data-raw/make_nobel.R
In PPBDS/primer.data: Data for Preceptor's Primer for Bayesian Data Science
library(tidyverse)
library(magrittr)
library(janitor)
library(jsonlite)
# Accessing data through Nobel Prize API. Last time run on 29.07.2021.
url <- "http://api.nobelprize.org/v1/laureate.json"
x <- fromJSON(url)
# This data comes as a list, so we need to convert it to a usable structure
# first.
x <- as_tibble(data.frame(x$laureates)) %>%
clean_names() %>%
select(-c(id, born_country_code, died_country_code)) %>%
rename(first_name = firstname,
last_name = surname)
# Extract variables from "prizes" list. I first created some new variables with
# empty values which will then be overwritten by the values in the list using
# the loop below.
new_cols <- tibble("year" = rep(NA_character_, 955),
"field" = rep(NA_character_, 955),
"share" = rep(NA_character_, 955),
"motivation" = rep(NA_character_, 955),
"aff_inst" = rep(NA_character_, 955),
"aff_city" = rep(NA_character_, 955),
"aff_country" = rep(NA_character_, 955))
x <- cbind(x, new_cols)
y <- tibble("first_name" = rep(NA_character_, 300),
"last_name" = rep(NA_character_, 300),
"born" = rep(NA_character_, 300),
"died" = rep(NA_character_, 300),
"born_country" = rep(NA_character_, 300),
"born_city" = rep(NA_character_, 300),
"died_country" = rep(NA_character_, 300),
"died_city" = rep(NA_character_, 300),
"gender" = rep(NA_character_, 300),
"prizes" = rep(NA_character_, 300),
"year" = rep(NA_character_, 300),
"field" = rep(NA_character_, 300),
"share" = rep(NA_character_, 300),
"motivation" = rep(NA_character_, 300),
"aff_inst" = rep(NA_character_, 300),
"aff_city" = rep(NA_character_, 300),
"aff_country" = rep(NA_character_, 300))
x <- rbind(x,y)
# City was missing for University of Delaware, which messes up the loop.
x[[10]][[826]][[5]][[1]][["city"]] <- "Newark, DE"
v <- 0
u <- -1
for (i in 1:955) {
if (nrow(as_tibble(x[[10]][[i]])) == 1) {
x$year[i] <- pull(x[[10]][[i]]["year"])
x$field[i] <- pull(x[[10]][[i]]["category"])
x$share[i] <- pull(x[[10]][[i]]["share"])
x$motivation[i] <- pull(x[[10]][[i]]["motivation"])
if (length(x[[10]][[i]][["affiliations"]][[1]][[1]]) == 0) {
x$aff_inst[i] <- "None"
x$aff_city[i] <- "None"
x$aff_country[i] <- "None"
} else {
if (nrow(as_tibble(x[[10]][[i]][["affiliations"]][[1]])) == 1) {
x$aff_inst[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["name"])
x$aff_city[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["city"])
x$aff_country[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["country"])
} else {
x$aff_inst[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "name"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "name"], sep = ", ")
x$aff_city[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "city"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "city"], sep = ", ")
x$aff_country[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "country"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "country"], sep = ", ")
}
}
} else {
for (v in 1:nrow(as_tibble(x[[10]][[i]]))) {
u <- u + 1
x$year[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "year"])
x$field[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "category"])
x$share[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "share"])
x$motivation[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "motivation"])
if (length(x[[10]][[i]][["affiliations"]][[v]][[1]]) == 0) {
x$aff_inst[955 + u] <- "None"
x$aff_city[955 + u] <- "None"
x$aff_country[955 + u] <- "None"
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
} else {
if (nrow(as_tibble(x[[10]][[i]][["affiliations"]][[v]])) == 1) {
x$aff_inst[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["name"])
x$aff_city[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["city"])
x$aff_country[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["country"])
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
} else {
x$aff_inst[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "name"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "name"], sep = ", ")
x$aff_city[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "city"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "city"], sep = ", ")
x$aff_country[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "country"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "country"], sep = ", ")
