Data Preparation

Load Data

# load data

library(tidyr)
library(dplyr)
library(tidyverse)
library(stringr)
library(ggplot2)

# all ages
temp1 <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/all-ages.csv", stringsAsFactors = FALSE)

# graduate students with ages>25
temp2 <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/grad-students.csv", stringsAsFactors = FALSE)

# recent graduates with ages<28
temp3 <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv", stringsAsFactors = FALSE)

# see the list of all 16 major categories
temp1$Major_category

# categorize stem, business and liberal arts
stem <- c("Agriculture & Natural Resources", "Biology & Life Science", "Health", "Computers & Mathematics", "Engineering", "Physical Sciences", "Social Science")

business <- c("Business")

liberalarts <- c("Communications & Journalism", "Education", "Industrial Arts & Consumer Services", "Law & Public Policy", "Psychology & Social Work", "Arts", "Humanities & Liberal Arts", "Interdisciplinary")

Tidy Data

# all_ages
all_ages <- temp1 %>%
  #drop_na() %>%
  mutate(Type = case_when(Major_category %in% liberalarts ~ "L",
                          Major_category %in% stem ~ "S",
                          TRUE ~ "B")) %>%
  arrange(Major_category) %>%
  group_by(Major_category) %>%
  mutate(Mean_median= round(mean(Median),0)) %>%
  select(Major_category, Type, Employed, Unemployed, Unemployment_rate, Median, P25th, P75th, Mean_median)
    
all_ages

# grad_students
grad_students <- temp2 %>%
  #drop_na() %>%
  mutate(Type = case_when(Major_category %in% liberalarts ~ "L",
                          Major_category %in% stem ~ "S",
                          TRUE ~ "B")) %>%
  arrange(Major_category) %>%
  group_by(Major_category) %>%
  mutate(Mean_grad_median= round(mean(Grad_median),0)) %>%
  select(Major_category, Type, Grad_employed, Grad_unemployed, Grad_unemployment_rate, Grad_median, Grad_P25, Grad_P75, Mean_grad_median) 

grad_students

recent_grads <- temp3 %>%
  #drop_na() %>%
  mutate(Type = case_when(Major_category %in% liberalarts ~ "L",
                          Major_category %in% stem ~ "S",
                          TRUE ~ "B")) %>%
  arrange(Major_category) %>%
  group_by(Major_category) %>%
  mutate(Mean_median= round(mean(Median),0)) %>%
  mutate(Mean_jobindex = round(mean(ifelse(Non_college_jobs==0, 0, Non_college_jobs/(Non_college_jobs + College_jobs))), 4)) %>%
  select(Rank, Major_category, Type, Employed, Unemployed, Unemployment_rate, Median, P25th, P75th, College_jobs, Non_college_jobs, Mean_jobindex, Mean_median)

recent_grads

Research question

You should phrase your research question in a way that matches up with the scope of inference your dataset allows for.

As STEM majors are becoming more popular, I would like to study the relationship between median income and different major categories in stem, business, and liberal arts.

Also, I would like to study the relationship between median income and the percentage of job requirement of being college graduates (Mean_jobindex) by different major categories in stem, business, and liberal arts, among the recent graduates.

Cases

What are the cases, and how many are there?

All_ages: There are 173 cases in total, with no age limits in each case.

Grad_students: There are 173 cases in total, with age >25.

Recent_grads: There are 173 cases in total, with age <28. Although one row has some NA values, I do not need the columns with NA, therefore I do not have to drop that row.

Each case represents one unique major_code and major offered by colleges in United States.

Data collection

Describe the method of data collection.

Every year, the U.S. Census Bureau contacts over 3.5 million households across the country to participate in the American Community Survey (ACS). ACS is an ongoing survey that provides vital information on a yearly basis about our nation and its people. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.

Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. (https://www.census.gov/programs-surveys/acs/about.html)

All three main .csv datasets are from American Community Survey 2010-2012 Public Use Microdata Series. They contain basic earnings and labor force information.

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

Type of study

What type of study is this (observational/experiment)?

This study is observational.

Data Source

If you collected the data, state self-collected. If not, provide a citation/link.

The datasets were obtained from github: https://github.com/fivethirtyeight/data/tree/master/college-majors.

