Visualising Inflation with plot_inflation

What is `plot_inflation_shocks()`?

Inflation numbers in isolation are only half the story. To understand why a rate moved, you need the economic context around it — when did a recession hit? When did fuel subsidies end? When did COVID disrupt supply chains?

plot_inflation_shocks() answers that need. It draws an inflation time series and automatically overlays vertical markers for significant Nigerian macroeconomic events, so the relationship between policy shocks and price movements is immediately visible — no manual annotation required.

Arguments

Argument	Type	Default	What it does
`data`	`data.frame`	—	Your dataset. Must contain a date column and a numeric value column
`date_col`	`character`	`"date"`	Name of the column holding dates
`val_col`	`character`	`"headline_yoy"`	Name of the column to plot on the y-axis
`title`	`character`	`"Nigeria Inflation Trends"`	Plot title

Output: A rendered plot in your active graphics device. The shock markers are drawn automatically — you do not configure them.

Built-in Shock Events

The function ships with markers for the following events. They are drawn as labelled vertical lines so you can immediately see where each shock falls on your series.

Event	Period
2016 Recession	Q1–Q4 2016
COVID-19 Pandemic	March 2020 – December 2021
2023 Fuel Subsidy Removal	June 2023

These represent structural breaks in the Nigerian economy that consistently appear in macroeconomic research. When your inflation series shows a sharp inflection, the markers tell you whether a known shock coincides with it.

1. Basic Usage

If your dataset comes from cbn("inflation"), the default column names already match the function’s expectations. One line produces a publication-quality chart.

library(opennaijR)

infl <- cbn("inflation")
#> Fetching raw data from 'https://www.cbn.gov.ng/api/GetAllInflationRates' ...
#> Applying canonicalization using 'standardize_cbn_inflation'
#> Creating new version '20260312T181900Z-ceabf'
#> Writing to pin 'cbn__inflation__d305a3e522e5ccc56286e414cfd231cd'

plot_inflation_shocks(infl)

What you will see: Headline year-on-year inflation plotted as a continuous line, with vertical bands or lines marking the three shock events, a labelled y-axis showing percentage points, and a clean title.

2. Switching Inflation Measures

Nigeria’s headline inflation figure is a weighted composite. Sometimes you need to look beneath it. Use the val_col argument to plot a different component and update the title to match.

# Food inflation — typically more volatile than headline
plot_inflation_shocks(
  data    = infl,
  val_col = "food_yoy",
  title   = "Nigeria Food Inflation Trends"
)

# Core inflation — strips out farm produce and energy
plot_inflation_shocks(
  data    = infl,
  val_col = "core_ex_farm_yoy",
  title   = "Nigeria Core Inflation Trends"
)

When to use each:

headline_yoy — overall price level; use for general briefings and policy summaries.
food_yoy — most sensitive to supply shocks, weather, and import costs; use when analysing food security or agricultural policy.
core_ex_farm_yoy — strips out food and energy to reveal underlying demand pressures; preferred in monetary policy analysis.

Comparing all three against the same shock markers reveals whether a price spike was broad-based or driven by a single component.

3. Focusing on a Specific Period

The full inflation series stretches back many years. For a presentation or report focused on recent volatility, filter your data frame before passing it to the function. The shock markers will automatically adjust to whichever events fall inside the filtered window.

# Zoom in on the post-COVID and subsidy-removal period
recent <- infl[infl$date >= as.Date("2020-01-01"), ]

plot_inflation_shocks(
  data  = recent,
  title = "Inflation Volatility: 2020 – Present"
)

# Isolate the 2016 recession era
recession_era <- infl[
  infl$date >= as.Date("2015-01-01") &
  infl$date <= as.Date("2018-12-01"), 
]

plot_inflation_shocks(
  data  = recession_era,
  title = "Inflation During the 2016 Recession"
)

Why filter rather than zoom the axis? Filtering restricts the data that enters the function, which keeps the y-axis scale honest for the period of interest. Axis zooming can visually exaggerate variation by compressing a small range.

4. Working with External or Custom Data

plot_inflation_shocks() is not limited to CBN data. Any data frame with a date column and a numeric column works — as long as you tell the function which column is which via date_col and val_col.

# A data frame with non-standard column names
external_df <- data.frame(
  period = seq(as.Date("2015-01-01"), as.Date("2024-01-01"), by = "month"),
  rate   = runif(109, 10, 30)
)

plot_inflation_shocks(
  data     = external_df,
  date_col = "period",
  val_col  = "rate",
  title    = "Custom Macroeconomic Series with Shock Overlay"
)

This makes the function reusable for NBS data, World Bank series, or any other monthly indicator you want to plot in the Nigerian macroeconomic context.

5. Deriving a Measure First, then Plotting

plot_inflation_shocks() integrates naturally with derive_measure(). Engineer a feature and plot it immediately — the result is a new column in the same data frame, so no extra steps are needed.

# Derive the food-headline gap, then visualise it
infl_gap <- derive_measure(
  infl,
  food_gap = food_yoy - headline_yoy,
  reason   = "Food premium over headline inflation"
)

plot_inflation_shocks(
  data    = infl_gap,
  val_col = "food_gap",
  title   = "Food Inflation Premium Over Headline"
)

A positive food_gap means food prices are rising faster than the overall basket — a signal of food-specific supply pressure rather than broad demand inflation.

6. Batch Comparison: Multiple Metrics in One View

par(mfrow) stacks multiple plots in a single graphics window. This is the fastest way to compare how different inflation components responded to the same shock.

metrics <- c(
  "headline_yoy",
  "food_yoy",
  "core_ex_farm_yoy"
)

labels <- c(
  "Headline Inflation",
  "Food Inflation",
  "Core Inflation (Ex-Farm)"
)

par(mfrow = c(3, 1), mar = c(4, 4, 3, 1))

for (i in seq_along(metrics)) {
  plot_inflation_shocks(
    data    = infl,
    val_col = metrics[i],
    title   = labels[i]
  )
}


# Reset graphics layout after use
par(mfrow = c(1, 1))

Reading the stacked output: Look at how the shock markers align — or misalign — across the three panels. If food inflation spikes sharply at the 2023 subsidy removal but core inflation barely moves, it tells you the shock was a supply-side, cost-push event rather than a demand-driven one. That is a meaningful policy finding.

7. Saving Plots for Reports

To export a chart for a policy brief or journal submission, wrap the function in png() or pdf():

png(
  filename = "nigeria_headline_inflation.png",
  width    = 2400,
  height   = 1500,
  res      = 300
)

plot_inflation_shocks(
  data  = infl,
  title = "Nigeria Headline Inflation with Macroeconomic Shocks"
)

dev.off()

For PDF output, replace png() with pdf(file = "...", width = 8, height = 5). PDF is preferred for academic submissions because it is vector-based and scales without loss of quality.

Workflow Position

plot_inflation_shocks() sits at the end of the opennaijR pipeline, after your data is clean and your features are derived:

cbn()  →  apply_projection()  →  derive_measure()  →  plot_inflation_shocks()

It is a communication tool — it turns an analysis-ready dataset into something a policymaker, student, or journalist can read at a glance.

Visualising Inflation with plot_inflation_shocks()

What is plot_inflation_shocks()?