Country

Indicator

Value type

Values present

Food exports (% of merchandise exports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Albania	13.4	+11.6%	47
Argentina	48.9	-7.2%	13
Armenia	9.58	-27.2%	63
Antigua & Barbuda	84.7	+6.49%	3
Australia	12.1	+3.78%	51
Azerbaijan	4.14	+45.9%	77
Belgium	13	+9.04%	48
Burkina Faso	7.09	-16.9%	69
Bulgaria	15.2	-13%	41
Bosnia & Herzegovina	7.06	+13.4%	70
Belize	95.7	+0.518%	1
Bolivia	22.2	-6.89%	29
Brazil	40.4	-4.11%	18
Barbados	43.5	-5.66%	17
Canada	13.5	-0.0761%	46
Switzerland	2.51	-2.18%	81
Chile	22.9	+3.29%	26
China	2.43	-1.2%	82
Cyprus	22.5	+5.75%	28
Czechia	5.44	+2.83%	75
Germany	6.28	+5.12%	74
Denmark	17.7	+3.62%	36
Dominican Republic	25	+8.12%	23
Ecuador	53.7	+5.99%	11
Egypt	21.9	+9.42%	30
Spain	18.6	+8%	34
Estonia	13.6	+16.6%	45
Finland	3.07	+11.2%	80
Fiji	64.3	-6.14%	8
United Kingdom	6.82	+4.16%	71
Georgia	45.7	+16.6%	15
Greece	21.7	+7.37%	31
Grenada	69.1	+12%	6
Guatemala	47.5	-2.33%	14
Guyana	1.99	-59.3%	83
Hong Kong SAR China	1.55	+0.203%	85
Croatia	15.2	-4.9%	40
Hungary	9	+5.23%	65
India	11.1	+1.04%	54
Ireland	7.93	-6.36%	68
Iceland	44	-1.52%	16
Israel	3.81	-3.04%	78
Italy	11	+8.68%	55
Japan	1.15	-1.61%	86
Kyrgyzstan	10.1	-7.37%	60
South Korea	1.63	-1.1%	84
Sri Lanka	27.3	+6.03%	22
Lithuania	19	+2.36%	33
Luxembourg	11.8	+8.09%	52
Latvia	22.5	+1.73%	27
Macao SAR China	17.8	-38.2%	35
Moldova	56.1	-2.19%	10
Maldives	94.4	-2.27%	2
Mexico	8.51	+1.74%	66
North Macedonia	10.6	+18.9%	56
Malta	9.66	-4.29%	62
Myanmar (Burma)	36.7	+28.9%	19
Montenegro	16.8	+22.8%	37
Mauritius	50.9	+3.36%	12
Malaysia	10.2	+7.1%	58
Namibia	28	+16.4%	21
Netherlands	16.3	+8.45%	38
Norway	10.2	+4.68%	59
New Zealand	67.1	+1.98%	7
Pakistan	24.4	+11.2%	24
Panama	73.6	+323%	4
Philippines	10.4	+23.7%	57
Poland	14.9	+3.43%	42
Portugal	14.4	+6.23%	44
Paraguay	69.5	-1.34%	5
French Polynesia	15.8	+54.9%	39
Romania	11.5	-11.5%	53
El Salvador	22.9	+6.13%	25
Suriname	6.37	+82.4%	73
Slovakia	4.66	+2.87%	76
Slovenia	3.68	-3.25%	79
Sweden	6.71	-1.07%	72
Togo	30.8	-3.69%	20
Thailand	14.7	-2.24%	43
Trinidad & Tobago	8.2	+21.3%	67
Turkey	12.3	+2.68%	49
Ukraine	59.1	-2.1%	9
United States	9.45	-1.01%	64
Uzbekistan	9.69	+11%	61
South Africa	12.2	+4.44%	50
Zimbabwe	21.6	+6.35%	32

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'TX.VAL.FOOD.ZS.UN'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'TX.VAL.FOOD.ZS.UN'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

Food exports (% of merchandise exports)

Year: 2024

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