library(tidyverse)
library(httr)
library(jsonlite)
library(gt)607 Assignment 10B Dylan Gold
Approach
In this assignment we have to use the Noble Prize Developer Zone to collect data and answer some data driven questions. First I will test out the API call. It looks like we do not need an account for this call. Once I experiment with some calls I can see how it works and create some questions.
I will look at laureates api, there is also one
url <- "https://api.nobelprize.org/2.1/laureates" # Testing simple api call on the laureates endpoint
response <- GET(url)
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
status_code(response)[1] 200Display data, use unnest to show more than the lists/dataframes nested inside.
laureates_df <- unnest(data$laureates)Warning: `cols` is now required when using `unnest()`.
ℹ Please use `cols = c(knownName, givenName, familyName, fullName, birth,
wikipedia, wikidata, sameAs, links, nobelPrizes, death)`.head(laureates_df)# A tibble: 6 × 40
id en se en1 se1 en2 se2 en3 se3 fileName gender date
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 745 A. Mich… A. M… A. M… A. M… Spen… Spen… A. M… A. M… spence male 1943…
2 745 A. Mich… A. M… A. M… A. M… Spen… Spen… A. M… A. M… spence male 1943…
3 102 Aage N.… Aage… Aage… Aage… Bohr Bohr Aage… Aage… bohr male 1922…
4 102 Aage N.… Aage… Aage… Aage… Bohr Bohr Aage… Aage… bohr male 1922…
5 779 Aaron C… Aaro… Aaron Aaron Ciec… Ciec… Aaro… Aaro… ciechan… male 1947…
6 779 Aaron C… Aaro… Aaron Aaron Ciec… Ciec… Aaro… Aaro… ciechan… male 1947…
# ℹ 28 more variables: year <chr>, place <df[,6]>, slug <chr>, english <chr>,
# id1 <chr>, url <chr>, sameAs <chr>, rel <chr>, href <chr>, action <chr>,
# types <chr>, title <chr>, class <list>, awardYear <chr>, category <df[,3]>,
# categoryFullName <df[,3]>, sortOrder <chr>, portion <chr>,
# dateAwarded <chr>, prizeStatus <chr>, motivation <df[,3]>,
# prizeAmount <int>, prizeAmountAdjusted <int>, affiliations <list>,
# links <list>, residences <list>, date1 <chr>, place1 <df[,6]>We can see Names and prize money here, we can see that unnesting it duplicated rows as they have the same ID. There are also dataframes still shown inside probably nested even deeper. I will have to fix this in the codebase but some inistial questions I can ask are could be based on money, something as simple as who made the most money? I could also ask something more complicated related to money like what was the average earning each year shown in a timeplot. Once I see more of the columns that are nested deeper like the place or category I could ask questions based on that as well.
Codebase
I will start off by loading in the data again.
library(tidyverse)
library(httr)
library(jsonlite)This time I will look at the other endpoint,
url <- "https://api.nobelprize.org/2.1/nobelPrizes" # Testing simple api call on the laureates endpoint
response <- GET(url)
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
status_code(response)[1] 200After looking at the data, I believe the nobelPrizes endpoint would be easier to work with and probably easier to formulate questions on.
I also noticed we were being cut off and only receiving 25 responses. Initially I was considering running loops for each of the categories and each of the years because in the documentation those are the ways you can modify the request but I decided to ask Chatgpt as well. I was surprised to find that you could actually just modify the request to give you more responses. Chatgpt recommended the following line of code
https://api.nobelprize.org/2.1/nobelPrizes?limit=25&offset=0 I modified this line to give me all the responses available.
url <- "https://api.nobelprize.org/2.1/nobelPrizes?limit=1000" # set the limit to 1000 to get all (682) rows
response <- GET(url)
data <- fromJSON(content(response, "text", encoding = "UTF-8"))
status_code(response)[1] 200df <- data$nobelPrizes
glimpse(df)Rows: 682
Columns: 9
$ awardYear <chr> "1901", "1901", "1901", "1901", "1901", "1902", "1…
$ category <df[,3]> <data.frame[26 x 3]>
$ categoryFullName <df[,3]> <data.frame[26 x 3]>
$ dateAwarded <chr> "1901-11-12", "1901-11-14", "1901-12-10", "1901…
$ prizeAmount <int> 150782, 150782, 150782, 150782, 150782, 141847,…
$ prizeAmountAdjusted <int> 10833458, 10833458, 10833458, 10833458, 10833458, …
$ links <list> [<data.frame[1 x 4]>], [<data.frame[1 x 4]>], [<da…
$ laureates <list> [<data.frame[1 x 7]>], [<data.frame[1 x 7]>], [<da…
$ topMotivation <df[,2]> <data.frame[26 x 2]>We now see that we have the data, all 682 rows. we can unnest the data and ask questions. I will follow through on my approach of asking questions relating to the prize amount and such.
