Assignment 10B: More JSON Practice
Exploring Nobel Prize Data Using JSON and R
Zaina Hassan
2026-11-03
Introduction
The Nobel Prize recognizes individuals and organizations for significant contributions to science, literature, peace, and economics. Since 1901, these awards have provided historical information about achievements across different fields.
For this assignment, I will use the Nobel Prize organization’s public API to retrieve JSON data and investigate four questions about prize categories, historical trends, gender representation, and geographic patterns.
My analysis will use R to retrieve, clean, transform, and visualize the data.
Approach
I plan to retrieve Nobel Prize data using the organization’s publicly
available API. I will use httr to send API requests,
jsonlite to parse the JSON responses, and
dplyr, tidyr, and purrr to
organize the nested information into tidy data frames. I will also use
ggplot2 to create visualizations.
The analysis will address four questions:
- Which Nobel Prize categories have awarded the most prizes?
- How has the number of Nobel Prizes awarded changed over time?
- What is the gender distribution of Nobel laureates across different prize categories?
- Which countries have produced the most Nobel laureates, and how do their birth countries compare with their institutional affiliations at the time of receiving the award?
The fourth question will involve joining and comparing data from different nested fields. I will also account for missing values and multiple affiliations when preparing the data.
Load Required Packages
# httr: Send HTTP requests to the Nobel Prize API
library(httr)
# jsonlite: Parse JSON data into R objects
library(jsonlite)
# dplyr: Filter, group, summarize, and join data
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# tidyr: Organize nested data into tidy structures
library(tidyr)
# purrr: Iterate through nested JSON lists
library(purrr)##
## Attaching package: 'purrr'## The following object is masked from 'package:jsonlite':
##
## flatten# ggplot2: Create data visualizations
library(ggplot2)
# tibble: Create tidy data frames
library(tibble)
# knitr: Display formatted tables
library(knitr)Retrieve Nobel Prize JSON Data
The Nobel Prize API provides information about laureates, including their names, gender, birthplaces, award years, prize categories, and institutional affiliations.
I will use version 2.1 of the API and retrieve the data in batches to account for pagination.
# Nobel Prize API endpoint
base_url <- "https://api.nobelprize.org/2.1/laureates"
# Function to retrieve one batch of laureates
get_laureates <- function(offset, limit = 100) {
response <- GET(
base_url,
query = list(
offset = offset,
limit = limit
)
)
stop_for_status(response)
json_text <- content(
response,
as = "text",
encoding = "UTF-8"
)
fromJSON(
json_text,
simplifyVector = FALSE
)
}
# Retrieve all available laureates
all_laureates <- list()
offset <- 0
batch_size <- 100
repeat {
batch <- get_laureates(
offset = offset,
limit = batch_size
)
records <- batch$laureates
if (length(records) == 0) {
break
}
all_laureates <- c(
all_laureates,
records
)
if (length(records) < batch_size) {
break
}
offset <- offset + batch_size
}
# Check number of records retrieved
length(all_laureates)## [1] 1028Transform JSON into Tidy Data
The JSON response contains nested lists for laureate information and Nobel Prize awards.
To make the data easier to analyze, I will extract relevant fields and create separate data frames for laureates, awards, and institutional affiliations.
