Case Study: Welcome to University
datING - BDA 503 - Fall 2017
Introduction
In this case study our group are going to explore university entrance examinations (YGS/LYS) data from 2017.
Dataset consists of undergraduate programs offered in 2017. Each program offers an availability (i.e. quota). Then students get placed according to their lists and their scores. Each program is filled with the students ranked by their scores until placements are equal to availability. Student placed to a a program with the highest score forms the maximum score of that program and the last student to be placed forms the minimum score.
MEF University management asks us to examine the data and provide insights that are useful to understand MEF University’s place among its competitors and in the undergraduate market. We decided to examine occupancy rate of quota which is found dividing the general placement into general quota. After assessment the university with analysis, we wil have an outcome and wil offer a suggestion to university management.
Clean Data and Assign New Population
- First we had a quick review of number of rows and columns “osym_data_2017” dataset.
dim(osym_data_2017)## [1] 11031 14- We filtered abroad universities that university management is not interested. (IDs that start with 3 or 4)
#Filtered abroad university
osym_data_2017_except_abroad <- osym_data_2017 %>%
filter(program_id<300000000)- Let’s see number of our new population excluding the abroad universities.
dim(osym_data_2017_except_abroad)## [1] 9812 14Analysis
- We found the occupancy rates of quota in all the universities.
Occupancy_rate_of_quota <- osym_data_2017_except_abroad %>%
group_by(university_name) %>%
summarise(total_quota = sum(general_quota),
total_placement = sum(general_placement),
Percentage=round((sum(general_placement)/sum(general_quota))*100)) %>%
arrange(Percentage)
Occupancy_rate_of_quota## # A tibble: 172 x 4
## university_name total_quota total_placement Percentage
## <chr> <dbl> <dbl> <dbl>
## 1 ANADOLU ÜNIVERSITESI 55767 12864 23
## 2 ISTANBUL RUMELI ÜNIVERSITESI 528 197 37
## 3 ALANYA HAMDULLAH EMIN PASA ÜNIVERSITESI 155 60 39
## 4 ALTINBAS ÜNIVERSITESI 1749 770 44
## 5 ISTINYE ÜNIVERSITESI 953 429 45
## 6 TÜRK HAVA KURUMU ÜNIVERSITESI 596 270 45
## 7 ANTALYA AKEV ÜNIVERSITESI 230 125 54
## 8 TOROS ÜNIVERSITESI 510 289 57
## 9 AVRASYA ÜNIVERSITESI 1366 796 58
## 10 ARDAHAN ÜNIVERSITESI 1130 664 59
## # ... with 162 more rows- We found the occupancy rates of quota universities in İstanbul.
Istanbul_Occupancy_rate_of_quota <- osym_data_2017_except_abroad %>%
filter(city == "İSTANBUL")%>%
group_by(university_name) %>%
summarise(total_quota = sum(general_quota),
total_placement = sum(general_placement),
Percentage=round((sum(general_placement)/sum(general_quota))*100)) %>%
arrange(Percentage)
Istanbul_Occupancy_rate_of_quota## # A tibble: 51 x 4
## university_name total_quota total_placement Percentage
## <chr> <dbl> <dbl> <dbl>
## 1 ISTANBUL RUMELI ÜNIVERSITESI 528 197 37
## 2 ALTINBAS ÜNIVERSITESI 1749 770 44
## 3 ISTINYE ÜNIVERSITESI 953 429 45
## 4 ISTANBUL GELISIM ÜNIVERSITESI 3950 2351 60
## 5 ISTANBUL GEDIK ÜNIVERSITESI 690 421 61
## 6 ISTANBUL KENT ÜNIVERSITESI 440 269 61
## 7 HALIÇ ÜNIVERSITESI 1403 886 63
## 8 ISTANBUL AREL ÜNIVERSITESI 1545 980 63
## 9 OKAN ÜNIVERSITESI 2393 1555 65
## 10 BIRUNI ÜNIVERSITESI 1077 759 70
## # ... with 41 more rows- Let’s visualize the occupancy rates of quota universities in İstanbul.
ggplot(Istanbul_Occupancy_rate_of_quota, aes(x=reorder(university_name,Percentage), y=Percentage)) +
geom_bar(stat = "identity", aes(fill=Istanbul_Occupancy_rate_of_quota$university_name=='MEF ÜNİVERSİTESİ')) +
labs(title="Occupancy Rate of Quota Universities in Istanbul",x="University",y="Quota Occupancy Rate",fill="") +
theme (axis.text.x=element_text (angle=-90,vjust=0.5, hjust=0)) +
scale_fill_manual(values = c('#f39c12', '#e67e22'),guide=FALSE)
- Then we found the occupancy rates of quota of MEF University to compare with all universities and İstanbul located universities.
