KLEBER PEREZ

DATA 606 - PROJECT PROPOSAL
SOURCE FILES AND RMARKDOWN ON RPUBS AND GITHUB

Load Libraries

# load libraries

library(kableExtra)
#suppressMessages(suppressWarnings(library(tidyverse)))
library(tidyr)
library(dplyr)
library(psych)
library(stringr)

Data Wrangling

Data source from fivethirtyeight and github.

# Load data and Subset data by category 
url1 <- "https://raw.githubusercontent.com/kleberperez1/CUNY-SPS-Data606-Project-Proposal/master/all_ages.csv"
all_ages <- url1 %>% 
  read.csv(stringsAsFactors = FALSE) %>% 
  tbl_df() %>% 
  arrange(Major_category)

kable(head(all_ages, 20)) %>%
  kable_styling("striped", "hovered", font_size = 12) %>%
  scroll_box(height = "500px")

Major_code	Major	Major_category	Total	Employed	Employed_full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th
1100	GENERAL AGRICULTURE	Agriculture & Natural Resources	128148	90245	74078	2423	0.0261471	50000	34000	80000
1101	AGRICULTURE PRODUCTION AND MANAGEMENT	Agriculture & Natural Resources	95326	76865	64240	2266	0.0286361	54000	36000	80000
1102	AGRICULTURAL ECONOMICS	Agriculture & Natural Resources	33955	26321	22810	821	0.0302483	63000	40000	98000
1103	ANIMAL SCIENCES	Agriculture & Natural Resources	103549	81177	64937	3619	0.0426789	46000	30000	72000
1104	FOOD SCIENCE	Agriculture & Natural Resources	24280	17281	12722	894	0.0491884	62000	38500	90000
1105	PLANT SCIENCE AND AGRONOMY	Agriculture & Natural Resources	79409	63043	51077	2070	0.0317909	50000	35000	75000
1106	SOIL SCIENCE	Agriculture & Natural Resources	6586	4926	4042	264	0.0508671	63000	39400	88000
1199	MISCELLANEOUS AGRICULTURE	Agriculture & Natural Resources	8549	6392	5074	261	0.0392304	52000	35000	75000
1302	FORESTRY	Agriculture & Natural Resources	69447	48228	39613	2144	0.0425633	58000	40500	80000
1303	NATURAL RESOURCES MANAGEMENT	Agriculture & Natural Resources	83188	65937	50595	3789	0.0543413	52000	37100	75000
6000	FINE ARTS	Arts	571961	386961	256747	29912	0.0717533	45000	30000	70000
6001	DRAMA AND THEATER ARTS	Arts	174817	135071	81519	11789	0.0802737	42000	29000	62000
6002	MUSIC	Arts	276262	192704	116142	11155	0.0547192	45000	30000	67000
6003	VISUAL AND PERFORMING ARTS	Arts	55141	41098	23479	4297	0.0946580	40000	27000	59000
6004	COMMERCIAL ART AND GRAPHIC DESIGN	Arts	504657	379980	266671	30330	0.0739197	46600	32000	70000
6005	FILM VIDEO AND PHOTOGRAPHIC ARTS	Arts	133508	107651	69303	10080	0.0856189	47000	30000	70000
6007	STUDIO ARTS	Arts	81008	58799	36943	5372	0.0837138	37600	24900	58000
6099	MISCELLANEOUS FINE ARTS	Arts	8511	6431	3802	1190	0.1561475	45000	30000	60000
1301	ENVIRONMENTAL SCIENCE	Biology & Life Science	106106	87602	65238	4736	0.0512898	52000	38000	75000
3600	BIOLOGY	Biology & Life Science	839454	583079	422788	36757	0.0593012	51000	35000	80000

Subset by Category

# Subsetting by Major Category 
all_ages_ag <- all_ages %>% 
  filter(Major_category == "Agriculture & Natural Resources")

kable(head(all_ages_ag, 20)) %>%
  kable_styling("striped", "hovered", font_size = 12) %>%
  scroll_box(height = "500px")

