NLB Project
The Payvolutionaries
3rd February 2025
1 Introduction
Good morning Professors,
We’re excited to present the work we’ve done so far—our beautifully crafted document! The project has been incredibly engaging, though R Studio has definitely been a worthy opponent. But fear not, we’re not giving up! We’ve cleaned up our questionnaire, manipulated the data, and even dared to explore some potential clustering. While we still have a few lingering questions, we’re hopeful that our exhausted faces will earn your understanding. We look forward to our consultations and can’t wait to dive deeper into this journey!
Sincerely,
The Payvolutionaries
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Load the CSV file
NLB_data <- read.csv("~/Desktop/NLB/nlb_data.csv", sep = ";")
# Remove columns 2 to 8
NLB_data <- NLB_data %>% select(-c(2:8, 101:116))
# Display the first few rows of the modified dataset
head(NLB_data)## status Q1 Q3a
## 1 Status Koliko ste stari? Gotovina
## 2 6 7 3
## 3 6 9 2
## 4 6 9 2
## 5 6 7 3
## 6 6 7 2
## Q3b
## 1 Plačilo z debetno ali kreditno kartico (uporaba fizične kartice)
## 2 2
## 3 5
## 4 4
## 5 5
## 6 5
## Q3c Q3d Q4a Q4b
## 1 Plačilo s telefonom Drugo (PayPal, Stripe,..) Nakupi do 10€ Nakupi med 11-99€
## 2 5 2 4 3
## 3 1 1 2 1
## 4 4 1 2 2
## 5 5 1 2 2
## 6 5 1 2 1
## Q4c Q4d
## 1 Nakupi med 100-1000€ Nakupi nad 1000€
## 2 2 1
## 3 1 1
## 4 1 1
## 5 1 1
## 6 1 1
## Q5
## 1 Kako se običajno odzovete, če prodajalec ne sprejema digitalnih plačil?
## 2 2
## 3 1
## 4 2
## 5 1
## 6 1
## Q5_4_text Q6a
## 1 Drugo (prosim navedite): Plača ali zaslužek iz študentskega dela
## 2 -2 3
## 3 -2 4
## 4 -2 3
## 5 -2 3
## 6 -2 4
## Q6b Q6c
## 1 Žepnina (od družinskih članov) Darila (ob rojstnih dnevih, praznikih...)
## 2 2 1
## 3 2 1
## 4 3 1
## 5 3 2
## 6 3 3
## Q6d
## 1 Delo na črno ali priložnostno delo (varstvo otrok, tutorstvo...)
## 2 1
## 3 4
## 4 4
## 5 4
## 6 4
## Q6e
## 1 Državne in/ali socialne pomoči
## 2 3
## 3 4
## 4 4
## 5 4
## 6 4
## Q6f
## 1 Državne in druge oblike štipendij (Zoisova, kadrovska...)
## 2 3
## 3 4
## 4 4
## 5 3
## 6 4
## Q6g
## 1 Donosi iz naložb (delnice, obveznice, kripto itd.)
## 2 3
## 3 3
## 4 2
## 5 4
## 6 4
## Q7
## 1 Ali vi varčujete denar? (Vprašanje NE zajema morebitnih privarčevanih sredstev, ki so jih za vas varčevali oziroma jih varčujejo starši ali drugi družinski člani)
## 2 1
## 3 1
## 4 1
## 5 1
## 6 2
## Q8a
## 1 NA
## 2 5
## 3 7
## 4 7
## 5 7
## 6 -2
## Q9a
## 1 Običajno denar porabim v obliki, v kateri sem ga prejel_a (ne spreminjam oblike prejetega denarja npr. polaganje gotovine na banko ali dvigovanje denarja iz kartice).
## 2 6
## 3 7
## 4 6
## 5 6
## 6 7
## Q9b
## 1 Zaradi varnosti svojih osebnih podatkov se počutim zaskrbljenega_o, ko uporabljam digitalne načine plačevanja (npr. spletno bančništvo, mobilne denarnice).
## 2 3
## 3 6
## 4 2
## 5 5
## 6 4
## Q9c
## 1 Digitalno plačevanje se mi zdi manj varno kakor gotovinsko plačevanje.
## 2 5
## 3 3
## 4 3
## 5 6
## 6 7
## Q9d
## 1 Več zaupanja imam v digitalne načine plačevanja, če ponujajo funkcije, kot so dvofaktorska avtentikacija ali biometrična preverjanja.
## 2 6
## 3 6
## 4 6
## 5 7
## 6 4
## Q9e
## 1 Počutim se varno, ko s seboj nosim gotovino.
## 2 4
## 3 3
## 4 3
## 5 4
## 6 4
## Q9f
## 1 Raje uporabljam digitalna plačila, ker so bolj priročna in prihranijo čas.
## 2 7
## 3 7
## 4 7
## 5 7
## 6 7
## Q9g
## 1 Raje uporabljam gotovino, da bi se izognil_a pretirani porabi, (ob porabljanju gotovine se bolj zavedam svojega zapravljanja in je le-to bolj zavestno).
## 2 6
## 3 1
## 4 1
## 5 1
## 6 4
## Q9h
## 1 Gotovino najpogosteje uporabim tam, kjer digitalna plačila niso mogoča, v nasprotnem primeru pa se raje poslužujem digitalnega plačevanja.
