
Explore inflation trends with R by analyzing datasets from Google and Kaggle, visualize correlations between inflation, interest rate, and unemployment, compare countries, and forecast 2025 inflation.
Discover how this course uses R to analyze inflation trends, covering 12 chapters from goals and tools to data sources, macroeconomics fundamentals, inflation calculations, visualization, and policies to lower inflation.
Tailored for microeconomics enthusiasts, aspiring data analysts, and future investors, this course uses R to analyze two Kaggle inflation datasets, clean and visualize data, and present actionable insights.
Identify tools, IDEs, and data sources for inflation analysis. Choose VS Code, RStudio, Tint R, or browser IDEs, and access Kegel data sets, World Bank data, and Google search.
Explore how to use R for data analysis, visualization, and econometric modeling of inflation and macroeconomic trends, with time series forecasting using packages like ggplot, dplyr, and forecast.
Explore macroeconomics and inflation, examining factors such as supply chain disruptions, exchange rate fluctuations, rising production costs, central bank printing, government overspending, and population growth shaping the general price level.
Compute the inflation rate using the CPI x+1 minus CPI x divided by CPI x formula, applying basic algebra, with 2015 and 2023 CPI values to yield about 28.3%.
Calculate the real interest rate from nominal rate and inflation using the formula (1+i)=(1+r)(1+π) and see how higher rates influence inflation, borrowing, saving, and currency.
Explore how high interest rates raise borrowing costs, dampen consumer spending, curb real estate activity, and shift stock market dynamics, guiding investors toward bonds for stability.
Install and launch RStudio, choose the right version for your operating system, then create a new R script to import datasets and run analyses.
Choose browser-based or local setup for RStudio, sign up with Google or other options, then create an R project, write and run scripts, and import Excel datasets in RStudio Cloud.
Sign up for Kaggle with Google and access inflation datasets. Download the global inflation data (1970–2022) and the inflation and unemployment rate data, then import them into studio cloud.
Upload the global inflation dataset to studio cloud, name file global inflation analysis, then import, view, and organize data for further analysis using read from text and required packages.
Load the R library and read the global inflation dataset, then inspect the data by viewing the head, structure, dimensions, and summary, and note the country frequency and column names.
Identify the top ten countries with the highest inflation rates from 1970 to 2022 using R and dplyr, by computing average inflation per country from a global dataset.
Visualize inflation data for a country using a scatter plot in R. Filter by country, reshape to long format with pivot_longer, and plot with ggplot2.
Analyze inflation trends during the 2008 crisis using R and dplyr on the five-year 2006–2010 dataset. Highlight country examples like Argentina, Belarus, Bolivia, and Botswana to illustrate varied impacts.
Compare inflation between the United States and China using dplyr in R, compute average inflations from 2000 to 2022, and contrast results to show higher inflation in the United States.
Forecasts future inflation in the United States using historical data by converting it to a time series and applying the forecast package in R to predict 2025.
Upload the inflation, interest, and unemployment dataset to studio cloud, import it via text reader, install the readr and Rcpp packages, and prepare the data for analysis in the course.
Explore dataset 2 in R by inspecting its head, structure, dimensions, and column names, then review the summary to understand 13,832 rows and 13 columns and plan cleaning.
Clean data set by removing missing values and duplicates using na.omit in R, save as cleaned_dataset.csv, and run the script to produce a ready-to-use data set.
Analyze the correlation between inflation rate and interest rate for Canada using the cleaned dataset, showing a weak negative correlation around -0.18 to -0.2 and inflation falls as rates rise.
Visualize the correlation between inflation and interest rate in Canada with a scatter plot using ggplot2 in R, highlighting the inverse relationship and axis labels.
Analyze the correlation between inflation and unemployment rate using a modified code workflow for Australia, demonstrating a negative correlation and setting up a future scatter plot visualization.
