TidyTuesday: Financial Inclusion Metrics

SDG 1: No Poverty

Author

Nichodemus Amollo

Published

February 14, 2023

Overview

This project explores the Financial Inclusion Metrics dataset from TidyTuesday, focusing on data visualization and analysis techniques aligned with SDG 1: No Poverty.

Load Required Packages

Data Import

Rows: 100
Columns: 4
$ id       <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
$ value    <dbl> -0.5604756, -0.7906531, 0.7680552, 0.8385636, 0.9678513, 2.68…
$ category <chr> "B", "B", "A", "B", "C", "C", "A", "B", "A", "B", "C", "C", "…
$ date     <date> 2023-02-14, 2023-02-15, 2023-02-16, 2023-02-17, 2023-02-18, …

Visualizations

Analysis & Insights

[Analysis content here]

References

⬅️ Back to TidyTuesday Index

---
title: "TidyTuesday: Financial Inclusion Metrics"
subtitle: "SDG 1: No Poverty"
author: "Nichodemus Amollo"
date: "2023-02-14"
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}
# **Financial Inclusion Metrics**

Analysis of financial inclusion metrics data from TidyTuesday 2023 - Week of 2023-02-14
:::

## Overview

This project explores the **Financial Inclusion Metrics** dataset from TidyTuesday, focusing on data visualization and analysis techniques aligned with **SDG 1: No Poverty**.

## 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)
```

## Data Import

```{r load-data, echo=FALSE}
# Load data using tidytuesdayR
# library(tidytuesdayR)
# tuesdata <- tt_load('2023-02-14')
# df <- tuesdata$data_name

# 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("2023-02-14"), by = "day", length.out = 100)
)

glimpse(df)
```

## Visualizations

```{r visualization, echo=FALSE}
ggplot(df, aes(x = date, y = value, color = category)) +
  geom_line(size = 1.2, alpha = 0.7) +
  labs(title = "Financial Inclusion Metrics") +
  theme_minimal()
```

## Analysis & Insights

[Analysis content here]

## References

- [TidyTuesday GitHub](https://github.com/rfordatascience/tidytuesday)
- [UN SDGs](https://sdgs.un.org/goals)

---

[⬅️ Back to TidyTuesday Index](index.qmd)