Assignment 10B: More JSON Practice — Exploring Nobel Prize Data

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Published

November 8, 2026

1 Introduction and analytical approach

This report uses the Nobel Prize public API to investigate patterns in Nobel laureates. It retrieves JSON data directly in R, extracts nested records, creates tidy data frames, and uses those data to answer four questions.

  1. How has the number of Nobel laureate awards changed over time?
  2. Which countries have the largest number of laureates by recorded country of birth?
  3. Which countries have the largest difference between the number of laureates born there and the number affiliated with institutions there when awarded?
  4. How does the recorded gender distribution vary across Nobel Prize categories?

Here, question 3 goes beyond simple counts: it joins birthplace and award-affiliation information using the unique laureate ID, then compares the two country-level measures.

The Nobel Prize API returns nested JSON. A laureate can have multiple prizes, and a prize record can contain multiple affiliations. The analysis therefore creates two tidy tables: one row per laureate and one row per laureate–prize–affiliation combination.

2 Retrieve JSON from the Nobel Prize API

The analysis uses the official Nobel Prize API v2.1 laureates endpoint. Each laureate record contains biographical information and an array of Nobel Prize records. Pagination is handled with offset and limit; the loop continues until all records reported by the API have been retrieved.

[1] 1028

The response is retained as a list of nested R objects. The next step extracts the fields needed for the four questions.

3 Transform nested JSON into tidy data frames

The API uses multilingual text objects such as {"en": "Sweden"}. The helper get_text() extracts the English value when available and otherwise returns a missing value. The code also accommodates absent optional fields. The sample of the data is as under:

Rows: 1,028
Columns: 5
$ laureate_id   <chr> "745", "102", "779", "259", "1004", "114", "982", "981",…
$ full_name     <chr> "A. Michael Spence", "Aage Niels Bohr", "Aaron Ciechanov…
$ gender        <chr> "male", "male", "male", "male", "male", "male", "male", …
$ birth_year    <int> 1943, 1922, 1947, 1926, 1948, 1926, 1961, 1976, 1939, 19…
$ birth_country <chr> "USA", "Denmark", "British Protectorate of Palestine", "…
Rows: 1,058
Columns: 5
$ laureate_id   <chr> "745", "102", "779", "259", "1004", "114", "114", "982",…
$ award_year    <int> 2001, 1975, 2004, 1982, 2021, 1979, 1979, 2019, 2019, 20…
$ category      <chr> "Economic Sciences", "Physics", "Chemistry", "Chemistry"…
$ award_country <chr> "USA", "Denmark", "Israel", "United Kingdom", NA, "Italy…
$ motivation    <chr> "for their analyses of markets with asymmetric informati…
[1] 0

** Unit of analysis.** laureates contains one row per laureate. laureate_awards contains one row per laureate–prize–affiliation. A laureate with multiple prizes or multiple listed affiliations can therefore appear more than once in the second table. This is intentional and is handled explicitly in the relevant summaries.

4 Question 1: How has the number of Nobel laureate awards changed over time?

4.1 Rationale

A yearly trend helps show how many individual laureates have been recognized over time. The measure below counts laureate–prize associations. A prize shared by three individuals contributes three laureate awards.

First ten award years in the retrieved data
award_year laureate_awards
1901 6
1902 7
1903 7
1904 6
1905 5
1906 6
1907 6
1908 7
1909 7
1910 5

Summary of annual laureate awards
first_year last_year minimum_annual_awards maximum_annual_awards average_annual_awards
1901 2026 1 15 8.42

** Interpretation.** The line chart identifies long-term changes and unusually high or low years in the number of laureate–prize associations. This is not the same as counting prize categories or prize ceremonies. Years with no award are not displayed because the data contain no laureate–prize records for those years.

5 Question 2 — Which countries have the largest number of laureates by recorded birthplace?

5.1 Rationale

This question examines the geographical distribution of laureates using the country recorded in each laureate’s birth information. Each laureate is counted once, even if that person received more than one Nobel Prize.

Top 15 recorded birth countries by number of laureates
birth_country laureates_born
USA 299
United Kingdom 94
Germany 82
France 61
Japan 31
Sweden 30
Canada 23
the Netherlands 20
Switzerland 19
Italy 18
Russia 18
Austria 16
Russian Empire 16
Austria-Hungary 13
Norway 13

Interpretation. The ranking identifies countries most frequently represented in the recorded birthplace data. It is descriptive, not a direct measure of national research performance. Population size, historical borders, migration, access to education, and record completeness may all influence the results.

6 Question 3 — Which countries have the largest difference between laureates born there and laureates affiliated there when awarded?

6.1 Rationale

This question compares two country-level measures for the same laureates:

  • Birth country: the country recorded in the laureate’s birth-place information.
  • Award affiliation country: the country recorded for an institution associated with the laureate’s prize.

The two tables are joined by laureate_id. Each laureate is counted once per country in each measure. The net balance is calculated as:

[ = - ]

A positive balance indicates more laureates born in the country than affiliated with it at the time of the award. A negative balance indicates more laureates affiliated with it than born there.

