R: tidytext NASA data

library(jsonlite)
metadata <- fromJSON("https://data.nasa.gov/data.json")
names(metadata$dataset)

class(metadata$dataset$title)
class(metadata$dataset$description)
class(metadata$dataset$keyword)

# ambil judul2
library(dplyr)
nasa_title <- data_frame(id = metadata$dataset$`_id`$`$oid`,
                         title = metadata$dataset$title)
nasa_title

# sampling 5 line
nasa_desc <- data_frame(id = metadata$dataset$`_id`$`$oid`,
                        desc = metadata$dataset$description)
nasa_desc %>%
   select(desc) %>%
      sample_n(5)

# cari keyword
library(tidyr)
nasa_keyword <- data_frame(id = metadata$dataset$`_id`$`$oid`,
                           keyword = metadata$dataset$keyword) %>%
                                 unnest(keyword)
nasa_keyword

library(tidytext)
nasa_title <- nasa_title %>%
    unnest_tokens(word, title) %>%
       anti_join(stop_words)
nasa_desc <- nasa_desc %>%
    unnest_tokens(word, desc) %>%
       anti_join(stop_words)
nasa_title
nasa_desc

# count & sort
nasa_title %>%
    count(word, sort = TRUE)
nasa_desc %>%
    count(word, sort = TRUE)

# buang keyword yang tidak ada artinya
my_stopwords <- data_frame(word = c(as.character(1:10),
                           "v1", "v03", "l2", "l3", "l4", "v5.2.0",
                           "v003", "v004", "v005", "v006", "v7"))
nasa_title <- nasa_title %>%
     anti_join(my_stopwords)
nasa_desc <- nasa_desc %>%
     anti_join(my_stopwords)

# supaya OCEAN & Ocean jadi sama
nasa_keyword <- nasa_keyword %>%
     mutate(keyword = toupper(keyword))

#
library(widyr)
title_word_pairs <- nasa_title %>%
     pairwise_count(word, id, sort = TRUE, upper = FALSE)
title_word_pairs

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