Midterm Exam ~ Data Structures and Algorithms
Jocelyn Irene Gani
October 29, 2020
1 Case 1
Assume you have collected some data set from ABC Property as we can see in the following table:
Id <- (1:10000)
Marketing_Name <- rep(c("Angel","Sherly","Vanessa","Irene","Julian",
"Jeffry","Nikita","Kefas","Siana","Lala",
"Fallen","Ardifo","Kevin","Juen","Jerrel",
"Imelda","Widi","Theodora","Elvani","Jonathan",
"Sofia","Abraham","Siti","Niko","Sefli",
"Bene", "Diana", "Pupe", "Andi", "Tatha",
"Endri", "Monika", "Hans", "Debora","Hanifa",
"James", "Jihan", "Friska","Ardiwan", "Bakti",
"Anthon","Amry", "Wiwik", "Bastian", "Budi",
"Leo","Simon","Matius","Arry", "Eliando"), 200)
Work_Exp <- rep(c(1.3,2.4,2.5,3.6,3.7,4.7,5.7,6.7,7.7,7.3,
5.3,5.3,10,9.3,3.3,3.3,3.4,3.4,3.5,5.6,
3.5,4.6,4.6,5.7,6.2,4.4,6.4,6.4,3.5,7.5,
4.6,3.7,4.7,4.3,5.2,6.3,7.4,2.4,3.4,8.2,
6.4,7.2,1.5,7.5,10,4.5,6.5,7.2,7.1,7.6),200)
City <- sample(c("Jakarta","Bogor","Depok","Tengerang","Bekasi"),10000, replace = T)
Cluster <- sample(c("Victoria","Palmyra","Winona","Tiara", "Narada",
"Peronia","Lavesh","Alindra","Sweethome", "Asera",
"Teradamai","Albasia", "Adara","Neon","Arana",
"Asoka", "Primadona", "Mutiara","Permata","Alamanda" ), 10000, replace=T)
Price <- sample(c(7000:15000),10000, replace = T)
Date_Sales <- sample(seq(as.Date("2018/01/01"), by = "day", length.out = 1000),10000, replace = T)
Advertisement <- sample(c(1:20), 10000, replace = T)
Data <- data.frame(Id,
Marketing_Name,
Work_Exp,
City,
Cluster,
Price,
Date_Sales,
Advertisement)
library(DT)## Warning: package 'DT' was built under R version 4.0.31.1 Task 1
Categorize the Price into three groups:
- \(\text{High} > 1200\)
- \(1000 \le \text{Medium} \le 12000\)
- \(\text{Low} < 10000\)
Assign it into a new variable called Class by using If and else if statement.
1.2 Task 2
Categorize the Price into six groups:
- 5 % Booking fee if the \(\text{Price} < 8000\)
- 6 % Booking fee if the \(8000 \le \text{Price} < 9000\)
- 7 % Booking fee if the \(9000 \le \text{Price} < 10000\)
- 8 % Booking fee if the \(10000 \le \text{Price} < 11000\)
- 9 % Booking fee if the \(11000 \le \text{Price} < 13000\)
- 10 % Booking fee if the \(13000 \le \text{Price} \le 15000\)
Assign it into a new variable called Booking_fee by using if and else if statements.
Data$Booking_Fee <- ifelse(Data$Price< 8000, Data$Price*0.05
,ifelse(Data$Price<9000 & Data$Price>8000, Data$Price*0.06,
ifelse(Data$Price<10000 & Data$Price>9000, Data$Price*0.07,
ifelse(Data$Price<11000 & Data$Price>10000, Data$Price*0.08,
ifelse(Data$Price>11000 & Data$Price<13000, Data$Price*0.09,
Data$Price*0.01)))))
View(Data)1.3 Task 3
According to the final date set that you have created at task 2, I assume you have been working as marketing at ABC Property company, how could you collected all information about your sales by using for statement.