I will use unnest_wider as we likely want to create more columns from the values as opposed to longer which would create more rows.
I will chain several unnest_wider calls on columns that are nested.
Because some have overlaping column names, we can use names_sep to attach the new column name to the original.
np_df <- df %>%
unnest_wider(category, names_sep = "_") %>% # both categorys have overlap. names_sep needed
unnest_wider(categoryFullName, names_sep = "_") %>%
unnest_wider(links) %>%
unnest_wider(laureates) %>%
unnest_wider(topMotivation)
head(np_df)# A tibble: 6 × 26
awardYear category_en category_no category_se categoryFullName_en
<chr> <chr> <chr> <chr> <chr>
1 1901 Chemistry Kjemi Kemi The Nobel Prize in…
2 1901 Literature Litteratur Litteratur The Nobel Prize in…
3 1901 Peace Fred Fred The Nobel Peace Pr…
4 1901 Physics Fysikk Fysik The Nobel Prize in…
5 1901 Physiology or Medicine Fysiologi el… Fysiologi … The Nobel Prize in…
6 1902 Chemistry Kjemi Kemi The Nobel Prize in…
# ℹ 21 more variables: categoryFullName_no <chr>, categoryFullName_se <chr>,
# dateAwarded <chr>, prizeAmount <int>, prizeAmountAdjusted <int>, rel <chr>,
# href <chr>, action <chr>, types <chr>, id <list<chr>>,
# knownName <list<df[,2]>>, fullName <list<df[,1]>>, portion <list<chr>>,
# sortOrder <list<chr>>, motivation <list<df[,3]>>, links <list<list>>,
# orgName <list<df[,2]>>, nativeName <list<chr>>, acronym <list<chr>>,
# en <chr>, se <chr>We now have all our columns unnested. I will select columns that I think are useful because there are many columns that are not.
Many columns are redundant, like date awarded and year, category and categoryFullname.
columns <- c("awardYear", "category_en", "prizeAmount", "prizeAmountAdjusted", "fullName", "portion")
np_data <- np_df %>%
select(all_of(columns))
head(np_data)# A tibble: 6 × 6
awardYear category_en prizeAmount prizeAmountAdjusted fullName portion
<chr> <chr> <int> <int> <list<d> <list<>
1 1901 Chemistry 150782 10833458 [1 × 1] [1]
2 1901 Literature 150782 10833458 [1 × 1] [1]
3 1901 Peace 150782 10833458 [2 × 1] [2]
4 1901 Physics 150782 10833458 [1 × 1] [1]
5 1901 Physiology or Medi… 150782 10833458 [1 × 1] [1]
6 1902 Chemistry 141847 10191492 [1 × 1] [1]We also have cases where there are multiple laureates for a single award. I want to see if there are cases where each person is not awarded the some portion. I will filter on this column with unique for this. This will also be my first question that I try to answer, How many times was the prize money split unequally?
We get 39 noble prizes were split unequally.
# This function checks the unique amount, if its greater than 1, meaning we have not all equal proportions return True, else false
check_unique <- function(x) {
return(length(unique(x)) > 1)
}
filtered_unequal <- np_data %>% filter(sapply(np_data$portion, check_unique))
head(filtered_unequal)# A tibble: 6 × 6
awardYear category_en prizeAmount prizeAmountAdjusted fullName portion
<chr> <chr> <int> <int> <list<d> <list<>
1 1903 Physics 141358 9857642 [3 × 1] [3]
2 1946 Chemistry 121524 3166301 [3 × 1] [3]
3 1947 Physiology or Medi… 146115 3685518 [3 × 1] [3]
4 1958 Physiology or Medi… 214559 3346838 [3 × 1] [3]
5 1963 Physics 265000 3590371 [3 × 1] [3]
6 1964 Physics 273000 3576149 [3 × 1] [3]nrow(filtered_unequal)[1] 39This makes making the data frame tidy a bit more difficult. We have to match each person to their proportion of the prize money rather than just divide by the number of people. We can still do this. We have 2 columns, one with names and another with their corresponding portions.
The rows need to be make into multiple rows so we use unnest longer.
I was not sure if I could unnest_longer while keeping corresponding rows together so I decided to use the LLM again, chatgpt.
I learn I can just give unnest_longer a list of columns and it will do exactly that.
I combine this with mutate to create new prizeAmounts and prizeAmountAdjusted.
