Introduction.
For this assignment, we will be working with two JSON files
available through the API at nobelprize.org, ask 4 interesting questions
and answer using data analysis.
Load Packages Required for the Project
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(dplyr)
library(jsonlite)
##
## Attaching package: 'jsonlite'
##
## The following object is masked from 'package:purrr':
##
## flatten
library(httr2)
Load Json files into R
I will get the prize and the Prize and Laureate files.
nobel_prize <- "http://api.nobelprize.org/v1/prize.json"
nobel_laureate <- "http://api.nobelprize.org/v1/laureate.json"
# read data from json files
nobelPrize <- fromJSON(nobel_prize)
nobelLaureate <- fromJSON(nobel_laureate)
# display names of main keys in the JSON files
names(nobelPrize)
## [1] "prizes"
names(nobelLaureate)
## [1] "laureates"
# examine sub elements in files
names(nobelPrize$prizes)
## [1] "year" "category" "laureates"
## [4] "overallMotivation"
names(nobelLaureate$laureates)
## [1] "id" "firstname" "surname" "born"
## [5] "died" "bornCountry" "bornCountryCode" "bornCity"
## [9] "diedCountry" "diedCountryCode" "diedCity" "gender"
## [13] "prizes"
Start cleaning and tyding data, as well as to create a data
frame.
nobelPrize <- nobelPrize$prizes %>%
unnest_wider(laureates) %>%
unnest(id, firstname, surname, motivation, share)
## Warning: `unnest()` has a new interface. See `?unnest` for details.
## ℹ Try `df %>% unnest(c(id, firstname, surname, motivation, share))`, with
## `mutate()` if needed.
# Create Data Frames with JSON Data
df_nobelPrize <- bind_rows(nobelPrize)
df_nobelLaureate <- bind_rows(nobelLaureate)
# Combine the two data frames by id to start asking questions
join_df <- inner_join(df_nobelPrize, df_nobelLaureate, by = "id") %>%
select(-c("firstname.y", "surname.y", "prizes")) %>%
as.data.frame()
head(join_df)
## year category id firstname.x surname.x
## 1 2023 chemistry 1029 Moungi Bawendi
## 2 2023 chemistry 1030 Louis Brus
## 3 2023 chemistry 1031 Aleksey Yekimov
## 4 2023 economics 1034 Claudia Goldin
## 5 2023 literature 1032 Jon Fosse
## 6 2023 peace 1033 Narges Mohammadi
## motivation
## 1 "for the discovery and synthesis of quantum dots"
## 2 "for the discovery and synthesis of quantum dots"
## 3 "for the discovery and synthesis of quantum dots"
## 4 "for having advanced our understanding of women’s labour market outcomes"
## 5 "for his innovative plays and prose which give voice to the unsayable"
## 6 "for her fight against the oppression of women in Iran and her fight to promote human rights and freedom for all"
## share overallMotivation born died bornCountry
## 1 3 <NA> 1961-00-00 0000-00-00 France
## 2 3 <NA> 1943-00-00 0000-00-00 USA
## 3 3 <NA> 1945-00-00 0000-00-00 USSR (now Russia)
## 4 1 <NA> 1946-00-00 0000-00-00 USA
## 5 1 <NA> 1959-09-29 0000-00-00 Norway
## 6 1 <NA> 1972-04-21 0000-00-00 Iran
## bornCountryCode bornCity diedCountry diedCountryCode
## 1 FR Paris <NA> <NA>
## 2 US Cleveland, OH <NA> <NA>
## 3 RU Leningrad (now St. Petersburg) <NA> <NA>
## 4 US New York, NY <NA> <NA>
## 5 NO Haugesund <NA> <NA>
## 6 IR Zanjan <NA> <NA>
## diedCity gender
## 1 <NA> male
## 2 <NA> male
## 3 <NA> male
## 4 <NA> female
## 5 <NA> male
## 6 <NA> female
Question 1: what is the country with most novel prize winners?
join_df %>%
group_by(bornCountry) %>%
top_n(15) %>%
filter(n() > 10) %>%
filter(!is.na(bornCountry)) %>%
ggplot() +
geom_bar(aes(x = bornCountry, fill = bornCountry)) +
ggtitle("Countries with most Nobel Prize Winners") +
ylab("Numbers of Winners") +
xlab("Birth Country") +
theme(plot.title = element_text(hjust = 0.3),
plot.subtitle = element_text(hjust = 0.3),
legend.title = element_blank(),
legend.position = "none",
axis.text.x = element_text(angle = 85, hjust = 1))
## Selecting by gender
Based on the data analysis and Graph, the country with most awards
is USA with over two hundred awards.