## 2 7
## 3 6
## 4 7
## 5 7
## 6 7
## Q31_2a Q31_2b Q31_2c
## 1 Enostavnost uporabe Hitrost transakcije Možnost uporabe v trgovinah
## 2 7 7 6
## 3 6 7 5
## 4 7 6 6
## 5 7 7 7
## 6 7 7 7
## Q31_2d Q31_2e
## 1 Varnost plačilnega sredstva Funkcije za sledenje in načrtovanje proračuna
## 2 6 3
## 3 7 7
## 4 6 4
## 5 7 7
## 6 7 7
## Q31_2f Q10a Q10b
## 1 Zasebnost uporabe Gotovina (Fizična) Debetna kartica
## 2 6 6 6
## 3 6 3 7
## 4 5 6 6
## 5 7 6 5
## 6 7 7 4
## Q10c Q10d
## 1 (Fizična) kreditna kartica Plačevanje s telefonom (Flik, Apple pay...)
## 2 6 7
## 3 7 3
## 4 4 6
## 5 4 6
## 6 4 4
## Q10e Q11a Q11b
## 1 Neobanke (Revolut, N26...) Gotovina (Fizična) debetna kartica
## 2 6 5 6
## 3 6 7 7
## 4 6 4 7
## 5 6 3 6
## 6 4 7 7
## Q11c Q11d Q11e
## 1 (Fizična) kreditna kartica Plačevanje s telefonom Neobanke (Revolut, N26...)
## 2 6 7 6
## 3 7 6 6
## 4 7 7 7
## 5 6 7 7
## 6 7 7 7
## Q12a Q12b Q12c
## 1 Gotovina (Fizična) debetna kartica (Fizična) kreditna kartica
## 2 6 7 7
## 3 6 6 6
## 4 7 7 7
## 5 6 7 7
## 6 7 7 7
## Q12d Q12e Q13a
## 1 Plačevanje s telefonom Neobanke (Revolut, N26...) Gotovina
## 2 7 6 5
## 3 6 6 5
## 4 7 7 4
## 5 6 6 1
## 6 7 4 7
## Q13b Q13c Q13d
## 1 (Fizična) debetna kartica (Fizična) kreditna kartica Plačevanje s telefonom
## 2 6 6 7
## 3 7 7 7
## 4 7 7 7
## 5 6 6 7
## 6 7 7 7
## Q13e Q14a Q14b
## 1 Neobanke (Revolut, N26...) Gotovina (Fizična) debetna kartica
## 2 7 7 6
## 3 7 7 1
## 4 7 6 5
## 5 7 7 4
## 6 7 4 7
## Q14c Q14d Q14e
## 1 (Fizična) kreditna kartica Plačevanje s telefonom Neobanke (Revolut, N26...)
## 2 5 5 6
## 3 1 1 1
## 4 5 5 5
## 5 4 4 4
## 6 7 7 7
## Q15a Q15b Q15c
## 1 Gotovina (Fizična) debetna kartica (Fizična) kreditna kartica
## 2 7 5 5
## 3 2 7 4
## 4 5 7 7
## 5 6 5 1
## 6 4 7 7
## Q15d Q15e
## 1 Plačevanje s telefonom Neobanke (Revolut, N26...)
## 2 3 3
## 3 1 6
## 4 7 7
## 5 5 5
## 6 7 7
## Q16a
## 1 Izbiram načine plačevanja, kot jih izbirajo moji prijatelji.
## 2 5
## 3 4
## 4 4
## 5 7
## 6 4
## Q16b
## 1 Izbiram načine plačevanja, kot jih izbirajo moji družinski člani.
## 2 2
## 3 4
## 4 4
## 5 7
## 6 4
## Q17 Q17_5_text
## 1 Kako si najpogosteje delite stroške med prijatelji? Drugo (prosim navedite):
## 2 2 -2
## 3 2 -2
## 4 2 -2
## 5 2 -2
## 6 2 -2
## Q18a Q18b
## 1 Lahko nakažem točno vsoto Da se izognem plačevanju z gotovino
## 2 1 0
## 3 1 1
## 4 1 1
## 5 1 1
## 6 1 1
## Q18c
## 1 Ker je proces hiter in priročen
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## Q18d
## 1 Ker imam zabeležene svoje transakcije in tako lažje vodim svoje finance
## 2 0
## 3 0
## 4 1
## 5 1
## 6 1
## Q18e Q18e_text
## 1 Drugo (prosim navedite): Drugo (prosim navedite):
## 2 0 -2
## 3 0 -2
## 4 0 -2
## 5 0 -2
## 6 0 -2
## Q19a Q19b Q19c
## 1 Goljufije (npr. kraja denarja) Razkritje osebnih podatkov Kraja identitete
## 2 3 5 3
## 3 5 5 6
## 4 3 3 3
## 5 5 5 5
## 6 7 7 7
## Q19d
## 1 Izguba dostopa zaradi hekerskega napada
## 2 3
## 3 7
## 4 3
## 5 6
## 6 7
## Q20
## 1 Ali ste bili vi (oz. kdo od vaših bližnjih) žrtev spletne goljufije?
## 2 2
## 3 3
## 4 4
## 5 4
## 6 1
## Q21a
## 1 Pogosteje uporabljam gotovino v neznanih ali sumljivih situacijah (npr. med potovanji).
## 2 0
## 3 0
## 4 -2
## 5 -2
## 6 -1
## Q21b
## 1 Pogosteje uporabljam digitalna plačilna sredstva, kot so virtualne ali enkratne kartice, ki jih omogočajo neobanke, v neznanih ali sumljivih situacijah (npr. med potovanji).
## 2 0
## 3 0
## 4 -2
## 5 -2
## 6 -1
## Q21c
## 1 Postal_a sem bolj previden_na pri spletnih plačilih.
## 2 1
## 3 1
## 4 -2
## 5 -2
## 6 -1
## Q21d
## 1 Preklopil_a sem na varnejše plačilne možnosti (npr. mobilne denarnice z avtentikacijo).
## 2 0
## 3 0
## 4 -2
## 5 -2
## 6 -1
## Q21e
## 1 Moje vedenje se ni spremenilo.
## 2 0
## 3 0
## 4 -2
## 5 -2
## 6 -1
## Q22
## 1 Če bi imeli možnost, ali bi popolnoma preklopili na digitalna plačila?
## 2 2
## 3 2
## 4 2
## 5 1
## 6 1
## Q23
## 1 Katerega spola ste?
## 2 1
## 3 2
## 4 2
## 5 2
## 6 2
## Q24
## 1 Katera je vaša najvišja dosežena stopnja dosežene izobrazbe?
## 2 6
## 3 5
## 4 4
## 5 4
## 6 4
## Q25 Q25_5_text
## 1 Kakšen je vaš trenutni status? Drugo:
## 2 1 -2
## 3 1 -2
## 4 1 -2
## 5 1 -2
## 6 1 -2
## Q26
## 1 Kakšna je višina vaših trenutnih neto mesečnih prihodkov?
## 2 2
## 3 2
## 4 3
## 5 2
## 6 4
## Q27
## 1 Katero banko trenutno uporabljate kot primarno banko?
## 2 2
## 3 2
## 4 7
## 5 1
## 6 1
## Q27_7_text
## 1 Drugo (prosim navedite):
## 2 -2
## 3 -2
## 4 Unicredit
## 5 -2
## 6 -22 Dataset overview
- Unit of observation: Each row represents a single respondent from the questionnaire, which was completed by young people aged 18 to 27.
- Sample size: The dataset includes responses from a total of 441 young individuals.
2.1 Variables description
Q1a - Age
Q3 - How many times a month do you use the following payment methods on average?
- Q3a - Cash
- Q3b - Debit or Credit (physical) cards
- Q3c - Phone
- Q3d - Other (PayPal, Stripe..)
Q4 - How often do you use cash payment for the following purchases?
- Q4a - Purchases up to €10
- Q4b - Purchases between €11 and €99
- Q4c - Purchases between €100 and €1000
- Q4d - Purchases over €1000
Q5 - How do you usually respond if a merchant does not accept digital payments?
- Q5a - I prefer to go elsewhere and pay with a digital payment method.
- Q5b - I pay in the form available, even if it means withdrawing cash from an ATM.