## Warning: package 'tidyverse' was built under R version 4.0.3## -- Attaching packages --------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## [1] 4 54 104 154 204
## [1] "Irene" "Irene" "Irene" "Irene" "Irene"
## [1] 3.6 3.6 3.6 3.6 3.6
## [1] "Jakarta" "Tengerang" "Bogor" "Tengerang" "Jakarta"
## [1] "Arana" "Sweethome" "Arana" "Permata" "Victoria"
## [1] 9844 13889 12247 12166 9447
## [1] "2020-04-26" "2018-03-15" "2018-06-03" "2019-08-21" "2020-04-27"
## [1] 4 9 7 18 1
## [1] "Low" "High" "High" "High" "Low"
## [1] 689.08 138.89 1102.23 1094.94 661.291.4 Task 4
If you will get pay 2% bonus of the Booking fee per-unit as marketing and also get an extra 1% bonus if you have been working at this company for more than 3 years. Please calculate the total bonus by using if, for, and break statements.
1.5 Task 5
In this section your are expected to be able to use all statements that you just learn earlier. So, please answer the following questions:
- Who is the best marketing?
library(dplyr)
Best_Marketing<-aggregate(Price~Marketing_Name,data=Data,sum)
Best_Marketing_Name<- Best_Marketing[order(Best_Marketing$Price,decreasing=T),]%>%
head(1)%>%
print()## Marketing_Name Price
## 18 Fallen 2252260- Which City and Cluster is the most profitable?
Best_City<-aggregate(Price~City,data=Data,sum)
Most_City_Profitable<- Best_City[order(Best_City$Price,decreasing=T),]%>%
head(1)%>%
print()## City Price
## 3 Depok 22383582Best_Cluster<-aggregate(Price~Cluster,data=Data,sum)
Most_Cluster_Profitable<- Best_Cluster[order(Best_Cluster$Price,decreasing=T),]%>%
head(1)%>%
print()## Cluster Price
## 6 Asera 5992982- Calculate your total advertisement cost, if you have to pay them $4 once.
library(dplyr)
list <- c()
for(i in "Irene"){
list[[i]] <- aggregate(Advertisement ~ Marketing_Name, data = Data, sum, T)
break
}
result <- list[[i]]
Total_Advertisement_Cost <- function(x){
Count <- result$Advertisement*4
Total <- paste(Count, sep = "", "$")
}
result$Advertisement_Cost <- Total_Advertisement_Cost(result$Advertisement)
result%>%filter(Marketing_Name=="Irene")%>%print()## Marketing_Name Advertisement Advertisement_Cost
## 1 Irene 1979 7916$- Calculate the average advertisement cost for each marketing.
Average_Advertisement_Cost <- aggregate(Advertisement ~ Marketing_Name, data=Data, mean)
print(Average_Advertisement_Cost)## Marketing_Name Advertisement