Extract Laureate Information
# Safely extract English-language values
get_english <- function(x) {
if (is.null(x) || is.null(x$en)) {
return(NA_character_)
}
as.character(x$en)
}
# Extract nested fields safely
get_nested <- function(x, path) {
value <- x
for (field in path) {
if (is.null(value) || is.null(value[[field]])) {
return(NULL)
}
value <- value[[field]]
}
value
}
# Convert laureate records into a tidy data frame
laureates_df <- map_dfr(
all_laureates,
function(person) {
tibble(
laureate_id = as.character(person$id),
name = get_english(
person$knownName
),
gender = if (is.null(person$gender)) {
NA_character_
} else {
as.character(person$gender)
},
birth_country = get_english(
get_nested(
person,
c("birth", "place", "country")
)
)
)
}
)
# Remove duplicate laureates
laureates_df <- laureates_df %>%
distinct(laureate_id, .keep_all = TRUE)
# Preview data
head(laureates_df)## # A tibble: 6 × 4
## laureate_id name gender birth_country
## <chr> <chr> <chr> <chr>
## 1 745 A. Michael Spence male USA
## 2 102 Aage N. Bohr male Denmark
## 3 779 Aaron Ciechanover male British Protectorate of Palestine
## 4 259 Aaron Klug male Lithuania
## 5 1004 Abdulrazak Gurnah male <NA>
## 6 114 Abdus Salam male India# Check structure
glimpse(laureates_df)## Rows: 1,028
## Columns: 4
## $ laureate_id <chr> "745", "102", "779", "259", "1004", "114", "982", "981",…
## $ name <chr> "A. Michael Spence", "Aage N. Bohr", "Aaron Ciechanover"…
## $ gender <chr> "male", "male", "male", "male", "male", "male", "male", …
## $ birth_country <chr> "USA", "Denmark", "British Protectorate of Palestine", "…Extract Nobel Prize Awards
A laureate can receive more than one Nobel Prize. Therefore, I will create a separate data frame containing one row per laureate-award combination.
awards_df <- map_dfr(
all_laureates,
function(person) {
prizes <- person$nobelPrizes
if (is.null(prizes) || length(prizes) == 0) {
return(tibble())
}
map_dfr(
prizes,
function(prize) {
tibble(
laureate_id = as.character(person$id),
award_year = as.integer(
prize$awardYear
),
category = get_english(
prize$category
)
)
}
)
}
)
# Remove duplicate award records
awards_df <- awards_df %>%
distinct()
# Join laureate information with awards
nobel_data <- awards_df %>%
left_join(
laureates_df,
by = "laureate_id"
)
# Preview combined data
head(nobel_data)## # A tibble: 6 × 6
## laureate_id award_year category name gender birth_country
## <chr> <int> <chr> <chr> <chr> <chr>
## 1 745 2001 Economic Sciences A. Michael Spen… male USA
## 2 102 1975 Physics Aage N. Bohr male Denmark
## 3 779 2004 Chemistry Aaron Ciechanov… male British Prot…
## 4 259 1982 Chemistry Aaron Klug male Lithuania
## 5 1004 2021 Literature Abdulrazak Gurn… male <NA>
## 6 114 1979 Physics Abdus Salam male India# Check missing values
colSums(is.na(nobel_data))## laureate_id award_year category name gender
## 0 0 0 31 31
## birth_country
## 34Extract Institutional Affiliations
Institutional affiliations are nested within individual Nobel Prize records. Some laureates have multiple affiliations, while others have none listed.
I will extract these affiliations separately so they can be compared with birth countries.
affiliations_df <- map_dfr(
all_laureates,
function(person) {
prizes <- person$nobelPrizes
if (is.null(prizes) || length(prizes) == 0) {
return(tibble())
}
map_dfr(
prizes,
function(prize) {
affiliations <- prize$affiliations
if (is.null(affiliations) ||
length(affiliations) == 0) {
return(tibble())
}
map_dfr(
affiliations,
function(affiliation) {
tibble(
laureate_id = as.character(person$id),
award_year = as.integer(
prize$awardYear
),
category = get_english(
prize$category
),
affiliation_country = get_english(
affiliation$country
)
)
}
)
}
)
}
)
# Remove duplicate affiliation records
affiliations_df <- affiliations_df %>%
distinct()
# Preview
head(affiliations_df)## # A tibble: 6 × 4
## laureate_id award_year category affiliation_country
## <chr> <int> <chr> <chr>
## 1 745 2001 Economic Sciences USA
## 2 102 1975 Physics Denmark
## 3 779 2004 Chemistry Israel
## 4 259 1982 Chemistry United Kingdom
## 5 114 1979 Physics Italy
## 6 114 1979 Physics United KingdomQuestion 1: Which Nobel Prize Categories Have Awarded the Most Prizes?
For this question, I will count the number of prizes awarded in each category.
Since multiple laureates can share a single prize, I will count unique combinations of award year and category rather than counting individual recipients.