- Q5c - I have not encountered such a situation yet.
- Q5d - Other (please specify).
Q6 - Which of the following income sources do you have, and how do you receive them?
- Q6a - Salary or earnings from student work
- Q6b - Pocket Money (from family members)
- Q6c - Gifts (for birthdays, holidays, etc.)
- Q6d - Unreported or occasional work (childcare, tutoring, etc.)
- Q6e - Government and/or social benefits
- Q6f - Government and other forms of scholarships (e.g., Zois, corporate)
- Q6g - Returns from investments (stocks, bonds, cryptocurrencies, etc.)
Q7 - Do you save money? (This question does NOT include savings from parents or family members.)
- Q7a - Yes
- Q7b - No
Q8a - In what form do you save money? (Digital vs. cash savings)
Q9 - Various attitudes towards digital and cash payments.
- Q9a - I usually spend money in the form in which I received it.
- Q9b - I feel concerned about the security of my personal information when using digital payment methods.
- Q9c - I find digital payments less secure than cash payments.
- Q9d - I have more confidence in digital payment methods if they offer features like two-factor authentication.
- Q9e - I feel safe when I carry cash with me.
- Q9f - I prefer to use digital payments because they are more convenient and save time.
- Q9g - I prefer to use cash to avoid overspending.
- Q9h - I use cash only when digital payments are not possible.
Q10 - How safe do you think the following payment methods are?
- Q10a - Cash
- Q10b - (Physical) Debit Card
- Q10c - (Physical) Credit Card
- Q10d - Paying with your phone (Flik, Apple Pay…)
- Q10e - Neobanks (Revolut, N26…)
Q11 - How easy do you find the following payment methods to use?
- Q11a - Cash
- Q11b - (Physical) Debit Card
- Q11c - (Physical) Credit Card
- Q11d - Paying with your phone (Flik, Apple Pay…)
- Q11e - Neobanks (Revolut, N26…)
Q12 - How accepted do you think the following payment methods are in stores in your environment?
- Q12a - Cash
- Q12b - (Physical) Debit Card
- Q12c - (Physical) Credit Card
- Q12d - Paying with your phone (Flik, Apple Pay…)
- Q12e - Neobanks (Revolut, N26…)
Q13 - What do you consider the fastest payment method?
- Q13a - Cash
- Q13b - (Physical) Debit Card
- Q13c - (Physical) Credit Card
- Q13d - Paying with your phone (Flik, Apple Pay…)
- Q13e - Neobanks (Revolut, N26…)
Q14 - Which payment method do you consider the most private?
- Q14a - Cash
- Q14b - (Physical) Debit Card
- Q14c - (Physical) Credit Card
- Q14d - Paying with your phone (Flik, Apple Pay…)
- Q14e - Neobanks (Revolut, N26…)
Q15 - Which payment method helps you control spending the most?
- Q15a - Cash
- Q15b - (Physical) Debit Card
- Q15c - (Physical) Credit Card
- Q15d - Paying with your phone (Flik, Apple Pay…)
- Q15e - Neobanks (Revolut, N26…)
Q16 - Social influence on payment method choice.
- Q16a - I choose the payment methods my friends choose.
- Q16b - I choose the payment methods my family members choose.
Q17 - How do you most often share expenses among friends?
- Q17a - With cash
- Q17b - Through mobile applications (Flik, Revolut, PayPal…)
- Q17c - By bank transfer
- Q17d - I don’t share expenses among friends
- Q17e - Other (please specify)
Q18 - Reasons for preferring digital payments.
- Q18a - I can quote the exact sum.
- Q18b - To avoid paying with cash.
- Q18c - Because the process is quick and convenient.
- Q18d - Because I have my transactions recorded and it is easier to manage finances.
- Q18e - Other (please specify).
Q19 - Concerns about digital payment security.
- Q19a - Fraud (e.g., stealing money)
- Q19b - Disclosure of Personal Information
- Q19c - Identity theft
- Q19d - Loss of access due to a hacker attack
Q20 - Experience with online fraud.
- Q20a - Yes, it happened to me.
- Q20b - Yes, I know people who have had this happen.
- Q20c - Yes, it has happened to me and others I know.
- Q20d - I’ve never encountered such a situation.
Q21 - How did this affect your behavior in further payment habits?
- Q21a - I use cash more often in unfamiliar or suspicious situations (e.g., when traveling).
- Q21b - I use digital payment methods (e.g., virtual or disposable cards facilitated by neobanks) more often in unfamiliar or suspicious situations.
- Q21c - I am more cautious with online payments.
- Q21d - I switched to more secure payment options (e.g., mobile wallets with authentication).
- Q21e - My behavior hasn’t changed.
Q22 - If we had the opportunity, would you switch to digital payments entirely?
- Q22a - Yes, immediately.
- Q22b - I would consider it, but I wouldn’t want to give up cash completely.
- Q22c - No, I prefer to use cash.
- Q22d - I don’t know.
Q23 - What is your gender?
- Q23a - Man.
- Q23b - Woman.
- Q23c - Another.
- Q23d - I don’t want to answer.
Q24 - What is your highest level of educational attainment?
- Q24a - Unfinished primary school.
- Q24b - Completed primary school.
- Q24c - Completed lower or secondary vocational education.
- Q24d - Completed secondary professional or general education.
- Q24e - Completed tertiary professional or tertiary professional education (including 1st Bologna level).
Q25 - What is your current status?
- Q25a - Student.
- Q25b - Employed.
- Q25c - Self-employed.
- Q25d - Unemployed.
- Q25e - Other (please specify).
Q26 - What is your current net monthly income?
- Q26a - 0-200 EUR.
- Q26b - 201-500 EUR.
- Q26c - 501-800 EUR.
- Q26d - 801-1300 EUR.
- Q26e - More than 1300 EUR.
Q27 - Which bank do you currently use as your primary bank?
- Q27a - NLB.
- Q27b - OTP.
- Q27c - Intesa Sanpaolo.
- Q27d - Sparkasse.
- Q27e - Addiko Bank.
- Q27f - Workers’ Savings Bank.
- Q27g - Other (please specify).
