Assignment 2-3
Ilgaz Yüksel
April 9, 2018
OSYM Data Tidy Analysis Exercise (Individual)
load("/cloud/project/osym_data_2017_v2.RData")
osym_data_2017 <- osym_data_2017 %>% mutate(general_quota = as.numeric(general_quota), general_placement=as.numeric(general_placement))
osym_data_2017## # A tibble: 11,465 x 14
## program_id university_name city faculty_name program_name exam_type
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 100110266 ABANT İZZET BAY… BOLU Bolu Sağlık … Hemşirelik YGS_2
## 2 100110487 ABANT İZZET BAY… BOLU Bolu Turizm … Gastronomi v… YGS_4
## 3 100110724 ABANT İZZET BAY… BOLU Bolu Turizm … Turizm İşlet… YGS_6
## 4 100130252 ABANT İZZET BAY… BOLU Bolu Turizm … Turizm İşlet… YGS_6
## 5 100110433 ABANT İZZET BAY… BOLU Diş Hekimliğ… Diş Hekimliği MF_3
## 6 100110609 ABANT İZZET BAY… BOLU Diş Hekimliğ… Diş Hekimliğ… MF_3
## 7 100110018 ABANT İZZET BAY… BOLU Eğitim Fakül… Bilgisayar v… MF_1
## 8 100110027 ABANT İZZET BAY… BOLU Eğitim Fakül… Fen Bilgisi … MF_2
## 9 100110036 ABANT İZZET BAY… BOLU Eğitim Fakül… İlköğretim M… MF_1
## 10 100110045 ABANT İZZET BAY… BOLU Eğitim Fakül… İngilizce Öğ… DİL_1
## # ... with 11,455 more rows, and 8 more variables: general_quota <dbl>,
## # general_placement <dbl>, min_score <dbl>, max_score <dbl>,
## # val_quota <dbl>, val_placement <dbl>, val_min_score <dbl>,
## # val_max_score <dbl>- Get the highest
max_scoreprograms from eachexam_type.
osym_data_2017 %>%
group_by(exam_type) %>%
summarise(maxscore = max(max_score))## # A tibble: 19 x 2
## exam_type maxscore
## <chr> <dbl>
## 1 DİL_1 543.
## 2 DİL_2 485.
## 3 DİL_3 490.
## 4 MF 408.
## 5 MF_1 537.
## 6 MF_2 527.
## 7 MF_3 564.
## 8 MF_4 575.
## 9 TM_1 561.
## 10 TM_2 458.
## 11 TM_3 558.
## 12 TS_1 527.
## 13 TS_2 536.
## 14 YGS_1 516.
## 15 YGS_2 473.
## 16 YGS_3 315.
## 17 YGS_4 466.
## 18 YGS_5 419.
## 19 YGS_6 519.- Plot the top 10 programs of İSTANBUL ÜNİVERSİTESİ in terms of total quota in a bar chart.
top_10_iu <- osym_data_2017 %>%
filter(university_name == "İSTANBUL ÜNİVERSİTESİ") %>%
arrange(desc(general_quota)) %>%
slice(1:10)
top_10_iu## # A tibble: 10 x 14
## program_id university_name city faculty_name program_name exam_type
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 105611289 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… Sosyoloji (… TM_3
## 2 105611298 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… İşletme (Aç… TM_1
## 3 105611183 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… Coğrafya (A… TS_1
## 4 105611305 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… Tarih (Açık… TS_2
## 5 105611314 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… İktisat (Aç… TM_1
## 6 105610501 İSTANBUL ÜNİVER… İSTAN… Hukuk Fakült… Hukuk TM_3
## 7 105611174 İSTANBUL ÜNİVER… İSTAN… Açık ve Uzak… Felsefe (Aç… TM_3
## 8 105630092 İSTANBUL ÜNİVER… İSTAN… Hukuk Fakült… Hukuk (İÖ) TM_3
## 9 105610634 İSTANBUL ÜNİVER… İSTAN… İstanbul Tıp… İstanbul Tıp MF_3
## 10 105610016 İSTANBUL ÜNİVER… İSTAN… Cerrahpaşa T… Cerrahpaşa … MF_3
## # ... with 8 more variables: general_quota <dbl>, general_placement <dbl>,
## # min_score <dbl>, max_score <dbl>, val_quota <dbl>,
## # val_placement <dbl>, val_min_score <dbl>, val_max_score <dbl>library(ggplot2)
ggplot(data = top_10_iu) +
geom_bar(aes(x=reorder(program_name,-general_quota), y=general_quota), stat = "identity") +
labs(x="Program Name", y="Quota") + theme(axis.text.x=element_text(angle=90))
- Calculate the fill rate (sum(
general_placement)/sum(general_quota)) per city and return the top 10 cities.
osym_data_2017 %>%
group_by(city) %>%
summarise(fillrate = sum(general_placement)/sum(general_quota)) %>%
arrange(desc(fillrate)) %>%
slice(1:10)## # A tibble: 10 x 2
## city fillrate
## <chr> <dbl>
## 1 GEBZE 1.02
## 2 TEKİRDAĞ 1.01
## 3 MANİSA 1.01
## 4 KOCAELİ 1.00
## 5 BURSA 1.00
## 6 SUMGAYIT - AZERBAYCAN 1.00
## 7 DİYARBAKIR 0.999
## 8 DENİZLİ 0.998
## 9 EDİRNE 0.998
## 10 SAKARYA 0.997- Find full (
general_placement==general_quota) Endüstri Mühendisliği programs (usegrepl) and draw a scatterplot of min_score vs max_score. Set transparency parameter (alpha) to 0.7. Set program colors according to whether it is a foundation university or state university. (Tip: State university programs ids start with 1, foundation 2, KKTC 3 and other abroad 4. You can usesubstrfunction.).
