Week 3: Global Coffee Production

agriculture

economics

maps

Exploring trends in coffee production, exports, and consumption

Published

March 9, 2025

Introduction

For this week’s Tidy Tuesday, I explored global coffee production data. Coffee is one of the world’s most traded agricultural commodities, with millions of people involved in its production, processing, and distribution. This analysis examines production trends, export patterns, and the relationship between price, variety, and growing conditions.

The Data

Our dataset includes information on coffee production, exports, domestic consumption, prices, and growing conditions across 15 major coffee-producing countries from 2015 to 2024.

Let’s take a look at the first few rows of our dataset:

# Using kable for better table formatting
head(coffee_data) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE)

country	year	production	exports	domestic_consumption	price	variety	altitude
Brazil	2015	52368	30310.483	22057.517	1.645784	Robusta	783
Vietnam	2015	26097	18635.728	7461.272	1.729525	Robusta	917
Colombia	2015	13251	7577.217	5673.783	4.361002	Arabica	1202
Indonesia	2015	11169	7603.348	3565.652	2.141132	Robusta	1025
Ethiopia	2015	6633	4157.706	2475.294	1.877499	Arabica	1348
Honduras	2015	5096	2784.810	2311.190	2.287659	Arabica	858

Production vs. Export Analysis

Let’s also look at a summary of production and export data by country:

# Calculate average production and exports by country
country_summary <- coffee_data %>%
  group_by(country) %>%
  summarize(
    avg_production = mean(production),
    avg_exports = mean(exports),
    export_ratio = mean(exports/production) * 100
  ) %>%
  arrange(desc(avg_production))

# Display nicely formatted table
country_summary %>%
  mutate(
    avg_production = round(avg_production, 0),
    avg_exports = round(avg_exports, 0),
    export_ratio = round(export_ratio, 1)
  ) %>%
  rename(
    Country = country,
    "Avg Production" = avg_production,
    "Avg Exports" = avg_exports,
    "Export Ratio (%)" = export_ratio
  ) %>%
  kable(caption = "Coffee Production and Export Summary by Country") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = FALSE) %>%
  row_spec(1:3, background = "#F8F8DC")  # Highlight top 3 producers

Coffee Production and Export Summary by Country
Country	Avg Production	Avg Exports	Export Ratio (%)
Brazil	54795	39372	71.9
Vietnam	26571	17204	64.6
Colombia	13525	9071	66.7
Indonesia	10959	7740	70.8
Ethiopia	7266	4325	59.6
Kenya	5276	3876	72.7
India	5237	3818	71.3
Nicaragua	5077	3550	70.8
Guatemala	5075	3383	66.2
Uganda	4936	3803	77.5
Tanzania	4878	3336	67.5
Honduras	4742	2843	61.3
Peru	4581	3065	67.0
Costa Rica	4529	2840	62.8
Mexico	4283	2898	67.2

Global Coffee Production Trends

First, let’s examine how coffee production has changed over the past decade for the top producing countries:

# Get top producers by average production
top_producers <- coffee_data %>%
  group_by(country) %>%
  summarize(avg_production = mean(production)) %>%
  arrange(desc(avg_production)) %>%
  head(5) %>%
  pull(country)

# Filter for top producers
top_producers_data <- coffee_data %>%
  filter(country %in% top_producers)

# Create plot
ggplot(top_producers_data, aes(x = year, y = production/1000, color = country)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 2) +
  labs(
    title = "Coffee Production Trends (2015-2024)",
    subtitle = "Top 5 Coffee Producing Countries",
    x = "Year",
    y = "Production (Millions of 60kg bags)",
    color = "Country"
  ) +
  scale_x_continuous(breaks = years) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  ) +
  scale_color_viridis(discrete = TRUE)

Production vs. Export

Next, let’s analyze the relationship between production and exports for different countries:

