Visualization Lab 5

Introduction

Welcome to my visualization la. We will explore the ‘completejourney’ dataset, an insight into customer interactions and experiences. This this report, we will employ the power of exploratory data visualization to uncover hidden patterns and trends that illuminate the dynamics of consumer behavior and satisfaction.Through a series of crafted plots, we delve into demographics, satisfaction levels, and journey duration of customers, seeking actionable insights that inform strategic decision-making. Join us as we decipher the narrative woven within the dataset, and explore the complexities of customer journeys and paving the way for enhanced engagement and organizational succss.

‘Completejourney’ dataset exploration

Plot 1

library(ggplot2)
library(completejourney)

## Welcome to the completejourney package! Learn more about these data
## sets at http://bit.ly/completejourney.

library(dplyr)

## 
## 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

ggplot(data = demographics, aes(x = income)) + geom_bar()

PLot 2

library(ggplot2)
library(completejourney)
library(dplyr)

transactions <- get_transactions()
merge_data = inner_join(demographics,transactions, by = "household_id") 

ggplot(data = merge_data, aes(x = household_size, y = income)) + geom_point()

Plot 3

library(ggplot2)
library(completejourney)
library(dplyr)
ggplot(data = demographics, aes(x = income, y = age)) + geom_point()