2.2 Data manipulation
#Remove first row
NLB_data <- NLB_data[-1, ]
#Remove all questionnaires with non valid status
library(dplyr)
NLB_data <- NLB_data %>%
filter(!status == 5)
#Remove status column
NLB_data <- NLB_data[ , -1]
#Remove all under 18/over 27
library(dplyr)
NLB_data <- NLB_data %>%
filter(!Q1 %in% c(1, 12))# Factoring
# Age (Q1)
NLB_data$Q1 <- factor(NLB_data$Q1,
levels = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11),
labels = c(18, 19, 20, 21, 22, 23, 24, 25, 26, 27))
# Q5
NLB_data$Q5 <- factor(NLB_data$Q5,
levels = c(1, 2, 3, 4),
labels = c("Pay digital elsewhere", "Pay as available", "Never occurred", "Other"))
# Q7
NLB_data$Q7 <- factor(NLB_data$Q7,
levels = c(1, 2),
labels = c("Yes", "No"))
# Q17
NLB_data$Q17 <- factor(NLB_data$Q17,
levels = c(1, 2, 3, 4, 5),
labels = c("Cash", "Mobile apps", "Bank transfer", "Don't share", "Other"))
# Q20
NLB_data$Q20 <- factor(NLB_data$Q20,
levels = c(1, 2, 3, 4),
labels = c("Yes - me", "Yes - others", "Yes - both", "No"))
# Q22
NLB_data$Q22 <- factor(NLB_data$Q22,
levels = c(1, 2, 3, 4),
labels = c("Fully digital", "Balance digital-cash", "Cash", "Don't know"))
# Q23
NLB_data$Q23 <- factor(NLB_data$Q23,
levels = c(1, 2, 3, 4),
labels = c("Man", "Woman", "Other", "Don't want to answer"))
# Q24
NLB_data$Q24 <- factor(NLB_data$Q24,
levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("Unfinished primary", "Primary school", "Vocational education", "High School", "Bachelor Degree", "Master Degree", "PhD" ))
# Q25
NLB_data$Q25 <- factor(NLB_data$Q25,
levels = c(1, 2, 3, 4, 5),
labels = c("Student", "Employed", "Self-employed", "Unemployed", "Other"))
# Q26
NLB_data$Q26 <- factor(NLB_data$Q26,
levels = c(1, 2, 3, 4, 5),
labels = c("0-200 EUR", "201-500 EUR", "501-800 EUR", "801-1300 EUR", "Above 1300 EUR"))
# Q27
NLB_data$Q27 <- factor(NLB_data$Q27,
levels = c(1, 2, 3, 4, 5, 6, 7),
labels = c("NLB", "OTP", "Intesa Sanpaolo", "Sparkasse", "Addiko Bank", "Delovska Hranilnica", "Other"))
head(NLB_data)## Q1 Q3a Q3b Q3c Q3d Q4a Q4b Q4c Q4d Q5 Q5_4_text Q6a Q6b
## 1 23 3 2 5 2 4 3 2 1 Pay as available -2 3 2
## 2 25 2 5 1 1 2 1 1 1 Pay digital elsewhere -2 4 2
## 3 25 2 4 4 1 2 2 1 1 Pay as available -2 3 3
## 4 23 3 5 5 1 2 2 1 1 Pay digital elsewhere -2 3 3
## 5 23 2 5 5 1 2 1 1 1 Pay digital elsewhere -2 4 3
## 6 25 3 3 1 2 4 3 2 1 Pay as available -2 3 4
## Q6c Q6d Q6e Q6f Q6g Q7 Q8a Q9a Q9b Q9c Q9d Q9e Q9f Q9g Q9h Q31_2a Q31_2b
## 1 1 1 3 3 3 Yes 5 6 3 5 6 4 7 6 7 7 7
## 2 1 4 4 4 3 Yes 7 7 6 3 6 3 7 1 6 6 7
## 3 1 4 4 4 2 Yes 7 6 2 3 6 3 7 1 7 7 6
## 4 2 4 4 3 4 Yes 7 6 5 6 7 4 7 1 7 7 7
## 5 3 4 4 4 4 No -2 7 4 7 4 4 7 4 7 7 7
## 6 1 1 3 3 4 No -2 7 3 5 7 7 5 6 5 5 3
## Q31_2c Q31_2d Q31_2e Q31_2f Q10a Q10b Q10c Q10d Q10e Q11a Q11b Q11c Q11d Q11e
## 1 6 6 3 6 6 6 6 7 6 5 6 6 7 6
## 2 5 7 7 6 3 7 7 3 6 7 7 7 6 6
## 3 6 6 4 5 6 6 4 6 6 4 7 7 7 7
## 4 7 7 7 7 6 5 4 6 6 3 6 6 7 7
## 5 7 7 7 7 7 4 4 4 4 7 7 7 7 7
## 6 7 7 4 4 7 6 6 5 5 6 7 7 6 6
## Q12a Q12b Q12c Q12d Q12e Q13a Q13b Q13c Q13d Q13e Q14a Q14b Q14c Q14d Q14e
## 1 6 7 7 7 6 5 6 6 7 7 7 6 5 5 6
## 2 6 6 6 6 6 5 7 7 7 7 7 1 1 1 1
## 3 7 7 7 7 7 4 7 7 7 7 6 5 5 5 5
## 4 6 7 7 6 6 1 6 6 7 7 7 4 4 4 4
## 5 7 7 7 7 4 7 7 7 7 7 4 7 7 7 7
## 6 6 7 7 6 6 2 7 7 6 6 5 7 7 7 7
## Q15a Q15b Q15c Q15d Q15e Q16a Q16b Q17 Q17_5_text Q18a Q18b Q18c Q18d
## 1 7 5 5 3 3 5 2 Mobile apps -2 1 0 1 0
## 2 2 7 4 1 6 4 4 Mobile apps -2 1 1 1 0
## 3 5 7 7 7 7 4 4 Mobile apps -2 1 1 1 1
## 4 6 5 1 5 5 7 7 Mobile apps -2 1 1 1 1
## 5 4 7 7 7 7 4 4 Mobile apps -2 1 1 1 1
## 6 7 6 5 3 5 3 5 Mobile apps -2 1 0 0 0
## Q18e Q18e_text Q19a Q19b Q19c Q19d Q20 Q21a Q21b Q21c Q21d Q21e
## 1 0 -2 3 5 3 3 Yes - others 0 0 1 0 0
## 2 0 -2 5 5 6 7 Yes - both 0 0 1 0 0
## 3 0 -2 3 3 3 3 No -2 -2 -2 -2 -2
## 4 0 -2 5 5 5 6 No -2 -2 -2 -2 -2
## 5 0 -2 7 7 7 7 Yes - me -1 -1 -1 -1 -1
## 6 0 -2 5 4 6 6 Yes - others 1 0 0 0 0
## Q22 Q23 Q24 Q25 Q25_5_text Q26
## 1 Balance digital-cash Man Master Degree Student -2 201-500 EUR
## 2 Balance digital-cash Woman Bachelor Degree Student -2 201-500 EUR
## 3 Balance digital-cash Woman High School Student -2 501-800 EUR
## 4 Fully digital Woman High School Student -2 201-500 EUR
## 5 Fully digital Woman High School Student -2 801-1300 EUR
## 6 Don't know Woman Master Degree Student -2 201-500 EUR
## Q27 Q27_7_text
## 1 OTP -2
## 2 OTP -2
## 3 Other Unicredit
## 4 NLB -2
## 5 NLB -2
## 6 NLB -23 Question for Prof. Marinšek
library(dplyr)
Replace NA values with column means for all numeric columns in NLB_data NLB_data <- NLB_data %>% mutate(across(where(is.numeric), ~ ifelse(is.na(.), mean(., na.rm = TRUE), .)))