Visualize the inflation-unemployment relationship in Australia with a scatter plot, mapping inflation on the x-axis and unemployment rate on the y-axis, using ggplot and filtered data.
Welcome to Macroeconomic Analysis: Investigating Inflation Trend with R course. This is a comprehensive data analysis course where you will be guided step by step on how to perform complex data analysis and visualisation on inflation data using R programming language. This course is a perfect combination of macroeconomics and statistics as you will learn all things about inflation from both perspectives. In the introduction session, you will learn basic fundamentals of R programming language, such as getting to know its use cases and features that R has but Python does not have. Then, continue by learning the basic fundamentals of macroeconomics and inflation, using case study examples to understand inflation better as well as getting to know factors that cause inflation. Afterward, you will also learn how to calculate inflation rate and interest rate as well as understanding the relationship between both of them, especially why in most cases raising the interest rate can help to lower the inflation rate. Before starting the project, you will be guided step by step on how to set up all necessary tools, such as installing R programming language and R Studio which is the IDE that will be used in our project. Meanwhile, for the data source, we are going to get our datasets from Kaggle which is one of the largest data science learning platforms that has a lot of datasets that can be downloaded for free. In the project section, you are going to be conducting analysis and visualisation on two different datasets from Kaggle. At the end of the project, you are also going to learn how to deliver data insights and summaries which highlight all your findings during the project. Last but not least, at the end of the course, we will also go over several solutions that can be implemented to lower inflation rate like effective monetary policy, cutting of unnecessary government spending, and fiscal responsibility.
First of all, before getting into the course, we need to ask ourselves these questions. Why should we learn about inflation? Why should we analyze inflation data using the R programming language? Well, let me ask you this question first, have you ever asked yourself, why a cup of coffee that cost two dollars five years ago but now the same product with the same quality costs three dollars? Or maybe why a gaming desk cost you a hundred dollars three years ago but today the same product from the same brand costs you two hundred dollars. Well, those are a few examples of inflation in real life where your cash today is no longer as valuable as it used to be a couple years ago and you might be wondering why? Well inflation reduces a currency’s purchasing power as what we discussed in the previous example where two dollars could buy you a cup of coffee five years ago but today you need three dollars. There are many factors out there that can potentially cause inflation however the most common factor is the oversupply of cash where there is more cash in the circulation than the actual demand. Let me give you an example, let’s say there is an island with population of a thousand people and there are only a hundred coins in the circulation, obviously in this case, the coin is very valuable as they are a thousand people who want to get those coins but there are only a hundred coins available but what if I told you the island’s ministry of treasury decided to create nine hundred more coins, so now there are a thousand coins in the circulation, obviously, now the value of one coin is definitely not as valuable as it used to be. Hence, what we are going to do in this course is to use data to investigate inflation patterns as well as forecast the inflation rate in the future based on the historical data.
Below are things that you can expect to learn from the course:
Learn basic fundamentals of macroeconomics and inflation, such as getting to know factors that cause inflation and example of inflation in real life
Learn basic fundamentals of R programming language and getting to know its use cases
Learn how to calculate inflation and interest rate
Learn how high interest impacts the market from several perspectives, such as cost of borrowing, consumer spending, real estate market and stock market
Learn how to find and download datasets from Kaggle
Learn how to upload and import data to RStudio Cloud
Learn how to clean data and remove all NA values from dataset using R
Learn how to find countries with highest inflation rate using R
Learn how to visualize inflation data for a specific country using scatter plot in R
Investigate inflation trend in 2008 economic crisis using R
Learn how to compare inflation between two countries using R
Learn how to forecast future inflation using R
Learn how to find correlation between inflation and interest rate, then, visualise the correlation using scatter plot
Learn how to find correlation between inflation and unemployment rate, then, visualise the correlation using scatter plot
Learn several policies that can be implemented to lower inflation rate
Learn how to analyse commodity prices using R
Learn how to find correlation between energy price and food price