Country

Indicator

Value type

Values present

Patent applications, nonresidents

Source: worldbank.org, 01.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	1	0%	98
Andorra	8	+60%	91
United Arab Emirates	2,354	+25.9%	27
Argentina	3,263	+27.4%	23
Armenia	8	+14.3%	91
Antigua & Barbuda	8	+167%	91
Australia	29,443	+9.35%	7
Austria	175	+1.16%	59
Azerbaijan	16	-11.1%	84
Belgium	415	+44.1%	44
Bangladesh	373	+3.04%	46
Bulgaria	6	-14.3%	93
Bahrain	326	+26.8%	48
Bosnia & Herzegovina	8	+60%	91
Belarus	110	+42.9%	66
Belize	27	+17.4%	79
Bolivia	227	+50.3%	55
Brazil	19,566	+2.67%	9
Brunei	137	+19.1%	63
Bhutan	7	+16.7%	92
Canada	32,445	+7.74%	6
Switzerland	267	-11.3%	51
Chile	2,680	+10.2%	24
China	159,019	+4.38%	2
Colombia	1,855	+5.88%	29
Cape Verde	1	-75%	98
Costa Rica	566	+8.02%	40
Cuba	86	+13.2%	69
Cyprus	2	+100%	97
Czechia	59	+5.36%	72
Germany	18,747	-5.53%	10
Djibouti	10	+100%	89
Dominica	11	+450%	88
Denmark	186	-14.3%	58
Dominican Republic	220	+6.28%	56
Algeria	581	+6.22%	38
Ecuador	373	-0.267%	46
Egypt	1,343	+9.28%	33
Spain	126	+1.61%	64
Estonia	1	-50%	98
Finland	105	+8.25%	67
France	1,373	-11%	32
United Kingdom	7,263	-16.1%	19
Georgia	164	+22.4%	60
Gambia	91	+37.9%	68
Greece	532	+46.6%	41
Grenada	16	0%	84
Guatemala	249	+21.5%	53
Hong Kong SAR China	21,542	+1.94%	8
Honduras	188	+0.535%	57
Croatia	11	-8.33%	88
Hungary	13	-53.6%	86
Indonesia	7,403	+8.06%	17
India	35,306	+4.98%	5
Ireland	42	-22.2%	74
Iran	581	-8.36%	38
Iceland	2	-33.3%	97
Israel	8,017	+23.7%	15
Italy	797	-15.8%	35
Jamaica	60	+27.7%	71
Jordan	322	+6.98%	49
Japan	66,748	+9.2%	3
Kenya	37	+5.71%	75
Kyrgyzstan	4	+300%	95
South Korea	51,753	+11.8%	4
Sri Lanka	273	+8.76%	50
Lithuania	9	-50%	90
Luxembourg	1,441	+112%	30
Latvia	4	+300%	95
Macao SAR China	35	-44.4%	76
Morocco	2,549	+4.55%	26
Monaco	3	-57.1%	96
Moldova	12	+9.09%	87
Madagascar	22	+4.76%	80
Mexico	15,044	+14.1%	11
Malta	5	0%	94
Mongolia	77	-6.1%	70
Mozambique	16	+14.3%	84
Mauritius	14	-26.3%	85
Malaysia	6,651	+13.9%	20
Netherlands	1,390	+68.5%	31
Norway	634	+12.4%	37
New Zealand	6,522	+20.4%	21
Oman	511	-1.54%	42
Pakistan	567	+0.89%	39
Panama	401	+35%	45
Peru	1,141	-0.0876%	34
Philippines	3,903	+11%	22
Papua New Guinea	29	-45.3%	77
Poland	111	+26.1%	65
Portugal	42	-84%	74
Paraguay	354	+9.6%	47
Qatar	722	+19.5%	36
Romania	45	-4.26%	73
Russia	11,408	+1.63%	13
Rwanda	2	+100%	97
Saudi Arabia	2,581	+13.5%	25
Singapore	12,566	+9.39%	12
El Salvador	144	+0.699%	62
San Marino	722	+1.83%	36
Serbia	18	+125%	82
São Tomé & Príncipe	14	0%	85
Slovakia	13	-13.3%	86
Slovenia	17	+41.7%	83
Sweden	425	-1.62%	43
Seychelles	28	+211%	78
Syria	20	+25%	81
Thailand	7,375	+10.7%	18
Tonga	2	-33.3%	97
Trinidad & Tobago	149	+34.2%	61
Turkey	242	+1.68%	54
Tuvalu	11	+10%	88
Uganda	4	-33.3%	95
Ukraine	2,091	+14.8%	28
United States	329,229	+0.502%	1
Uzbekistan	252	+8.62%	52
St. Vincent & Grenadines	11	+120%	88
Vietnam	7,468	+11.9%	16
Yemen	22	+175%	80
South Africa	9,156	+49%	14
Zambia	13	+30%	86

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 = 'IP.PAT.NRES'

# 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 <- 'IP.PAT.NRES'

# 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))

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Patent applications, nonresidents

Year: 2021

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