10B:NobelPrize.org

Introduction

This assignment uses the Nobel Prize API to explore patterns in Nobel Prize awards using structured JSON data. The goal is to move beyond simple summaries and investigate how laureates, prize categories, and countries are connected.

Approach

The analysis begins by retrieving JSON data from the Nobel Prize API, specifically from the laureates and nobelPrizes endpoints. These endpoints provide detailed, nested data on individuals and prize records.

The JSON data is loaded into R using the jsonlite package and then transformed into tidy data frames using dplyr and tidyr. Because the API data is nested (e.g., multiple prizes per laureate and multiple affiliations per prize), functions such as unnest() are used to flatten the structure into a tabular format suitable for analysis.

Several related data frames are created, including:

1. a laureates table with demographic information,
2. a prize-level table linking individuals to awards,
3. and an affiliations table capturing institutional and country information.

These tables are then used to answer four questions. The analysis includes grouping, filtering, and joining across datasets to uncover patterns. At least one question involves comparing birth country and affiliation country, requiring a join between multiple data frames.

The results are presented using tables and visualizations to clearly communicate the findings.

library(jsonlite)
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

library(tidyr)
library(purrr)

Attaching package: 'purrr'

The following object is masked from 'package:jsonlite':

    flatten

library(stringr)
library(ggplot2)
library(knitr)

Helper function

`%||%` <- function(x, y) {
  if (is.null(x) || length(x) == 0) y else x
}

Retrieve JSON data

laureates_url <- "https://api.nobelprize.org/2.1/laureates"
prizes_url <- "https://api.nobelprize.org/2.1/nobelPrizes"

laureates_raw <- fromJSON(laureates_url, simplifyVector = FALSE)
prizes_raw <- fromJSON(prizes_url, simplifyVector = FALSE)

Build tidy data frames

Laureates table

laureates_tbl <- map_dfr(laureates_raw$laureates, function(x) {
  tibble(
    laureate_id = x$id %||% NA_character_,
    known_name = x$knownName$en %||% NA_character_,
    given_name = x$givenName$en %||% NA_character_,
    family_name = x$familyName$en %||% NA_character_,
    full_name = x$fullName$en %||% NA_character_,
    gender = x$gender %||% NA_character_,
    birth_date = x$birth$date %||% NA_character_,
    birth_year = x$birth$year %||% NA_character_,
    birth_city = x$birth$place$city$en %||% NA_character_,
    birth_country = x$birth$place$country$en %||% NA_character_,
    birth_continent = x$birth$place$continent$en %||% NA_character_,
    death_date = x$death$date %||% NA_character_
  )
})

laureates_tbl %>% slice_head(n = 5)

# A tibble: 5 × 12
  laureate_id known_name      given_name family_name full_name gender birth_date
  <chr>       <chr>           <chr>      <chr>       <chr>     <chr>  <chr>     
1 745         A. Michael Spe… A. Michael Spence      A. Micha… male   1943-00-00
2 102         Aage N. Bohr    Aage N.    Bohr        Aage Nie… male   1922-06-19
3 779         Aaron Ciechano… Aaron      Ciechanover Aaron Ci… male   1947-10-01
4 259         Aaron Klug      Aaron      Klug        Aaron Kl… male   1926-08-11
5 1004        Abdulrazak Gur… Abdulrazak Gurnah      Abdulraz… male   1948-00-00
# ℹ 5 more variables: birth_year <chr>, birth_city <chr>, birth_country <chr>,
#   birth_continent <chr>, death_date <chr>

Prize-level table

prize_tbl <- map_dfr(laureates_raw$laureates, function(x) {
  laureate_id <- x$id %||% NA_character_
  full_name <- x$fullName$en %||% x$knownName$en %||% NA_character_

  if (length(x$nobelPrizes) == 0) return(NULL)

  map_dfr(x$nobelPrizes, function(p) {
    tibble(
      laureate_id = laureate_id,
      full_name = full_name,
      award_year = p$awardYear %||% NA_character_,
      category = p$category$en %||% NA_character_,
      category_full = p$categoryFullName$en %||% NA_character_,
      portion = p$portion %||% NA_character_,
      date_awarded = p$dateAwarded %||% NA_character_,
      prize_status = p$prizeStatus %||% NA_character_,
      motivation = p$motivation$en %||% NA_character_,
      prize_amount = p$prizeAmount %||% NA_real_,
      prize_amount_adjusted = p$prizeAmountAdjusted %||% NA_real_
    )
  })
})

prize_tbl %>% slice_head(n = 5)