## 1 Abraham 10.805
## 2 Amry 9.535
## 3 Andi 10.435
## 4 Angel 10.730
## 5 Anthon 10.515
## 6 Ardifo 10.175
## 7 Ardiwan 10.950
## 8 Arry 10.775
## 9 Bakti 10.415
## 10 Bastian 10.485
## 11 Bene 10.570
## 12 Budi 11.255
## 13 Debora 9.605
## 14 Diana 11.585
## 15 Eliando 11.275
## 16 Elvani 10.825
## 17 Endri 10.385
## 18 Fallen 10.440
## 19 Friska 9.815
## 20 Hanifa 9.875
## 21 Hans 9.905
## 22 Imelda 10.440
## 23 Irene 9.890
## 24 James 10.565
## 25 Jeffry 10.945
## 26 Jerrel 10.455
## 27 Jihan 10.200
## 28 Jonathan 10.645
## 29 Juen 11.260
## 30 Julian 10.195
## 31 Kefas 10.705
## 32 Kevin 9.860
## 33 Lala 10.370
## 34 Leo 10.570
## 35 Matius 10.730
## 36 Monika 10.725
## 37 Nikita 10.780
## 38 Niko 10.530
## 39 Pupe 10.440
## 40 Sefli 10.735
## 41 Sherly 10.720
## 42 Siana 10.815
## 43 Simon 10.675
## 44 Siti 10.660
## 45 Sofia 10.345
## 46 Tatha 10.480
## 47 Theodora 10.945
## 48 Vanessa 10.680
## 49 Widi 10.165
## 50 Wiwik 10.545- Calculate the Total Revenue (in Monthly)
library(dplyr)
library(tidyverse)
Data$Revenue <- Data$Price - Data$Booking_Fee
Monthly_Revenue <- Data%>%separate(Date_Sales,c("Year","Month","Day"), sep = "-")%>%
select(Year,Month, Revenue)
Monthly_Revenue <- aggregate(Revenue ~ Month + Year, data = Monthly_Revenue,sum)
Monthly_Revenue## Month Year Revenue
## 1 01 2018 3142933
## 2 02 2018 2785752
## 3 03 2018 3365445
## 4 04 2018 2966820
## 5 05 2018 3165520
## 6 06 2018 3035318
## 7 07 2018 3167802
## 8 08 2018 3326887
## 9 09 2018 3257464
## 10 10 2018 3069426
## 11 11 2018 2947576
## 12 12 2018 3171052
## 13 01 2019 3258454
## 14 02 2019 3039338
## 15 03 2019 3067721
## 16 04 2019 3111915
## 17 05 2019 3525306
## 18 06 2019 3147552
## 19 07 2019 3235537
## 20 08 2019 3160480
## 21 09 2019 3444583
## 22 10 2019 3159982
## 23 11 2019 3195404
## 24 12 2019 3543213
## 25 01 2020 2981103
## 26 02 2020 2866803
## 27 03 2020 3258863
## 28 04 2020 2958305
## 29 05 2020 3443967
## 30 06 2020 3051025
## 31 07 2020 3041338
## 32 08 2020 3224513
## 33 09 2020 26123962 Case 2
Suppose you have a market research project to maintain some potential customers at your company. Let’s assume you are working at ABC insurance company. To do so, you want to collect the following data set:
- Marital_Status : assign random marital status (“Yes”, “No”)
- Address : assign random address (JABODETABEK)
- Work_Location : assign random working location (JABODETABEK)
- Age : assign a sequence of random numbers (from 19 to 60)
- Academic : assign random academic levels (“J.School”,“H.School”, “Undergraduate”, “Master”, “Phd”)
- Job : 10 random jobs for each academic levels
- Grade : 5 random grades for each Jobs
- Income : assign the possible income for each Jobs
- Spending : assign the possible spending for each Jobs
- Number_of_children: assign a random number in between 0 and 10 (according to marital status)
- Private_vehicle : assign the possible private vehicle for each person (“Car”, “motorcycle”, “Public”)
- Home : “Rent”, “Own”, “Credit”
2.1 Task 1
Please provide me a data set about the information of 50000 customers refers to each variable above!
