---
title: "TidyTuesday: Income Inequality & Health"
subtitle: "SDG 10: Reduced Inequalities"
author: "Nichodemus Amollo"
date: "2021-06-08"
format:
html:
toc: true
toc-depth: 2
code-fold: show
code-tools: true
code-copy: true
theme:
light: [cosmo, ../../custom.scss]
dark: [darkly, ../../custom.scss]
css: ../../styles.scss
---
::: {.hero-banner}
# **Income Inequality & Health**
Analysis of income inequality & health data from TidyTuesday 2021 - Week of 2021-06-08
:::
## Overview
This project explores the **Income Inequality & Health** dataset from TidyTuesday, focusing on data visualization and analysis techniques.
**SDG Alignment:** SDG 10: Reduced Inequalities
## Load Required Packages
```{r load-packages, echo=FALSE, message=FALSE, warning=FALSE}
library(tidyverse)
library(lubridate)
library(here)
library(showtext)
library(ggtext)
library(patchwork)
library(plotly) # For interactive visualizations
# Optional: Load additional packages based on analysis needs
# library(sf) # For spatial data
# library(rnaturalearth) # For map data
# library(gganimate) # For animations
```
## Data Import
```{r load-data, echo=FALSE}
# Load data using tidytuesdayR
# library(tidytuesdayR)
# tuesdata <- tt_load('2021-06-08')
# df <- tuesdata$data_name
# Alternative: Direct CSV download
# df <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-06-08/data.csv')
# Sample data for demonstration
set.seed(123)
df <- data.frame(
id = 1:100,
value = cumsum(rnorm(100)),
category = sample(c("A", "B", "C"), 100, replace = TRUE),
date = seq.Date(from = as.Date("2021-06-08"), by = "day", length.out = 100)
)
glimpse(df)
summary(df)
```
## Data Exploration
```{r data-exploration, echo=FALSE}
# Explore data structure
head(df)
str(df)
# Check for missing values
colSums(is.na(df))
# Summary statistics
summary(df)
```
## Data Wrangling
```{r data-wrangling, echo=FALSE}
# Clean and prepare data
df_clean <- df %>%
filter(!is.na(value)) %>%
mutate(
category = as.factor(category),
value_group = cut(value, breaks = 5, labels = c("Low", "Medium-Low", "Medium", "Medium-High", "High"))
)
# Group by category if applicable
df_summary <- df_clean %>%
group_by(category) %>%
summarise(
mean_value = mean(value, na.rm = TRUE),
median_value = median(value, na.rm = TRUE),
count = n()
)
```
## Visualizations
### Visualization 1: Main Analysis
```{r visualization-1, echo=FALSE}
p1 <- ggplot(df_clean, aes(x = date, y = value, color = category)) +
geom_line(size = 1.2, alpha = 0.7) +
geom_point(size = 2, alpha = 0.8) +
scale_color_manual(values = c("#667eea", "#764ba2", "#f093fb")) +
labs(
title = "Income Inequality & Health",
subtitle = "Time series analysis",
x = "Date",
y = "Value",
color = "Category",
caption = "Source: TidyTuesday | Visualization: Nichodemus Amollo"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 18, hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5, color = "gray50"),
legend.position = "bottom"
)
print(p1)
```
### Visualization 2: Distribution Analysis
```{r visualization-2, echo=FALSE}
p2 <- ggplot(df_clean, aes(x = category, y = value, fill = category)) +
geom_violin(alpha = 0.7) +
geom_boxplot(width = 0.2, alpha = 0.5) +
scale_fill_manual(values = c("#667eea", "#764ba2", "#f093fb")) +
labs(
title = "Distribution by Category",
x = "Category",
y = "Value"
) +
theme_minimal()
print(p2)
```
### Interactive Visualization
```{r visualization-interactive, echo=FALSE}
# Create interactive plotly visualization
p_interactive <- plot_ly(
df_clean,
x = ~date,
y = ~value,
color = ~category,
type = "scatter",
mode = "lines+markers",
hovertemplate = "<b>Date:</b> %{x}<br><b>Value:</b> %{y}<extra></extra>"
) %>%
layout(
title = "Income Inequality & Health",
xaxis = list(title = "Date"),
yaxis = list(title = "Value"),
hovermode = "x unified"
)
p_interactive
```
## Analysis & Insights
### Key Findings
1. **Finding 1**: [Description of key insight]
2. **Finding 2**: [Description of key insight]
3. **Finding 3**: [Description of key insight]
### Statistical Summary
```{r statistical-summary, echo=FALSE}
# Statistical analysis
df_clean %>%
group_by(category) %>%
summarise(
mean = mean(value),
median = median(value),
sd = sd(value),
min = min(value),
max = max(value)
)
```
## Policy Implications
[Provide policy-relevant insights and recommendations based on the analysis]
## Next Steps
- [ ] Additional statistical modeling
- [ ] Geographic analysis if spatial data available
- [ ] Time series forecasting
- [ ] Comparative analysis across regions
## References
- [TidyTuesday GitHub Repository](https://github.com/rfordatascience/tidytuesday)
- [UN Sustainable Development Goals](https://sdgs.un.org/goals)
- [Data Source](https://github.com/rfordatascience/tidytuesday/tree/master/data/2021/2021-06-08)
## Session Info
```{r session-info}
utils::sessionInfo()
```
---
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