library(DATA606)
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## Welcome to CUNY DATA606 Statistics and Probability for Data Analytics
## This package is designed to support this course. The text book used
## is OpenIntro Statistics, 3rd Edition. You can read this by typing
## vignette('os3') or visit www.OpenIntro.org.
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## The getLabs() function will return a list of the labs available.
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library('ggplot2')
1) Introduction: The millions of American college students heading back to campus face a grim reality: A college degree is no guarantee of economic success. But through their choice of major, they can take at least some steps toward boosting their odds. The main objective of this project is to study Which college majors offer the good prediction in terms of employment rate and salary. In this project, we are going to use American Community Survey data to reasearch the employment rate and salary from 2010-2012.
2) Data
Case: 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.
Variables: The explanatory variables are the counts of employed and unemployed college degree holders and the statistics of their income. These data are numerical.
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.
Type of Study: This is an observational Study.
3) Exploratory data analysis
suppressMessages(suppressWarnings(library(tidyr)))
suppressMessages(suppressWarnings(library(dplyr)))
suppressMessages(suppressWarnings(library(psych)))
suppressMessages(suppressWarnings(library(stringr)))
url1 <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv"
recent_grads <- url1 %>% read.csv(stringsAsFactors = FALSE) %>% tbl_df() %>% arrange(Major_category)
recent_grads_ag <- recent_grads %>% filter(Major_category == "Engineering")
head(recent_grads_ag)
## # A tibble: 6 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen
## <int> <int> <chr> <int> <int> <int> <chr> <dbl>
## 1 1 2419 PETR~ 2339 2057 282 Engineering 0.121
## 2 2 2416 MINI~ 756 679 77 Engineering 0.102
## 3 3 2415 META~ 856 725 131 Engineering 0.153
## 4 4 2417 NAVA~ 1258 1123 135 Engineering 0.107
## 5 5 2405 CHEM~ 32260 21239 11021 Engineering 0.342
## 6 6 2418 NUCL~ 2573 2200 373 Engineering 0.145
## # ... with 13 more variables: Sample_size <int>, Employed <int>,
## # Full_time <int>, Part_time <int>, Full_time_year_round <int>,
## # Unemployed <int>, Unemployment_rate <dbl>, Median <int>, P25th <int>,
## # P75th <int>, College_jobs <int>, Non_college_jobs <int>,
## # Low_wage_jobs <int>
rct_art <- recent_grads %>% filter(Major_category == "Arts")
rct_bio <- recent_grads %>% filter(Major_category == "Biology & Life Science")
rct_bsn <- recent_grads %>% filter(Major_category == "Business")
rct_cj <- recent_grads %>% filter(Major_category == "Communications & Journalism")
rct_com <- recent_grads %>% filter(Major_category == "Computers & Mathematics")
rct_ed <- recent_grads %>% filter(Major_category == "Education")
rct_hlt <- recent_grads %>% filter(Major_category == "Health")
rct_la <- recent_grads %>% filter(Major_category == "Humanities & Liberal Arts" | Major_category == "Interdisciplinary")
rct_ia <- recent_grads %>% filter(Major_category == "Industrial Arts & Consumer Services")
rct_law <- recent_grads %>% filter(Major_category == "Law & Public Policy")
rct_sci <- recent_grads %>% filter(Major_category == "Physical Sciences")
rct_psy <- recent_grads %>% filter(Major_category == "Psychology & Social Work")
rct_ssc <- recent_grads %>% filter(Major_category == "Social Science")
url2 <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/grad-students.csv"
grad_stdnt <- url2 %>% read.csv(stringsAsFactors = FALSE) %>% tbl_df() %>% arrange(Major_category)
grad_ag <- grad_stdnt %>% filter(Major_category == "Engineering")
head(grad_ag)
## # A tibble: 6 x 22
## Major_code Major Major_category Grad_total Grad_sample_size Grad_employed
## <int> <chr> <chr> <int> <int> <int>
## 1 2504 MECH~ Engineering 6065 111 4442
## 2 2599 MISC~ Engineering 14816 315 12433
## 3 2503 INDU~ Engineering 19885 408 14752
## 4 2502 ELEC~ Engineering 28155 521 22501
## 5 2500 ENGI~ Engineering 11724 219 9471
## 6 2403 ARCH~ Engineering 6466 143 4857
## # ... with 16 more variables: Grad_full_time_year_round <int>,
## # Grad_unemployed <int>, Grad_unemployment_rate <dbl>,
## # Grad_median <dbl>, Grad_P25 <int>, Grad_P75 <dbl>,
## # Nongrad_total <int>, Nongrad_employed <int>,
## # Nongrad_full_time_year_round <int>, Nongrad_unemployed <int>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>,
## # Nongrad_P25 <int>, Nongrad_P75 <dbl>, Grad_share <dbl>,
## # Grad_premium <dbl>
grad_art <- grad_stdnt %>% filter(Major_category == "Arts")