# Create one record per awarded prize
prizes_df <- awards_df %>%
distinct(
award_year,
category
)
# Count prizes by category
category_counts <- prizes_df %>%
count(
category,
name = "total_prizes",
sort = TRUE
)
# Display results
kable(
category_counts,
caption = "Number of Nobel Prizes Awarded by Category"
)| category | total_prizes |
|---|---|
| Physics | 120 |
| Literature | 119 |
| Chemistry | 118 |
| Physiology or Medicine | 117 |
| Peace | 107 |
| Economic Sciences | 58 |
Visualization
ggplot(
category_counts,
aes(
x = reorder(category, total_prizes),
y = total_prizes,
fill = category
)
) +
geom_col(
show.legend = FALSE
) +
coord_flip() +
labs(
title = "Nobel Prizes Awarded by Category",
x = "Prize Category",
y = "Number of Prizes"
) +
theme_minimal(base_size = 13)
Answer
top_category <- category_counts %>%
slice_max(
total_prizes,
n = 1,
with_ties = TRUE
)
cat(
"The category or categories with the most awarded prizes:",
paste(top_category$category, collapse = ", "),
"\nNumber of prizes:",
top_category$total_prizes[1]
)## The category or categories with the most awarded prizes: Physics
## Number of prizes: 120Physics has received the highest number of Nobel Prizes, with 120 awards, followed closely by Literature (119), Chemistry (118), and Physiology or Medicine (117). The small differences between these categories suggest that prizes have been awarded relatively consistently across the original fields. Peace has received slightly fewer awards (107), while Economic Sciences has the lowest total (58), largely because it was introduced in 1969 rather than 1901.
Question 2: How Has the Number of Nobel Prizes Awarded Changed Over Time?
For this question, I will examine how the number of prizes awarded has changed over time.
I will group the data by award year and count the number of distinct prizes awarded each year.
# Count prizes awarded each year
yearly_prizes <- prizes_df %>%
count(
award_year,
name = "total_prizes"
) %>%
arrange(award_year)
# Display first observations
head(yearly_prizes, 10)## # A tibble: 10 × 2
## award_year total_prizes
## <int> <int>
## 1 1901 5
## 2 1902 5
## 3 1903 5
## 4 1904 5
## 5 1905 5
## 6 1906 5
## 7 1907 5
## 8 1908 5
## 9 1909 5
## 10 1910 5# Summary statistics
summary(yearly_prizes$total_prizes)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 5.000 5.195 6.000 6.000Visualization
ggplot(
yearly_prizes,
aes(
x = award_year,
y = total_prizes
)
) +
geom_line(
color = "steelblue",
linewidth = 0.8
) +
geom_point(
color = "darkblue",
size = 1.2
) +
scale_y_continuous(
breaks = 0:6
) +
labs(
title = "Nobel Prizes Awarded Over Time",
x = "Award Year",
y = "Number of Prizes"
) +
theme_minimal(base_size = 13)
Compare Prizes by Decade
decade_summary <- prizes_df %>%
mutate(
decade = floor(award_year / 10) * 10
) %>%
group_by(decade) %>%
summarise(
total_prizes = n(),
.groups = "drop"
)
kable(
decade_summary,
caption = "Nobel Prizes Awarded by Decade"
)| decade | total_prizes |
|---|---|
| 1900 | 45 |
| 1910 | 36 |
| 1920 | 44 |
| 1930 | 44 |
| 1940 | 32 |
| 1950 | 48 |
| 1960 | 49 |
| 1970 | 59 |
| 1980 | 60 |
| 1990 | 60 |
| 2000 | 60 |
| 2010 | 60 |
| 2020 | 42 |
Answer
highest_year <- yearly_prizes %>%
slice_max(
total_prizes,
n = 1,
with_ties = TRUE
)
cat(
"The highest annual number of prizes awarded was",
max(yearly_prizes$total_prizes),
"\nYears:",
paste(highest_year$award_year, collapse = ", ")
)## The highest annual number of prizes awarded was 6
## Years: 1969, 1970, 1971, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026The number of Nobel Prizes awarded annually has remained relatively consistent throughout history, although there were noticeable fluctuations during the earlier decades. Several years had fewer than five prizes awarded, particularly during periods of global conflict. Beginning in 1969, the introduction of Economic Sciences increased the number of available categories from five to six. Since then, six prizes have generally been awarded each year, with very few exceptions. The graph shows that the annual number of prizes has become more consistent over time.
