Country

Indicator

Value type

Values present

ICT goods imports (% total goods imports)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Angola	3.3	-20.3%	72
Albania	0.21	-88.3%	100
Andorra	5.89	-1.17%	36
Argentina	7.95	-12.5%	21
Armenia	9.05	+78.1%	18
Australia	8.52	-14.8%	20
Austria	6.33	-7.18%	33
Azerbaijan	4.54	-17.6%	53
Burundi	1.91	-35%	93
Belgium	3.24	-11.2%	75
Benin	1.58	-37.1%	95
Bulgaria	4.8	-18.4%	50
Bahrain	3.91	-19.7%	61
Bosnia & Herzegovina	2.39	-11.2%	89
Belize	2.6	-2.99%	81
Bermuda	2.4	-15.2%	88
Bolivia	2.49	-20.7%	84
Brazil	6.13	-31.7%	34
Barbados	3.88	-30.5%	63
Brunei	1.53	-14%	96
Botswana	2.5	+10.1%	83
Canada	6.75	-9.15%	31
Chile	5.5	-38.8%	41
China	22.1	-6.62%	3
Cyprus	3.81	-19.8%	64
Czechia	16.7	-1.24%	5
Germany	7.48	-12.3%	24
Denmark	6.89	-7.02%	29
Dominican Republic	2.87	-42.3%	79
Egypt	2.55	-47.4%	82
Spain	4.33	-18%	55
Estonia	6.13	-19.4%	34
Finland	5.73	-17%	39
Fiji	4.13	-22.9%	56
France	5.35	-17.1%	43
United Kingdom	7.26	-3.97%	25
Georgia	5.13	+5.99%	45
Greece	3.97	-24.2%	60
Grenada	3.51	-31.2%	69
Guatemala	5.82	-6.28%	38
Guyana	0.96	-45.5%	98
Hong Kong SAR China	57.2	-0.643%	1
Croatia	3.55	-24.3%	68
Hungary	10	-23.2%	14
India	7.64	-18.6%	22
Ireland	11.3	-10.6%	11
Iceland	5.84	-24.3%	37
Israel	9.27	-9.82%	17
Italy	3.89	-25.3%	62
Jamaica	2.67	-27%	80
Jordan	3.16	-26.2%	76
Japan	11.3	-13.8%	12
Kazakhstan	7.53	-12.9%	23
Kyrgyzstan	5.03	+26.4%	46
South Korea	15.3	-6.61%	6
Lebanon	5	+64.5%	48
Sri Lanka	3.01	-51.5%	77
Lithuania	4.7	-16.8%	52
Luxembourg	3.25	-12.4%	74
Latvia	7.24	-19.6%	26
Macao SAR China	13.3	-15.1%	8
Morocco	3.64	-10.8%	67
Moldova	3.79	-7.56%	66
Mexico	13	-14.6%	10
North Macedonia	3.81	-8.63%	64
Malta	9.41	+1.95%	16
Myanmar (Burma)	0.78	-68%	99
Montenegro	3.8	+6.15%	65
Mozambique	1.89	-25%	94
Mauritius	4.99	-2.92%	49
Namibia	2.46	+12.8%	86
Niger	1.29	-43.4%	97
Nicaragua	2.24	-11.5%	91
Netherlands	9.57	-25.2%	15
Norway	5.98	-11%	35
New Zealand	6.92	-9.54%	28
Panama	3.99	-22.8%	59
Philippines	19.4	-10.9%	4
Poland	6.34	-32%	32
Portugal	5.59	-19.1%	40
Paraguay	13.6	-7.36%	7
French Polynesia	4.49	-13.2%	54
Qatar	5.02	-25.4%	47
Romania	6.96	-2.93%	27
Rwanda	4.03	-24.2%	58
Singapore	31.1	-5.35%	2
El Salvador	5.38	-17.5%	42
Serbia	3.5	-13.6%	70
São Tomé & Príncipe	3.37	-16.2%	71
Suriname	2.05	+1.49%	92
Slovakia	10.7	-14.4%	13
Slovenia	3.26	-21.6%	73
Sweden	8.69	-11.4%	19
Togo	2.48	+27.2%	85
Turkey	2.97	-27.6%	78
Tanzania	2.42	-25.8%	87
Ukraine	4.73	-11.8%	51
Uruguay	5.32	-7.16%	44
United States	13	-7.29%	9
Uzbekistan	4.07	-3.1%	57
South Africa	6.76	-14.2%	30
Zimbabwe	2.3	+6.98%	90

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 = 'TM.VAL.ICTG.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 <- 'TM.VAL.ICTG.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

ICT goods imports (% total goods imports)

Year: 2022

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