grad_bio <- grad_stdnt %>% filter(Major_category == "Biology & Life Science")
grad_bsn <- grad_stdnt %>% filter(Major_category == "Business")
grad_cj <- grad_stdnt %>% filter(Major_category == "Communications & Journalism")
grad_com <- grad_stdnt %>% filter(Major_category == "Computers & Mathematics")
grad_ed <- grad_stdnt %>% filter(Major_category == "Education")
grad_hlt <- grad_stdnt %>% filter(Major_category == "Health")
grad_la <- grad_stdnt %>% filter(Major_category == "Humanities & Liberal Arts" | Major_category == "Interdisciplinary")
grad_ia <- grad_stdnt %>% filter(Major_category == "Industrial Arts & Consumer Services")
grad_law <- grad_stdnt %>% filter(Major_category == "Law & Public Policy")
grad_sci <- grad_stdnt %>% filter(Major_category == "Physical Sciences")
grad_psy <- grad_stdnt %>% filter(Major_category == "Psychology & Social Work")
grad_ssc <- grad_stdnt %>% filter(Major_category == "Social Science")
summary(recent_grads$Employed)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3608 11797 31193 31433 307933
summary(grad_stdnt$Grad_employed)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1008 12659 28930 94037 109944 915341
hist(recent_grads$Employed)
hist(grad_stdnt$Grad_employed)
summary(recent_grads$Median)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22000 33000 36000 40151 45000 110000
summary(grad_stdnt$Grad_median)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 47000 65000 75000 76756 90000 135000
hist(recent_grads$Median, main = "Histogram for Median Income Recent Grads", xlab = "Median Income by Major Recent Grads (USD)", col = "yellow")
hist(grad_stdnt$Grad_median, main = "Histogram for Median Income Grad Students", xlab = "Median Income by Major Grad Student (USD)", col = "dark blue")
4) Inference:
H0 : There is no correlation between Major: “Engineering” and the employed rate from Graduates and Recent-Graduates.
HA: There is an correlation between Major: “Engineering” and the employed rate from Graduates and Recent-Graduates.
summary(grad_stdnt$Grad_median)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 47000 65000 75000 76756 90000 135000
summary(grad_stdnt$Grad_employed)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1008 12659 28930 94037 109944 915341
summary(grad_ag)
## Major_code Major Major_category Grad_total
## Min. :1401 Length:29 Length:29 Min. : 3940
## 1st Qu.:2406 Class :character Class :character 1st Qu.: 11722
## Median :2413 Mode :character Mode :character Median : 20177
## Mean :2490 Mean : 73535
## 3rd Qu.:2499 3rd Qu.: 82102
## Max. :5008 Max. :482767
## Grad_sample_size Grad_employed Grad_full_time_year_round
## Min. : 66 Min. : 2673 Min. : 1905
## 1st Qu.: 243 1st Qu.: 9299 1st Qu.: 8018
## Median : 408 Median : 14752 Median : 12467
## Mean : 1464 Mean : 56364 Mean : 48553
## 3rd Qu.: 1758 3rd Qu.: 66432 3rd Qu.: 56355
## Max. :10070 Max. :371723 Max. :324080
## Grad_unemployed Grad_unemployment_rate Grad_median Grad_P25
## Min. : 79 Min. :0.01154 Min. : 72000 Min. :48400
## 1st Qu.: 304 1st Qu.:0.02871 1st Qu.: 84500 1st Qu.:56000
## Median : 603 Median :0.03600 Median : 98000 Median :70000
## Mean : 2244 Mean :0.03932 Mean : 94328 Mean :65972
## 3rd Qu.: 1592 3rd Qu.:0.04575 3rd Qu.:102000 3rd Qu.:74000
## Max. :13974 Max. :0.09464 Max. :124000 Max. :85000
## Grad_P75 Nongrad_total Nongrad_employed
## Min. :102000 Min. : 5643 Min. : 3797
## 1st Qu.:111000 1st Qu.: 15759 1st Qu.: 10596
## Median :135000 Median : 35992 Median : 26479
## Mean :131234 Mean :116624 Mean : 85648
## 3rd Qu.:145000 3rd Qu.:131402 3rd Qu.: 96312
## Max. :200000 Max. :645159 Max. :471850
## Nongrad_full_time_year_round Nongrad_unemployed Nongrad_unemployment_rate
## Min. : 3165 Min. : 0 Min. :0.00000
## 1st Qu.: 9044 1st Qu.: 547 1st Qu.:0.04147
## Median : 21870 Median : 1475 Median :0.04862
## Mean : 74034 Mean : 4557 Mean :0.04670
## 3rd Qu.: 82220 3rd Qu.: 5047 3rd Qu.:0.05259
## Max. :413582 Max. :24278 Max. :0.08262
## Nongrad_median Nongrad_P25 Nongrad_P75 Grad_share
## Min. : 61000 Min. :40000 Min. : 85000 Min. :0.1780
## 1st Qu.: 70000 1st Qu.:48000 1st Qu.:100000 1st Qu.:0.3048
## Median : 78000 Median :52000 Median :105000 Median :0.3881
## Mean : 79928 Mean :53828 Mean :110345 Mean :0.3934
## 3rd Qu.: 87000 3rd Qu.:60000 3rd Qu.:118000 3rd Qu.:0.4924
## Max. :126000 Max. :75000 Max. :215000 Max. :0.7154
## Grad_premium
## Min. :-0.0250
## 1st Qu.: 0.1266
## Median : 0.2069
## Mean : 0.1922
## 3rd Qu.: 0.2588
## Max. : 0.4286
ggplot(grad_stdnt, aes(x=grad_stdnt$Major_category, y = Grad_median)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