Question 3: What Is the Gender Distribution of Nobel Laureates Across Prize Categories?
For this question, I will compare gender representation across Nobel Prize categories.
I will calculate both the number and percentage of male and female laureate-award records in each category.
Organizations and records without a male or female gender classification will be excluded from this particular comparison.
# Filter to individual laureates
gender_data <- nobel_data %>%
filter(
gender %in% c("male", "female")
)
# Calculate counts and percentages
gender_summary <- gender_data %>%
count(
category,
gender,
name = "total"
) %>%
group_by(category) %>%
mutate(
percentage = round(
total / sum(total) * 100,
2
)
) %>%
ungroup()
# Display table
kable(
gender_summary,
caption = "Gender Distribution Across Nobel Prize Categories"
)| category | gender | total | percentage |
|---|---|---|---|
| Chemistry | female | 8 | 3.96 |
| Chemistry | male | 194 | 96.04 |
| Economic Sciences | female | 3 | 2.97 |
| Economic Sciences | male | 98 | 97.03 |
| Literature | female | 19 | 15.45 |
| Literature | male | 104 | 84.55 |
| Peace | female | 21 | 18.58 |
| Peace | male | 92 | 81.42 |
| Physics | female | 5 | 2.16 |
| Physics | male | 226 | 97.84 |
| Physiology or Medicine | female | 14 | 5.96 |
| Physiology or Medicine | male | 221 | 94.04 |
Visualization
ggplot(
gender_summary,
aes(
x = category,
y = percentage,
fill = gender
)
) +
geom_col(
position = "stack"
) +
coord_flip() +
scale_fill_manual(
values = c(
"female" = "#D87093",
"male" = "#4682B4"
)
) +
labs(
title = "Gender Distribution Across Nobel Prize Categories",
x = "Prize Category",
y = "Percentage of Laureate Awards",
fill = "Gender"
) +
theme_minimal(base_size = 13)
Identify the Category With the Highest Female Representation
female_representation <- gender_summary %>%
filter(
gender == "female"
) %>%
arrange(
desc(percentage)
)
kable(
female_representation,
caption = "Female Representation by Prize Category"
)| category | gender | total | percentage |
|---|---|---|---|
| Peace | female | 21 | 18.58 |
| Literature | female | 19 | 15.45 |
| Physiology or Medicine | female | 14 | 5.96 |
| Chemistry | female | 8 | 3.96 |
| Economic Sciences | female | 3 | 2.97 |
| Physics | female | 5 | 2.16 |
Answer
top_female_category <- female_representation %>%
slice(1)
cat(
"The category with the highest female representation is",
top_female_category$category,
"\nFemale representation:",
top_female_category$percentage,
"%"
)## The category with the highest female representation is Peace
## Female representation: 18.58 %The results show a substantial gender imbalance across all Nobel Prize categories, with male laureates accounting for the majority of awards. Peace and Literature have the highest percentages of female recipients, while Physics and Economic Sciences have some of the lowest. Although women have received recognition across all six categories, their representation remains considerably lower than that of men. Using percentages rather than counts makes these differences easier to compare across categories with different numbers of recipients.
Question 4: Which Countries Have Produced the Most Nobel Laureates, and How Do Birth Countries Compare With Institutional Affiliations?
For this question, I will investigate geographic patterns among Nobel laureates.
First, I will identify the countries associated with the largest numbers of unique laureates by birthplace.
Next, I will compare birth countries with the countries of institutional affiliations recorded at the time of an award.
This analysis requires joining information from multiple nested JSON fields.