Define the calculate_mode function before using it in mutate() calculate_mode <- function(x) { # Remove NA values x <- x[!is.na(x)]
# Return the most frequent value mode_value <- names(sort(table(x), decreasing = TRUE))[1] return(mode_value) }
Apply the function in dplyr library(dplyr)
NLB_data <- NLB_data %>% mutate(across(where(~ !is.numeric(.)), ~ ifelse(is.na(.), calculate_mode(.), .)))
View the updated dataset print(NLB_data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
4 Descriptive statistics
## Q1 Q3a Q3b Q3c
## 23 :64 Length:304 Length:304 Length:304
## 22 :44 Class :character Class :character Class :character
## 25 :43 Mode :character Mode :character Mode :character
## 20 :40
## 24 :30
## 21 :23
## (Other):60
## Q3d Q4a Q4b Q4c
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q4d Q5 Q5_4_text
## Length:304 Pay digital elsewhere: 52 Length:304
## Class :character Pay as available :195 Class :character
## Mode :character Never occurred : 42 Mode :character
## Other : 15
##
##
##
## Q6a Q6b Q6c Q6d
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q6e Q6f Q6g Q7
## Length:304 Length:304 Length:304 Yes:233
## Class :character Class :character Class :character No : 71
## Mode :character Mode :character Mode :character
##
##
##
##
## Q8a Q9a Q9b Q9c
## Min. :-2.000 Length:304 Length:304 Length:304
## 1st Qu.: 1.000 Class :character Class :character Class :character
## Median : 4.000 Mode :character Mode :character Mode :character
## Mean : 3.395
## 3rd Qu.: 7.000
## Max. : 7.000
##
## Q9d Q9e Q9f Q9g
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q9h Q31_2a Q31_2b Q31_2c
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q31_2d Q31_2e Q31_2f Q10a
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q10b Q10c Q10d Q10e
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q11a Q11b Q11c Q11d
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q11e Q12a Q12b Q12c
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q12d Q12e Q13a Q13b
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q13c Q13d Q13e Q14a
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q14b Q14c Q14d Q14e
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q15a Q15b Q15c Q15d
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q15e Q16a Q16b Q17
## Length:304 Length:304 Length:304 Cash : 28
## Class :character Class :character Class :character Mobile apps :250
## Mode :character Mode :character Mode :character Bank transfer: 14
## Don't share : 8
## Other : 4
##
##
## Q17_5_text Q18a Q18b Q18c
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q18d Q18e Q18e_text Q19a
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q19b Q19c Q19d Q20
## Length:304 Length:304 Length:304 Yes - me : 22
## Class :character Class :character Class :character Yes - others: 96
## Mode :character Mode :character Mode :character Yes - both : 11
## No :172
## NA's : 3
##
##
## Q21a Q21b Q21c Q21d
## Length:304 Length:304 Length:304 Length:304
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Q21e Q22 Q23
## Length:304 Fully digital : 78 Man :120
## Class :character Balance digital-cash:171 Woman :182
## Mode :character Cash : 44 Other : 1
## Don't know : 10 Don't want to answer: 1
## NA's : 1
##
##
## Q24 Q25 Q25_5_text
## Unfinished primary : 0 Student :236 Length:304
## Primary school : 8 Employed : 49 Class :character
## Vocational education: 2 Self-employed: 7 Mode :character
## High School :132 Unemployed : 3
## Bachelor Degree : 99 Other : 9
## Master Degree : 61
## PhD : 2
## Q26 Q27 Q27_7_text
## 0-200 EUR :48 NLB :121 Length:304
## 201-500 EUR :91 OTP :109 Class :character
## 501-800 EUR :58 Intesa Sanpaolo : 17 Mode :character
## 801-1300 EUR :48 Sparkasse : 7
## Above 1300 EUR:57 Addiko Bank : 8
## NA's : 2 Delovska Hranilnica: 19
## Other : 23library(dplyr)
# Convert character columns in specified ranges to numeric
NLB_data <- NLB_data %>%
mutate(across(c(2:9, 11:18, 21:66, 68:78, 80:84, 89, 92),
~ ifelse(!is.na(.), as.numeric(.), .)))## Warning: There were 5 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(...)`.
## Caused by warning in `ifelse()`:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 4 remaining warnings.## Q1 Q3a Q3b Q3c Q3d
## 23 :64 Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 22 :44 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:1.000
## 25 :43 Median :2.000 Median :4.00 Median :4.000 Median :1.000
## 20 :40 Mean :2.737 Mean :3.51 Mean :3.332 Mean :1.546
## 24 :30 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:5.000 3rd Qu.:2.000
## 21 :23 Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## (Other):60
## Q4a Q4b Q4c Q4d
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.00 Median :1.000 Median :1.000
## Mean :2.421 Mean :2.03 Mean :1.576 Mean :1.398
## 3rd Qu.:3.000 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:1.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
##
## Q5 Q5_4_text Q6a Q6b
## Pay digital elsewhere: 52 Min. :-2 Min. :1.000 Min. :1.000
## Pay as available :195 1st Qu.:-2 1st Qu.:3.000 1st Qu.:1.750
## Never occurred : 42 Median :-2 Median :3.000 Median :3.000
## Other : 15 Mean :-2 Mean :2.905 Mean :2.651
## 3rd Qu.:-2 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :-2 Max. :4.000 Max. :5.000
## NA's :15
## Q6c Q6d Q6e Q6f Q6g
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000
## Median :1.00 Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :1.28 Mean :3.039 Mean :3.796 Mean :3.539 Mean :3.674
## 3rd Qu.:1.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q7 Q8a Q9a Q9b Q9c
## Yes:233 Min. :-2.000 Min. :1.000 Min. :1.000 Min. :1.000
## No : 71 1st Qu.: 1.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:2.000
## Median : 4.000 Median :6.000 Median :3.000 Median :4.000
## Mean : 3.395 Mean :5.316 Mean :3.115 Mean :3.655
## 3rd Qu.: 7.000 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. : 7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q9d Q9e Q9f Q9g
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:6.000 1st Qu.:1.000
