Introduction

This project aims to demonstrate the skills and techniques learned throughout the data visualization specialization by creating a series of compelling and informative graphics. The dataset used in this project consists of categorical values and their corresponding numerical values. The dataset is simple but provides a good basis for showcasing various types of visualizations including bar charts, line plots, box plots, heatmaps, interactive plots, animated plots, scatter plot, and pie chart.

Load Data

# Load the dataset
data <- read.csv("sample_data.csv")
head(data)
##   Category Values
## 1        A     23
## 2        B     45
## 3        C     12
## 4        D     67
## 5        E     34

Visualization 1: Bar Chart of Category Distribution

# Bar chart
library(ggplot2)
ggplot(data, aes(x=Category, y=Values)) + 
  geom_bar(stat="identity") + 
  ggtitle("Distribution of Categories") + 
  xlab("Category") + 
  ylab("Values")

Visualization 2: Line Plot of Category Values

# Line plot
ggplot(data, aes(x=Category, y=Values, group=1)) + 
  geom_line() + 
  geom_point() + 
  ggtitle("Values by Category") + 
  xlab("Category") + 
  ylab("Values")

Visualization 3: Box Plot of Values by Category

# Box plot
ggplot(data, aes(x=Category, y=Values)) + 
  geom_boxplot() + 
  ggtitle("Box Plot of Values by Category") + 
  xlab("Category") + 
  ylab("Values")

Visualization 4: Heatmap of Values

# Heatmap
library(reshape2)
data_melt <- melt(data)
## Using Category as id variables
ggplot(data_melt, aes(x=variable, y=Category, fill=value)) + 
  geom_tile() + 
  ggtitle("Heatmap of Values") + 
  xlab("Variable") + 
  ylab("Category")

Visualization 5: Interactive Plot

## 
## 載入套件：'plotly'
## 下列物件被遮斷自 'package:ggplot2':
## 
##     last_plot
## 下列物件被遮斷自 'package:stats':
## 
##     filter
## 下列物件被遮斷自 'package:graphics':
## 
##     layout

Visualization 6: Animated Plot

Visualization 7: Scatter Plot

Visualization 8: Pie chart

# Conclusion Overall, these visualizations offer a clear and detailed view of the dataset, allowing for a deeper understanding of the categorical values and their distributions. The use of different types of plots, including static, interactive, and animated, ensures a well-rounded analysis, catering to various analytical needs and preferences.