Country

Indicator

Value type

Values present

Hospital beds (per 1,000 people)

Source: worldbank.org, 01.09.2025

Year: 2020

Country	Value	Value change, %	Rank
Afghanistan	0.34	-10.5%	107
Albania	2.91	+2.11%	52
United Arab Emirates	1.94	+3.74%	74
Argentina	3.2	-13.7%	45
Armenia	4.59	+9.81%	27
Antigua & Barbuda	3.29	+1.54%	44
Austria	7.06	-1.81%	7
Belgium	5.52	-0.719%	19
Benin	0.47	+11.9%	105
Burkina Faso	0.2	-50%	110
Bulgaria	7.77	+1.44%	5
Bahamas	2.69	-0.37%	57
Bolivia	1.36	+7.94%	85
Brazil	2.43	+8.48%	63
Barbados	5.66	-1.39%	16
Brunei	3.88	+4.86%	39
Bhutan	2.12	+1.92%	70
Botswana	2.26	-1.74%	67
Canada	2.56	+1.19%	60
Switzerland	4.48	-2.4%	28
Chile	2.03	-0.49%	72
China	5	+3.52%	24
Colombia	1.69	-2.87%	78
Costa Rica	1.15	+5.5%	90
Cuba	4.19	+0.48%	35
Czechia	6.6	-0.901%	10
Germany	7.8	-1.39%	4
Dominica	3	-0.662%	49
Denmark	2.59	-0.385%	59
Dominican Republic	1.42	+13.6%	81
Ecuador	1.32	-5.04%	86
Egypt	1.13	-7.38%	91
Eritrea	1	-0.99%	96
Spain	2.95	0%	51
Estonia	4.46	-1.55%	29
Finland	2.83	-15.5%	54
France	6	-1.64%	13
United Kingdom	2.43	-0.816%	63
Georgia	4.94	+6.47%	25
Gambia	1.11	+18.1%	92
Guatemala	0.43	-8.51%	106
Guyana	2.25	+3.21%	68
Honduras	0.71	+9.23%	101
Croatia	5.6	+0.358%	17
Haiti	5.03	+3.29%	23
Hungary	6.76	-2.17%	8
Indonesia	1.4	+18.6%	82
India	1.62	-0.613%	80
Ireland	2.91	+0.345%	52
Iraq	1.2	0%	88
Iceland	2.83	+1.07%	54
Israel	3.07	-1.6%	48
Italy	3.18	+0.633%	47
Jamaica	1.71	-1.16%	77
Jordan	1.37	+0.735%	84
Japan	12.7	-1.24%	1
Kazakhstan	6.72	+31%	9
Kyrgyzstan	4.19	-0.238%	35
St. Kitts & Nevis	4.32	0%	32
South Korea	12.7	+1.85%	2
Kuwait	2.35	+17.5%	65
Laos	1.27	-24.9%	87
Lebanon	2.73	0%	56
Libya	3.2	0%	45
St. Lucia	1.74	+32.8%	76
Sri Lanka	4.02	+0.5%	37
Lithuania	5.96	-4.18%	14
Luxembourg	4.19	-1.64%	35
Latvia	5.3	-2.21%	21
Morocco	0.73	+4.29%	100
Moldova	5.57	-3.97%	18
Maldives	5.04	-20.8%	22
Mexico	1	+3.09%	96
North Macedonia	4.22	+0.716%	34
Malta	4.38	+6.57%	30
Myanmar (Burma)	1.06	+1.92%	94
Montenegro	3.81	0%	40
Mongolia	8.22	+3.53%	3
Mozambique	0.7	-1.41%	102
Mauritius	3.64	+0.275%	41
Malaysia	1.97	+1.55%	73
Niger	0.27	-6.9%	109
Nicaragua	0.94	-2.08%	98
Netherlands	2.91	-3.64%	52
Norway	3.4	-2.02%	43
Nepal	0.28	-3.45%	108
New Zealand	2.51	-1.57%	61
Oman	1.16	+5.45%	89
Panama	1.92	-1.03%	75
Peru	1.64	+3.8%	79
Philippines	0.99	+1.02%	97
Poland	6.11	+0.328%	12
Portugal	3.53	+0.57%	42
Paraguay	1.01	+12.2%	95
Romania	7.06	+0.857%	7
Russia	7.08	+0.141%	6
Rwanda	0.74	+2.78%	99
Saudi Arabia	2.18	+1.4%	69
Sudan	0.66	0%	103
Singapore	2.63	+1.15%	58
El Salvador	1.08	+1.89%	93
Serbia	5.35	+0.187%	20
Suriname	2.89	+6.25%	53
Slovakia	5.68	-1.39%	15
Slovenia	4.25	-2.97%	33
Sweden	2.05	-0.966%	71
Seychelles	3.19	-1.85%	46
Syria	1.39	+10.3%	83
Thailand	2.33	+4.95%	66
Tajikistan	4.36	+0.23%	31
Turkmenistan	4.09	-0.969%	36
Trinidad & Tobago	1.92	+1.05%	75
Tunisia	2.38	+33%	64
Turkey	2.99	+5.28%	50
Tanzania	0.63	+3.28%	104
Ukraine	6.26	-6.15%	11
Uruguay	2.49	+0.81%	62
United States	2.74	-0.364%	55
Uzbekistan	4.78	+2.8%	26
St. Vincent & Grenadines	3.98	+0.505%	38
Venezuela	0.99	+6.45%	97

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 = 'SH.MED.BEDS.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 <- 'SH.MED.BEDS.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

Hospital beds (per 1,000 people)

Year: 2020

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