## Median :6.000 Median :4.000 Median :7.000 Median :4.000
## Mean :5.359 Mean :4.135 Mean :6.046 Mean :3.441
## 3rd Qu.:7.000 3rd Qu.:5.000 3rd Qu.:7.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q9h Q31_2a Q31_2b Q31_2c Q31_2d
## Min. :1.000 Min. :1.000 Min. :1 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:6.000 1st Qu.:6 1st Qu.:6.000 1st Qu.:5.000
## Median :6.000 Median :7.000 Median :7 Median :7.000 Median :7.000
## Mean :5.704 Mean :6.105 Mean :6 Mean :6.168 Mean :6.076
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7 Max. :7.000 Max. :7.000
##
## Q31_2e Q31_2f Q10a Q10b
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :6.000 Median :6.000 Median :5.000
## Mean :5.201 Mean :5.658 Mean :5.503 Mean :5.138
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q10c Q10d Q10e Q11a
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000
## Median :5.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.947 Mean :5.174 Mean :4.826 Mean :5.102
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q11b Q11c Q11d Q11e Q12a
## Min. :3.000 Min. :3.00 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.00 1st Qu.:7.00 1st Qu.:4.000 1st Qu.:6.000
## Median :7.000 Median :7.00 Median :7.00 Median :6.000 Median :7.000
## Mean :6.148 Mean :6.02 Mean :6.48 Mean :5.592 Mean :6.227
## 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.00 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.00 Max. :7.00 Max. :7.000 Max. :7.000
##
## Q12b Q12c Q12d Q12e Q13a
## Min. :3.000 Min. :2.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:6.000 1st Qu.:6.000 1st Qu.:5.00 1st Qu.:4.000 1st Qu.:2.750
## Median :7.000 Median :7.000 Median :7.00 Median :5.000 Median :4.000
## Mean :6.484 Mean :6.336 Mean :6.03 Mean :5.076 Mean :4.076
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:7.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000 Max. :7.000
##
## Q13b Q13c Q13d Q13e
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:5.000 1st Qu.:7.000 1st Qu.:4.000
## Median :7.000 Median :7.000 Median :7.000 Median :6.000
## Mean :6.174 Mean :6.122 Mean :6.526 Mean :5.684
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q14a Q14b Q14c Q14d
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000
## Median :7.000 Median :4.000 Median :4.000 Median :4.000
## Mean :5.681 Mean :4.457 Mean :4.457 Mean :4.395
## 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:6.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q14e Q15a Q15b Q15c
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :5.000 Median :5.000 Median :5.000
## Mean :4.484 Mean :4.852 Mean :5.155 Mean :5.007
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q15d Q15e Q16a Q16b
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.000
## Median :5.000 Median :5.000 Median :3.000 Median :4.000
## Mean :5.161 Mean :5.115 Mean :3.125 Mean :3.339
## 3rd Qu.:7.000 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
##
## Q17 Q17_5_text Q18a Q18b
## Cash : 28 Min. :-2 Min. :-2.0000 Min. :-2.0000
## Mobile apps :250 1st Qu.:-2 1st Qu.: 1.0000 1st Qu.: 0.0000
## Bank transfer: 14 Median :-2 Median : 1.0000 Median : 0.0000
## Don't share : 8 Mean :-2 Mean : 0.5132 Mean : 0.1579
## Other : 4 3rd Qu.:-2 3rd Qu.: 1.0000 3rd Qu.: 1.0000
## Max. :-2 Max. : 1.0000 Max. : 1.0000
## NA's :4
## Q18c Q18d Q18e Q18e_text
## Min. :-2.0000 Min. :-2.0000 Min. :-2.0000 Min. :-2.000
## 1st Qu.: 1.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:-2.000
## Median : 1.0000 Median : 0.0000 Median : 0.0000 Median :-2.000
## Mean : 0.5164 Mean : 0.1349 Mean :-0.2368 Mean :-1.997
## 3rd Qu.: 1.0000 3rd Qu.: 1.0000 3rd Qu.: 0.0000 3rd Qu.:-2.000
## Max. : 1.0000 Max. : 1.0000 Max. : 1.0000 Max. :-1.000
## NA's :7
## Q19a Q19b Q19c Q19d
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 4.000 1st Qu.: 4.000 1st Qu.: 3.000 1st Qu.: 4.000
## Median : 5.000 Median : 4.000 Median : 4.000 Median : 5.000
## Mean : 4.507 Mean : 4.457 Mean : 4.273 Mean : 4.849
## 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 6.000 3rd Qu.: 6.000
## Max. : 7.000 Max. : 7.000 Max. : 7.000 Max. : 7.000
##
## Q20 Q21a Q21b Q21c
## Yes - me : 22 Min. :-2.000 Min. :-2.000 Min. :-2.0000
## Yes - others: 96 1st Qu.:-2.000 1st Qu.:-2.000 1st Qu.:-2.0000
## Yes - both : 11 Median :-2.000 Median :-2.000 Median :-2.0000
## No :172 Mean :-1.039 Mean :-1.072 Mean :-0.8651
## NA's : 3 3rd Qu.: 0.000 3rd Qu.: 0.000 3rd Qu.: 1.0000
## Max. : 1.000 Max. : 1.000 Max. : 1.0000
##
## Q21d Q21e Q22
## Min. :-2.000 Min. :-2.000 Fully digital : 78
## 1st Qu.:-2.000 1st Qu.:-2.000 Balance digital-cash:171
## Median :-2.000 Median :-2.000 Cash : 44
## Mean :-1.033 Mean :-1.079 Don't know : 10
## 3rd Qu.: 0.000 3rd Qu.: 0.000 NA's : 1
## Max. : 1.000 Max. : 1.000
##
## Q23 Q24 Q25
## Man :120 Unfinished primary : 0 Student :236
## Woman :182 Primary school : 8 Employed : 49
## Other : 1 Vocational education: 2 Self-employed: 7
## Don't want to answer: 1 High School :132 Unemployed : 3
## Bachelor Degree : 99 Other : 9
## Master Degree : 61
## PhD : 2
## Q25_5_text Q26 Q27 Q27_7_text
## Min. :-2 0-200 EUR :48 NLB :121 Min. :-2
## 1st Qu.:-2 201-500 EUR :91 OTP :109 1st Qu.:-2
## Median :-2 501-800 EUR :58 Intesa Sanpaolo : 17 Median :-2
## Mean :-2 801-1300 EUR :48 Sparkasse : 7 Mean :-2
## 3rd Qu.:-2 Above 1300 EUR:57 Addiko Bank : 8 3rd Qu.:-2
## Max. :-2 NA's : 2 Delovska Hranilnica: 19 Max. :-2
## NA's :9 Other : 23 NA's :234.1 Categorical variables
- Q17 – Preferred Payment Method for Expense Sharing When it comes to sharing expenses, the majority of respondents (250) prefer using mobile apps. A smaller group (28) opts for cash, while 14 respondents use bank transfers. Eight respondents do not share expenses with others, and four selected “Other” as their method of choice. This highlights the dominance of mobile apps as the most convenient and preferred option for managing shared expenses.
