Building an Interactive Data Dashboard with R, World Bank Data, and Streamlit

Introduction

R is a powerful programming language for statistical computing and data visualization. When combined with the vast datasets available from the World Bank and the interactivity of Streamlit, we can create insightful and interactive data dashboards with minimal effort. In this blog, we will walk through the process of using R to analyze World Bank data and then display the results in a Streamlit dashboard.

Why Use R for World Bank Data Analysis?

1. Data Handling: R provides numerous packages (e.g., tidyverse, dplyr) that make data cleaning and manipulation easier.
2. Statistical Analysis: R is built for statistical computing, making it an ideal choice for analyzing socio-economic and financial data.
3. Data Visualization: Packages like ggplot2 and plotly help create professional-quality visualizations.
4. APIs and Data Integration: The WDI package in R allows seamless access to World Bank datasets.

Getting Started

1. Install Necessary Packages

To begin, install the required R packages by running:

install.packages("tidyverse")
install.packages("WDI")
install.packages("plotly")
install.packages("shiny")

2. Fetch World Bank Data

Using the WDI package, we can easily fetch global economic data. Below is an example of retrieving GDP data for multiple countries:

library(WDI)
library(tidyverse)

data <- WDI(indicator = "NY.GDP.MKTP.CD", country = c("US", "IN", "CN", "BR"), start = 2000, end = 2023)
head(data)

3. Data Cleaning and Transformation

We often need to clean and reshape the data before visualization:

data_clean <- data %>%
  rename(GDP = NY.GDP.MKTP.CD) %>%
  mutate(year = as.factor(year))

4. Create Visualizations

Using ggplot2, we can generate interactive visualizations:

library(ggplot2)

ggplot(data_clean, aes(x = year, y = GDP, color = country)) +
  geom_line() +
  theme_minimal() +
  labs(title = "GDP Growth Over Time", x = "Year", y = "GDP (USD)")

Deploying with Streamlit

While R has Shiny for web applications, we can also use Python’s Streamlit for an interactive frontend. First, install Streamlit and Pandas in Python:

pip install streamlit pandas

1. Convert R Output to CSV

Save your processed R data as a CSV file:

write.csv(data_clean, "worldbank_gdp.csv", row.names = FALSE)

2. Build a Streamlit App

Create a app.py file and add the following Python code:

import streamlit as st
import pandas as pd
import plotly.express as px

st.title("World Bank GDP Analysis")
data = pd.read_csv("worldbank_gdp.csv")

fig = px.line(data, x="year", y="GDP", color="country", title="GDP Growth Over Time")
st.plotly_chart(fig)

3. Run the Streamlit App

Execute the following command in the terminal:

streamlit run app.py

Conclusion

By combining R’s powerful statistical capabilities with Streamlit’s interactive frontend, we can create a dynamic and insightful dashboard using World Bank data. This setup is ideal for economists, data analysts, and researchers looking to explore large datasets in an accessible way. Try it out and enhance your data storytelling capabilities!

Would you like to explore additional datasets or advanced visualizations? Let us know in the comments!