Dependent Variable

What is the response variable? Is it quantitative or qualitative?

My response variable is Median income. It is quantitative.

Independent Variable

You should have two independent variables, one quantitative and one qualitative.

Major_category is qualitative. Mean_jobindex is quantitative.

Relevant summary statistics

Provide summary statistics for each the variables. Also include appropriate visualizations related to your research question (e.g. scatter plot, boxplots, etc). This step requires the use of R, hence a code chunk is provided below. Insert more code chunks as needed.

summary(all_ages)

##  Major_category         Type              Employed         Unemployed    
##  Length:173         Length:173         Min.   :   1492   Min.   :     0  
##  Class :character   Class :character   1st Qu.:  17281   1st Qu.:  1101  
##  Mode  :character   Mode  :character   Median :  56564   Median :  3619  
##                                        Mean   : 166162   Mean   :  9725  
##                                        3rd Qu.: 142879   3rd Qu.:  8862  
##                                        Max.   :2354398   Max.   :147261  
##  Unemployment_rate     Median           P25th           P75th       
##  Min.   :0.00000   Min.   : 35000   Min.   :24900   Min.   : 45800  
##  1st Qu.:0.04626   1st Qu.: 46000   1st Qu.:32000   1st Qu.: 70000  
##  Median :0.05472   Median : 53000   Median :36000   Median : 80000  
##  Mean   :0.05736   Mean   : 56816   Mean   :38697   Mean   : 82506  
##  3rd Qu.:0.06904   3rd Qu.: 65000   3rd Qu.:42000   3rd Qu.: 95000  
##  Max.   :0.15615   Max.   :125000   Max.   :78000   Max.   :210000  
##   Mean_median   
##  Min.   :43000  
##  1st Qu.:46080  
##  Median :53222  
##  Mean   :56816  
##  3rd Qu.:62400  
##  Max.   :77759

# Median Income from All Ages
ggplot(all_ages, aes(x=Median, fill=..count..)) + 
    geom_histogram(bins=30, color="black") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    ggtitle("Median Income from All Ages") +
    xlab("Median Salary") + ylab("Count")

# Mean of Median Income from All Ages by Major Category
ggplot(all_ages, aes(x= reorder(Major_category, Mean_median), y=Mean_median, fill=Mean_median)) +
    geom_bar(stat="identity", position=position_dodge(), width = 0.8) +
    coord_flip() + 
    ggtitle("Mean of Median Income from All Ages by Major Category") +
    xlab("Major Category") + ylab("Median Salary") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    geom_text(aes(label=Mean_median), color='white', size=3, vjust=0.4, hjust=1.1)

summary(grad_students)

##  Major_category         Type           Grad_employed    Grad_unemployed
##  Length:173         Length:173         Min.   :  1008   Min.   :    0  
##  Class :character   Class :character   1st Qu.: 12659   1st Qu.:  453  
##  Mode  :character   Mode  :character   Median : 28930   Median : 1179  
##                                        Mean   : 94037   Mean   : 3506  
##                                        3rd Qu.:109944   3rd Qu.: 3329  
##                                        Max.   :915341   Max.   :35718  
##  Grad_unemployment_rate  Grad_median        Grad_P25        Grad_P75     
##  Min.   :0.00000        Min.   : 47000   Min.   :24500   Min.   : 65000  
##  1st Qu.:0.02607        1st Qu.: 65000   1st Qu.:45000   1st Qu.: 93000  
##  Median :0.03665        Median : 75000   Median :50000   Median :108000  
##  Mean   :0.03934        Mean   : 76756   Mean   :52597   Mean   :112087  
##  3rd Qu.:0.04805        3rd Qu.: 90000   3rd Qu.:60000   3rd Qu.:130000  
##  Max.   :0.13851        Max.   :135000   Max.   :85000   Max.   :294000  
##  Mean_grad_median
##  Min.   :55000   
##  1st Qu.:66571   
##  Median :80292   
##  Mean   :76756   
##  3rd Qu.:85545   
##  Max.   :94328

# Median Income from Graduate Students
ggplot(grad_students, aes(x=Grad_median, fill=..count..)) + 
    geom_histogram(bins=30, color="black") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    ggtitle("Median Income from Grad Students") +
    xlab("Median Salary") + ylab("Count")