Major_code	Major	Major_category	Total	Employed	Employed_full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th
1100	GENERAL AGRICULTURE	Agriculture & Natural Resources	128148	90245	74078	2423	0.0261471	50000	34000	80000
1101	AGRICULTURE PRODUCTION AND MANAGEMENT	Agriculture & Natural Resources	95326	76865	64240	2266	0.0286361	54000	36000	80000
1102	AGRICULTURAL ECONOMICS	Agriculture & Natural Resources	33955	26321	22810	821	0.0302483	63000	40000	98000
1103	ANIMAL SCIENCES	Agriculture & Natural Resources	103549	81177	64937	3619	0.0426789	46000	30000	72000
1104	FOOD SCIENCE	Agriculture & Natural Resources	24280	17281	12722	894	0.0491884	62000	38500	90000
1105	PLANT SCIENCE AND AGRONOMY	Agriculture & Natural Resources	79409	63043	51077	2070	0.0317909	50000	35000	75000
1106	SOIL SCIENCE	Agriculture & Natural Resources	6586	4926	4042	264	0.0508671	63000	39400	88000
1199	MISCELLANEOUS AGRICULTURE	Agriculture & Natural Resources	8549	6392	5074	261	0.0392304	52000	35000	75000
1302	FORESTRY	Agriculture & Natural Resources	69447	48228	39613	2144	0.0425633	58000	40500	80000
1303	NATURAL RESOURCES MANAGEMENT	Agriculture & Natural Resources	83188	65937	50595	3789	0.0543413	52000	37100	75000

Apply Filter

# Apply Filter on category 
value_list <- c("Arts", "Biology & Life Science", "Business") #, "Communications & Journalism", "Computers & Mathematics", 
                #"Education", "Engineering", "Health", "Humanities & Liberal Arts", "Industrial Arts & Consumer Services", 
                #"Law & Public Policy", "Physical Sciences", "Psychology & Social Work", "Social Science")
all_ages_value <- all_ages %>% 
  filter(Major_category %in% value_list)

kable(all_ages_value) %>%
  kable_styling("striped", "hovered", font_size = 12) %>%
  scroll_box(height = "500px")