The converting from fraction to numeric I also asked chatgpt to help.
tidy_np <- np_data %>%
unnest_longer(c(fullName, portion), keep_empty = TRUE) %>% # Sometimes the prize has no laureates.
unnest_wider(fullName) %>% # Got put into a vector again, unnest
mutate(
portion = map_dbl(portion, function(x) { # Generated Code modified slightly, converts fraction to numeric
if (is.na(x)) return(1) # Returns 1 if na
if (!grepl("/", x)) return(as.numeric(x)) # if whole number returns the number, ex 1 -> 1
s <- strsplit(x, "/")[[1]] # Splits by /
as.numeric(s[1]) / as.numeric(s[2]) # Divides numerator and denominator
}),
prizeAmount = prizeAmount * portion, # Multiply by the portion we updated from fraction char to numeric.
prizeAmountAdjusted = prizeAmountAdjusted * portion
)
head(tidy_np)# A tibble: 6 × 6
awardYear category_en prizeAmount prizeAmountAdjusted en portion
<chr> <chr> <dbl> <dbl> <chr> <dbl>
1 1901 Chemistry 150782 10833458 Jaco… 1
2 1901 Literature 150782 10833458 Sull… 1
3 1901 Peace 75391 5416729 Jean… 0.5
4 1901 Peace 75391 5416729 Fréd… 0.5
5 1901 Physics 150782 10833458 Wilh… 1
6 1901 Physiology or Medicine 150782 10833458 Emil… 1 We now have a tidy version of the data that we can ask more questions about. We already answered how many awards had multiple laureates.
My other questions will be what category has made the most adjusted prize money?
Who has made the most adjusted prize money?
And how has the total reward money changed each year for each category?
For the category that has made the most adjusted prize money we can see Chemisty, Peace, Physics, Physiology or Medicine, all made the same amount while literature is shortly after, and economic sciences being close to half the other cateogories. We will look at a graph in the last question that may answer this.
money_by_category <- tidy_np %>%
group_by(category_en) %>%
summarize(Total_Prize_Adjusted = sum(prizeAmountAdjusted)) %>%
arrange(desc(Total_Prize_Adjusted)) %>%
ungroup()
money_by_category# A tibble: 6 × 2
category_en Total_Prize_Adjusted
<chr> <dbl>
1 Chemistry 901155136
2 Peace 901155136
3 Physics 901155136
4 Physiology or Medicine 901155136
5 Literature 900952440
6 Economic Sciences 537198604For the who has made the most money. We can see that much of the money did not have someone to claim it. Turns out also that not many people have won more than 1 nobel prize. I checked some people who have like Marie Curie and their adjusted total was summed up properly.
money_by_person <- tidy_np %>%
group_by(en) %>%
summarize(Total_Prize_Adjusted = sum(prizeAmountAdjusted)) %>%
arrange(desc(Total_Prize_Adjusted)) %>%
ungroup()
money_by_person %>%
gt() %>%
cols_label(
'en' = "Laureates",
'Total_Prize_Adjusted' = "Total Prize Money Adjusted"
) %>%
tab_header(title = md("Individual Noble Prize Money Total")) %>%
tab_options(container.height = 500, container.overflow.y = TRUE) # Set container height and container overflow to add scrolling for large tables.| Individual Noble Prize Money Total | |
| Laureates | Total Prize Money Adjusted |
|---|---|
| NA | 429362061.3 |
| Sir Vidiadhar Surajprasad Naipaul | 15547541.0 |
| Imre Kertész | 15218228.0 |
| James Earl Carter | 15218228.0 |
| John M. Coetzee | 14930730.0 |
| Shirin Ebadi | 14930730.0 |
| Elfriede Jelinek | 14874529.0 |
| Wangari Muta Maathai | 14874529.0 |
| Harold Pinter | 14809494.0 |
| Edmund S. Phelps | 14608749.0 |
| Orhan Pamuk | 14608749.0 |
| Roger D. Kornberg | 14608749.0 |
| Gao Xingjian | 14331095.0 |
| Kim Dae-jung | 14331095.0 |
| Doris Lessing | 14291742.0 |
| Gerhard Ertl | 14291742.0 |
| Barack Hussein Obama | 13857393.0 |
| Herta Müller | 13857393.0 |
| Jean-Marie Gustave Le Clézio | 13817016.0 |
| Martti Ahtisaari | 13817016.0 |
| Paul Krugman | 13817016.0 |
| Liu Xiaobo | 13681477.0 |
| Mario Vargas Llosa | 13681477.0 |
| Robert G. Edwards | 13681477.0 |
| Dan Shechtman | 13335208.0 |
| Tomas Tranströmer | 13335208.0 |
| Ahmed H. Zewail | 12707531.0 |
| Günter Blobel | 12707531.0 |
| Günter Grass | 12707531.0 |
| Robert A. Mundell | 12707531.0 |
| Louise Glück | 12361835.0 |
| Amartya Sen | 12283299.0 |
| José Saramago | 12283299.0 |
| Dario Fo | 12105174.0 |
| Stanley B. Prusiner | 12105174.0 |
| Abdulrazak Gurnah | 12096939.0 |
| Wisława Szymborska | 12000958.0 |
| Robert E. Lucas Jr. | 11732784.0 |