# Mean of Median Income from Grad Students by Major Category
ggplot(grad_students, aes(x= reorder(Major_category, Mean_grad_median), y=Mean_grad_median, fill=Mean_grad_median)) +
    geom_bar(stat="identity", position=position_dodge(), width = 0.8) +
    coord_flip() + 
    ggtitle("Mean of Median Income from Grad Students by Major Category") +
    xlab("Major Category") + ylab("Median Salary") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    geom_text(aes(label=Mean_grad_median), color='white', size=3, vjust=0.4, hjust=1.1)

summary(recent_grads)

##       Rank     Major_category         Type              Employed     
##  Min.   :  1   Length:173         Length:173         Min.   :     0  
##  1st Qu.: 44   Class :character   Class :character   1st Qu.:  3608  
##  Median : 87   Mode  :character   Mode  :character   Median : 11797  
##  Mean   : 87                                         Mean   : 31193  
##  3rd Qu.:130                                         3rd Qu.: 31433  
##  Max.   :173                                         Max.   :307933  
##    Unemployed    Unemployment_rate     Median           P25th      
##  Min.   :    0   Min.   :0.00000   Min.   : 22000   Min.   :18500  
##  1st Qu.:  304   1st Qu.:0.05031   1st Qu.: 33000   1st Qu.:24000  
##  Median :  893   Median :0.06796   Median : 36000   Median :27000  
##  Mean   : 2416   Mean   :0.06819   Mean   : 40151   Mean   :29501  
##  3rd Qu.: 2393   3rd Qu.:0.08756   3rd Qu.: 45000   3rd Qu.:33000  
##  Max.   :28169   Max.   :0.17723   Max.   :110000   Max.   :95000  
##      P75th         College_jobs    Non_college_jobs Mean_jobindex   
##  Min.   : 22000   Min.   :     0   Min.   :     0   Min.   :0.2863  
##  1st Qu.: 42000   1st Qu.:  1675   1st Qu.:  1591   1st Qu.:0.3201  
##  Median : 47000   Median :  4390   Median :  4595   Median :0.4839  
##  Mean   : 51494   Mean   : 12323   Mean   : 13284   Mean   :0.4894  
##  3rd Qu.: 60000   3rd Qu.: 14444   3rd Qu.: 11783   3rd Qu.:0.6145  
##  Max.   :125000   Max.   :151643   Max.   :148395   Max.   :0.7027  
##   Mean_median   
##  Min.   :30100  
##  1st Qu.:33062  
##  Median :36900  
##  Mean   :40151  
##  3rd Qu.:42745  
##  Max.   :57383

# Median Income from Recent Graduates
ggplot(recent_grads, aes(x=Median, fill=..count..)) + 
    geom_histogram(bins=30, color="black") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    ggtitle("Median Income from Recent Grads") +
    xlab("Median Salary") + ylab("Count")

# Mean of Median Income from Recent Graduates by Major Category
ggplot(recent_grads, aes(x= reorder(Major_category, Mean_median), y=Mean_median, fill=Mean_median)) +
    geom_bar(stat="identity", position=position_dodge(), width = 0.8) +
    coord_flip() + 
    ggtitle("Mean of Median Income from Recent Graduates by Major Category") +
    xlab("Major Category") + ylab("Median Salary") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    geom_text(aes(label=Mean_median), color='white', size=3, vjust=0.4, hjust=1.1)

# Mean of Job Index from Recent Graduates by Major Category
ggplot(recent_grads, aes(x= reorder(Major_category, Mean_jobindex), y=Mean_jobindex, fill=Mean_jobindex)) +
    geom_bar(stat="identity", position=position_dodge(), width = 0.8) +
    coord_flip() + 
    ggtitle("Mean of Job Index from Recent Graduates by Major Category") +
    xlab("Major Category") + ylab("Job Index") +
    scale_fill_gradient(low='royalblue4', high='royalblue1') +
    geom_text(aes(label=Mean_jobindex), color='white', size=3, vjust=0.4, hjust=1.1)

DATA 606 Data Project Proposal

Sin Ying Wong

11/02/2019