Major_code	Major	Major_category	Total	Employed	Employed_full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th
6000	FINE ARTS	Arts	571961	386961	256747	29912	0.0717533	45000	30000	70000
6001	DRAMA AND THEATER ARTS	Arts	174817	135071	81519	11789	0.0802737	42000	29000	62000
6002	MUSIC	Arts	276262	192704	116142	11155	0.0547192	45000	30000	67000
6003	VISUAL AND PERFORMING ARTS	Arts	55141	41098	23479	4297	0.0946580	40000	27000	59000
6004	COMMERCIAL ART AND GRAPHIC DESIGN	Arts	504657	379980	266671	30330	0.0739197	46600	32000	70000
6005	FILM VIDEO AND PHOTOGRAPHIC ARTS	Arts	133508	107651	69303	10080	0.0856189	47000	30000	70000
6007	STUDIO ARTS	Arts	81008	58799	36943	5372	0.0837138	37600	24900	58000
6099	MISCELLANEOUS FINE ARTS	Arts	8511	6431	3802	1190	0.1561475	45000	30000	60000
1301	ENVIRONMENTAL SCIENCE	Biology & Life Science	106106	87602	65238	4736	0.0512898	52000	38000	75000
3600	BIOLOGY	Biology & Life Science	839454	583079	422788	36757	0.0593012	51000	35000	80000
3601	BIOCHEMICAL SCIENCES	Biology & Life Science	75322	52594	37103	4056	0.0715975	53000	33000	82000
3602	BOTANY	Biology & Life Science	14135	9284	6333	327	0.0340235	50000	32000	75000
3603	MOLECULAR BIOLOGY	Biology & Life Science	28197	20221	13366	1303	0.0605371	45000	30000	70000
3604	ECOLOGY	Biology & Life Science	45368	36708	25677	1888	0.0489170	47500	32000	73000
3605	GENETICS	Biology & Life Science	6362	4747	3498	206	0.0415910	48000	33000	80000
3606	MICROBIOLOGY	Biology & Life Science	68885	45422	33990	2435	0.0508807	60000	40000	85000
3607	PHARMACOLOGY	Biology & Life Science	5015	3481	2579	57	0.0161108	60000	35000	105000
3608	PHYSIOLOGY	Biology & Life Science	43984	31394	20207	1692	0.0511395	50000	30000	75000
3609	ZOOLOGY	Biology & Life Science	55395	35714	26152	1815	0.0483626	55000	34000	85000
3611	NEUROSCIENCE	Biology & Life Science	13676	8987	5446	665	0.0688976	35000	28000	52000
3699	MISCELLANEOUS BIOLOGY	Biology & Life Science	29389	22298	16508	1114	0.0475824	52000	33500	72800
4006	COGNITIVE SCIENCE AND BIOPSYCHOLOGY	Biology & Life Science	6898	5527	3639	284	0.0488728	53000	31500	93000
6200	GENERAL BUSINESS	Business	2148712	1580978	1304646	85626	0.0513775	60000	40000	95000
6201	ACCOUNTING	Business	1779219	1335825	1095027	75379	0.0534147	65000	42500	100000
6202	ACTUARIAL SCIENCE	Business	9763	7846	6880	466	0.0560635	72000	53000	115000
6203	BUSINESS MANAGEMENT AND ADMINISTRATION	Business	3123510	2354398	1939384	147261	0.0588653	58000	39500	86000
6204	OPERATIONS LOGISTICS AND E-COMMERCE	Business	57200	47341	41104	2141	0.0432683	65000	45000	90000
6205	BUSINESS ECONOMICS	Business	75547	57983	48471	3816	0.0617486	65000	45000	100000
6206	MARKETING AND MARKETING RESEARCH	Business	1114624	890125	704912	51839	0.0550329	56000	38500	90000
6207	FINANCE	Business	816548	670681	561073	34166	0.0484729	65000	45000	100000
6209	HUMAN RESOURCES AND PERSONNEL MANAGEMENT	Business	187274	142879	116466	9241	0.0607481	54000	38000	80000
6210	INTERNATIONAL BUSINESS	Business	86064	66453	51012	5106	0.0713537	54000	38600	80000
6211	HOSPITALITY MANAGEMENT	Business	200854	163393	122499	8862	0.0514470	49000	33000	70000
6212	MANAGEMENT INFORMATION SYSTEMS AND STATISTICS	Business	156673	134478	118249	6186	0.0439771	72000	50000	100000
6299	MISCELLANEOUS BUSINESS & MEDICAL ADMINISTRATION	Business	102753	77471	61603	4308	0.0526786	53000	36000	83000

Graduate Students

# Load graduate students file and subset data

url2 <- "https://raw.githubusercontent.com/kleberperez1/CUNY-SPS-Data606-Project-Proposal/master/grad_students.csv"
grad_stdnt <- url2 %>% read.csv(stringsAsFactors = FALSE) %>% tbl_df() %>% arrange(Major_category)

grad_ag <- grad_stdnt %>% 
  filter(Major_category == "Agriculture & Natural Resources")

kable(head(grad_ag, 20)) %>%
  kable_styling("striped", "hovered", font_size = 12) %>%
  scroll_box(height = "500px")

