| tle: “OSYM_ANALYTICS” |
| thor: “Explorers” |
| te: “24 Ekim 2017” |
| tput: html_document |
About The Task
Suppose, MEF University management asks you to examine the data and provide insights that are useful to understand MEF University’s place among its competitors and in the undergraduate market. Our technical team cleaned up the data for you as best as they can (you can check the raw data from here). Data is provided with the following commands and necessary information can be found below. You should explicitly state your code and process with clear communication. Assume management knows a bit of R and would like to reproduce your work in case there is any problem with the calculations. The university is not interested in universities abroad (IDs that start with 3 or 4).
Loading DataSet
load("osym_data_2017.RData")Library Loading and Summary
library(tidyverse)
library(highcharter)
library(data.table)
library(stringr)
osym_data_2017<-data.table(osym_data_2017)
summary(osym_data_2017)## program_id university_name city
## Min. :100110018 Length:11031 Length:11031
## 1st Qu.:105510394 Class :character Class :character
## Median :200210431 Mode :character Mode :character
## Mean :167009509
## 3rd Qu.:204011062
## Max. :409710072
## faculty_name program_name exam_type
## Length:11031 Length:11031 Length:11031
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## general_quota general_placement min_score max_score
## Min. : 1.00 Min. : 0.00 Min. :180.0 Min. :180.0
## 1st Qu.: 9.00 1st Qu.: 6.00 1st Qu.:235.9 1st Qu.:280.2
## Median : 32.00 Median : 23.00 Median :281.1 Median :341.1
## Mean : 46.07 Mean : 37.65 Mean :295.1 Mean :338.3
## 3rd Qu.: 60.00 3rd Qu.: 61.00 3rd Qu.:351.9 3rd Qu.:398.2
## Max. :9000.00 Max. :2050.00 Max. :543.7 Max. :569.1
## val_quota val_placement val_min_score val_max_score
## Min. : 0.000 Min. : 0.0000 Min. :180.0 Min. :180.0
## 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:180.0 1st Qu.:180.0
## Median : 0.000 Median : 0.0000 Median :180.0 Median :180.0
## Mean : 1.131 Mean : 0.4299 Mean :207.6 Mean :209.7
## 3rd Qu.: 2.000 3rd Qu.: 0.0000 3rd Qu.:180.0 3rd Qu.:180.0
## Max. :225.000 Max. :16.0000 Max. :524.6 Max. :526.8osymlocal<-osym_data_2017[substr(as.character(program_id),1,1)<=2,]
setkey(osymlocal,program_id)
sutunadlari<-colnames(osymlocal)
osymlocal[,program_statu:=ifelse((str_detect(program_name,"(%25 Burslu)") | str_detect(program_name,"(%50 Burslu)"))==TRUE,"Ucretli"
,ifelse(str_detect(program_name,"(İÖ)"),"IO","Burslu veya Devlet")),by=sutunadlari]
osymlocal[,MEF:=ifelse(substring(university_name,1,3)=="MEF",1,0),by=sutunadlari]
MEF<-osymlocal[MEF==1, ][order(-max_score)]
HP<-MEF[,max(max_score),.(exam_type,program_statu)]
setnames(HP,"V1","max_score")
setkey(MEF,max_score,exam_type,program_statu)
setkey(HP,max_score,exam_type,program_statu)
MEFHP<-MEF[HP,nomatch=0]
MEFHP## program_id university_name city
## 1: 207210067 MEF ÜNYVERSYTESY YSTANBUL
## 2: 207210376 MEF ÜNYVERSYTESY YSTANBUL
## 3: 207210049 MEF ÜNYVERSYTESY YSTANBUL
## 4: 207210252 MEF ÜNYVERSYTESY YSTANBUL
## 5: 207210358 MEF ÜNYVERSYTESY YSTANBUL
## 6: 207210419 MEF ÜNYVERSYTESY YSTANBUL
## 7: 207210076 MEF ÜNYVERSYTESY YSTANBUL
## 8: 207210394 MEF ÜNYVERSYTESY YSTANBUL
## 9: 207210331 MEF ÜNYVERSYTESY YSTANBUL
## faculty_name
## 1: Yktisadi, Ydari ve Sosyal Bilimler Fakültesi
## 2: E<U+00F0>itim Fakültesi
## 3: Yktisadi, Ydari ve Sosyal Bilimler Fakültesi
## 4: Sanat, Tasarym ve Mimarlyk Fakültesi
## 5: Hukuk Fakültesi
## 6: E<U+00F0>itim Fakültesi
## 7: Mühendislik Fakültesi
## 8: E<U+00F0>itim Fakültesi
## 9: Hukuk Fakültesi (Tam Burslu)
## program_name exam_type
## 1: Y<U+00FE>letme (Yngilizce) (%50 Burslu) TM_1
## 2: Ylkö<U+00F0>retim Matematik Ö<U+00F0>retmenli<U+00F0>i (Yngilizce) (Tam Burslu) MF_1
## 3: Y<U+00FE>letme (Yngilizce) (Tam Burslu) TM_1
## 4: Mimarlyk (Yngilizce) (%50 Burslu) MF_4
## 5: Hukuk (%50 Burslu) TM_3
## 6: Yngilizce Ö<U+00F0>retmenli<U+00F0>i (Yngilizce) (%50 Burslu) DYL_1
## 7: Bilgisayar Mühendisli<U+00F0>i (Yngilizce) (Tam Burslu) MF_4
## 8: Yngilizce Ö<U+00F0>retmenli<U+00F0>i (Yngilizce) (Tam Burslu) DYL_1
## 9: Hukuk (Tam Burslu) TM_3
## general_quota general_placement min_score max_score val_quota
## 1: 22 20 218.3906 283.9486 0
## 2: 2 2 386.4204 386.6900 0
## 3: 4 4 