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Uber_case_analysis

Jackyieym 2024-04-20

Introduction

This project is conduct under the Google Data Analytics Capstone and follows Track B, which is performing own case study. The company I choose is Uber, an American multinational transportation company that provides ride-hailing services, courier services, food delivery, and freight transport. The goal of this project is to analyse how some of the fundemental factors of people affect their usage of Uber and provide insights to Uber’s strategy in tailoring their ride-hailing service.

Data collection

The data used in this project is collected from survey answer. The survey was distributed in the university campus and received 32 responses. The questions asked about the gender, their major, usage of Uber, frequency, price, number of people, tier of services, main alternative, rating on important and improvement factors, including price, safety, convenient and customer services. Due to the data was collected in campus, its respondents are mostly limited to university students.

Question

Some question are set up for analysis using chisq.test 1. Is gender related to the score of important factors when using Uber In this question, Importance_Price, Importance_Price, Importance_Price, Importance_Price are selected for analysis, representing Price, Safety, Convenient and Customer Service respectively to Gender. For null hypothesis (H0), no relation between gender and the score of important factors For alternative hypothesis (H1), there is relation between gender and the score of important factors

Setting up Environment

library(ggplot2)
library(ggcorrplot)
library(nFactors)
library(GGally)
library(dplyr)
library(patchwork)
library(readr)

Load the data file

UberProjectRawData <- read_csv("./UberProjectRawData.csv")
## # A tibble: 32 × 18
##    `Response ID` Gender  Major OnlineTaxiService Frequency Time  Cost  NumPeople
##            <dbl> <chr>   <chr> <chr>             <chr>     <chr> <chr>     <dbl>
##  1     127966762 Female  Fina… Yes               Often     Morn… $50-…         1
##  2     127992777 Male    Acco… Yes               Sometimes Noon… $50-…         2
##  3     128824959 Male    Fina… Yes               Selfdom   Midn… $75-…         1
##  4     129122186 Male    Acco… Yes               Always    Midn… $125…         1
##  5     129123976 Prefer… Mark… Yes               Often     Midn… more…         2
##  6     129123992 Male    Fina… Yes               Selfdom   Midn… more…         1
##  7     129124004 Female  Entr… Yes               Always    Night $100…         1
##  8     129124015 Female  Mark… Yes               Sometimes Night $100…         2
##  9     129124024 Male    I am… Yes               Sometimes Midn… more…         4
## 10     129124046 Male    Huma… Yes               Selfdom   Midn… more…         3
## # ℹ 22 more rows
## # ℹ 10 more variables: `Std-or-Prem` <chr>, MainTrans <chr>,
## #   Importance_Price <dbl>, Importance_Safety <dbl>, Importance_Conv <dbl>,
## #   Importance_Cus <dbl>, Improve_Price <dbl>, Improve_Safety <dbl>,
## #   Improve_Conv <dbl>, Improve_Cus <dbl>

Data cleaning

After filtering out those did not use Uber, columns required for analysis are selected and arrange according to gender.

s1<-UberProjectRawData %>% 
  filter(OnlineTaxiService=="Yes") %>% 
  select(2,11,12,13,14) %>% 
  arrange(Gender)
## # A tibble: 29 × 5
##    Gender Importance_Price Importance_Safety Importance_Conv Importance_Cus
##    <chr>             <dbl>             <dbl>           <dbl>          <dbl>
##  1 Female                5                 5               5              5
##  2 Female                5                 4               5              3
##  3 Female                3                 3               5              2
##  4 Female                1                 3               5              4
##  5 Female                5                 5               4              4
##  6 Female                5                 4               3              5
##  7 Female                5                 5               4              3
##  8 Female                3                 3               3              3
##  9 Female                5                 3               4              2
## 10 Female                5                 5               4              3
## # ℹ 19 more rows

Data visualisation

To have a quick glance on the data to get the rating by the respondents, we can use bar chart to represent the vote of rating on different factors by genders. Putting four chart together will have a better comparison.

bar_Price<-ggplot(s1, aes(x=Importance_Price, fill=Gender))+
  geom_bar(position = position_dodge(preserve = "single"))
bar_Safety<-ggplot(s1, aes(x=Importance_Safety,fill=Gender))+
  geom_bar(position = position_dodge(preserve = "single"))
bar_Conv<-ggplot(s1, aes(x=Importance_Conv,fill=Gender))+
  geom_bar(position = position_dodge(preserve = "single"))
bar_Cus<-ggplot(s1, aes(x=Importance_Cus, fill=Gender))+
  geom_bar(position = position_dodge(preserve = "single"))
bar_Price+bar_Safety+bar_Conv+bar_Cus+
  plot_annotation("Votes on score of different factors per gender (frequency)", 
                  theme=theme(plot.title=element_text(hjust=0.5)))

However, due to some catagory of gender have less response in absolute number compare to other options, their vote on rating may under-represented. Therefore, converting the data into proportion will provide a better representation.

port1<-as.data.frame(with(s1, prop.table(table(Gender, Importance_Price), margin=1)))
port2<-as.data.frame(with(s1, prop.table(table(Gender, Importance_Safety), margin=1)))
port3<-as.data.frame(with(s1, prop.table(table(Gender, Importance_Conv), margin=1)))
port4<-as.data.frame(with(s1, prop.table(table(Gender, Importance_Cus), margin=1)))

porp_bar_Price<-ggplot(port1, aes(x=Importance_Price, y=Freq, fill=Gender))+
  geom_col(position="dodge")+
  labs(y="Proportion of count")
porp_bar_Safety<-ggplot(port2, aes(x=Importance_Safety, y=Freq, fill=Gender))+
  geom_col(position="dodge")+
  labs(y="Proportion of count")
porp_bar_Conv<-ggplot(port3, aes(x=Importance_Conv, y=Freq, fill=Gender))+
  geom_col(position="dodge")+
  labs(y="Proportion of count")
porp_bar_Cus<-ggplot(port4, aes(x=Importance_Cus, y=Freq, fill=Gender))+
  geom_col(position="dodge")+
  labs(y="Proportion of count")
porp_bar_Price+porp_bar_Safety+porp_bar_Conv+porp_bar_Cus+
  plot_annotation("Votes on score of different factors per gender (proportion)", 
                  theme=theme(plot.title=element_text(hjust=0.5)))

Testing relationship

Performing the chisq.test can provide us the relationship between the gender and the factors

chisq.test(s1$Gender, s1$Importance_Price)
## 
##  Pearson's Chi-squared test
## 
## data:  s1$Gender and s1$Importance_Price
## X-squared = 4.7338, df = 6, p-value = 0.5784

chisq.test(s1$Gender, s1$Importance_Safety)
## 
##  Pearson's Chi-squared test
## 
## data:  s1$Gender and s1$Importance_Safety
## X-squared = 5.5935, df = 6, p-value = 0.4702

chisq.test(s1$Gender, s1$Importance_Conv)
## 
##  Pearson's Chi-squared test
## 
## data:  s1$Gender and s1$Importance_Conv
## X-squared = 3.9441, df = 6, p-value = 0.6842

chisq.test(s1$Gender, s1$Importance_Cus)
## 
##  Pearson's Chi-squared test
## 
## data:  s1$Gender and s1$Importance_Cus
## X-squared = 5.4759, df = 8, p-value = 0.7057

Conclusion for question 1

Because p-value of all factors is greater than 0.05, H0 is not rejected and meaning there is no relation between gender and the score of important factors.