Question 2: What is the count of awards between male and
female?
print(join_df$gender)
## [1] "male" "male" "male" "female" "male" "female" "male" "male"
## [9] "female" "female" "male" "female" "male" "male" "male" "male"
## [17] "male" "female" "male" "org" "org" "male" "male" "male"
## [25] "male" "male" "male" "male" "male" "male" "male" "female"
## [33] "male" "male" "male" "male" "male" "male" "female" "female"
## [41] "male" "male" "female" "org" "male" "male" "female" "male"
## [49] "male" "male" "male" "male" "male" "male" "female" "male"
## [57] "male" "male" "male" "male" "male" "male" "male" "male"
## [65] "female" "male" "male" "male" "male" "female" "male" "female"
## [73] "male" "male" "female" "male" "male" "male" "male" "male"
## [81] "male" "male" "org" "male" "male" "male" "male" "male"
## [89] "male" "male" "male" "male" "male" "male" "male" "male"
## [97] "male" "male" "male" "male" "male" "male" "male" "male"
## [105] "female" "org" "male" "male" "male" "male" "female" "male"
## [113] "male" "male" "male" "male" "male" "female" "male" "male"
## [121] "male" "male" "female" "male" "male" "male" "male" "male"
## [129] "male" "male" "female" "org" "male" "male" "male" "male"
## [137] "male" "male" "male" "male" "male" "male" "org" "male"
## [145] "male" "male" "male" "male" "male" "male" "male" "female"
## [153] "female" "female" "male" "male" "male" "male" "male" "male"
## [161] "male" "male" "male" "male" "male" "male" "male" "male"
## [169] "male" "male" "male" "male" "male" "female" "female" "male"
## [177] "female" "male" "male" "male" "male" "female" "female" "male"
## [185] "male" "male" "male" "male" "male" "male" "male" "male"
## [193] "male" "male" "female" "male" "male" "male" "male" "male"
## [201] "female" "org" "male" "male" "male" "male" "male" "male"
## [209] "male" "male" "male" "male" "org" "male" "male" "male"
## [217] "male" "male" "male" "male" "male" "male" "male" "org"
## [225] "male" "male" "male" "male" "male" "male" "male" "male"
## [233] "male" "male" "male" "female" "female" "male" "male" "male"
## [241] "male" "female" "male" "male" "male" "male" "male" "female"
## [249] "male" "male" "male" "male" "male" "male" "male" "male"
## [257] "male" "male" "male" "male" "male" "male" "male" "male"
## [265] "male" "male" "male" "male" "male" "male" "male" "male"
## [273] "male" "org" "male" "male" "male" "male" "male" "male"
## [281] "male" "male" "male" "male" "male" "male" "male" "male"
## [289] "male" "male" "male" "male" "male" "male" "male" "male"
## [297] "male" "org" "male" "male" "male" "male" "male" "male"
## [305] "male" "male" "male" "male" "male" "male" "male" "male"
## [313] "male" "male" "male" "male" "male" "male" "male" "org"
## [321] "female" "male" "male" "male" "male" "male" "male" "male"
## [329] "male" "male" "female" "male" "male" "male" "male" "male"
## [337] "male" "male" "male" "male" "male" "male" "male" "male"
## [345] "org" "male" "male" "male" "female" "male" "male" "male"
## [353] "male" "male" "male" "male" "male" "male" "male" "male"
## [361] "male" "male" "male" "male" "male" "male" "female" "male"
## [369] "male" "male" "male" "male" "male" "male" "male" "male"
## [377] "female" "male" "male" "male" "male" "male" "female" "female"
## [385] "male" "male" "male" "male" "male" "male" "male" "male"
## [393] "male" "male" "male" "male" "male" "male" "male" "male"
## [401] "male" "male" "male" "male" "male" "male" "male" "male"
## [409] "male" "male" "male" "male" "male" "org" "male" "male"
## [417] "male" "male" "female" "male" "male" "male" "male" "male"
## [425] "male" "male" "male" "male" "male" "male" "male" "male"
## [433] "male" "male" "male" "male" "male" "male" "male" "female"
## [441] "male" "male" "male" "male" "org" "male" "male" "male"
## [449] "male" "male" "male" "male" "male" "male" "male" "male"
## [457] "male" "male" "male" "male" "male" "male" "male" "female"
## [465] "male" "male" "male" "female" "male" "male" "male" "male"
## [473] "male" "male" "male" "male" "male" "org" "male" "male"
## [481] "male" "male" "male" "male" "male" "male" "male" "male"
## [489] "male" "male" "male" "male" "male" "male" "male" "male"
## [497] "male" "male" "male" "male" "female" "male" "male" "male"
## [505] "male" "male" "male" "male" "male" "male" "male" "male"
## [513] "male" "male" "male" "male" "male" "male" "male" "male"
## [521] "male" "org" "male" "male" "male" "male" "male" "female"
## [529] "male" "male" "male" "female" "female" "male" "male" "male"
## [537] "male" "male" "male" "male" "male" "male" "male" "male"
## [545] "male" "male" "male" "male" "male" "male" "male" "male"
## [553] "male" "male" "male" "male" "male" "male" "male" "male"
## [561] "male" "male" "male" "male" "male" "male" "male" "male"
## [569] "male" "male" "male" "male" "male" "male" "male" "male"
## [577] "male" "male" "male" "male" "male" "male" "male" "male"
## [585] "male" "male" "male" "male" "male" "male" "male" "male"