- Q22 – Savings Preference Respondents show varied saving preferences. A majority (171) save their money in a balanced form, splitting between digital and cash. Fully digital savings are preferred by 78 respondents, while 44 save entirely in cash. A smaller group (10 respondents) is unsure of their savings method.
- Q23 – Gender The gender distribution indicates that the majority of respondents (182) are women, while 120 are men. Additionally, 1 respondent identified as “Other,” and another preferred not to disclose their gender.
- Q24 – Education Level The most common level of education is high school completion, with 132 respondents. Following this, 99 have a bachelor’s degree, 61 hold a master’s degree, and 2 have a PhD. Vocational education was reported by 2 respondents, and 8 completed only primary school, while none reported having incomplete primary education.
- Q25 – Employment Status The majority of respondents (236) are students. Employed individuals account for 49 responses, followed by 7 self-employed, 3 unemployed, and 9 selecting “other” as their employment status.
- Q26 – Monthly Income Regarding income, the largest group of respondents (91) earns between 201-500 EUR per month. Others reported the following income ranges: 0-200 EUR (48), 501-800 EUR (58), 801-1300 EUR (48), and above 1300 EUR (57).
- Q27 – Bank Association Respondents are primarily associated with two major banks: NLB (121 respondents) and OTP (109 respondents). Smaller numbers of respondents use other banks such as Intesa Sanpaolo (17), Sparkasse (7), and Addiko Bank (8).
4.2 Numerical variables
Respondents show varying frequencies of cash usage based on the purchase amount. For small purchases (up to €10), the average usage is moderate, with a mean of 2.421 and a median of 2, indicating that cash is used occasionally but not regularly. Medium-small purchases (€11 to €99) have a slightly higher mean of 2.737 but still lean towards occasional use, with a median of 2. In contrast, for medium-large purchases (€100 to €1000), cash usage drops significantly, reflected by a mean of 1.576 and a median of 1, suggesting infrequent or rare use. This trend continues for large purchases (over €1000), where cash is almost never used, with a mean of 1.398 and a median of 1. Overall, cash usage declines as the purchase value increases, indicating a preference for alternative payment methods for higher-value transactions.
Respondents show a stronger tendency to be influenced by their family than by their friends when choosing payment methods. For Q16a (friends’ influence), the mean is 3.125 and the median is 3, indicating a neutral to slightly agreeing stance, with many respondents either disagreeing or moderately agreeing. In contrast, Q16b (family’s influence) has a higher mean of 3.339 and a median of 4, suggesting a more significant agreement with the statement. While both questions have a 1st quartile of 1, indicating strong disagreement from a subset of respondents, the 3rd quartile for family influence is 5, compared to 4 for friends, confirming that family has a stronger impact on respondents’ payment choices.
5 Clustering ideas
NLB_CluStd <- as.data.frame(scale(NLB_data[c("Q3a", "Q6a", "Q8a", "Q9g", "Q11a")]))
head(NLB_CluStd)## Q3a Q6a Q8a Q9g Q11a
## 1 0.2495495 0.1553635 0.4648959 1.2515560 -0.05416764
## 2 -0.6987386 1.7840011 1.0441105 -1.1936434 1.00821695
## 3 -0.6987386 0.1553635 1.0441105 -1.1936434 -0.58535993
## 4 0.2495495 0.1553635 1.0441105 -1.1936434 -1.11655222
## 5 -0.6987386 1.7840011 -1.5623551 0.2734763 1.00821695
## 6 0.2495495 0.1553635 -1.5623551 1.2515560 0.47702466NLB_CluStd$Dissimilarity <- sqrt(NLB_CluStd$Q3a^2 + NLB_CluStd$Q6a^2 + NLB_CluStd$Q8a^2 + NLB_CluStd$Q9g^2 +NLB_CluStd$Q11a^2)
head(NLB_CluStd[order(-NLB_CluStd$Dissimilarity), ], n = 10)## Q3a Q6a Q8a Q9g Q11a Dissimilarity
## 204 2.1461256 -3.101912 -1.5623551 1.2515560 1.0082170 4.387660
## 191 2.1461256 -3.101912 0.4648959 1.7405959 -0.5853599 4.220919
## 299 -0.6987386 -3.101912 0.1752886 -1.1936434 -2.1789368 4.038981
## 216 0.2495495 -3.101912 -1.5623551 -1.1936434 -1.1165522 3.846630
## 214 1.1978375 -3.101912 -0.6935332 0.2734763 1.0082170 3.553724
## 184 1.1978375 -3.101912 1.0441105 0.2734763 -0.5853599 3.544612
## 277 0.2495495 -3.101912 -1.5623551 0.2734763 -0.5853599 3.541542
## 267 -0.6987386 -3.101912 0.4648959 -0.7046035 -1.1165522 3.474101
## 36 2.1461256 -1.473274 1.0441105 1.7405959 1.0082170 3.451483
## 211 2.1461256 1.784001 -1.5623551 0.7625161 1.0082170 3.439099Three potential outliers (204, 191 and 299).
## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaDistances <- get_dist(NLB_CluStd,
method = "euclidian")
fviz_dist(Distances,
gradient = list(low = "red4",
mid = "grey",
high = "white"))
## $hopkins_stat
## [1] 0.6800118
##
## $plot
## NULLNot the best one, we’ll find the perfect one :)
library(factoextra)
library(NbClust)
fviz_nbclust(NLB_CluStd, kmeans, method = "wss") +
labs(subtitle = "Elbow Method")
library(NbClust)
NbClust(NLB_CluStd,
distance = "euclidean",
min.nc = 2, max.nc = 10,
method = "kmeans",
index = "all")