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
}
}
}
}
}
# Convert to tibble and remove leftover NA-rows.
x %<>%
tibble() %>%
filter(is.na(field) == FALSE)
# Changing variable types and cleaning up.
x %<>%
mutate(gender = str_to_title(gender),
field = str_to_title(field),
gender = as.factor(gender),
field = as.factor(field),
born = as.Date(born),
died = as.Date(died),
year = as.integer(year),
share = as.integer(share),
motivation = substr(motivation, 2, nchar(motivation)-1),
born_country = str_replace(born_country, "\\(", ""),
born_country = str_replace(born_country, "\\)", ""),
born_country = sub(".*now ", "", born_country),
born_country = str_replace(born_country, "Faroe Islands Denmark", "Denmark"),
born_country = str_replace(born_country, "the Netherlands", "Netherlands"),
died_country = str_replace(died_country, "\\(", ""),
died_country = str_replace(died_country, "\\)", ""),
died_country = sub(".*now ", "", died_country),
died_country = str_replace(died_country, "Faroe Islands Denmark", "Denmark"),
died_country = str_replace(died_country, "the Netherlands", "Netherlands"),
born_city = str_replace(born_city, "\\(", ""),
born_city = str_replace(born_city, "\\)", ""),
died_city = sub(".*now ", "", died_city),
died_city = str_replace(died_city, "\\(", ""),
died_city = str_replace(died_city, "\\)", ""),
died_city = sub(".*now ", "", died_city))
# Arrange.
x %<>%
arrange(field, year)
# Sort variables and remove useless stuff.
x %<>%
select(-prizes, -aff_country) %>%
select(1:2, year, field, share, gender, everything())
# Save.
nobel <- x
usethis::use_data(nobel, overwrite = T)
PPBDS/primer.data documentation built on April 9, 2024, 5:36 p.m.
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library(tidyverse)
library(magrittr)
library(janitor)
library(jsonlite)
# Accessing data through Nobel Prize API. Last time run on 29.07.2021.
url <- "http://api.nobelprize.org/v1/laureate.json"
x <- fromJSON(url)
# This data comes as a list, so we need to convert it to a usable structure
# first.
x <- as_tibble(data.frame(x$laureates)) %>%
clean_names() %>%
select(-c(id, born_country_code, died_country_code)) %>%
rename(first_name = firstname,
last_name = surname)
# Extract variables from "prizes" list. I first created some new variables with
# empty values which will then be overwritten by the values in the list using
# the loop below.
new_cols <- tibble("year" = rep(NA_character_, 955),
"field" = rep(NA_character_, 955),
"share" = rep(NA_character_, 955),
"motivation" = rep(NA_character_, 955),
"aff_inst" = rep(NA_character_, 955),
"aff_city" = rep(NA_character_, 955),
"aff_country" = rep(NA_character_, 955))
x <- cbind(x, new_cols)
y <- tibble("first_name" = rep(NA_character_, 300),
"last_name" = rep(NA_character_, 300),
"born" = rep(NA_character_, 300),
"died" = rep(NA_character_, 300),
"born_country" = rep(NA_character_, 300),
"born_city" = rep(NA_character_, 300),
"died_country" = rep(NA_character_, 300),
"died_city" = rep(NA_character_, 300),
"gender" = rep(NA_character_, 300),
"prizes" = rep(NA_character_, 300),
"year" = rep(NA_character_, 300),
"field" = rep(NA_character_, 300),
"share" = rep(NA_character_, 300),
"motivation" = rep(NA_character_, 300),
"aff_inst" = rep(NA_character_, 300),
"aff_city" = rep(NA_character_, 300),
"aff_country" = rep(NA_character_, 300))
x <- rbind(x,y)