# Create bar chart comparing production and exports
ggplot(country_summary, aes(x = reorder(country, avg_production))) +
  geom_bar(aes(y = avg_production/1000, fill = "Production"), stat = "identity", alpha = 0.7) +
  geom_bar(aes(y = avg_exports/1000, fill = "Exports"), stat = "identity", alpha = 0.9) +
  coord_flip() +
  labs(
    title = "Coffee Production vs. Exports by Country",
    subtitle = "Average values for 2015-2024",
    x = NULL,
    y = "Coffee (Millions of 60kg bags)",
    fill = NULL
  ) +
  scale_fill_manual(values = c("Production" = "#8C510A", "Exports" = "#DFC27D")) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

Price vs. Altitude

Let’s explore the relationship between coffee growing altitude and price:

# Calculate average altitude and price by country and variety
altitude_price <- coffee_data %>%
  group_by(country, variety) %>%
  summarize(
    avg_altitude = mean(altitude),
    avg_price = mean(price)
  )

# Create scatter plot
ggplot(altitude_price, aes(x = avg_altitude, y = avg_price, color = variety, label = country)) +
  geom_point(size = 4, alpha = 0.8) +
  geom_text(hjust = -0.2, vjust = 0, size = 3) +
  labs(
    title = "Relationship Between Growing Altitude and Coffee Price",
    subtitle = "Average altitude vs. price by country and variety",
    x = "Altitude (meters above sea level)",
    y = "Price (USD per pound)",
    color = "Variety"
  ) +
  scale_color_manual(values = c("Arabica" = "#1B9E77", "Robusta" = "#D95F02")) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

Domestic Consumption Trends

Finally, let’s examine domestic consumption trends in major coffee-producing countries:

# Calculate proportion of production consumed domestically
consumption_trends <- coffee_data %>%
  group_by(country, year) %>%
  summarize(
    consumption_ratio = domestic_consumption / production * 100
  )

# Focus on interesting countries with varying domestic consumption patterns
selected_countries <- c("Brazil", "Ethiopia", "Vietnam", "Colombia", "Mexico")

# Filter for selected countries
selected_consumption <- consumption_trends %>%
  filter(country %in% selected_countries)

# Create line plot
ggplot(selected_consumption, aes(x = year, y = consumption_ratio, color = country)) +
  geom_line(linewidth = 1.2) +
  geom_point(size = 2) +
  labs(
    title = "Domestic Coffee Consumption Trends",
    subtitle = "Percentage of production consumed domestically",
    x = "Year",
    y = "Domestic Consumption (%)",
    color = "Country"
  ) +
  scale_x_continuous(breaks = years) +
  scale_y_continuous(limits = c(0, 100)) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  ) +
  scale_color_viridis(discrete = TRUE)

Key Findings

From our analysis, we can observe several interesting patterns:

Production Dominance: Brazil consistently leads global coffee production by a significant margin, followed by Vietnam. Together, these two countries account for nearly half of global coffee production.
Export Patterns: While some countries like Vietnam export the vast majority of their coffee, others like Ethiopia and Brazil retain a significant proportion for domestic consumption.
Altitude and Price Relationship: There’s a strong positive correlation between growing altitude and coffee price. Countries growing Arabica coffee at high altitudes, like Colombia and Ethiopia, generally command higher prices.
Variety Premium: Arabica coffee consistently fetches higher prices than Robusta, reflecting consumer preferences and the different flavor profiles of these varieties.
Domestic Consumption Trends: Brazil not only leads in production but also has the highest domestic consumption among producing countries, with approximately 30-40% of its production consumed locally.

Conclusions

This analysis highlights the complex dynamics of the global coffee market. Production is concentrated in a few major countries, but various factors including growing conditions, variety, and local consumption patterns create a diverse landscape.

For coffee-producing countries, these findings suggest several potential strategies: - Higher-altitude regions may benefit from focusing on premium Arabica production - Countries with established domestic markets (like Brazil) have more stable demand - Smaller producers might differentiate themselves through specialty varieties or sustainable practices

For further research, it would be valuable to examine: - The impact of climate change on growing regions - Fair trade certification and its effect on prices - The relationship between coffee quality scores and price premiums

Resources and Further Reading

International Coffee Organization: ICO
Specialty Coffee Association: SCA
Coffee Research Institute: CRI