Marital_Status <- sample(c("Yes","No"),50000,replace=T)
Address <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000,replace=T)
Work_Location <- sample(c("Jakarta","Bogor","Depok","Tangerang","Bekasi"),50000,replace=T)
Age <- sample(c(19:60),50000,replace=T)
Academic <- sample(c("J.School","H.School","Undergraduate","Master","Phd"),50000,replace=T)
Job <- ifelse(Academic =="J.School",
sample(c("Konstruksi","Cleaning Service","Operator Warnet","Baby Sitter",
"Supir Pribadi","Tukang Kebun","Staf Gudang","Helper","Security",
"Petani"
),length(Academic=="J.School"),replace=T),
ifelse(Academic=="H.School",
sample(c("Customer Service","Penulis","Input Data","Asisten Pilot",
"Anak Buah Kapal","Asisten Dokter Gigi","Staf Algikultur",
"operator produksi","Pengajar Bimbel","Polisi"
),length(Academic=="H.School"),replace=T),
ifelse(Academic=="Undergraduate",
sample(c("Admin", "Akuntan", "Freelance designer","Perawat","Pengacara",
"Jaksa","Hakim","Aktuaria","Make-up Artist","Selebgram"
),length(Academic=="Undergraduate"),replace=T),
ifelse(Academic=="Master",
sample(c("Engineer", "Arsitek", "Guru","Psikolog","Pengacara","Data Analyst",
"Data Scientist", "Aktuaria","Dosen", "Hakim"
),length(Academic=="Master"),replace=T),
sample(c("Hakim","Dosen", "Dokter","Arsitek","Pilot", "Ahli bedah Syaraf",
"Guru","Aktuaria", "Data Analyst","Ahli bedah jantung"
),length(Academic=="Phd"),replace=T)))))
Grade <- sample(c("Staff","Manager","Senior Manager",
"General Manager","Director"),50000, replace=T)
Income <- sample(c(3500000:10000000),50000,replace=T)
Spending <- sample(c(2000000:7000000),50000,replace=T)
Number_of_children <- ifelse(Marital_Status=="Yes",
sample((c(0:10)),
length(Marital_Status=="Yes"),replace=T),"0")
Private_vehicle <- sample(c("Car","Motorcycle","Public"),50000,replace=T)
Home <- sample(c("Rent","Own","Credit"),50000, replace=T)
Data02 <- data.frame(Marital_Status,
Address,
Work_Location,
Age,
Academic,
Job,
Grade,
Income,
Spending,
Number_of_children,
Private_vehicle,
Home)
library(DT)
datatable(Data02)## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html2.2 Task 2
What kind of important summary statistics you can get from your data set?
## Marital_Status Address Work_Location Age
## Length:50000 Length:50000 Length:50000 Min. :19.00
## Class :character Class :character Class :character 1st Qu.:29.00
## Mode :character Mode :character Mode :character Median :40.00
## Mean :39.51
## 3rd Qu.:50.00
## Max. :60.00
## Academic Job Grade Income
## Length:50000 Length:50000 Length:50000 Min. :3500141
## Class :character Class :character Class :character 1st Qu.:5146040
## Mode :character Mode :character Mode :character Median :6769894
## Mean :6765918
## 3rd Qu.:8398170
## Max. :9999985
## Spending Number_of_children Private_vehicle Home
## Min. :2000001 Length:50000 Length:50000 Length:50000
## 1st Qu.:3253430 Class :character Class :character Class :character
## Median :4497450 Mode :character Mode :character Mode :character
## Mean :4497859
## 3rd Qu.:5749360
## Max. :69999432.3 Task 3
According to your calculations and analysis, which customers are potential for you to maintain?
2.4 Task 4
The following types of insurance are available in Indonesia:
- Life insurance
- Health Insurance
- Vehicle Insurance
- Home and property insurance
- Education insurance
- Business Insurance
- General insurance
- Credit Insurance
- Marine Insurance
- Travel Insurance
Which insurance product will you provide to your customers?
# Calculation & Analysis (to make your answer below are reasonable)
Insurance <- ifelse(Data02$Age>50,"Life insurance","Education insurance")# Add new variable to your data set (assign product for each customers)
Data02$Insurance <- ifelse(Data02$Age>50,"Life insurance",
ifelse(Data02$Age>40,"Health insurance",
ifelse(Data02$Age>30,"Business insurance",
ifelse(Data02$Age>20,"Travel insurance",
ifelse(Data02$Age>10,"Education insurance",
ifelse(Data02$Marital_Status=="Yes","Home and property insurance",
ifelse(Data02$Private_vehicle=="Car","Vehicle insurance",
"General insurance")))))))
View(Data02)