## [593] "male" "male" "male" "male" "male" "male" "male" "male"
## [601] "male" "male" "male" "male" "org" "male" "male" "male"
## [609] "male" "male" "male" "male" "male" "male" "male" "male"
## [617] "male" "male" "male" "male" "male" "male" "male" "male"
## [625] "male" "male" "female" "male" "male" "male" "male" "male"
## [633] "org" "male" "male" "male" "male" "male" "male" "female"
## [641] "male" "male" "male" "male" "male" "male" "male" "male"
## [649] "male" "male" "org" "org" "male" "female" "male" "male"
## [657] "male" "male" "male" "male" "male" "male" "male" "male"
## [665] "male" "male" "male" "male" "male" "male" "male" "male"
## [673] "male" "male" "male" "male" "male" "male" "male" "male"
## [681] "male" "male" "male" "male" "male" "male" "male" "male"
## [689] "male" "male" "male" "male" "male" "male" "male" "male"
## [697] "male" "male" "male" "male" "male" "male" "male" "male"
## [705] "male" "male" "male" "male" "male" "male" "male" "male"
## [713] "male" "male" "male" "male" "org" "male" "male" "male"
## [721] "male" "male" "male" "male" "male" "male" "male" "male"
## [729] "male" "male" "male" "male" "male" "male" "male" "male"
## [737] "male" "male" "male" "male" "male" "male" "male" "male"
## [745] "male" "male" "male" "male" "male" "male" "male" "male"
## [753] "male" "male" "male" "male" "male" "male" "male" "male"
## [761] "male" "male" "org" "org" "male" "male" "female" "male"
## [769] "male" "male" "male" "male" "female" "male" "male" "male"
## [777] "male" "female" "male" "male" "male" "male" "male" "male"
## [785] "male" "org" "male" "male" "male" "male" "male" "male"
## [793] "male" "male" "male" "male" "male" "male" "male" "female"
## [801] "org" "male" "male" "male" "male" "male" "male" "male"
## [809] "male" "male" "male" "male" "male" "male" "male" "male"
## [817] "male" "male" "female" "male" "male" "male" "male" "male"
## [825] "male" "male" "male" "male" "male" "male" "male" "male"
## [833] "male" "male" "male" "male" "male" "male" "male" "male"
## [841] "male" "female" "male" "male" "male" "male" "male" "male"
## [849] "male" "male" "male" "male" "male" "male" "male" "male"
## [857] "male" "female" "male" "male" "male" "male" "male" "male"
## [865] "male" "male" "male" "male" "female" "male" "male" "male"
## [873] "male" "male" "male" "male" "male" "male" "male" "male"
## [881] "male" "male" "male" "male" "male" "male" "male" "male"
## [889] "male" "male" "male" "male" "male" "male" "male" "male"
## [897] "male" "male" "male" "male" "male" "male" "male" "male"
## [905] "male" "male" "male" "male" "male" "male" "male" "org"
## [913] "male" "male" "male" "male" "male" "male" "male" "male"
## [921] "male" "male" "male" "male" "male" "male" "male" "male"
## [929] "male" "male" "male" "male" "female" "male" "male" "male"
## [937] "male" "male" "male" "male" "org" "male" "male" "male"
## [945] "female" "male" "male" "male" "male" "male" "male" "male"
## [953] "male" "male" "male" "male" "male" "male" "male" "male"
## [961] "male" "male" "male" "male" "male" "male" "male" "male"
## [969] "male" "male" "male" "female" "male" "male" "male" "male"
## [977] "male" "org" "male" "male" "male" "male" "male" "male"
## [985] "male" "female" "male" "male" "male" "male" "male" "male"
## [993] "male" "male" "male" "male" "male" "male" "male" "male"
join_df %>%
group_by(gender) %>%
ggplot() +
geom_bar(aes(x = gender, fill = gender), position = "dodge") +
ggtitle("Nobel Prizes by Gender") +
xlab("Awards by gender") +
ylab("Count") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(fill = "Gender")
Based on the graph above there is a huge difference of awards
between male and female with over 800 awards for males against less than
a 100 for females.
Question 3: What is the count of awards winned by USA per category
in the last century?
join_df %>%
group_by(bornCountry, category) %>%
filter(year %in% 2000:2023) %>%
filter(bornCountry %in% "USA") %>%
ggplot() +
geom_bar(aes(x = bornCountry, fill = category), position = "dodge") +
ggtitle("Nobel Prizes by Category") +
xlab("Nobel Prizes winned by the US in the last century") +
ylab("Count") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 85, hjust = 1)) +
labs(fill = "Category")
The most awards by category winned by the US is Economics with 35 in
the 21st century.
Question 3: number of females awarded per category in the last
century?
join_df %>%
group_by(gender, category) %>%
filter(bornCountry %in% "USA") %>%
filter(year %in% 2000:2023) %>%
filter(gender %in% "female")%>%
ggplot() +
geom_bar(mapping = aes(x = gender, fill = category),position = "dodge")+
ggtitle("females awarded in last century") +
xlab("Females Awarded") +
ylab("category") +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(fill = "Category")
There is a total of 9 US females awarded with the nobel prize in the
21st century.
Conclussion:
I was able to succesfully upload the JSON files, clean and organize
the data and analize it, in order to answer the questions with data
analysis.