MEF_Occupancy_rate_of_quota <- osym_data_2017_except_abroad %>%
filter(university_name == "MEF ÜNİVERSİTESİ")%>%
group_by(faculty_name) %>%
summarise(total_quota = sum(general_quota),
total_placement = sum(general_placement),
Percentage=round((sum(general_placement)/sum(general_quota))*100)) %>%
arrange(Percentage)
MEF_Occupancy_rate_of_quota## # A tibble: 6 x 4
## faculty_name total_quota total_placement Percentage
## <chr> <dbl> <dbl> <dbl>
## 1 Egitim Fakültesi 130 95 73
## 2 Iktisadi, Idari ve Sosyal Bilimler Fakültesi 190 176 93
## 3 Hukuk Fakültesi 153 153 100
## 4 Hukuk Fakültesi (Tam Burslu) 17 17 100
## 5 Mühendislik Fakültesi 212 212 100
## 6 Sanat, Tasarim ve Mimarlik Fakültesi 115 115 100- Let’s visualize the occupancy rates of quota of MEF University with respect to faculty.
ggplot(MEF_Occupancy_rate_of_quota, aes(x=reorder(faculty_name,Percentage),y=Percentage)) +
geom_bar(stat = "identity",colour="black", fill="#e67e22", width=.5,) +
labs(title="Quota Occupancy Rate of Each Faculty of MEF University",x="Faculty",y="Percentage",fill="") +
theme (axis.text.x=element_text(angle=0,vjust=1, hjust=1)) +
coord_flip()
- Means of occupancy rates of quota.
All_universities_mean<-round(mean(Occupancy_rate_of_quota$Percentage))
İstanbul_universities_mean<-round(mean(Istanbul_Occupancy_rate_of_quota$Percentage))
MEF_Rate<- Istanbul_Occupancy_rate_of_quota %>%
select(university_name, Percentage) %>%
filter(university_name == "MEF ÜNİVERSİTESİ")- We arranged the means and visualize to compare the occupancy rates of quota.
Percentages=data.frame(name=c("All_Universities", "Istanbul","MEF") , value=c(All_universities_mean,İstanbul_universities_mean,MEF_Rate$Percentage))
ggplot(Percentages, aes(x=reorder(name,value), y=value)) +
geom_bar(stat = "identity",colour="black", fill="#e67e22", width=.5,)+
labs(title="Comparing Quota Occupancy Rate",x="Percentage",y="Quota Occupancy Rate",fill="") +
theme (axis.text.x=element_text(angle=0,vjust=2, hjust=2)) +
coord_flip()
- As a result, we saw that 3 programs are mentioned below in MEF University have below according its quota.
MEF_Program_Rate <- osym_data_2017_except_abroad %>%
filter(university_name == "MEF ÜNİVERSİTESİ")%>%
group_by(program_name) %>%
summarise(total_quota = sum(general_quota),
total_placement = sum(general_placement),
Percentage=round((sum(general_placement)/sum(general_quota))*100)) %>%
filter(Percentage < 100)
MEF_Program_Rate## # A tibble: 3 x 4
## program_name total_quota total_placement Percentage
## <chr> <dbl> <dbl> <dbl>
## 1 Ekonomi (Ingilizce) (%50 Burslu) 22 10 45
## 2 Isletme (Ingilizce) (%50 Burslu) 22 20 91
## 3 Rehberlik ve Psikolojik Danismanlik (Ingilizce) (%50 Burslu) 60 25 42Conclusion
According to the universites’ occupancy rates of quota, MEF University has 3 program which is not full quota. On the other hand, occupancy rates of quota is higher than universities in Turkey /İstanbul. We suggest an offer to MEF University Management to decrease the quota of 3 non-full programs.