endustri_muhendisligi <- filter(osym_data_2017, grepl("Endüstri Mühendisliği", program_name))
full_endustri_muhendisligi <- endustri_muhendisligi %>% filter(general_quota==general_placement)
full_endustri_muhendisligi## # A tibble: 119 x 14
## program_id university_name city faculty_name program_name exam_type
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 106510023 ABDULLAH GÜL ÜN… KAYS… Mühendislik … Endüstri Müh… MF_4
## 2 202910136 ALTINBAŞ ÜNİVER… İSTA… Mühendislik … Endüstri Müh… MF_4
## 3 202910366 ALTINBAŞ ÜNİVER… İSTA… Mühendislik … Endüstri Müh… MF_4
## 4 101011032 ANADOLU ÜNİVERS… ESKİ… Mühendislik … Endüstri Müh… MF_4
## 5 206410262 ANTALYA BİLİM Ü… ANTA… Mühendislik … Endüstri Müh… MF_4
## 6 200210997 ATILIM ÜNİVERSİ… ANKA… Mühendislik … Endüstri Müh… MF_4
## 7 200210413 ATILIM ÜNİVERSİ… ANKA… Mühendislik … Endüstri Müh… MF_4
## 8 200210404 ATILIM ÜNİVERSİ… ANKA… Mühendislik … Endüstri Müh… MF_4
## 9 200510701 BAHÇEŞEHİR ÜNİV… İSTA… Mühendislik … Endüstri Müh… MF_4
## 10 200510507 BAHÇEŞEHİR ÜNİV… İSTA… Mühendislik … Endüstri Müh… MF_4
## # ... with 109 more rows, and 8 more variables: general_quota <dbl>,
## # general_placement <dbl>, min_score <dbl>, max_score <dbl>,
## # val_quota <dbl>, val_placement <dbl>, val_min_score <dbl>,
## # val_max_score <dbl>ggplot(data = full_endustri_muhendisligi, aes(x= min_score, y= max_score)) + geom_point(col="blue", alpha=0.7) + labs(x="Minimum score", y="Maximum score")
- Find the top 10 faculties with the highest quotas and draw a bar chart. Ignore similar names and typos in faculty names.
top_10_faculty <- osym_data_2017 %>%
arrange(desc(general_quota)) %>%
slice(1:10)
ggplot(data = top_10_faculty) + geom_bar(aes(x=reorder(program_name,-general_quota), y=general_quota), stat = "identity") +
labs(x="Program Name", y="Quota") + theme(axis.text.x=element_text(angle=90))
Find all full medicine programs (Tıp but not Tıp Mühendisliği) of foundation universities group by university calculate total quotas per university and maximum max_score and minimum min_score as bounds, ordered and colored by total quota. (Tip: Use
geom_crossbar)Freestyle: Do an analysis that compares the mechanical engineering (Makine Mühendisliği) and civil engineering (İnşaat Mühendisliği) programs.
Top 5 universities for these programs were compared according to minimum acceptance score. As a result, it seems that the most succesfull students prefer mechanical engineering over civil engineering.
top_5avg_mech <- filter(osym_data_2017, general_quota>20, grepl("Makine Mühendisliği", program_name)) %>%
arrange(desc(min_score)) %>%
slice(1:5) %>%
summarise("Average min scores" = mean(min_score))
top_5avg_mech## # A tibble: 1 x 1
## `Average min scores`
## <dbl>
## 1 483.top_5avg_civil <- filter(osym_data_2017, general_quota>20, grepl("İnşaat Mühendisliği", program_name)) %>%
arrange(desc(min_score)) %>%
slice(1:5) %>%
summarise("Average min scores" = mean(min_score))
top_5avg_civil## # A tibble: 1 x 1
## `Average min scores`
## <dbl>
## 1 452.- Freestyle: Compare Koç University with Bilkent University.
Koç University has higher minimum acceptance score in all exam types than the Bilkent University.
koc <- osym_data_2017 %>%
filter(grepl("KOÇ ÜNİVERSİTESİ", university_name)) %>%
group_by(exam_type) %>% summarise(min_score = mean(min_score))
koc## # A tibble: 9 x 2
## exam_type min_score
## <chr> <dbl>
## 1 DİL_1 462.
## 2 MF_1 451.
## 3 MF_2 487.
## 4 MF_3 457.
## 5 MF_4 462.
## 6 TM_1 440.
## 7 TM_3 447.
## 8 TS_1 461.
## 9 TS_2 443.bilkent <- osym_data_2017 %>%
filter(grepl("BİLKENT ÜNİVERSİTESİ", university_name)) %>%
group_by(exam_type) %>% summarise(min_score = mean(min_score))
bilkent## # A tibble: 11 x 2
## exam_type min_score
## <chr> <dbl>
## 1 DİL_1 440.
## 2 MF_1 385.
## 3 MF_2 378.
## 4 MF_3 418.
## 5 MF_4 446.
## 6 TM_1 368.
## 7 TM_2 327.
## 8 TM_3 397.
## 9 TS_1 362.
## 10 YGS_1 361.
## 11 YGS_6 295.- Freestyle: Do your best.