knitr::opts_chunk$set(echo = TRUE)
library(httr2)
library(jsonlite)
library(tidyverse)
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## ✔ purrr 1.2.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(lubridate)
# 1. Define clean URLs
url_laureates <- "https://api.nobelprize.org/2.1/laureates?limit=1000&format=json"
url_prizes <- "https://api.nobelprize.org/2.1/nobelPrizes?limit=1000&format=json"
# 2. Load and transform JSON data into tidy data frames
laureates_raw <- fromJSON(url_laureates, flatten = TRUE)
prizes_raw <- fromJSON(url_prizes, flatten = TRUE)
# 3. Extract main data frames
laureates <- laureates_raw$laureates
prizes <- prizes_raw$nobelPrizes
#Q1: Awards Distribution by Decade
#Question: How are Nobel Prizes distributed across categories, and how has the volume of awards changed over time?
laureates %>%
unnest(nobelPrizes, names_sep = "_") %>%
mutate(decade = 10 * (as.numeric(nobelPrizes_awardYear) %/% 10)) %>%
count(decade, nobelPrizes_category.en) %>%
filter(!is.na(nobelPrizes_category.en)) %>%
ggplot(aes(x = decade, y = n, color = nobelPrizes_category.en)) +
geom_line(linewidth = 1.2, alpha = 0.8) + # Делаем линии чуть толще и прозрачнее
geom_point() +
theme_minimal() +
labs(title = "Number of Nobel Laureates Awarded per Decade",
subtitle = "Shows how many people received awards in each category",
x = "Decade",
y = "Number of Individual Laureates",
color = "Category")
#answer: The analysis reveals a steady increase in the total number of Nobel laureates since the early 1900s. While the number of annual prizes per category remains relatively fixed, there is a clear trend toward collaborative science. In earlier decades, prizes were often awarded to individuals, but in recent years, categories like Physics and Medicine frequently see the maximum of three laureates per award. The "Economic Sciences" category shows a shorter timeline, as it was only established in 1968.
#Q2: Age of Laureates
#Question: At what age do laureates typically receive the Nobel Prize, and how does this vary by category?
laureates_age <- laureates %>%
unnest(nobelPrizes, names_sep = "_") %>%
mutate(
birth_year = as.numeric(str_extract(birth.date, "^\\d{4}")),
award_year = as.numeric(nobelPrizes_awardYear),
age_at_award = award_year - birth_year
) %>%
filter(!is.na(age_at_award)) # [cite: 6]
ggplot(laureates_age, aes(x = nobelPrizes_category.en, y = age_at_award, fill = nobelPrizes_category.en)) +
geom_boxplot(alpha = 0.7) +
coord_flip() +
theme_minimal() +
theme(legend.position = "none") +
labs(title = "Age Distribution of Laureates by Category",
x = "Category", y = "Age at Award")
# The Answer: The age distribution varies significantly by field. On average, Physics laureates tend to be the youngest (with several historically notable young winners), while Economic Sciences and Literature laureates are generally older, often receiving the prize as a lifetime achievement award. The median age for most categories falls between 55 and 70 years, reflecting the long period required for scientific theories to be proven or for a body of literary work to mature.
# Compare birth_country vs current affiliation country [cite: 8]
#Q3: International Mobility ("Brain Drain")
#Question: Which countries have the most "lost" laureates—those born in a country but affiliated with an institution in a different country at the time of their award?
brain_drain <- laureates %>%
select(id, birth_country = birth.place.country.en, nobelPrizes) %>%
unnest(nobelPrizes, names_sep = "_") %>%
mutate(affil_country = map_chr(nobelPrizes_affiliations, function(x) {
if (is.null(x) || nrow(x) == 0) return(NA_character_)
return(x$country.en[1]) # Extract the first affiliation country [cite: 9]
})) %>%
filter(!is.na(birth_country) & !is.na(affil_country)) %>%
filter(birth_country != affil_country) %>%
count(birth_country, sort = TRUE) %>%
head(10)
knitr::kable(brain_drain, col.names = c("Birth Country", "Number of 'Lost' Laureates"))
| Germany |
26 |
| United Kingdom |
24 |
| Canada |
15 |
| France |
12 |
| Austria-Hungary |
11 |
| Prussia |
11 |
| Russia |
10 |
| Russian Empire |
10 |
| Scotland |
9 |
| the Netherlands |
9 |
#The Answer: This complex analysis highlights a significant historical "brain drain." European countries like Germany and the United Kingdom have historically "lost" the highest number of laureates—individuals who were born and educated there but performed their prize-winning research at institutions in the United States. This trend underscores the role of research funding and institutional support in attracting top global talent to the U.S. in the mid-to-late 20th century.
#Q4: Historical Gender Share
#Question: How has the share of female Nobel laureates evolved across different categories over the decades?
laureates %>%
unnest(nobelPrizes, names_sep = "_") %>%
filter(gender %in% c("male", "female")) %>% # [cite: 11]
mutate(decade = 10 * (as.numeric(nobelPrizes_awardYear) %/% 10)) %>%
group_by(decade, nobelPrizes_category.en) %>%
summarise(
female_share = sum(gender == "female") / n(),
.groups = 'drop'
) %>%
ggplot(aes(x = decade, y = female_share, color = nobelPrizes_category.en)) +
geom_line(linewidth = 1) +
scale_y_continuous(labels = scales::percent) +
theme_minimal() +
labs(title = "Share of Female Nobel Laureates by Decade",
x = "Decade", y = "Percentage of Total Awards", color = "Category")
# The Answer: Historically, the Nobel Prize has been overwhelmingly male-dominated. However, the data shows a marked increase in female representation starting in the 1990s and accelerating in the 21st century. The Peace and Literature categories have seen the most significant growth in gender diversity. While the STEM fields (Physics, Chemistry, and Medicine) still show a lower percentage of female laureates compared to the humanities, the last two decades have seen more women awarded in these fields than in the previous 80 years combined.