# City was missing for University of Delaware, which messes up the loop.
x[[10]][[826]][[5]][[1]][["city"]] <- "Newark, DE"
v <- 0
u <- -1
for (i in 1:955) {
if (nrow(as_tibble(x[[10]][[i]])) == 1) {
x$year[i] <- pull(x[[10]][[i]]["year"])
x$field[i] <- pull(x[[10]][[i]]["category"])
x$share[i] <- pull(x[[10]][[i]]["share"])
x$motivation[i] <- pull(x[[10]][[i]]["motivation"])
if (length(x[[10]][[i]][["affiliations"]][[1]][[1]]) == 0) {
x$aff_inst[i] <- "None"
x$aff_city[i] <- "None"
x$aff_country[i] <- "None"
} else {
if (nrow(as_tibble(x[[10]][[i]][["affiliations"]][[1]])) == 1) {
x$aff_inst[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["name"])
x$aff_city[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["city"])
x$aff_country[i] <- pull(x[[10]][[i]][["affiliations"]][[1]]["country"])
} else {
x$aff_inst[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "name"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "name"], sep = ", ")
x$aff_city[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "city"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "city"], sep = ", ")
x$aff_country[i] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[1]])[1, "country"], as_tibble(x[[10]][[i]][["affiliations"]][[1]])[2, "country"], sep = ", ")
}
}
} else {
for (v in 1:nrow(as_tibble(x[[10]][[i]]))) {
u <- u + 1
x$year[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "year"])
x$field[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "category"])
x$share[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "share"])
x$motivation[955 + u] <- pull(as_tibble(x[[10]][[i]])[v, "motivation"])
if (length(x[[10]][[i]][["affiliations"]][[v]][[1]]) == 0) {
x$aff_inst[955 + u] <- "None"
x$aff_city[955 + u] <- "None"
x$aff_country[955 + u] <- "None"
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
} else {
if (nrow(as_tibble(x[[10]][[i]][["affiliations"]][[v]])) == 1) {
x$aff_inst[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["name"])
x$aff_city[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["city"])
x$aff_country[955 + u] <- pull(x[[10]][[i]][["affiliations"]][[v]]["country"])
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
} else {
x$aff_inst[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "name"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "name"], sep = ", ")
x$aff_city[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "city"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "city"], sep = ", ")
x$aff_country[955 + u] <- paste(as_tibble(x[[10]][[i]][["affiliations"]][[v]])[1, "country"], as_tibble(x[[10]][[i]][["affiliations"]][[v]])[2, "country"], sep = ", ")
x$first_name[955 + u] <- x$first_name[i]
x$last_name[955 + u] <- x$last_name[i]
x$born[955 + u] <- x$born[i]
x$died[955 + u] <- x$died[i]
x$born_country[955 + u] <- x$born_country[i]
x$born_city[955 + u] <- x$born_city[i]
x$died_country[955 + u] <- x$died_country[i]
x$died_city[955 + u] <- x$died_city[i]
x$gender[955 + u] <- x$gender[i]
}
}
}
}
}
# Convert to tibble and remove leftover NA-rows.
x %<>%
tibble() %>%
filter(is.na(field) == FALSE)
# Changing variable types and cleaning up.
x %<>%
mutate(gender = str_to_title(gender),
field = str_to_title(field),
gender = as.factor(gender),
field = as.factor(field),
born = as.Date(born),
died = as.Date(died),
year = as.integer(year),
share = as.integer(share),
motivation = substr(motivation, 2, nchar(motivation)-1),
born_country = str_replace(born_country, "\\(", ""),
born_country = str_replace(born_country, "\\)", ""),
born_country = sub(".*now ", "", born_country),
born_country = str_replace(born_country, "Faroe Islands Denmark", "Denmark"),
born_country = str_replace(born_country, "the Netherlands", "Netherlands"),
died_country = str_replace(died_country, "\\(", ""),
died_country = str_replace(died_country, "\\)", ""),
died_country = sub(".*now ", "", died_country),
died_country = str_replace(died_country, "Faroe Islands Denmark", "Denmark"),
died_country = str_replace(died_country, "the Netherlands", "Netherlands"),
born_city = str_replace(born_city, "\\(", ""),
born_city = str_replace(born_city, "\\)", ""),
died_city = sub(".*now ", "", died_city),
died_city = str_replace(died_city, "\\(", ""),
died_city = str_replace(died_city, "\\)", ""),
died_city = sub(".*now ", "", died_city))
# Arrange.
x %<>%
arrange(field, year)
# Sort variables and remove useless stuff.
x %<>%
select(-prizes, -aff_country) %>%
select(1:2, year, field, share, gender, everything())
# Save.
nobel <- x
usethis::use_data(nobel, overwrite = T)
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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