SPS_Data607_Week10B_DC

Assignment 10B: More JSON Practice

The Nobel Prize organization provides public APIs for accessing structured Nobel Prize data.
Using one or both of the APIs available at the Nobel Prize Developer Zone, your task is to use JSON data to investigate and answer four interesting, data-driven questions.

At least one of your questions should go beyond simple counts and require joining, filtering, or comparing fields across the data (e.g., “Which country lost the most Nobel laureates—born there but awarded as a citizen of another country?”).

Requirements

1. Use one or both Nobel Prize APIs to retrieve data in JSON format.

2. In R, load and transform the JSON data into tidy data frames.

3. Formulate four questions that can be answered from the data.

4. For each question:

   • Describe the question

   • Show the code used

   • Present the answer (table, summary, or plot)

Deliverables

Submit a single Quarto (.qmd) file that includes:

• Your four questions

• All R code used to retrieve and process the data

• The resulting answers

You may complete this assignment individually or in a small group.

The Nobel Prize API link

https://api.nobelprize.org/2.1/laureates

https://api.nobelprize.org/2.1/nobelPrizes

library(httr)
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)




if(all(file.exists(c("df_laureates.json", "df_nobel.json")))) {
  # load both files
  print("Cache files exits\n")

  
  json1 <- readLines("df_laureates.json", warn = FALSE)
  json2 <- readLines("df_nobel.json", warn = FALSE)
  
}else{
  # load from API
  #url1 <- "https://api.nobelprize.org/2.1/laureates"
  url1 <- "https://api.nobelprize.org/2.1/laureates?limit=1018"
  #url2 <- "https://api.nobelprize.org/2.1/nobelPrizes"
  url2 <- "https://api.nobelprize.org/2.1/nobelPrizes?limit=682"
  res1 <- GET(url1)
  res2 <- GET(url2)
  
  json1 <- content(res1, as = "text", encoding = "UTF-8")
  json2 <- content(res2, as = "text", encoding = "UTF-8")
  writeLines(json1, "df_laureates.json")
  writeLines(json2, "df_nobel.json")
  
  
}

[1] "Cache files exits\n"

# identical(df_laureates, df_laureates_0)
# identical(df_nobel, df_nobel_0)
data1 <- fromJSON(json1, flatten = TRUE)
data2 <- fromJSON(json2, flatten = TRUE)
js_laureates <- data1$laureates
js_nobel <- data2$nobelPrizes

library(tidyr)
df_nobelprizes <- js_nobel %>%
  select(awardYear,laureates, category.en)%>%
  unnest(laureates) %>%
  transmute(
    l_id = id,
    laureate_name = fullName.en,
    award_year = awardYear,
    category = category.en
  )
#df_nobelprizes

df_laureates <- js_laureates %>%
  select(id, gender, 'birth.year', 'birth.place.city.en', 'birth.place.countryNow.en','birth.place.continent.en')%>%

  transmute(
    l_id=id,
    gender=gender,
    birth_year=birth.year,
    city=birth.place.city.en,
    birth_country=birth.place.countryNow.en,
    birth_continent=birth.place.continent.en
  )
#df_laureates
#names(df_laureates)

Combine both data frames

df_combined_nobel <- left_join(df_nobelprizes,df_laureates,by ="l_id")
#df_combined_nobel

Q1: Comparing in gender

library(ggplot2)
ggplot(df_combined_nobel, aes(x = gender,fill = gender)) +
  geom_bar() +
  geom_text(stat = "count",
            aes(label = after_stat(count)),
            vjust = -0.5) +
  labs(title = "Count by Gender",
       x = "Gender",
       y = "Count")

Q2: How many Asians were awarded, and how are they distributed by country?

df_combined_nobel %>%
  filter(birth_continent == "Asia") %>%
  count(birth_country, sort = TRUE) %>%
  
  ggplot(aes(x = reorder(birth_country,n), y=n,fill = birth_country)) +
  geom_col() +
  # geom_text(stat = "count",
  #           aes(label = after_stat(count)),
  #           vjust = -0.5) +
  geom_text(
            aes(label = n),
            hjust = -0.2) +
  coord_flip() +
  labs(title = "Asian Nobel Prizes by Country",
       x = "Country",
       y = "Count") +
  theme_minimal()

Q3 Which categories were Japan, India, and China awarded in?

df_combined_nobel %>%
  filter(birth_country %in% c("Japan", "China", "India")) %>%
  count(birth_country, category) %>%
  ggplot(aes(x = birth_country, y = n, fill = category)) +
  geom_col(position = "dodge") +
  geom_text(aes(label = n),
            position = position_dodge(width = 0.9),
            vjust = -0.3) +
  labs(title = "Counts by Japan, India and China w Category",
       x = "Country",
       y = "Count") 

Q4: Comparing the distribution of Nobel Prizes across different continents.

df_combined_nobel %>%
  #filter(birth_country %in% c("Japan", "China", "India")) %>%
  count(birth_continent, category) %>%
  ggplot(aes(x = birth_continent, y = n, fill = category)) +
  geom_col(position = "dodge") +
  coord_flip()+
  geom_text(aes(label = n),
            position = position_dodge(width = 0.9),
            vjust = -0.3) +
  labs(title = "Counts by Continent w Category",
       x = "Continent",
       y = "Count") 

Conlusion

By using the Nobel Prize API, this project also provided hands-on experience in working with JSON data, including retrieving, parsing, and converting API responses into structured data frames in R.That explored Nobel Prize data using R to uncover patterns in awards across categories, countries, and time periods. Through data cleaning, transformation, and visualization, we identified clear trends in how Nobel Prizes are distributed globally.

LLMS used:

• OpenAI. (2025). ChatGPT (Version 5.2) [Large language model]. https://chat.openai.com. Accessed Apr 19, 2026.