| Seamus Heaney | 11732784.0 |
| Marie Curie, née Skłodowska | 11730739.5 |
| George A. Olah | 11696265.0 |
| Kenzaburo Oe | 11696265.0 |
| Derek Walcott | 11613791.0 |
| Gary S. Becker | 11613791.0 |
| Georges Charpak | 11613791.0 |
| Rigoberta Menchú Tum | 11613791.0 |
| Rudolph A. Marcus | 11613791.0 |
| Kazuo Ishiguro | 11603589.0 |
| Richard H. Thaler | 11603589.0 |
| K. Barry Sharpless | 11494737.2 |
| Toni Morrison | 11436789.0 |
| Claudia Goldin | 11314967.0 |
| Jon Fosse | 11314967.0 |
| Narges Mohammadi | 11314967.0 |
| Abiy Ahmed Ali | 11178104.0 |
| Olga Tokarczuk | 11178104.0 |
| Peter Handke | 11178104.0 |
| Annie Ernaux | 11162900.0 |
| Svante Pääbo | 11162900.0 |
| Han Kang | 11000000.0 |
| László Krasznahorkai | 11000000.0 |
| Maria Corina Machado | 11000000.0 |
| Aung San Suu Kyi | 10968389.0 |
| Nadine Gordimer | 10968389.0 |
| Pierre-Gilles de Gennes | 10968389.0 |
| Richard R. Ernst | 10968389.0 |
| Ronald H. Coase | 10968389.0 |
| Emil Adolf von Behring | 10833458.0 |
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| Sully Prudhomme | 10833458.0 |
| Wilhelm Conrad Röntgen | 10833458.0 |
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| Christian Matthias Theodor Mommsen | 10191492.0 |
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| Bjørnstjerne Martinus Bjørnson | 9857642.0 |
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| Svante August Arrhenius | 9857642.0 |
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| Ivan Petrovich Pavlov | 9822844.0 |
| Lord Rayleigh (John William Strutt) | 9822844.0 |
| Sir William Ramsay | 9822844.0 |
| Baroness Bertha Sophie Felicita von Suttner, née Countess Kinsky von Chinic und Tettau | 9629677.0 |
| Henryk Sienkiewicz | 9629677.0 |
| Johann Friedrich Wilhelm Adolf von Baeyer | 9629677.0 |
| Philipp Eduard Anton von Lenard | 9629677.0 |
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| Giosuè Carducci | 9384824.0 |
| Henri Moissan | 9384824.0 |
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| Theodore Roosevelt Jr. | 9384824.0 |
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| Count Maurice (Mooris) Polidore Marie Bernhard Maeterlinck | 9266329.0 |
| Wilhelm Wien | 9266329.0 |
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| Selma Ottilia Lovisa Lagerlöf | 8958535.0 |
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| Kofi Atta Annan | 7773770.5 |
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| Edward C. Prescott | 7437264.5 |
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| Barry J. Marshall | 7404747.0 |
| J. Robin Warren | 7404747.0 |
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| Roy J. Glauber | 7404747.0 |
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| Craig C. Mello | 7304374.5 |
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| John C. Mather | 7304374.5 |
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| Daniel L. McFadden | 7165547.5 |
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| Albert Arnold (Al) Gore Jr. | 7145871.0 |
| Albert Fert | 7145871.0 |
| Peter Grünberg | 7145871.0 |
| Charles Kuen Kao | 6928696.5 |
| Elinor Ostrom | 6928696.5 |
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| Linus Carl Pauling | 6926106.0 |
| Harald zur Hausen | 6908508.0 |
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| Sir Norman Angell (Ralph Lane) | 6845037.0 |
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| Andre Geim | 6840738.5 |
| Konstantin Novoselov | 6840738.5 |
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| Werner Karl Heisenberg | 6675842.0 |
| Christopher A. Sims | 6667604.0 |
| Ralph M. Steinman | 6667604.0 |
| Saul Perlmutter | 6667604.0 |
| Thomas J. Sargent | 6667604.0 |
| Camilo José Cela | 6629077.0 |
| The 14th Dalai Lama (Tenzin Gyatso) | 6629077.0 |
| Trygve Haavelmo | 6629077.0 |
| Erik Axel Karlfeldt | 6623733.0 |
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| Arthur Henderson | 6534637.0 |
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| Hans Fischer | 6407146.0 |
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| Lars Olof Jonathan (Nathan) Söderblom | 6407146.0 |
| Sinclair Lewis | 6407146.0 |
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| Gerardus 't Hooft | 6353765.5 |
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| Carl von Ossietzky | 6319387.0 |