Countries with the largest positive birthplace-minus-affiliation balance
country born_count affiliated_count net_balance
Austria-Hungary 11 0 11
Prussia 11 0 11
Russian Empire 10 0 10
Canada 19 10 9
Russia 12 3 9
Scotland 9 0 9
the Netherlands 18 10 8
India 8 1 7
China 8 2 6
Hungary 8 2 6
Countries with the largest negative birthplace-minus-affiliation balance
country born_count affiliated_count net_balance
USA 271 410 -139
United Kingdom 83 97 -14
Switzerland 15 25 -10
Israel 0 6 -6
USSR 4 9 -5
Germany 72 76 -4
Denmark 7 10 -3
Sweden 17 18 -1
Tunisia 0 1 -1
Argentina 2 2 0

** Interpretation.** Countries with a positive balance have more laureates born there than laureates affiliated with institutions there in the award records. Countries with a negative balance have more award affiliations than recorded births. These differences may reflect international migration, historical changes in borders and country names, institutional mobility, or missing affiliation information.

** Important qualification:** award affiliation is not citizenship or nationality. In addition, some Nobel categories, particularly Literature and many Peace awards, may not have institutional affiliations. Consequently, this analysis is an indicator of differences between recorded birthplace and award affiliation, not a direct count of emigrants or immigrants.

7 Question 4 — How does recorded gender representation vary across Nobel Prize categories?

7.1 Rationale

The gender distribution can differ across Nobel Prize categories. This analysis compares the number and percentage of laureate–prize associations by recorded gender within each category. Organizations and records without a gender value are excluded.

Laureate awards by category and recorded gender
category gender laureate_awards percentage
Chemistry female 8 4.0
Chemistry male 194 96.0
Economic Sciences female 3 3.0
Economic Sciences male 98 97.0
Literature female 19 15.4
Literature male 104 84.6
Peace female 21 18.6
Peace male 92 81.4
Physics female 5 2.2
Physics male 226 97.8
Physiology Or Medicine female 14 6.0
Physiology Or Medicine male 221 94.0

Interpretation. Percentages make categories with different total award counts easier to compare. The results describe the historical distribution of Nobel laureates, not the current gender composition of the academic or professional fields represented by the categories. The API’s gender field is not applicable to organizations and may be missing for some records.

8 Data quality checks

The following checks make missing information and potential join problems visible.

Data quality and completeness checks
check value
Laureate records retrieved 1028
Distinct laureate IDs 1028
Laureates with missing birth country 31
Laureate–prize–affiliation rows 1058
Award rows with missing affiliation country 273
Award rows with missing gender after join 31
Award IDs without a matching laureate record 0

9 Overall conclusion

The four questions demonstrate how public JSON data can be converted into tidy analytical tables and used to investigate historical patterns in Nobel Prizes.

  1. The annual trend describes how the number of individual laureate–prize associations has changed over time.
  2. The birthplace analysis identifies countries most frequently represented in the recorded birth data.
  3. The birthplace-versus-affiliation comparison demonstrates a relational analysis: it joins two attributes at the laureate level and compares their country distributions.
  4. The gender analysis compares representation across categories using within-category percentages.

Together, these analyses demonstrate JSON retrieval, nested-data extraction, data cleaning, joining, grouping, aggregation, visualization, and interpretation. The findings are descriptive: they identify patterns and differences but do not establish why those patterns occurred.

10 Limitations

  • API updates: Results reflect the data available when the report is rendered. New awards and corrections can change the results.
  • Historical country labels: Borders and country names have changed. A historical birthplace label may not correspond directly to a present-day country.
  • Missing information: Some records lack birth-country, gender, or affiliation information. Excluding missing values can affect rankings and comparisons.
  • Affiliation is not citizenship: The country associated with an award affiliation does not necessarily represent the laureate’s nationality or citizenship.
  • Multiple affiliations: Some laureates have more than one institutional affiliation. A laureate may therefore be associated with more than one country.
  • Categories without institutional affiliations: Literature and many Peace awards may not list institutional affiliations, reducing the coverage of Question 3.
  • Organizations: Nobel Prizes can be awarded to organizations. Gender analysis is therefore restricted to individual laureates with a recorded gender.
  • Descriptive analysis: These analyses identify associations and historical patterns; they do not establish causation.

11 Way forward

The analysis could be extended in several ways:

  1. Standardize country names: Develop a documented country crosswalk to harmonize historical country labels with present-day names while retaining the original API values.
  2. Investigate mobility more rigorously: Distinguish birthplace, citizenship, nationality, and institutional affiliation rather than treating them as equivalent.
  3. Examine trends by decade: Compare country and gender patterns over time to identify changes in representation.
  4. Compare categories statistically: Use appropriate statistical tests to examine whether gender distributions differ across categories, while considering the historical number of awards.
  5. Validate unusual records: Manually inspect laureates with multiple affiliations, missing birthplaces, or historical country labels.
  6. Improve reproducibility: Record the retrieval date and preserve a copy of the raw JSON response so future results can be compared with the same snapshot.

12 References