library("tidyverse")

## Registered S3 methods overwritten by 'ggplot2':
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##   [.quosures     rlang
##   c.quosures     rlang
##   print.quosures rlang

## ── Attaching packages ────────────── tidyverse 1.2.1 ──

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## ✔ tibble  2.1.2     ✔ dplyr   0.8.1
## ✔ tidyr   0.8.3     ✔ stringr 1.4.0
## ✔ readr   1.3.1     ✔ forcats 0.4.0

## ── Conflicts ───────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library("rjson")
library("magicfor")

myDOI <- readr::read_csv(here::here("data/BalciSdoi.txt"), col_names = "DOI", col_types = "c")

myDOI <- myDOI %>% 
    mutate(
        apitallies = paste0("https://api.scite.ai/tallies/", DOI)
    ) %>% 
    rownames_to_column()

magicfor::magic_for(silent = TRUE)
json_data <- for (i in 1:(dim(myDOI)[1]-1)) {
    json_name <- paste0("Article", myDOI$rowname[i])
    json_data <- rjson::fromJSON(file = myDOI$apitallies[i])
    put(json_name, json_data)
}
jsonDF <- magicfor::magic_result_as_dataframe()
magicfor::magic_free()

jsonDF <- dplyr::bind_rows(jsonDF$json_data, .id = "meta_information")

df <- jsonDF %>% 
    filter(total > 0) %>% 
    select(doi,
           contradicting,
           mentioning,
           supporting,
           unclassified
           ) %>% 
    gather(key = feature, value = number, -doi)

library(ggplot2)

ggplot(data = df) +
  aes(x = doi, fill = feature, color = feature, weight = number) +
  geom_bar(position = 'fill') +
  labs(x = 'DOI',
    y = 'Percentage Of Article Citation Features') +
  theme_minimal() +
  coord_flip()