You can drop the code into any Julia project (v1.6 or newer) and call analyze_country("France") (or any other ISO‑2/ISO‑3 code or common name) to get a ready‑to‑use CountryReport object. | Step | Action | |------|--------| | 1️⃣ Load data | Sends a GET request to https://restcountries.com/v3.1/name/<country> and parses the JSON response. | | 2️⃣ Build a struct | Packs the most useful fields into a CountryInfo struct (population, area, GDP placeholder, etc.). | | 3️⃣ Compute derived metrics | • Population density (people / km²) • “Economic weight” proxy (population × GDP per‑capita, if you provide a GDP table) | | 4️⃣ Pretty‑print | A short, human‑readable summary that can be printed directly in the REPL or logged. |
Fetches basic data for the given country from the REST Countries API, computes population density, and (optionally) merges a GDP‑per‑capita value from a user‑provided `gdp_table`. julia pais anal
if resp.status != 200 error("Could not fetch data for \"$(name_or_code)\" (HTTP $(resp.status)).") end You can drop the code into any Julia project (v1
# Turn a dictionary of currencies (e.g. "USD"=>"name"=>"United States dollar","symbol"=>"$") into a vector of strings. function currencies_from_dict(dict::Dict) return [string(v["name"], " (", get(v, "symbol", "?"), ")") for (_, v) in dict] end | | 3️⃣ Compute derived metrics | •
# ---- 3️⃣ Compute derived metrics -------------------------------- density = info.area_km2 > 0 ? info.population / info.area_km2 : missing
# ---------------------------------------------------------------- # 3️⃣ Core function: fetch + analyse # ---------------------------------------------------------------- """ analyze_country(name_or_code::AbstractString; gdp_table=nothing)
