This document demonstrates how to download and analyze global GDP data using R, utilizing the wbstats and WDI packages to retrieve and visualize various indicators from the World Bank.

This document provides a guide to downloading and analyzing global GDP data using R. It demonstrates how to use the wbstats and WDI packages to retrieve a variety of economic indicators from the World Bank, including GDP, GDP per capita, and the sectoral contributions of agriculture, industry, and services to GDP. The document includes R code for data acquisition, cleaning, and creating visualizations to compare the economic performance of different countries over time. The Python section is a placeholder for future content.

R

write R code to download annual world GDP data , include gdp,gdp per capita,year,sector,country name

wbstats package

Code

#install.packages("wbstats")
library(wbstats)
library(ggplot2)
library(tidyverse)

data from World Bank:https://data.worldbank.org/indicator/NY.GDP.MKTP.CD

Code

industry_search_results <- wbsearch(pattern = "% of GDP")
print(industry_search_results)

Code

# Define the indicators you want to download
# "NY.GDP.MKTP.CD" for GDP (current US$)
# "NY.GDP.PCAP.CD" for GDP per capita (current US$)

#NV.AGR.TOTL.ZS Agriculture, forestry, and fishing, value added (% of GDP)
#NV.IND.TOTL.ZS Industry (including construction), value added (% of GDP)
#NV.SRV.TOTL.ZS Services, value added (% of GDP)
gdp_indicators <- c("NY.GDP.MKTP.CD", "NY.GDP.PCAP.CD",
                    "NV.AGR.TOTL.ZS","NV.IND.TOTL.ZS","NV.SRV.TOTL.ZS" )


# Download the data
world_gdp_data <- wb_data(
  indicator = gdp_indicators,
  start_date = 2000, # You can change the start and end years as needed
  end_date = 2023
)

# Print the first few rows of the data to see the structure
head(world_gdp_data)

# You can also use summary() to get a quick overview of the data
summary(world_gdp_data)

# To get the data with country names, you can merge with wb_countries()
#world_gdp_data_with_names <- merge(world_gdp_data, wb_countries(), by = "country_code", all.x = TRUE)

# Print the first few rows of the merged data
#head(world_gdp_data_with_names)

# Clean up the column names
world_gdp_data_with_names <- world_gdp_data %>%
  rename(
    GDP = NY.GDP.MKTP.CD,
    GDP_per_capita = NY.GDP.PCAP.CD,
    
    Agriculture_of_GDP=NV.AGR.TOTL.ZS,
    Industry_of_GDP=NV.IND.TOTL.ZS,
    Services_of_GDP=NV.SRV.TOTL.ZS,
    Year = date,
    Country_Name = country
  )

# Select only the columns you need
final_gdp_data <- world_gdp_data_with_names %>%mutate(total=Agriculture_of_GDP+Industry_of_GDP+Services_of_GDP)
  

# Print the first few rows of the final data
head(final_gdp_data)

Code

# Create the line chart

china_thailand_data=final_gdp_data |> filter(Country_Name %in% c("China", "Thailand","United States","Japan","Korea, Rep.")) |> mutate(Year=as.numeric(Year))

gdp_per_capita_plot <- ggplot(data = china_thailand_data, aes(x = Year, y = Services_of_GDP, color = Country_Name)) +
  geom_line(size = 1) +
  labs(
    title = "Services_of_GDP: China vs Thailand vs US",
    #subtitle = "Services_of_GDP",
    x = "Year",
    y = "Services_of_GDP",
    color = "Country_Name"
  ) +
  theme_minimal() +
  theme(
    text = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5)
  )

gdp_per_capita_plot

Code

gdp_per_capita_plot <- ggplot(data = china_thailand_data, aes(x = Year, y = GDP_per_capita, color = Country_Name)) +
  geom_line(size = 1) +
  labs(
    title = "GDP Per Capita: China vs Thailand",
    subtitle = "Comparison of GDP per Capita (current US$)",
    x = "Year",
    y = "GDP Per Capita (USD)",
    color = "Country_Name"
  ) +
  theme_minimal() +
  theme(
    text = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5)
  )

gdp_per_capita_plot

WDI package

Code

library(WDI)
library(ggplot2)
library(tidyverse)

Code

# Download GDP data from World Bank
gdp_data <- WDI(
  country = "all",
  indicator = c(
    gdp = "NY.GDP.MKTP.CD",               # GDP (current US$)
    gdp_per_capita = "NY.GDP.PCAP.CD",    # GDP per capita (current US$)
    agriculture = "NV.AGR.TOTL.ZS",       # Agriculture value added (% of GDP)
    industry = "NV.IND.TOTL.ZS",          # Industry value added (% of GDP)
    services = "NV.SRV.TOTL.ZS"           # Services value added (% of GDP)
  ),
  start = 2000,
  end = 2023,
  extra = TRUE
)

