Objective

Inflation is the price rise in goods and services over a period of time. The period from 2013 to 2024 has been marked by significant economic challenges in India, particularly in terms of inflation and the cost of living. This open data storytelling explores the trends in inflation through the lens of the Consumer Price Index (CPI), which helps us to measure inflation for three key sectors: Rural, Urban, and combined (Rural+Urban). By leveraging data visualization techniques, I want to tell a compelling story that not only presents the data but also provides insights and context, making the information accessible and engaging to a broad audience.

Source: Government of India. (2024). All India consumer price index (Rural/Urban) up to February 2024. data.gov.in. Retrieved from https://data.gov.in/resource/all-india-consumer-price-index-ruralurban-upto-february-2024

#Packages 
library(ggplot2) 
library(dplyr)  
library(knitr) 
library(tidyverse)
library(plotly)
library(readr)
library(readxl)

Audience:

The audience for this data visualization primarily includes a diverse group of stakeholders who are interested in understanding the trends and implications of inflation and the cost of living in India over the past decade. The key audiences are Policymakers and Government Officials,Economists and Researchers,Business Leaders and Investors,Academia and Students,General Public and Media,Non-Governmental Organizations (NGOs) and Advocacy Groups.

# Load the dataset
data <- read_csv("All_India_Index_Upto_Feb24.csv")
colnames(data) <- c("Sector", "Year", "Month", "Cereals_and_products", "Meat_and_fish", "Egg",
  "Milk_and_products","Oils_and_fats", "Fruits", "Vegetables","Pulses_and_products", 
  "Sugar_and_Confectionery","Spices","Non-alcoholic_beverages", "Prepared_meals_snacks_sweets_etc", 
  "Food_and_beverages", "Pan_tobacco_intoxicants", "Clothing", "Footwear", "Clothing_and_footwear", 
  "Housing", "Fuel_and_light", "Household_goods_and_services", "Health","Transport_and_communication", 
  "Recreation_and_amusement", "Education","Personal_care_and_effects", "Miscellaneous", "General_index")
data$Housing <- as.numeric(data$Housing)
data$Sector <- as.factor(data$Sector)
data$Month <- as.factor(data$Month)
# Handle missing values by replacing them with mean of that column
cleaned_data <-data %>% mutate(across(where(is.numeric),~ifelse(is.na(.),mean(., na.rm = TRUE),.)))

Visualisation-1:Consumer Food Price Index (CFPI)

# Adding the food product columns so as to compute CFPI
cleaned_data <- cleaned_data %>%
  mutate(CFPI = rowSums(across(c(Cereals_and_products, Meat_and_fish, Egg, Milk_and_products, 
  Oils_and_fats, Fruits, Vegetables, Pulses_and_products, 
  Sugar_and_Confectionery, Spices)))/10)

# Interactive plot for Consumer Food Price Index (taking food products) grouped by Year and Sector
data_summary_food <- cleaned_data %>%  group_by(Year, Sector) %>%summarize(CFPI = mean(CFPI))
ggplotly(ggplot(data_summary_food, aes(x = Year, y = CFPI, color = Sector)) +
geom_line(size = 1) +  geom_point(size = 2) +
labs(title = "Consumer Food Price Index of India (CFPI) by Sector from 2013 to 2024",
     x = "Year",y = "Consumer Food Price Index",color = "Sector")+ theme_minimal() + 
     scale_x_continuous(breaks = 2013:2024))

Inference from CFPI

• Overall Upward Trend (2013-2024): This period saw a consistent increase in food prices across all three sectors. It indicates the general inflationary trend affecting the food prices over the years.
• 2016-2017 Plateau: Minimal change observed in CFPI across all sectors during this period, indicating a temporary stabilization in food prices. This period shows stabilization, possibly due to balanced supply and demand or effective economic policies.
• 2019-2020 Surge: Significant increase in CFPI starting from 2019, potentially due to economic factors such as supply chain disruptions caused by the COVID-19. The COVID-19 had a major impact and could have contributed to this sharp rise.
• 2022-2024 Spike: Another notable increase in CFPI, reflecting heightened inflationary pressures. The spike here suggests significant inflation, possibly due to global and local economic conditions affecting food prices.

