Country

Indicator

Value type

Values present

Gross fixed capital formation (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	25	+5.14%	31
Albania	23.4	-2.76%	46
Argentina	15.8	-14.7%	107
Armenia	21.7	+3.93%	67
Australia	24.3	+4.18%	37
Austria	24.3	-2.28%	35
Azerbaijan	16.8	-3.47%	101
Belgium	24.4	-0.198%	33
Benin	34.7	+1.59%	4
Burkina Faso	16.5	-3.98%	103
Bangladesh	30.7	-0.81%	10
Bulgaria	17.9	-4.05%	93
Bahamas	25.7	+18.3%	28
Belarus	23.8	+5.52%	40
Bermuda	11.3	+0.754%	120
Brazil	17	+3.79%	99
Brunei	28.2	-4.3%	18
Botswana	28.5	+11.1%	17
Central African Republic	15.4	+5.58%	109
Canada	22.7	-0.84%	53
Switzerland	25.1	-2.98%	30
Chile	23.5	-3.11%	45
Côte d’Ivoire	24.5	+5.79%	32
Cameroon	21.4	+11.8%	72
Congo - Kinshasa	32.9	+4.41%	5
Congo - Brazzaville	26.5	+6.56%	21
Colombia	16.5	+0.0233%	102
Comoros	11.7	+0.0252%	118
Costa Rica	15.8	-2.19%	108
Cyprus	20.5	-4.35%	79
Czechia	26.2	-4.15%	24
Germany	20.9	-2.99%	75
Djibouti	26.3	+7.24%	23
Denmark	22.2	-1.6%	59
Dominican Republic	26.1	-9.88%	26
Ecuador	18.4	-7.26%	91
Egypt	11.7	-22.7%	119
Spain	19.5	-0.89%	87
Estonia	26.1	-6.52%	27
Ethiopia	20.5	-7.54%	80
Finland	21.5	-8.14%	71
France	22.3	-3.34%	56
Gabon	18.1	+3.32%	92
United Kingdom	17.4	-1.3%	96
Georgia	22	+2.75%	62
Ghana	9.8	+1.51%	123
Guinea	32.1	+31.1%	6
Gambia	39	+42.9%	2
Guinea-Bissau	22.8	+5.89%	50
Equatorial Guinea	9.07	-2.57%	124
Greece	15.3	+0.724%	110
Guatemala	16.1	-1.39%	106
Hong Kong SAR China	16.2	-4.33%	105
Honduras	23.9	+0.717%	39
Croatia	23.7	+5.37%	41
Haiti	9.94	-28.3%	122
Hungary	23.4	-8.65%	47
Indonesia	29.1	-0.0243%	14
India	29.6	-2.69%	12
Ireland	17.2	-25.8%	97
Iran	26.7	-0.309%	20
Iraq	20.6	+34.7%	77
Iceland	26.4	+6.4%	22
Israel	22.7	-6.86%	52
Italy	22	-2.46%	63
Kenya	17.7	+2.5%	95
Cambodia	31.6	-3.35%	8
Libya	14.8	-9.97%	111
Sri Lanka	18.8	+5.34%	89
Lithuania	22.5	-4.99%	55
Luxembourg	14.3	-10.9%	115
Latvia	23.1	-6.4%	48
Macao SAR China	13.8	-5.77%	116
Morocco	26.1	+2.86%	25
Moldova	20	+2.82%	84
Madagascar	22.6	+13.9%	54
Mexico	24.2	+0.977%	38
Mali	21.6	-0.353%	68
Malta	17.9	-5.3%	94
Montenegro	20.2	+3.11%	82
Mongolia	26.8	+5.83%	19
Mauritius	21	+3.62%	74
Malaysia	20.6	+6.84%	78
Namibia	23.7	-9.34%	42
Niger	18.7	-12.2%	90
Nicaragua	22.9	+6.83%	49
Netherlands	19.7	-1.81%	86
Norway	21.9	+0.996%	65
Nepal	24.3	-1.06%	36
Pakistan	11.2	-8.84%	121
Peru	20.8	-1.96%	76
Philippines	23.6	-0.271%	44
Poland	16.9	-5.68%	100
Puerto Rico	14.6	+3.95%	112
Portugal	19.8	-1.26%	85
Paraguay	21	+1.57%	73
Palestinian Territories	21.8	-9.17%	66
Romania	25.7	-4.87%	29
Russia	22.1	+0.606%	61
Rwanda	29.1	+9.19%	15
Saudi Arabia	28.7	+2.86%	16
Sudan	2.87	+32.2%	126
Senegal	32.1	-9.38%	7
Singapore	21.9	-2.22%	64
Sierra Leone	29.5	+48.9%	13
El Salvador	22.2	+1.03%	60
Somalia	22.7	+1.5%	51
Serbia	23.6	+1.08%	43
Slovakia	20.3	-3.55%	81
Slovenia	20.1	-5.99%	83
Sweden	24.4	-2.48%	34
Seychelles	17.2	-16.5%	98
Chad	14.4	-3.33%	114
Togo	22.3	-2.89%	57
Thailand	22.2	-2.98%	58
Tunisia	13.4	-15.1%	117
Turkey	31	-2.69%	9
Tanzania	41.4	-3.22%	1
Uganda	21.5	-2.42%	70
Ukraine	18.9	-5.07%	88
Uruguay	16.2	-5.57%	104
United States	21.6	+0.819%	69
Uzbekistan	37.1	+14.3%	3
Samoa	30.5	-11.6%	11
South Africa	14.5	-2.78%	113
Zimbabwe	3.57	-70.7%	125

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 = 'NE.GDI.FTOT.ZS'

# 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 <- 'NE.GDI.FTOT.ZS'

# 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

Gross fixed capital formation (% of GDP)

Year: 2024

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