Assignment 10b

Author

Anastasiia Gmyrina

Approach

For this assignment, I will use the Nobel Prize API to explore patterns among Nobel Prize winners. I will retrieve the data in JSON format and use R to clean, transform, and analyze it.

I will focus on four questions:

Is there an ideal age to win a Nobel Prize? I will compare the age distribution of laureates across different prize categories.
Do certain Nobel categories have more exceptionally young winners? I will calculate the percentage of laureates who received their awards before age 40.
Has women’s representation among Nobel laureates increased over time? I will examine how the percentage of female winners has changed across decades.
Which countries produce Nobel laureates, and how has that changed over time? I will compare birth countries across decades to identify geographic trends.

I will use httr2 to retrieve the JSON data and tidyverse to organize it into separate laureate and prize data frames. I will then join these datasets using laureate IDs and calculate additional variables such as age at the time of the award and award decade.

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title: "Assignment 10b"
author: "Anastasiia Gmyrina"
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## Approach

For this assignment, I will use the Nobel Prize API to explore patterns among Nobel Prize winners. I will retrieve the data in JSON format and use R to clean, transform, and analyze it.

I will focus on four questions:

1.  **Is there an ideal age to win a Nobel Prize?** I will compare the age distribution of laureates across different prize categories.

2.  **Do certain Nobel categories have more exceptionally young winners?** I will calculate the percentage of laureates who received their awards before age 40.

3.  **Has women's representation among Nobel laureates increased over time?** I will examine how the percentage of female winners has changed across decades.

4.  **Which countries produce Nobel laureates, and how has that changed over time?** I will compare birth countries across decades to identify geographic trends.

I will use `httr2` to retrieve the JSON data and `tidyverse` to organize it into separate laureate and prize data frames. I will then join these datasets using laureate IDs and calculate additional variables such as age at the time of the award and award decade.