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| Emmanuelle Charpentier | 6180917.5 |
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| David Trimble | 6141649.5 |
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| Ardem Patapoutian | 6048469.5 |
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| David W.C. MacMillan | 6048469.5 |
| Dmitry Andreyevich Muratov | 6048469.5 |
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| Maria Ressa | 6048469.5 |
| Carlos Filipe Ximenes Belo | 6000479.0 |
| James A. Mirrlees | 6000479.0 |
| José Ramos-Horta | 6000479.0 |
| Peter C. Doherty | 6000479.0 |
| Rolf M. Zinkernagel | 6000479.0 |
| William Vickrey | 6000479.0 |
| Albert von Szent-Györgyi Nagyrápolt | 5963742.0 |
| Edgar Algernon Robert Gascoyne-Cecil, 1st Viscount Cecil of Chelwood | 5963742.0 |
| Roger Martin du Gard | 5963742.0 |
| Maurice Allais | 5874628.0 |
| Naguib Mahfouz | 5874628.0 |
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| Alfred G. Gilman | 5848132.5 |
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| Clifford G. Shull | 5848132.5 |
| Martin Rodbell | 5848132.5 |
| Edmond H. Fischer | 5806895.5 |
| Edwin G. Krebs | 5806895.5 |
| Rainer Weiss | 5801794.5 |
| Corneille Jean François Heymans | 5745118.0 |
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| Douglass C. North | 5718394.5 |
| Frederik Willem de Klerk | 5718394.5 |
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| Kary B. Mullis | 5718394.5 |
| Michael Smith | 5718394.5 |
| Nelson Rolihlahla Mandela | 5718394.5 |
| Phillip A. Sharp | 5718394.5 |
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| Robert W. Fogel | 5718394.5 |
| Russell A. Hulse | 5718394.5 |
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| Tasuku Honjo | 5690400.0 |
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| Drew Weissman | 5657483.5 |
| Katalin Karikó | 5657483.5 |
| Adolf Otto Reinhold Windaus | 5637915.0 |
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| James Peebles | 5589052.0 |
| David Baker | 5500000.0 |
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| Geoffrey Hinton | 5500000.0 |
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| Leland H. Hartwell | 5182513.7 |
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| Val Logsdon Fitch | 1827040.5 |
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| Edward Lawrie Tatum | 836709.5 |
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| John Howard Northrop | 791575.2 |
| Wendell Meredith Stanley | 791575.2 |
Finally I want to see the change in adjusted prize money each year for each category. We can see that all the values are actually ontop of eachother so the prize money is actually kept the same for most awards, we can see in a particular year the prize money of the literature reward was a little lower. This would also explain how our first question led many categories having the same amount, Economic Sciences also started later on as a category.
yearly_money <- tidy_np %>%
group_by(awardYear, category_en) %>%
summarize(Total_Prize_Adjusted = sum(prizeAmountAdjusted)) %>%
ungroup() %>%
mutate(awardYear = as.Date(paste0(awardYear, "-01-01")))
yearly_money %>%
ggplot(aes(x = awardYear, y = Total_Prize_Adjusted, group = category_en, color = category_en)) +
geom_point(alpha = .3) + # Points overlap, make transparaent
theme_minimal() +
labs(
title = "Nobel Prize Money Over Time by Category",
x = "Year",
y = "Adjusted Prize Money",
color = "Category"
)
Conclusion
In conclusion we were able to use the noble prize api to create and answer questions about our topic. We asked some questions that required filtering/grouping by. We also took advantage of tidyverses’ unnest_wider to unwrap nested layers in the json that was returned to us in an api response. By combining different functions like unnest_wider and unnest_longer I was able to create a tidy version of the api response. Through this dataframe I answered questions like how many prizes awarded had muliple people with unequal portions of the prize money, or how has the adjusted prize money changed from year to year. Some ways I could expand on this project are to look at the other api endpoint for more detailed data on the laureates or ask more questions about the api endpoint I used already.