## *** : The Hubert index is a graphical method of determining the number of clusters.
## In the plot of Hubert index, we seek a significant knee that corresponds to a
## significant increase of the value of the measure i.e the significant peak in Hubert
## index second differences plot.
## 
## *** : The D index is a graphical method of determining the number of clusters.
## In the plot of D index, we seek a significant knee (the significant peak in Dindex
## second differences plot) that corresponds to a significant increase of the value of
## the measure.
##
## *******************************************************************
## * Among all indices:
## * 5 proposed 2 as the best number of clusters
## * 2 proposed 3 as the best number of clusters
## * 6 proposed 4 as the best number of clusters
## * 1 proposed 5 as the best number of clusters
## * 2 proposed 6 as the best number of clusters
## * 1 proposed 7 as the best number of clusters
## * 1 proposed 9 as the best number of clusters
## * 5 proposed 10 as the best number of clusters
##
## ***** Conclusion *****
##
## * According to the majority rule, the best number of clusters is 4
##
##
## *******************************************************************## $All.index
## KL CH Hartigan CCC Scott Marriot TrCovW TraceW
## 2 1.0377 84.6416 49.1694 14.8593 1139.345 2.281962e+14 51481.33 1290.0514
## 3 0.2444 73.5515 53.2049 14.0886 1401.196 2.169760e+14 38381.23 1109.4232
## 4 2.0836 75.1860 12.7837 17.1720 1668.964 1.598657e+14 28890.53 942.7775
## 5 0.3270 61.7817 53.3464 15.9155 1788.854 1.683838e+14 27521.34 904.2456
## 6 5.4645 68.6824 22.0934 19.0813 2023.107 1.122043e+14 18129.29 767.3398
## 7 1.3134 64.9423 18.4025 18.6996 2142.910 1.029797e+14 15402.32 714.3766
## 8 0.3878 61.5350 24.8668 20.5237 2209.750 1.079562e+14 13312.61 672.6954
## 9 1.1985 61.2671 21.7164 21.4497 2446.190 6.277320e+13 11879.58 620.5624
## 10 3.5310 60.6752 12.6659 22.1796 2575.570 5.063583e+13 10341.32 578.0120
## Friedman Rubin Cindex DB Silhouette Duda Pseudot2 Beale Ratkowsky
## 2 14.6649 2.3487 0.4076 1.8808 0.2105 0.9753 4.0211 0.0965 0.2946
## 3 17.1954 2.7311 0.4592 1.6764 0.2030 1.0533 -6.8310 -0.1929 0.2999
## 4 18.2173 3.2139 0.3938 1.4672 0.2277 1.4457 -33.6041 -1.1705 0.3095
## 5 22.1979 3.3509 0.4345 1.5311 0.2255 3.2181 -84.7792 -2.5634 0.2899
## 6 27.1141 3.9487 0.4014 1.4495 0.2336 1.8988 -21.7745 -1.7584 0.2958
## 7 26.6437 4.2415 0.4030 1.5645 0.2140 1.4445 -18.4638 -1.1366 0.2807
## 8 26.9003 4.5043 0.4118 1.4442 0.2353 3.7139 -54.0748 -2.6357 0.2676
## 9 39.8438 4.8827 0.3582 1.3539 0.2332 3.4538 -40.4964 -2.5726 0.2631
## 10 39.6847 5.2421 0.3448 1.3475 0.2392 2.7310 -38.0301 -2.2862 0.2536
## Ball Ptbiserial Frey McClain Dunn Hubert SDindex Dindex SDbw
## 2 645.0257 0.3809 0.2363 0.7479 0.0727 0.0013 1.9147 1.9727 0.8195
## 3 369.8077 0.4401 0.1702 1.2028 0.0884 0.0016 1.6701 1.8433 0.7350
## 4 235.6944 0.4850 0.0547 1.6643 0.0824 0.0017 1.4301 1.6858 0.6234
## 5 180.8491 0.4922 0.2166 1.7104 0.0920 0.0020 1.6545 1.6537 0.6393
## 6 127.8900 0.5109 0.5116 2.3001 0.0935 0.0023 1.4946 1.5161 0.5492
## 7 102.0538 0.4822 -0.1088 3.0160 0.1151 0.0023 1.7395 1.4455 0.4988
## 8 84.0869 0.4981 0.3687 2.9932 0.1075 0.0023 1.5378 1.4143 0.4656
## 9 68.9514 0.4818 0.2400 3.5066 0.0935 0.0027 1.8485 1.3503 0.4710
## 10 57.8012 0.4739 0.8551 3.9086 0.0988 0.0028 1.7652 1.3067 0.4360
##
## $All.CriticalValues
## CritValue_Duda CritValue_PseudoT2 Fvalue_Beale
## 2 0.7432 54.9395 0.9967
## 3 0.7293 50.1032 1.0000
## 4 0.6981 47.1404 1.0000
## 5 0.5853 87.1490 1.0000
## 6 0.5802 33.2833 1.0000
## 7 0.5569 47.7335 1.0000
## 8 0.4772 81.0803 1.0000
## 9 0.4889 59.5851 1.0000
## 10 0.4772 65.7408 1.0000
##
## $Best.nc
## KL CH Hartigan CCC Scott Marriot TrCovW
## Number_clusters 6.0000 2.0000 5.0000 10.0000 4.0000 4.000000e+00 3.00
## Value_Index 5.4645 84.6416 40.5628 22.1796 267.7683 6.562841e+13 13100.09
## TraceW Friedman Rubin Cindex DB Silhouette Duda
## Number_clusters 4.0000 9.0000 4.0000 10.0000 10.0000 10.0000 2.0000
## Value_Index 128.1139 12.9435 -0.3458 0.3448 1.3475 0.2392 0.9753
## PseudoT2 Beale Ratkowsky Ball PtBiserial Frey McClain
## Number_clusters 2.0000 2.0000 4.0000 3.000 6.0000 1 2.0000
## Value_Index 4.0211 0.0965 0.3095 275.218 0.5109 NA 0.7479
## Dunn Hubert SDindex Dindex SDbw
## Number_clusters 7.0000 0 4.0000 0 10.000
## Value_Index 0.1151 0 1.4301 0 0.436
##
## $Best.partition
## [1] 1 2 2 2 4 4 2 2 1 2 2 4 1 2 4 2 4 2 4 2 4 4 1 1 2 3 3 2 1 2 2 2 2 1 2 1 2
## [38] 1 2 2 2 1 2 2 4 3 2 2 3 1 4 2 3 1 2 2 1 2 4 3 4 4 4 2 1 1 2 2 1 2 2 2 3 1
## [75] 2 2 2 4 1 2 1 2 2 1 4 1 2 1 2 3 2 1 2 1 2 2 3 1 4 1 2 2 4 4 2 4 4 2 2 1 2
## [112] 2 4 1 1 2 1 1 2 2 1 2 2 1 2 2 1 2 2 2 2 1 1 4 2 3 3 1 4 2 2 3 1 2 4 1 2 2
## [149] 1 2 3 2 1 1 4 4 1 2 4 3 3 2 2 2 1 2 4 1 1 2 2 1 1 2 2 2 1 2 4 4 2 2 4 3 2
## [186] 4 1 1 4 2 3 3 2 2 2 2 2 3 4 1 1 3 3 3 1 2 2 3 4 1 1 1 1 3 2 3 1 2 1 4 1 2
## [223] 3 2 2 4 1 1 4 2 4 2 4 2 2 1 1 1 2 2 1 3 3 3 4 3 4 2 4 4 4 4 4 1 1 1 1 3 2
## [260] 1 4 2 2 2 2 2 3 3 3 4 4 4 3 2 3 3 3 2 1 4 4 3 2 3 2 4 2 1 1 4 3 2 1 2 2 2
## [297] 4 4 3 2 4 1 4 26 Conclusion
Will we follow the code? Or will we revolutionize our findings? Keep up on the next episode
The Payvolutionaries