Python

IMF

--- title: "世界GDP" subtitle: "Global GDP" author: "Tony D" date: "2025-04-24" categories: - AI - R - Python execute: warning: false error: false eval: false image: 'images/download.jpg' --- This document demonstrates how to download and analyze global GDP data using R, utilizing the `wbstats` and `WDI` packages to retrieve and visualize various indicators from the World Bank. This document provides a guide to downloading and analyzing global GDP data using R. It demonstrates how to use the `wbstats` and `WDI` packages to retrieve a variety of economic indicators from the World Bank, including GDP, GDP per capita, and the sectoral contributions of agriculture, industry, and services to GDP. The document includes R code for data acquisition, cleaning, and creating visualizations to compare the economic performance of different countries over time. The Python section is a placeholder for future content. # R write R code to download annual world GDP data , include gdp,gdp per capita,year,sector,country name ## wbstats package ```{r} #install.packages("wbstats") library(wbstats) library(ggplot2) library(tidyverse) ``` data from World Bank:https://data.worldbank.org/indicator/NY.GDP.MKTP.CD ```{r} industry_search_results <- wbsearch(pattern = "% of GDP") print(industry_search_results) ``` ```{r} # Define the indicators you want to download # "NY.GDP.MKTP.CD" for GDP (current US$) # "NY.GDP.PCAP.CD" for GDP per capita (current US$) #NV.AGR.TOTL.ZS Agriculture, forestry, and fishing, value added (% of GDP) #NV.IND.TOTL.ZS Industry (including construction), value added (% of GDP) #NV.SRV.TOTL.ZS Services, value added (% of GDP) gdp_indicators <- c("NY.GDP.MKTP.CD", "NY.GDP.PCAP.CD", "NV.AGR.TOTL.ZS","NV.IND.TOTL.ZS","NV.SRV.TOTL.ZS" ) # Download the data world_gdp_data <- wb_data( indicator = gdp_indicators, start_date = 2000, # You can change the start and end years as needed end_date = 2023 ) # Print the first few rows of the data to see the structure head(world_gdp_data) # You can also use summary() to get a quick overview of the data summary(world_gdp_data) # To get the data with country names, you can merge with wb_countries() #world_gdp_data_with_names <- merge(world_gdp_data, wb_countries(), by = "country_code", all.x = TRUE) # Print the first few rows of the merged data #head(world_gdp_data_with_names) # Clean up the column names world_gdp_data_with_names <- world_gdp_data %>% rename( GDP = NY.GDP.MKTP.CD, GDP_per_capita = NY.GDP.PCAP.CD, Agriculture_of_GDP=NV.AGR.TOTL.ZS, Industry_of_GDP=NV.IND.TOTL.ZS, Services_of_GDP=NV.SRV.TOTL.ZS, Year = date, Country_Name = country ) # Select only the columns you need final_gdp_data <- world_gdp_data_with_names %>%mutate(total=Agriculture_of_GDP+Industry_of_GDP+Services_of_GDP) # Print the first few rows of the final data head(final_gdp_data) ``` ```{r} # Create the line chart china_thailand_data=final_gdp_data |> filter(Country_Name %in% c("China", "Thailand","United States","Japan","Korea, Rep.")) |> mutate(Year=as.numeric(Year)) gdp_per_capita_plot <- ggplot(data = china_thailand_data, aes(x = Year, y = Services_of_GDP, color = Country_Name)) + geom_line(size = 1) + labs( title = "Services_of_GDP: China vs Thailand vs US", #subtitle = "Services_of_GDP", x = "Year", y = "Services_of_GDP", color = "Country_Name" ) + theme_minimal() + theme( text = element_text(size = 12), plot.title = element_text(hjust = 0.5, face = "bold"), plot.subtitle = element_text(hjust = 0.5) ) gdp_per_capita_plot ``` ```{r} gdp_per_capita_plot <- ggplot(data = china_thailand_data, aes(x = Year, y = GDP_per_capita, color = Country_Name)) + geom_line(size = 1) + labs( title = "GDP Per Capita: China vs Thailand", subtitle = "Comparison of GDP per Capita (current US$)", x = "Year", y = "GDP Per Capita (USD)", color = "Country_Name" ) + theme_minimal() + theme( text = element_text(size = 12), plot.title = element_text(hjust = 0.5, face = "bold"), plot.subtitle = element_text(hjust = 0.5) ) gdp_per_capita_plot ``` ## WDI package ```{r} library(WDI) library(ggplot2) library(tidyverse) ``` ```{r} # Download GDP data from World Bank gdp_data <- WDI( country = "all", indicator = c( gdp = "NY.GDP.MKTP.CD", # GDP (current US$) gdp_per_capita = "NY.GDP.PCAP.CD", # GDP per capita (current US$) agriculture = "NV.AGR.TOTL.ZS", # Agriculture value added (% of GDP) industry = "NV.IND.TOTL.ZS", # Industry value added (% of GDP) services = "NV.SRV.TOTL.ZS" # Services value added (% of GDP) ), start = 2000, end = 2023, extra = TRUE ) ``` # Python ## IMF