Major_code	Major	Major_category	Grad_total	Grad_sample_size	Grad_employed	Grad_full_time_year_round	Grad_unemployed	Grad_unemployment_rate	Grad_median	Grad_P25	Grad_P75	Nongrad_total	Nongrad_employed	Nongrad_full_time_year_round	Nongrad_unemployed	Nongrad_unemployment_rate	Nongrad_median	Nongrad_P25	Nongrad_P75	Grad_share	Grad_premium
1101	AGRICULTURE PRODUCTION AND MANAGEMENT	Agriculture & Natural Resources	17488	386	13104	11207	473	0.0348383	67000	41600	100000	89169	71781	61335	1869	0.0253768	55000	38000	80000	0.1639649	0.2181818
1100	GENERAL AGRICULTURE	Agriculture & Natural Resources	44306	764	28930	23024	874	0.0293249	68000	45000	104000	123984	86631	72409	2352	0.0264320	50000	34000	80000	0.2632717	0.3600000
1302	FORESTRY	Agriculture & Natural Resources	24713	487	16831	14102	725	0.0412964	78000	52000	110000	67649	46815	39048	1885	0.0387064	59000	42000	80000	0.2675667	0.3220339
1303	NATURAL RESOURCES MANAGEMENT	Agriculture & Natural Resources	29357	659	23394	19087	711	0.0294960	70000	50000	100000	77101	60690	48256	3413	0.0532424	53000	38000	75000	0.2757613	0.3207547
1105	PLANT SCIENCE AND AGRONOMY	Agriculture & Natural Resources	30983	624	22782	18312	735	0.0312540	67000	45000	100000	76190	60241	49506	1899	0.0305600	50000	35000	75000	0.2890933	0.3400000
1102	AGRICULTURAL ECONOMICS	Agriculture & Natural Resources	14800	305	10592	8768	216	0.0199852	80000	53000	120000	33049	25557	22496	734	0.0279183	63000	40000	99000	0.3093064	0.2698413
1106	SOIL SCIENCE	Agriculture & Natural Resources	3335	61	2284	1641	34	0.0146678	65000	50000	91000	6242	4654	3917	264	0.0536804	65000	41000	89000	0.3482301	0.0000000
1103	ANIMAL SCIENCES	Agriculture & Natural Resources	56807	1335	47755	39047	596	0.0123265	70300	48000	104000	94910	74896	61629	3101	0.0397579	48000	32000	75000	0.3744274	0.4645833
1199	MISCELLANEOUS AGRICULTURE	Agriculture & Natural Resources	5032	98	2758	2276	261	0.0864525	54000	45000	81000	8092	5978	4707	239	0.0384430	55000	39000	78000	0.3834197	-0.0181818
1104	FOOD SCIENCE	Agriculture & Natural Resources	14521	266	10857	8074	370	0.0329563	72000	50000	110000	22853	16298	12431	681	0.0401084	63000	40000	92000	0.3885321	0.1428571

Recent Graduates

# Load graduate students file and subset data
url3 <- "https://raw.githubusercontent.com/kleberperez1/CUNY-SPS-Data606-Project-Proposal/master/recent_grads.csv"
rct_grad <- url3 %>% read.csv(stringsAsFactors = FALSE) %>% tbl_df() %>% arrange(Major_category)

rct_ag <- rct_grad %>% 
  filter(Major_category == "Agriculture & Natural Resources")

kable(head(rct_ag, 20)) %>%
  kable_styling("striped", "hovered", font_size = 12) %>%
  scroll_box(height = "500px")

Rank	Major_code	Major	Total	Men	Women	Major_category	ShareWomen	Sample_size	Employed	Full_time	Part_time	Full_time_year_round	Unemployed	Unemployment_rate	Median	P25th	P75th	College_jobs	Non_college_jobs	Low_wage_jobs
22	1104	FOOD SCIENCE	NA	NA	NA	Agriculture & Natural Resources	NA	36	3149	2558	1121	1735	338	0.0969315	53000	32000	70000	1183	1274	485
64	1101	AGRICULTURE PRODUCTION AND MANAGEMENT	14240	9658	4582	Agriculture & Natural Resources	0.3217697	273	12323	11119	2196	9093	649	0.0500308	40000	25000	50000	1925	6221	1362
65	1100	GENERAL AGRICULTURE	10399	6053	4346	Agriculture & Natural Resources	0.4179248	158	8884	7589	2031	5888	178	0.0196425	40000	30000	50000	2418	4717	839
72	1102	AGRICULTURAL ECONOMICS	2439	1749	690	Agriculture & Natural Resources	0.2829028	44	2174	1819	620	1528	182	0.0772496	40000	27000	54000	535	893	94
108	1303	NATURAL RESOURCES MANAGEMENT	13773	8617	5156	Agriculture & Natural Resources	0.3743556	152	11797	10722	2613	6954	842	0.0666192	35000	25000	42000	4333	5808	1405
112	1302	FORESTRY	3607	3156	451	Agriculture & Natural Resources	0.1250347	48	3007	2473	891	1763	322	0.0967257	35000	28600	48000	1096	1692	327
113	1106	SOIL SCIENCE	685	476	209	Agriculture & Natural Resources	0.3051095	4	613	488	185	383	0	0.0000000	35000	18500	44000	355	144	0
144	1105	PLANT SCIENCE AND AGRONOMY	7416	4897	2519	Agriculture & Natural Resources	0.3396710	110	6594	5798	1246	4522	314	0.0454545	32000	22900	40000	2089	3545	1231
153	1103	ANIMAL SCIENCES	21573	5347	16226	Agriculture & Natural Resources	0.7521439	255	17112	14479	5353	10824	917	0.0508625	30000	22000	40000	5443	9571	2125
162	1199	MISCELLANEOUS AGRICULTURE	1488	404	1084	Agriculture & Natural Resources	0.7284946	24	1290	1098	335	936	82	0.0597668	29000	23000	42100	483	626	31