# A tibble: 5 × 11
  laureate_id full_name   award_year category category_full portion date_awarded
  <chr>       <chr>       <chr>      <chr>    <chr>         <chr>   <chr>       
1 745         A. Michael… 2001       Economi… The Sveriges… 1/3     2001-10-10  
2 102         Aage Niels… 1975       Physics  The Nobel Pr… 1/3     1975-10-17  
3 779         Aaron Ciec… 2004       Chemist… The Nobel Pr… 1/3     2004-10-06  
4 259         Aaron Klug  1982       Chemist… The Nobel Pr… 1       1982-10-18  
5 1004        Abdulrazak… 2021       Literat… The Nobel Pr… 1       2021-10-07  
# ℹ 4 more variables: prize_status <chr>, motivation <chr>, prize_amount <int>,
#   prize_amount_adjusted <int>

Affiliations table

affiliations_tbl <- map_dfr(laureates_raw$laureates, function(x) {
  laureate_id <- x$id %||% NA_character_
  full_name <- x$fullName$en %||% x$knownName$en %||% NA_character_

  if (length(x$nobelPrizes) == 0) return(NULL)

  map_dfr(x$nobelPrizes, function(p) {
    award_year <- p$awardYear %||% NA_character_
    category <- p$category$en %||% NA_character_

    if (length(p$affiliations) == 0) return(NULL)

    map_dfr(p$affiliations, function(a) {
      tibble(
        laureate_id = laureate_id,
        full_name = full_name,
        award_year = award_year,
        category = category,
        affiliation_name = a$name$en %||% NA_character_,
        affiliation_city = a$city$en %||% NA_character_,
        affiliation_country = a$country$en %||% NA_character_,
        affiliation_continent = a$continent$en %||% NA_character_
      )
    })
  })
})

affiliations_tbl %>% slice_head(n = 5)

# A tibble: 5 × 8
  laureate_id full_name    award_year category affiliation_name affiliation_city
  <chr>       <chr>        <chr>      <chr>    <chr>            <chr>           
1 745         A. Michael … 2001       Economi… Stanford Univer… Stanford, CA    
2 102         Aage Niels … 1975       Physics  Niels Bohr Inst… Copenhagen      
3 779         Aaron Ciech… 2004       Chemist… Technion - Isra… Haifa           
4 259         Aaron Klug   1982       Chemist… MRC Laboratory … Cambridge       
5 114         Abdus Salam  1979       Physics  International C… Trieste         
# ℹ 2 more variables: affiliation_country <chr>, affiliation_continent <chr>

Prize-centered table from the Nobel Prizes endpoint

prizes_tbl <- map_dfr(prizes_raw$nobelPrizes, function(x) {
  tibble(
    award_year = x$awardYear %||% NA_character_,
    category = x$category$en %||% NA_character_,
    category_full = x$categoryFullName$en %||% NA_character_,
    date_awarded = x$dateAwarded %||% NA_character_,
    prize_amount = x$prizeAmount %||% NA_real_,
    prize_amount_adjusted = x$prizeAmountAdjusted %||% NA_real_
  )
})

prizes_tbl %>% slice_head(n = 5)

# A tibble: 5 × 6
  award_year category               category_full      date_awarded prize_amount
  <chr>      <chr>                  <chr>              <chr>               <int>
1 1901       Chemistry              The Nobel Prize i… 1901-11-12         150782
2 1901       Literature             The Nobel Prize i… 1901-11-14         150782
3 1901       Peace                  The Nobel Peace P… 1901-12-10         150782
4 1901       Physics                The Nobel Prize i… 1901-11-12         150782
5 1901       Physiology or Medicine The Nobel Prize i… 1901-10-30         150782
# ℹ 1 more variable: prize_amount_adjusted <int>

Question 1

Which Nobel Prize categories have been awarded most often?

q1 <- prize_tbl %>%
  count(category, sort = TRUE)

q1 %>%
  kable(caption = "Number of laureate records by Nobel Prize category")

Number of laureate records by Nobel Prize category

category	n
Chemistry	11
Physics	5
Peace	3
Economic Sciences	2
Literature	2
Physiology or Medicine	2

q1 %>%
  ggplot(aes(x = reorder(category, n), y = n)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Nobel Prize Categories by Number of Laureate Records",
    x = "Category",
    y = "Count"
  )

Answer: Chemistry appears most frequently in this dataset, followed by Physics and Peace. Other categories such as Economic Sciences, Literature, and Physiology or Medicine appear less often. This suggests that, within the sampled data, scientific fields, particularly Chemistry, are more prominently represented.