Graduates Students in each category
list <- aggregate(Grad_employed ~ Major_category, grad_stdnt, sum)
ggplot(list, aes(x=Major_category, y=Grad_employed, label=Grad_employed)) + geom_col() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+ labs(title = '# of Employed by major category type') + geom_text(size = 3, position = position_stack(vjust = 0.5), color='white')
summary(recent_grads$Median)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22000 33000 36000 40151 45000 110000
summary(recent_grads$Employed)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 3608 11797 31193 31433 307933
summary(recent_grads_ag)
## Rank Major_code Major Total
## Min. : 1.00 Min. :1401 Length:29 Min. : 720
## 1st Qu.:10.00 1st Qu.:2406 Class :character 1st Qu.: 2906
## Median :17.00 Median :2413 Mode :character Median : 4790
## Mean :22.62 Mean :2490 Mean :18537
## 3rd Qu.:31.00 3rd Qu.:2499 3rd Qu.:18968
## Max. :67.00 Max. :5008 Max. :91227
## Men Women Major_category ShareWomen
## Min. : 488 Min. : 77 Length:29 Min. :0.07745
## 1st Qu.: 2200 1st Qu.: 506 Class :character 1st Qu.:0.15304
## Median : 4419 Median : 1385 Mode :character Median :0.22712
## Mean :14080 Mean : 4458 Mean :0.23889
## 3rd Qu.:12953 3rd Qu.: 6548 3rd Qu.:0.32222
## Max. :80320 Max. :20957 Max. :0.45146
## Sample_size Employed Full_time Part_time
## Min. : 3.0 Min. : 604 Min. : 524 Min. : 126
## 1st Qu.: 26.0 1st Qu.: 2449 1st Qu.: 2038 1st Qu.: 343
## Median : 71.0 Median : 4428 Median : 4175 Median : 1040
## Mean : 169.9 Mean :14496 Mean :13168 Mean : 2936
## 3rd Qu.: 183.0 3rd Qu.:15604 3rd Qu.:14879 3rd Qu.: 2724
## Max. :1029.0 Max. :76442 Max. :71298 Max. :13101
## Full_time_year_round Unemployed Unemployment_rate Median
## Min. : 340 Min. : 16 Min. :0.006334 Min. : 40000
## 1st Qu.: 1449 1st Qu.: 78 1st Qu.:0.042876 1st Qu.: 50000
## Median : 3413 Median : 400 Median :0.059824 Median : 57000
## Mean : 9964 Mean :1028 Mean :0.063334 Mean : 57383
## 3rd Qu.:11326 3rd Qu.:1019 3rd Qu.:0.075038 3rd Qu.: 60000
## Max. :54639 Max. :4650 Max. :0.177226 Max. :110000
## P25th P75th College_jobs Non_college_jobs
## Min. :25000 Min. : 50000 Min. : 350 Min. : 50
## 1st Qu.:35000 1st Qu.: 60000 1st Qu.: 1394 1st Qu.: 649
## Median :40000 Median : 67000 Median : 2446 Median : 2121
## Mean :41555 Mean : 70448 Mean : 9302 Mean : 3530
## 3rd Qu.:45000 3rd Qu.: 75000 3rd Qu.: 8306 3rd Qu.: 3896
## Max. :95000 Max. :125000 Max. :52844 Max. :16384
## Low_wage_jobs
## Min. : 0.0
## 1st Qu.: 142.0
## Median : 372.0
## Mean : 864.8
## 3rd Qu.: 789.0
## Max. :4221.0
ggplot(recent_grads, aes(x=recent_grads$Major_category, y = Median)) + geom_bar(stat="identity") + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
Recent Graduates Students in each category
list <- aggregate(Employed ~ Major_category, recent_grads, sum)
ggplot(list, aes(x=Major_category, y=Employed, label=Employed)) + geom_col() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+ labs(title = '# of Recent Graduates Employed by major category type') + geom_text(size = 3, position = position_stack(vjust = 0.5), color='white')
5) Conclusion
From the graph and the data that give us a flavor of the data that graduate students clearly have much higher median income compared to students who recently completed undergraduates; but there seems not have strong correlation between chose Major: Enginnering and the employed rate from the Graduates and Recent-Graduates. Due to the uncertainty of the data collection process these findings cannot be generalized to the population of all Americans, but I would say Majoy: Engineering’s employed rate is not the highest from the major category, but Engineering’s median income is the highest one.