 business = 4 Communications & Journalism = 5 Computers and Mathematics = 6 Education = 7 Engineering = 8 Health = 9 Humanities and Liberal Arts = 10 Industrial Arts and Consumer Services = 11 Indterdisciplinary = 12 Law & Public Policy = 13 Physical Sciences = 14 Psychology and Social Work = 15 Social Science = 16
Hypothesis null and Hypothesis Alternative - H0: μArts, So = μ1 = μ2 = μ3 …= μ16 HA: Median salary varies by major selected.
summary(aov(recentgrads$Median ~ recentgrads$Major_category))## Df Sum Sq Mean Sq F value
## recentgrads$Major_category 15 12948122198 863208147 14
## Residuals 157 9681069941 61662866
## Pr(>F)
## recentgrads$Major_category <0.0000000000000002 ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Per our textbook (pg 289), we will use the F statistic also called the F-Test to come to a conclusion that the alternative hypothesis is the correct choice as the p value is close to zero (0.0000000000000002). Median salary is not the same across majors.
Part 5 - Conclusion
- Stick to Engineering and Technology - it will lead to higher paying jobs as an undergrad and have the least Share of women.
- Looking at ratios of men to women to decide on picking a major may help to stand out as a women. Engineering majors tend to have a low percentage of men to woemen. there are culture trends related to woment diversity inititives like #girlpower and #equalpay. It may be beneficial to try to enter the workforce in a male dominted major as it could be a good way to stand out from other applicants.
References
https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/ by Ben Casselmen
All data is from American Community Survey 2010-2012 Public Use Microdata Series.
Download data here: http://www.census.gov/programs-surveys/acs/data/pums.html
Documentation here: http://www.census.gov/programs-surveys/acs/technical-documentation/pums.html
Open Intro Statistics 4th Ed. by David Diez, Mine Cetinkaya-Rundel and Christpher Bar Chapter 7 "Comparing many means wiht ANOVA
Shiny App: https://bencasselman.shinyapps.io/new-test/