Question 2

Which birth countries have produced the most Nobel laureates?

q2 <- laureates_tbl %>%
  filter(!is.na(birth_country), birth_country != "") %>%
  count(birth_country, sort = TRUE)

q2 %>%
  slice_head(n = 15) %>%
  kable(caption = "Top 15 birth countries by number of laureates")

Top 15 birth countries by number of laureates

birth_country	n
USA	4
Germany	2
India	2
Japan	2
Prussia	2
Argentina	1
Belgium	1
British Mandate of Palestine	1
British Protectorate of Palestine	1
Denmark	1
Egypt	1
Ethiopia	1
France	1
French Algeria	1
Lithuania	1

q2 %>%
  slice_head(n = 15) %>%
  ggplot(aes(x = reorder(birth_country, n), y = n)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Top 15 Birth Countries of Nobel Laureates",
    x = "Birth Country",
    y = "Number of Laureates"
  )

Answer: The United States appears most frequently in this dataset, followed by several countries such as Germany, India, Japan, and Prussia with smaller counts. Most other countries appear only once, indicating that Nobel laureates in this sample are concentrated in a few countries, with a long tail of less-represented nations.

Note: The presence of historical country names such as Prussia and French Algeria reflects changes in geopolitical boundaries over time, which can affect how laureates are categorized by birthplace.

Question 3

Which institutions appear most often as Nobel Prize affiliations?

q3 <- affiliations_tbl %>%
  filter(!is.na(affiliation_name), affiliation_name != "") %>%
  count(affiliation_name, affiliation_country, sort = TRUE)

q3 %>%
  slice_head(n = 15) %>%
  kable(caption = "Top 15 affiliations in Nobel Prize records")

Top 15 affiliations in Nobel Prize records

affiliation_name	affiliation_country	n
Asahi Kasei Corporation	Japan	1
Berlin University	Germany	1
California Institute of Technology (Caltech)	USA	1
Goettingen University	Germany	1
Hokkaido University	Japan	1
Imperial College	United Kingdom	1
Institut d’Optique Graduate School – Université Paris-Saclay	France	1
International Centre for Theoretical Physics	Italy	1
Johns Hopkins University	USA	1
Kaiser-Wilhelm-Institut (now Max-Planck-Institut) für Biochemie	Germany	1
MRC Laboratory of Molecular Biology	United Kingdom	1
Massachusetts Institute of Technology (MIT)	USA	1
Meijo University	Japan	1
Munich University	Germany	1
Niels Bohr Institute	Denmark	1

Answer: Each institution in this dataset appears only once, meaning no single affiliation clearly dominates. This suggests that, within this sample, Nobel Prize winners are distributed across a wide range of institutions rather than concentrated in a few. However, the lack of repeated affiliations is likely due to the limited size of the dataset rather than reflecting the full distribution of Nobel Prize institutions.

Question 4

Which birth countries most often differ from the laureate’s affiliation country at the time of the award?

birth_vs_affiliation <- laureates_tbl %>%
  select(laureate_id, full_name, birth_country) %>%
  inner_join(
    affiliations_tbl %>%
      select(laureate_id, award_year, category, affiliation_country),
    by = "laureate_id"
  ) %>%
  filter(
    !is.na(birth_country), birth_country != "",
    !is.na(affiliation_country), affiliation_country != "",
    birth_country != affiliation_country
  )

q4 <- birth_vs_affiliation %>%
  count(birth_country, affiliation_country, sort = TRUE)

q4 %>%
  slice_head(n = 20) %>%
  kable(caption = "Birth country and affiliation country mismatches")

Birth country and affiliation country mismatches

birth_country	affiliation_country	n
British Mandate of Palestine	Israel	1
British Protectorate of Palestine	Israel	1
Egypt	USA	1
India	Italy	1
India	USA	1
India	United Kingdom	1
Lithuania	United Kingdom	1
New Zealand	USA	1
Prussia	Germany	1
Prussia	USA	1

q4_birth_loss <- birth_vs_affiliation %>%
  count(birth_country, sort = TRUE)

q4_birth_loss %>%
  slice_head(n = 15) %>%
  ggplot(aes(x = reorder(birth_country, n), y = n)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Birth Countries Most Often Different from Award Affiliation Country",
    x = "Birth Country",
    y = "Number of Mismatch Records"
  )

Answer: The results show that India has the highest number of mismatch cases in this dataset, followed by Prussia, while the remaining birth countries appear only once. The detailed table shows that these mismatches involve affiliations in countries such as the United States, the United Kingdom, Italy, Germany, and Israel. Together, the table and graph suggest that Nobel laureates in this sample often received their awards while affiliated with institutions outside their country of birth.

Conclusion

This analysis used JSON data from the Nobel Prize API to examine relationships between laureates, prize categories, and countries. After transforming the nested API responses into tidy tables, four questions were explored using filtering, grouping, and joins.

The results showed patterns in prize categories, birth countries, and institutional affiliations within the dataset. The comparison between birth country and affiliation country indicated that many laureates received their awards while affiliated with institutions outside their country of birth, highlighting the international nature of Nobel recognition.

Because the analysis is based on a limited sample of API data, the findings should be interpreted as illustrative rather than definitive. Overall, this assignment demonstrates how nested JSON data can be converted into tidy data frames in R and how combining multiple tables enables more meaningful analysis beyond simple counts.