Part A: Most Common Birth Countries
# Count unique laureates by birth country
birth_country_summary <- laureates_df %>%
filter(
!is.na(birth_country),
birth_country != ""
) %>%
count(
birth_country,
name = "total_laureates",
sort = TRUE
)
# Display top 10 countries
top_birth_countries <- birth_country_summary %>%
slice_head(n = 10)
kable(
top_birth_countries,
caption = "Top 10 Birth Countries of Nobel Laureates"
)| birth_country | total_laureates |
|---|---|
| USA | 299 |
| United Kingdom | 94 |
| Germany | 82 |
| France | 61 |
| Japan | 31 |
| Sweden | 30 |
| Canada | 23 |
| the Netherlands | 20 |
| Switzerland | 19 |
| Italy | 18 |
Visualization
ggplot(
top_birth_countries,
aes(
x = reorder(birth_country, total_laureates),
y = total_laureates
)
) +
geom_col(
fill = "steelblue"
) +
coord_flip() +
labs(
title = "Top 10 Birth Countries of Nobel Laureates",
x = "Birth Country",
y = "Number of Unique Laureates"
) +
theme_minimal(base_size = 13)
Part B: Compare Birth Countries and Affiliation Countries
I will join the affiliation records with laureate birth-country information using the unique laureate ID.
To avoid counting repeated institutional records, I will retain distinct combinations of laureate, award, and affiliation country.
# Join birth country and affiliation information
country_comparison <- affiliations_df %>%
left_join(
laureates_df %>%
select(
laureate_id,
name,
birth_country
),
by = "laureate_id"
) %>%
filter(
!is.na(birth_country),
!is.na(affiliation_country),
birth_country != "",
affiliation_country != ""
) %>%
distinct(
laureate_id,
award_year,
category,
birth_country,
affiliation_country,
.keep_all = TRUE
)
# Preview comparison data
head(country_comparison)## # A tibble: 6 × 6
## laureate_id award_year category affiliation_country name birth_country
## <chr> <int> <chr> <chr> <chr> <chr>
## 1 745 2001 Economic Scien… USA A. M… USA
## 2 102 1975 Physics Denmark Aage… Denmark
## 3 779 2004 Chemistry Israel Aaro… British Prot…
## 4 259 1982 Chemistry United Kingdom Aaro… Lithuania
## 5 114 1979 Physics Italy Abdu… India
## 6 114 1979 Physics United Kingdom Abdu… IndiaIdentify Laureates With Different Birth and Affiliation Countries
# Compare birthplace with institutional location
different_countries <- country_comparison %>%
filter(
birth_country != affiliation_country
)
# Count differences by birth country
country_differences <- different_countries %>%
group_by(birth_country) %>%
summarise(
total_laureates = n_distinct(laureate_id),
.groups = "drop"
) %>%
arrange(
desc(total_laureates)
)
# Display top 10
kable(
head(country_differences, 10),
caption = "Birth Countries With Laureates Affiliated Abroad"
)| birth_country | total_laureates |
|---|---|
| Germany | 29 |
| United Kingdom | 24 |
| Canada | 15 |
| France | 14 |
| Austria-Hungary | 11 |
| Prussia | 11 |
| Russia | 10 |
| Russian Empire | 10 |
| Scotland | 9 |
| the Netherlands | 9 |
Compare the Most Common Birth and Affiliation Countries
# Count unique laureates by affiliation country
affiliation_summary <- country_comparison %>%
group_by(affiliation_country) %>%
summarise(
total_laureates = n_distinct(laureate_id),
.groups = "drop"
) %>%
arrange(
desc(total_laureates)
)
# Display top 10
kable(
head(affiliation_summary, 10),
caption = "Top 10 Institutional Affiliation Countries"
)| affiliation_country | total_laureates |
|---|---|
| USA | 410 |
| United Kingdom | 97 |
| Germany | 76 |
| France | 40 |
| Switzerland | 25 |
| Japan | 22 |
| Sweden | 18 |
| Canada | 10 |
| Denmark | 10 |
| the Netherlands | 10 |
Visualization
# Compare countries within records containing both fields
birth_counts <- country_comparison %>%
group_by(birth_country) %>%
summarise(
total = n_distinct(laureate_id),
.groups = "drop"
) %>%
rename(country = birth_country) %>%
mutate(type = "Birth Country")
affiliation_counts <- affiliation_summary %>%
rename(
country = affiliation_country,
total = total_laureates
) %>%
mutate(type = "Affiliation Country")
# Combine summaries
combined_countries <- bind_rows(
birth_counts,
affiliation_counts
)
# Select top countries by combined totals
top_countries <- combined_countries %>%
group_by(country) %>%
summarise(
