Patent applications, residents

The indicator of 'Patent applications, residents' serves as a powerful metric in assessing the innovative capacity and technological advancement of a country's population. It reflects the number of applications filed by residents for patents, showcasing the extent of inventive activity within a nation. This measure is crucial for policymakers, economists, and researchers as it provides insight into the health of the innovation ecosystem in different regions.

Importance of this indicator cannot be overstated. A higher number of patent applications often correlates with significant investments in research and development, resulting in technological breakthroughs and economic growth. In contrast, a low count could highlight potential stagnation or limitations in a country's innovation landscape. For instance, the median patent application value for 2021 stood at 146.0. This figure provides a baseline to evaluate countries and regions, where a higher score indicates a robust inventive environment.

Examining the top five areas for patent applications in 2021 reveals stark differences in levels of innovation. China led with a staggering 1,426,644 applications, highlighting its dominance in global technological advancements and heavy investment in research initiatives. The United States followed with 262,244 applications, a traditional leader in innovation, underscoring its commitment to maintaining an edge in technology and research. Japan and South Korea, with 222,452 and 186,245 applications respectively, reflect a strong historical emphasis on innovation, especially in electronics and automotive industries. Germany, known for its engineering prowess, had 39,822 applications, which, while respectable, demonstrates a relative decline compared to its historical performance in the innovation landscape.

On the other end of the spectrum, the bottom five areas exhibit exceedingly low application numbers, with Cape Verde, Cyprus, and Samoa each filing only one application, and Brunei and El Salvador with two. The minuscule figures highlight the challenges faced by these regions, such as underdeveloped research infrastructures, limited access to technology, and inadequate support for inventors and innovators. These challenges can create a cycle of underperformance, where low patent activity leads to fewer innovations and subsequent economic slowdowns.

Looking at world values for patent applications from 1985 to 2021 paints a picture of global innovation trends. The total number of patent applications has risen significantly, from 651,100 in 1985 to a peak of 2,385,200 in 2021. This upward trajectory illustrates a growing recognition of the importance of intellectual property in encouraging creativity and technological innovation. The data also reveals fluctuations and an occasional decline in patent applications, such as from 2018 to 2019, emphasizing that while the overall trend is positive, it can be impacted by external factors such as economic recessions or changes in policy.

The factors impacting patent applications in different regions are complex and multifaceted. Key contributors include economic conditions, educational systems, government policies, and cultural attitudes towards innovation. Countries with strong educational systems producing a skilled workforce are more likely to see higher patent applications. Furthermore, supportive governmental policies that incentivize research and development, such as tax credits or grants for innovators, can further enhance the number of patents filed.

Strategies to increase patent applications may involve improving access to education and research funding, fostering collaborations between institutions and industry, and encouraging innovation-friendly regulations. An emphasis on strengthening the intellectual property laws can also motivate individuals and companies to invest time and resources into new ideas without fear of exploitation or theft of their innovations.

However, the indicator is not without its flaws. Relying solely on patent applications as an innovation metric can be misleading. For example, not all inventions are patented, and individuals may choose to keep their innovations as trade secrets or forego the patent process due to costs or time constraints. Furthermore, differences in patent laws and processes across countries can lead to inconsistencies in data. Regions with less rigorous patent processes may show inflated application numbers, while those with stringent laws may appear to lag behind despite having strong innovation capabilities.

In summary, 'Patent applications, residents' serves as a vital indicator of a country's innovative landscape. Understanding the nuances behind this metric, including its relationship with other indicators such as investment in R&D and education levels, provides invaluable context. By analyzing the top and bottom regions, one can glean insight into the driving forces behind innovation. Addressing the challenges faced by lower-performing areas and leveraging strategies to stimulate patent activity and, consequently, invention can enhance the global innovation ecosystem, contributing to long-term economic growth and societal advancement.

                    
# 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 = 'IP.PAT.RESD'

# 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 <- 'IP.PAT.RESD'

# 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))