Research question

Which college majors offer the best opportunities in terms of unemployment rate and salary?

Cases

All_ages - Each case represents majors offered by colleges and universities in the US. There are 173 majors represented. These data include both undergrads and grad students.

Grad Students - Each case represents majors offered by colleges and universities in the US. There are 173 majors represented. These data include only grad students aged 25+ years.

Recent Grads - Each case represents majors offered by colleges and universities in the US. There are 173 majors represented. These data include only undergraduate students aged < 28 years. These data also include gender statistics.

Data collection

These Data were collated by the fivethirtyeight website and was posted to their github page college major.

Type of study

This is an observational Study

Explanatory Variables

The explanatory variables are the counts of employed and unemployed college degree holders and the statistics of their income. These data are numerical.

Relevant summary statistics

First we will look at overall unemployment rate for the 3 categories: all ages, recent grads, and grad students.

summary(all_ages$Unemployment_rate)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.04626 0.05472 0.05736 0.06904 0.15615

summary(rct_grad$Unemployment_rate)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.05031 0.06796 0.06819 0.08756 0.17723

summary(grad_stdnt$Grad_unemployment_rate)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.02607 0.03665 0.03934 0.04805 0.13851

DT::datatable(summary(all_ages), options = list(pageLength = 7)) 

DT::datatable(summary(all_ages_ag), list(pageLength = 7)) 

DT::datatable(summary(rct_grad), list(pageLength = 7)) 

Unemployment Rate

unempl <- cbind(all_ages$Unemployment_rate, rct_grad$Unemployment_rate, grad_stdnt$Grad_unemployment_rate)
barplot(unempl/nrow(unempl), names.arg = c("All", "Recent Grad", "Grad Student"), 
        xlab = "Unemployment Rate", col = heat.colors(nrow(unempl)))

It appears that people holding only a Bachelor’s degree have nearly twice as high median unemployment as those with higher degrees.
We will also look at median income for the three categories.

Median Income by Major All Ages (USD)

summary(all_ages$Median)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   35000   46000   53000   56816   65000  125000

colors = c("red", "yellow", "green", "violet", "orange", "blue", "pink", "cyan")
hist(all_ages$Median, main = "Histogram for Median Income All Ages", 
     xlab = "Median Income by Major All Ages (USD)", col = colors)

Median Income by Major Recent Graduates (USD)

options(scipen = 999) 
summary(rct_grad$Median)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   22000   33000   36000   40151   45000  110000

hist(rct_grad$Median, main = "Histogram for Median Income Recent Grads", 
     xlab = "Median Income by Major Recent Grads (USD)", col = colors)

Median Income by Graduate Students (USD)

summary(grad_stdnt$Grad_median)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   47000   65000   75000   76756   90000  135000

hist(grad_stdnt$Grad_median, main = "Histogram for Median Income Grad Students", 
     xlab = "Median Income by Major Grad Student (USD)", col = colors)

All, Recent Graduate and Graduate Students Median Salary

medsal <- cbind(all_ages$Median, rct_grad$Median, grad_stdnt$Grad_median)
barplot(medsal/nrow(medsal), names.arg = c("All", "Recent Grad", "Grad Student"), 
        xlab = "Median Salary", col = heat.colors(nrow(unempl)))

---

KLEBER PEREZ

DATA 606 - PROJECT PROPOSAL
SOURCE FILES AND RMARKDOWN ON RPUBS AND GITHUB

Please email to: Kleber Perez for any suggestion.

    DATA 606 PROJECT PROPOSAL - MSDS 2019 Program.