combined_total = sum(total),
.groups = "drop"
) %>%
slice_max(
combined_total,
n = 10,
with_ties = FALSE
) %>%
pull(country)
# Plot comparison
combined_countries %>%
filter(
country %in% top_countries
) %>%
ggplot(
aes(
x = reorder(country, total),
y = total,
fill = type
)
) +
geom_col(
position = "dodge"
) +
coord_flip() +
labs(
title = "Birth Countries vs. Institutional Affiliation Countries",
x = "Country",
y = "Number of Unique Laureates",
fill = "Country Type"
) +
theme_minimal(base_size = 13)
Answer
# Identify leading birth country
top_birth_country <- birth_country_summary %>%
slice(1)
# Identify leading affiliation country
top_affiliation_country <- affiliation_summary %>%
slice(1)
# Count unique laureates with differing countries
total_different <- different_countries %>%
summarise(
total = n_distinct(laureate_id)
) %>%
pull(total)
cat(
"Country with the most Nobel laureates by birthplace:",
top_birth_country$birth_country,
"\nNumber of unique laureates:",
top_birth_country$total_laureates,
"\n\nCountry with the most laureates by institutional affiliation:",
top_affiliation_country$affiliation_country,
"\nNumber of unique laureates:",
top_affiliation_country$total_laureates,
"\n\nNumber of unique laureates with at least one affiliation",
"country different from their birth country:",
total_different
)## Country with the most Nobel laureates by birthplace: USA
## Number of unique laureates: 299
##
## Country with the most laureates by institutional affiliation: USA
## Number of unique laureates: 410
##
## Number of unique laureates with at least one affiliation country different from their birth country: 272This analysis compares the geographic origins of Nobel laureates with the locations of their institutional affiliations.
The United States has produced the largest number of Nobel laureates by birthplace, followed by the United Kingdom, Germany, and France. When comparing birth countries with institutional affiliations, the United States also has the highest number of affiliated laureates, with a noticeably larger affiliation count than birth count.
This suggests that American institutions have attracted researchers and other Nobel laureates from different countries. Other countries, including the United Kingdom and Germany, show similar patterns, although the differences are smaller. The results highlight the international nature of Nobel Prize achievements and the importance of institutional affiliations in understanding geographic patterns. However, affiliation does not necessarily indicate citizenship or permanent migration.
Conclusion
I used the Nobel Prize API to retrieve JSON data and analyze historical, demographic, and geographic patterns among Nobel Prize laureates. Using R packages such as httr, jsonlite, dplyr, tidyr, and ggplot2, I transformed nested JSON data into tidy data frames and created tables and visualizations to answer four questions.
The results showed that Physics has received the most Nobel Prizes, although the original prize categories have relatively similar totals. The historical analysis also showed that the number of prizes awarded annually became more consistent following the introduction of Economic Sciences in 1969. Gender comparisons revealed that male laureates continue to account for the majority of awards across all categories, particularly in Physics and Economic Sciences. Finally, the geographic analysis showed that the United States leads in both laureate birthplaces and institutional affiliations, highlighting the international connections between researchers and the institutions where they work.
One of the main challenges was working with nested JSON structures, particularly when extracting information about awards and institutional affiliations. Separating the data into multiple data frames and joining them using laureate IDs made the analysis more manageable. I also had to consider missing values and multiple affiliations to avoid misleading comparisons.
Data Source
Nobel Prize Outreach. Nobel Prize API, Version 2.1.
API endpoint: https://api.nobelprize.org/2.1/laureates
Developer documentation: https://www.nobelprize.org/about/developer-zone-2/