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' --- # 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