393.8451 398.7630 0
## 4: 33 33 350.5076 412.3419 0
## 5: 25 25 403.8408 416.0686 0
## 6: 32 32 292.9123 424.9534 0
## 7: 3 3 429.7522 445.0601 0
## 8: 4 4 447.1165 456.0084 0
## 9: 17 17 432.7763 459.0143 0
## val_placement val_min_score val_max_score program_statu MEF
## 1: 0 180 180 Ucretli 1
## 2: 0 180 180 Burslu veya Devlet 1
## 3: 0 180 180 Burslu veya Devlet 1
## 4: 0 180 180 Ucretli 1
## 5: 0 180 180 Ucretli 1
## 6: 0 180 180 Ucretli 1
## 7: 0 180 180 Burslu veya Devlet 1
## 8: 0 180 180 Burslu veya Devlet 1
## 9: 0 180 180 Burslu veya Devlet 1Data Preparation & Cleaning
osymlocal[, c("program_name1", "program_name2","program_name3","program_name4","program_name5") := tstrsplit(str_trim(program_name), "(", fixed=TRUE)]
osymlocal$program_name1<-str_trim(osymlocal$program_name1)
osymlocal$program_name2<-str_trim(osymlocal$program_name2)
osymlocal$program_name3<-str_trim(osymlocal$program_name3)
osymlocal$program_name4<-str_trim(osymlocal$program_name4)
osymlocal$program_name5<-str_trim(osymlocal$program_name5)
osymlocal<-osymlocal[program_name2!="KKTC Uyruklu)" | is.na(program_name2)==TRUE]
osymlocal<-osymlocal[program_name3!="KKTC Uyruklu)" | is.na(program_name3)==TRUE]
osymlocal<-osymlocal[program_name3!="KKTC Vatandaşları)" | is.na(program_name3)==TRUE]
osymlocal<-osymlocal[program_name2!="UOLP - Köln Üniversitesi)" | is.na(program_name2)==TRUE]
osymlocal<-osymlocal[program_name2!="Açıköğretim)" | is.na(program_name2)==TRUE]
osymlocal<-osymlocal[program_name3!="Açıköğretim)" | is.na(program_name3)==TRUE]
osymlocal<-osymlocal[program_name2!="Uzaktan Öğretim)" | is.na(program_name2)==TRUE]
osymlocal<-osymlocal[program_name2!="Tam Burslu)" | is.na(program_name2)==TRUE]
osymlocal<-osymlocal[program_name3!="Tam Burslu)" | is.na(program_name3)==TRUE]Programs at MEF University
MEFPrograms<-osymlocal[MEF==1,list(mean(min_score),sum(general_quota),sum(general_placement)
,sum(general_placement)/sum(general_quota)),.(university_name,city,program_name1)][order(-V1)]
setnames(MEFPrograms,"V1","min_score")
setnames(MEFPrograms,"V2","general_quota")
setnames(MEFPrograms,"V3","general_placement")
setnames(MEFPrograms,"V4","fullness")
setnames(MEFPrograms,"program_name1","program_name")
hchart(MEFPrograms[order(-min_score)],type="column",hcaes(x=program_name,y=min_score,group=university_name)) %>%
hc_add_theme(hc_theme_google())hchart(MEFPrograms[order(-min_score)],type="column",hcaes(x=program_name,y=fullness,group=university_name)) %>%
hc_add_theme(hc_theme_google())The Scores of Business Adminstration
programs<-osymlocal[,list(mean(min_score),sum(general_quota),sum(general_placement),sum(general_placement)/sum(general_quota)),.(university_name,city,program_name1)]
setnames(programs,"V1","min_score")
setnames(programs,"V2","general_quota")
setnames(programs,"V3","general_placement")
setnames(programs,"V4","fullness")
setnames(programs,"program_name1","program_name")
hchart(programs[program_name=="İşletme",][order(-min_score)],type="column",hcaes(x=university_name,y=min_score,group=program_name)) %>%
hc_add_theme(hc_theme_google())ggplot(programs[program_name=="İşletme",],aes(x=reorder(university_name,-min_score),y=min_score,fill=university_name=="MEF ÜNİVERSİTESİ")) +
geom_bar(stat="identity",position="dodge",) +
facet_wrap(~program_name) +
theme(axis.text.x = element_text(angle=90)) +
ggtitle("İşletme Min_Scores")
ggplot(programs[program_name=="İşletme" & city=="İSTANBUL",],aes(x=reorder(university_name,-min_score),y=min_score,fill=university_name=="MEF ÜNİVERSİTESİ")) +
geom_bar(stat="identity",position="dodge",) +
facet_wrap(~program_name) +
theme(axis.text.x = element_text(angle=90)) +
ggtitle("İşletme Min_Scores")
The Scores of Law Program
ggplot(programs[program_name=="Hukuk",],aes(x=reorder(university_name,-min_score),y=min_score,fill=university_name=="MEF ÜNİVERSİTESİ")) +
geom_bar(stat="identity",position="dodge",) +
facet_wrap(~program_name) +
theme(axis.text.x = element_text(angle=90)) +
ggtitle("Hukuk Min_Scores")
ggplot(programs[program_name=="Hukuk" & city=="İSTANBUL",],aes(x=reorder(university_name,-min_score),y=min_score,fill=university_name=="MEF ÜNİVERSİTESİ")) +
geom_bar(stat="identity",position="dodge",) +
facet_wrap(~program_name) +
theme(axis.text.x = element_text(angle=90)) +
ggtitle("Hukuk Min_Scores")