Visualisation-2:Consumer Price Index

# Interactive plot for Consumer Price Index (CPI) grouped by Year and Sector
data_summary_cpi <- cleaned_data %>%  group_by(Year, Sector) %>%
                    summarize(General_index = mean(General_index))
ggplotly(ggplot(data_summary_cpi, aes(x = Year, y = General_index, 
        color = Sector)) + geom_line(size = 1) +  geom_point(size = 2) +
        labs(title = "Consumer Price Index of India by Sector from 2013 to 2024",
        x = "Year", y = "Consumer Price Index",color = "Sector") +
        theme_minimal() + scale_x_continuous(breaks = 2013:2024))

Inference from CPI

• Overall Upward Trend (2013-2024): The CPI for all sectors (Rural, Urban, and Rural+Urban) shows a consistent upward trend from 2013 to 2024.This indicates a continuous increase in the cost of living across all sectors, reflecting overall inflationary pressures in the economy. The rising CPI suggests that the general price level of goods and services has been increasing steadily, making it more expensive for consumers to maintain their standard of living.

• 2013-2019 Linear Increase: A linear increase in CPI can be seen across all sectors from 2013 to 2019.

• 2019-2024 Surge: Steep rise in CPI, reflecting the impact of the COVID-19 pandemic on consumer prices of goods and services.

• Inflation is caused due to mismatch in demand and supply of goods and services in the market. Printing of currencies will also contribute to inflation. We can infer from the visualisation that the all sectors were hit by inflation in the 2013-2024 period particularly the rural sector worsening the cost of living.

Visualisation-3:Inflation Rate

# Load the Inflation dataset from World bank
inflation <- read_excel("Inflation.xlsx")

# Interactive plot for showing rate of Inflation (Rural+Urban) in India
ggplotly(ggplot(inflation, aes(x = Year, y = Inflation_rate)) +
geom_line(size = 1, color = "blue") +  geom_point(size = 2, color = "red") +
labs(title = "Inflation rate in India for Rural+ Urban from 2013 to 2023",x = "Year",
     y = "Rate of Inflation (%)")+ theme_minimal() + scale_x_continuous(breaks = 2013:2023))

Inference from rate of Inflation

Source: World Bank. (n.d.). Inflation, consumer prices (annual %) - India. Retrieved from https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?end=2022&locations=IN&start=2013

• 2013-2015 Drop: Sharp decline in inflation rate, reflecting stabilization efforts. This period saw a significant reduction in inflation, likely due to the economic policies aimed at keeping inflation at bay.

• 2015-2017 Minimum: Inflation rate reaches the lowest point in the period, indicating robust monetary policy was in place.

• 2017-2020 Increase: Significant rise in inflation rate, potentially due to economic disruptions caused by COVID-19 pandemic.

• 2020-2023 Fluctuations: Volatile inflation rates, influenced by global and domestic economic factors.

• Understanding these trends is crucial for policymakers, businesses, and consumers to navigate the economic landscape effectively. Policymakers need to consider measures to mitigate the impact of rising prices on consumers, particularly in rural areas where the increase has been more pronounced. For businesses, adjusting pricing strategies and supply chain management can help address the challenges posed by inflation. Consumers have to adapt their spending and saving habits to cope with the increasing cost of living.

References:

1.Government of India. (2024). All India consumer price index (Rural/Urban) up to February 2024. data.gov.in. Retrieved from https://data.gov.in/resource/all-india-consumer-price-index-ruralurban-upto-february-2024

2.World Bank. (n.d.). Inflation, consumer prices (annual %) - India. Retrieved from https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?end=2022&locations=IN&start=2013

3.ggplot2: Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag. Retrieved from https://ggplot2.tidyverse.org

4.plotly: Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC. Retrieved from https://plotly-r.com

5.Macrotrends. (n.d.). India inflation rate 1960-2023. Retrieved from https://www.macrotrends.net/global-metrics/countries/IND/india/inflation-rate-cpi