library(tidyverse)
osym_data_2017 <- osym_data_2017 %>% mutate(general_quota = as.numeric(general_quota), general_placement = as.numeric(general_placement)) head(osym_data_2017)
First of all, osym data is printed for understanding format of data.
print(osym_data_2017)
max_score
programs from each exam_type
.osym_data_2017%>% group_by(exam_type)%>% arrange(desc(max_score))%>% slice(1)
filtered_boğaziçi <- osym_data_2017 %>% filter(university_name==“BOĞAZİÇİ ÜNİVERSİTESİ”)
filtered_boğaziçi %>% arrange(desc(max_score))
osym_data_2017%>% filter(university_name==‘BOĞAZİÇİ ÜNİVERSİTESİ’)%>% arrange(desc(max_score))%>% slice(1:10)%>% ggplot()+geom_bar(aes(x=reorder(program_name,-max_score),y=max_score),stat=“Identity”) + theme(axis.text.x=element_text(angle=90))
Com <- osym_data_2017 %>% filter(grepl(“Bilgisayar Mühendisliği”, program_name))
Mech <- osym_data_2017 %>% filter(grepl(“Makine Mühendisliği”, program_name))
Com %>% mutate(avg_score= (max_score + min_score)/2, Fill_rate= general_placement/general_quota) %>% arrange(desc(avg_score)) %>% slice(1:10)
Mech %>% mutate(avg_score= (max_score + min_score)/2, Fill_rate= general_placement/general_quota) %>% arrange(desc(avg_score)) %>% slice(1:10)
com2 <-osym_data_2017 %>% select (university_name, program_name, max_score, general_placement, general_quota ) %>% filter(program_name == “Bilgisayar Mühendisliği” ) %>% summarise(mean_com2 = mean(max_score) , fillrate = sum(general_placement)/sum(general_quota)) com2
mech2 <-osym_data_2017 %>% select (university_name, program_name, max_score, general_placement, general_quota ) %>% filter(program_name == “Makine Mühendisliği” ) %>% summarise(mean_mech2 = mean(max_score) , fillrate = sum(general_placement)/sum(general_quota)) mech2
Max score for computer engineering departments is higher than mechanical engineering departmens in average, moreover fill rate to the computer engineering departments is higher than to the mechanical engineering programs.